Analyse des profils génétiques 2016 à 2022
Pierre-Alexandre QUITTET
2025-10-03
- 1 SESSION INFORMATION
- 2 PACKAGE, DATA AND FUNCTIONS
- 3 DATA IMPORTATION
- 4 DATA CLEANING AND FORMATTING
- 5 FORMATAGE DES DONNEES GENOTYPES POUR CERVUS
- 6 IDENTIFICATION DES INDIVIDUS A TESTER AVEC CERVUS
- 7 RAW PARENTAGE COMPARISON
- 8 ANALYSE CERVUS
Purpose: Analyse des profils génétiques 2016 à
2022
Author: Pierre-Alexandre QUITTET
Contact: pierre-alexandre.quittet@cefe.cnrs.fr
Code created: 25 septembre 2025
Last updated: 21 janvier 2026
1 SESSION INFORMATION
Ci-dessous les informations issues de sessionInfo()
permettant la reproducibilité du code.
# sessionInfo()
## R version 4.5.0 (2025-04-11 ucrt)
## Platform: x86_64-w64-mingw32/x64
## Running under: Windows 11 x64 (build 26100)
##
## Matrix products: default
## LAPACK version 3.12.1
##
## locale:
## [1] LC_COLLATE=French_France.utf8 LC_CTYPE=French_France.utf8
## [3] LC_MONETARY=French_France.utf8 LC_NUMERIC=C
## [5] LC_TIME=French_France.utf8
##
## time zone: Europe/Paris
## tzcode source: internal
##
## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
##
## loaded via a namespace (and not attached):
## [1] digest_0.6.37 R6_2.6.1 fastmap_1.2.0 xfun_0.52
## [5] cachem_1.1.0 knitr_1.50 htmltools_0.5.8.1 rmarkdown_2.29
## [9] lifecycle_1.0.4 cli_3.6.5 sass_0.4.10 jquerylib_0.1.4
## [13] compiler_4.5.0 rstudioapi_0.17.1 tools_4.5.0 evaluate_1.0.4
## [17] bslib_0.9.0 yaml_2.3.10 rlang_1.1.6 jsonlite_2.0.0
2 PACKAGE, DATA AND FUNCTIONS
# PACKAGES
library(RODBC) # connection to DBMS
library(tidyverse)
library(xlsx)
# ROOT DIRECTORY
dir = "C:/Users/quittet/Documents/marmot-quantitative-genetics"
# IMPORT DATA
load(paste0(dir, "/1_DATA_ANALYSIS/3_Data/mat_info_id.rds")) # information de capture (use for candidate fathers)
load(paste0(dir, "/1_DATA_ANALYSIS/3_Data/pedigree_data.rds")) # pédigrée social
load(paste0(dir, "/1_DATA_ANALYSIS/3_Data/pheno.rds")) # données phéno
# LOAD FUNCTIONS
dir <- "C:/Users/quittet/Documents/marmot-quantitative-genetics/PEDIGREE_GENETIQUE/CONSTRUCTION_PROFIL_GENETIQUE"
source(paste0(dir, "/CERVUS_functions.R"))
3 DATA IMPORTATION
On importe les données des différentes tables de la base de données
marmotte (http://umr5558-marmottes.univ-lyon1.fr/phpPgAdmin-master/)
et les profils lus sur Genemapper. A noter que cette étape nécessite de
configurer un accès dsn à la base de données pour pouvoir faire le lien
entre la BDD et R via le package RODBC.
channel <- RODBC::odbcConnect(dsn = "PostgreSQL30") # DSN name given during configuration for BDD access
3.1 Table “parentee_genetique”
parentee_genetique <- sqlQuery(channel = channel,
query = paste0("
SELECT
parentee_genetique.individu_id AS individual_id,
parentee_genetique.mere_id AS mother,
parentee_genetique.pere_id AS father,
parentee_genetique.pere_lod AS father_lod,
parentee_genetique.pere_delta AS father_delta,
date_part('year', individu.date_emergence) AS annee_emergence,
individu.sexe,
capture.territoire_id
FROM
parentee_genetique
LEFT JOIN individu ON parentee_genetique.individu_id = individu.id
LEFT JOIN capture ON parentee_genetique.individu_id = capture.individu_id
LEFT JOIN territoire ON territoire.id = capture.territoire_id
LEFT JOIN zone_etude ON territoire.zone_etude_id = zone_etude.id
WHERE
zone_etude.nom::text = 'sassiere'::text
"))
# FORMATAGE
parentee_genetique <- parentee_genetique[!duplicated(parentee_genetique$individual_id), ]
parentee_genetique <- parentee_genetique[colnames(parentee_genetique) != "territoire_id"]
parentee_genetique <- parentee_genetique |>
mutate(father = if_else(father == "unknow", NA_character_, father)) # replace wrong levels of "father
# SAVE
save(parentee_genetique, file = paste0(dir, "/parentee_genetique.rds"))
# LOAD
load(paste0(dir, "/parentee_genetique.rds"))
3.2 Table “analyse_microsat”
microsatellite_data <- sqlQuery(channel = channel,
query = paste0("
SELECT
analyse_microsat.id AS id_file,
analyse_microsat.file_genet AS fsa,
analyse_microsat.individu_id AS individual_id,
analyse_microsat.marqueur AS marker,
analyse_microsat.allele_1,
analyse_microsat.allele_2,
analyse_microsat.longueur_1,
analyse_microsat.longueur_2,
analyse_microsat.hauteur_1,
analyse_microsat.hauteur_2,
date_part('year', individu.date_emergence) AS annee_emergence,
individu.sexe,
capture.territoire_id
FROM
analyse_microsat
LEFT JOIN individu ON analyse_microsat.individu_id = individu.id
LEFT JOIN capture ON analyse_microsat.individu_id = capture.individu_id
LEFT JOIN territoire ON territoire.id = capture.territoire_id
LEFT JOIN zone_etude ON territoire.zone_etude_id = zone_etude.id
WHERE
zone_etude.nom::text = 'sassiere'::text
"))
# FORMATAGE
microsatellite_data <- microsatellite_data[!duplicated(microsatellite_data$id_file), ] # remove useless doublons
microsatellite_data <- microsatellite_data[colnames(microsatellite_data) != "territoire_id"]
# standardized name of marker (e.g. ms46 = MS46)
microsatellite_data$marker <- toupper(microsatellite_data$marker)
# GET OF ANALYSIS ASSOCIATED WITH FSA
#' on ajoute seulement 2013 / 2015 car les autres données sont en fouilli total... L'idée est de voir si certains mismatchs futurs sont présents dans ces analyses
#' # 2013
lst_data_fsa_2013 <- c(list.files(path = paste0(dir, "/FSA/2013/Mix1"), full.names = FALSE),
list.files(path = paste0(dir, "/FSA/2013/Mix2"), full.names = FALSE))
microsatellite_data$year_analysis <- NA
microsatellite_data[microsatellite_data$fsa %in% lst_data_fsa_2013, "year_analysis"] <- "2013"
#' 2015
lst_data_fsa_2015 <- c(list.files(path = paste0(dir, "/FSA/2015/Mix1"), full.names = FALSE),
list.files(path = paste0(dir, "/FSA/2015/Mix2"), full.names = FALSE))
microsatellite_data[microsatellite_data$fsa %in% lst_data_fsa_2015, "year_analysis"] <- "2015"
# SAVE
save(microsatellite_data, file = paste0(dir, "/microsatellite_data.rds"))
# LOAD
load(paste0(dir, "/microsatellite_data.rds"))
Vérification : On vérifie si on a bien 17 marqueurs
3.3 Profils génétiques lus sur Genemapper
J’importe ici les lectures de profils génétiques .txt (tab) lus avec
Genemapper. Ces fichiers .txt sont directement importés
depuis Genemapper. Attention à bien ajouter à ces données brutes les
mêmes colonnes que moi pour que le script fonctionne (à définir dans
Genemapper lors de l’exportation) : “Sample.File” / “Sample.Name” /
“Marker” / “Allele.1” / “Allele.2” / “Allele.3” / “Size.1” / “Size.2” /
“Size.3” / “Height.1” / “Height.2”.
# AJOUT DES PROFILS DE 2013 / 2015 POUR VERIFICATION
pg_2013 <- read.table(file = paste0(dir, "/LECTURE_PROFIL/txt/LECTURE_PROFIL_2013.txt"), header = T, sep = "\t", strip.white = T)[1:11]
pg_2015 <- read.table(file = paste0(dir, "/LECTURE_PROFIL/txt/LECTURE_PROFIL_2015.txt"), header = T, sep = "\t", strip.white = T)[1:11]
# AJOUT DES PROFILS LUS SUR GENEMAPPER (2016 to 2022)
# read .txt
pg_2017 <- read.table(file = paste0(dir, "/LECTURE_PROFIL/txt/LECTURE_PROFIL_2017.txt"), header = T, sep = ",", strip.white = T)[1:11] # pg = profil genetique
pg_2020 <- read.table(file = paste0(dir, "/LECTURE_PROFIL/txt/LECTURE_PROFIL_2020.txt"), header = T, sep = ",", strip.white = T)[1:11]
pg_2022 <- read.table(file = paste0(dir, "/LECTURE_PROFIL/txt/LECTURE_PROFIL_2022.txt"), header = T, sep = ",", strip.white = T)[1:11]
pg_2023_PK1_PK2 <- read.table(file = paste0(dir, "/LECTURE_PROFIL/txt/LECTURE_PROFIL_2023_PK1_PK2.txt"), header = T, sep = ",", strip.white = T)[1:11]
pg_2023_PK3_PK4 <- read.table(file = paste0(dir, "/LECTURE_PROFIL/txt/LECTURE_PROFIL_2023_PK3_PK4.txt"), header = T, sep = ",", strip.white = T)[1:11]
pg_2023_PK5 <- read.table(file = paste0(dir, "/LECTURE_PROFIL/txt/LECTURE_PROFIL_2023_PK5.txt"), header = T, sep = ",", strip.white = T)[1:11]
# add year of analysis for future stats
pg_2013$year_analysis <- "2013_2"
pg_2015$year_analysis <- "2015_2"
pg_2017$year_analysis <- "2017"
pg_2020$year_analysis <- "2020"
pg_2022$year_analysis <- "2022"
pg_2023_PK1_PK2$year_analysis <- "2023_1"
pg_2023_PK3_PK4$year_analysis <- "2023_2"
pg_2023_PK5$year_analysis <- "2023_3"
# merge datasets
pg <- rbind(pg_2017, pg_2020, pg_2022, pg_2023_PK1_PK2, pg_2023_PK3_PK4, pg_2023_PK5)
# pg <- rbind(pg_2013, pg_2015, pg_2017, pg_2020, pg_2022, pg_2023_PK1_PK2, pg_2023_PK3_PK4, pg_2023_PK5)
# rm(list = c("pg_2013", "pg_2015", "pg_2017", "pg_2020", "pg_2022", "pg_2023_PK1_PK2", "pg_2023_PK3_PK4", "pg_2023_PK5"))
#
# pg <- rbind(pg_2017, pg_2020, pg_2022, pg_2023_PK1_PK2, pg_2023_PK3_PK4, pg_2023_PK5)
# rm(list = c("pg_2017", "pg_2020", "pg_2022", "pg_2023_PK1_PK2", "pg_2023_PK3_PK4", "pg_2023_PK5"))
# reformat SampleName columns
pg$Sample.Name <- gsub(pattern = "1_|2_|_2", x = pg$Sample.Name, replacement = "")
# remove white space and replace by NA
pg[] <- lapply(pg, function(x) {
if (is.character(x)) gsub(" ", "", x) else x
})
pg[pg == ""] <- NA
# rename colnames
colnames(pg) <- c(
"fsa",
"individual_id",
"marker",
"allele_1",
"allele_2",
"allele_3",
"longueur_1",
"longueur_2",
"longueur_3",
"hauteur_1",
"hauteur_2",
"year_analysis"
)
# standardized name of marker (e.g. ms46 = MS46)
pg$marker <- toupper(pg$marker)
# ON SUPPRIME LE MARQUEUR ST10 DE 2023 PK5 car a foiré...
pg[pg$marker == "ST10" & pg$year_analysis == "2023_3", c("allele_1", "allele_2")] <- NA
# QUICK VIEW OF DATA
head(pg)## fsa individual_id marker allele_1 allele_2 allele_3 longueur_1
## 1 1_1360_E07_028.fsa 1360 MA091 173 175 NA 173.62
## 2 1_1360_E07_028.fsa 1360 BIBL1 97 107 NA 98.87
## 3 1_1360_E07_028.fsa 1360 MS6 158 <NA> NA 158.32
## 4 1_1360_E07_028.fsa 1360 BIBL20 208 216 NA 206.82
## 5 1_1360_E07_028.fsa 1360 MS56 106 108 NA 107.40
## 6 1_1360_E07_028.fsa 1360 MS47 184 186 NA 184.84
## longueur_2 longueur_3 hauteur_1 hauteur_2 year_analysis
## 1 175.60 NA 10140 6435 2017
## 2 108.16 NA 8785 5704 2017
## 3 NA NA 24372 NA 2017
## 4 214.53 NA 7420 4567 2017
## 5 109.22 NA 14562 11281 2017
## 6 186.80 NA 7050 4379 2017
Vérification : On vérifie que l’on a bien 17 marqueurs :
## [1] 17
Vérification : On calcule le nombre d’individu génotypé entre 2016 et 2022 :
## [1] 483
D. Allainé avait importé dans la BDD les lectures automatiques GENOSCREEN. Elles sont pleines de fautes car elles n’ont pas été lues directement et beaucoup de marqueurs ne sont pas correctement calibrés. Je supprime ces données pour les remplacer par celle que j’ai vraiment lues.
microsatellite_data <- microsatellite_data[!microsatellite_data$individual_id %in% pg$individual_id,]
3.4 Table “individu”
Les informations de cette table permettent d’avoir le sexe, l’année d’émergence et la plus ancienne capture de tous les individus.
individu <- sqlQuery(
channel = channel,
query = paste0(
"
WITH captures_uniques AS (
SELECT
c.*,
ROW_NUMBER() OVER (
PARTITION BY c.individu_id
ORDER BY c.date_capture ASC
) AS rn
FROM capture c
)
SELECT
i.id AS individual_id,
date_part('year', i.date_emergence) AS annee_emergence,
i.sexe,
cu.territoire_id,
date_part('year', cu.date_capture) AS annee_capture
FROM individu i
LEFT JOIN captures_uniques cu
ON i.id = cu.individu_id AND cu.rn = 1
LEFT JOIN territoire t ON t.id = cu.territoire_id
LEFT JOIN zone_etude z ON z.id = t.zone_etude_id
WHERE z.nom::text = 'sassiere'::text
"
))
individu <- individu[!duplicated(individu$individual_id),] # remove duplicated row
individu <- individu[colnames(individu) != "territoire_id"]
# SAVE
save(individu, file = paste0(dir, "/individu_microsat.rds"))
# LOAD
load(paste0(dir, "/individu_microsat.rds"))
Vérification : pas de NA dans les sexes des individus
Notre jeu de données contient 1 individu(s) avec
sexe == NA…
## fsa individual_id marker allele_1 allele_2 allele_3
## 170 1_1718_E03_012.fsa 1718 BIBL1 97 <NA> NA
## 653 1_1776_O17_065.fsa 1776 MS53 140 142 NA
## 1136 2_1737_N07_020.fsa 1737 MA002 279 <NA> NA
## 1619 2_1788_J21_087.fsa 1788 ST10 118 120 NA
## 2102 1_F06_1891_038.fsa 1891 BIBL4 190a <NA> NA
## 2585 2_C07_1895_059.fsa 1895 ST10 118 132 NA
## longueur_1 longueur_2 longueur_3 hauteur_1 hauteur_2 year_analysis sexe
## 170 98.74 NA NA 9121 NA 2017 f
## 653 141.22 143.44 NA 7950 5449 2017 m
## 1136 279.79 NA NA 2292 NA 2017 m
## 1619 118.98 120.88 NA 5752 4529 2017 f
## 2102 191.37 NA NA 20059 NA 2020 m
## 2585 118.55 132.19 NA 33980 7878 2020 m
## annee_emergence
## 170 2016
## 653 2016
## 1136 2016
## 1619 2016
## 2102 2018
## 2585 2018Ces observations sont issus de l’individu Tnegatif qui
doit être un teste de méthode, on supprime cette observation.
Vérification : le nombre d’individus avec des années d’émergence NA
Parmi les individus nouvellement génotypés, 28 n’ont pas de date d’émergence, probablement des individus capturés adultes. Les ID de ces individus sont :
## [1] 1417 1606 1625 1710 1730 1790 1791 1792 1843 1844 1845 1846 1847 1850 1913
## [16] 1943 1989 2078 2079 2080 2166 2167 2168 2169 2170 2171 2177 2205
3.5 Table “statut”
J’importe cette table pour vérifier le statut reproducteur des individus en cas de doute.
statut_individu <- sqlQuery(
channel = channel,
query = paste0(
"SELECT
capture.individu_id,
statut.age_obs,
statut.statut_reproducteur,
individu.sexe,
date_part('year', capture.date_capture) AS annee_capture
FROM statut
LEFT JOIN capture ON capture.id = statut.capture_id
LEFT JOIN individu ON individu.id = capture.individu_id
ORDER BY statut.capture_id;"
))
# SAVE
save(statut_individu, file = paste0(dir, "/statut_individu.rds"))
# LOAD
load(paste0(dir, "/statut_individu.rds"))
3.6 Save/load import BDD
# save(microsatellite_data, file = paste0(dir, "/microsatellite_data.rds"))
# save(individu, file = paste0(dir, "/parent.rds"))
# save(parentee_genetique, file = paste0(dir, "/parentee_genetique.rds"))
# load(paste0(dir, "/microsatellite_data.rds"))
# load(paste0(dir, "/parent.rds"))
# load(file = paste0(dir, "/parentee_genetique.rds"))
4 DATA CLEANING AND FORMATTING
Nombre d’individu génotypé dans la BDD : 1442
4.1 Formattage
On va :
- homogénéiser le codage des homozygotes
- identifier et supprimer les allèles et marqueurs non présents dans
la référence de Aurélie Cohas (2008)
- cas de re-test de certains individus, déterminer quel profil sera
conservé
- calculer le nombre d’individus n’ayant pas été génotypés.
4.1.1 RECODE DES HOMOZYGOTES
Cette étape est nécessaire car certains haplotypes sont notés 155/0 ou 155/155 ou 155/NA. On homogénise le tout.
# Fonction d'homogénéisation des homozygotes
recoding_homozygotis <- function(x){
if(!is.na(x[1]) && x[1] != 0 && (is.na(x[2]) || (!is.na(x[2]) && x[2] == 0))){
x <- c(x[1], x[1])
} else if (!is.na(x[2]) && x[2] != 0 && (is.na(x[1]) || (!is.na(x[1]) && x[1] == 0))){
x <- c(x[2], x[2])
} else if (is.na(x[1]) && (!is.na(x[2]) && x[2] == 0)){
x <- c(NA, NA)
} else {
x <- c(x[1], x[2])
}
return(x)
}
# application de la fonction sur les données de la BDD
microsatellite_data[c("allele_1", "allele_2")] <- t(apply(microsatellite_data[c("allele_1", "allele_2")],
1,
recoding_homozygotis, simplify = T))
# ... sur les données lues sur Genemapper
pg[c("allele_1", "allele_2")] <- t(apply(pg[c("allele_1", "allele_2")],
1,
recoding_homozygotis, simplify = T))
4.1.2 FORMATAGE DU NOM
On homogénise le noms des allèles en enlevant les lettres e.g. 180a 180b deviennent 180 et les “?” des allèles incertains.
pg$allele_1 <- gsub(pattern = "a|b|c|\\?", x = pg$allele_1, replacement = "")
pg$allele_2 <- gsub(pattern = "a|b|c|\\?", x = pg$allele_2, replacement = "")
4.1.3 ALLELES DE REFERENCE
On ne conserve que les allèles présents dans l’appendix de Cohas et
al. (2008). On précise NA pour les autres.
# ALLELES DE REFERENCES
cohas_alleles <- list(
"BIBL1" = c("95", "97", "101", "103", "107", "109", "0"),
"BIBL18" = c("132", "137", "143", "145", "147", "149", "0"),
"BIBL20" = c("206", "208", "216", "218", "220", "222", "0"),
"BIBL31" = c("157", "159", "161", "163", "0"),
"BIBL4" = c("175", "178", "188", "190", "192", "0"),
"MA002" = c("265", "271", "279", "281", "283", "0"),
"MA018" = c("296", "298", "0"),
"MA066" = c("231", "233", "241", "0"),
"MA091" = c("159", "167", "169", "171", "173", "175", "177", "179", "188", "0"),
"MS41" = c("184", "186", "0"),
"MS45" = c("107", "109", "111", "0"),
"MS47" = c("176", "180", "182", "184", "186", "188", "190", "0"),
"MS53" = c("132", "140", "142", "144", "148", "0"),
"MS56" = c("104", "106", "108", "110", "0"),
"MS6" = c("142", "158", "160", "0"),
"ST10" = c("116", "118", "120", "130", "132", "134", "136", "0")
)
On fait le bilan des allèles problématiques (i.e. non-présent dans la référence) par marqueur :
##
## ____________________
## MARKER : BIBL1
## ____________________
## * Number problematic loci : 0
## * Problematic alleles :
##
## ____________________
## MARKER : BIBL18
## ____________________
## * Number problematic loci : 1
## * Problematic alleles : 133
##
## ____________________
## MARKER : BIBL20
## ____________________
## * Number problematic loci : 0
## * Problematic alleles :
##
## ____________________
## MARKER : BIBL31
## ____________________
## * Number problematic loci : 0
## * Problematic alleles :
##
## ____________________
## MARKER : BIBL4
## ____________________
## * Number problematic loci : 3
## * Problematic alleles : 179 186
##
## ____________________
## MARKER : MA002
## ____________________
## * Number problematic loci : 7
## * Problematic alleles : 277 267 275
##
## ____________________
## MARKER : MA018
## ____________________
## * Number problematic loci : 0
## * Problematic alleles :
##
## ____________________
## MARKER : MA066
## ____________________
## * Number problematic loci : 3
## * Problematic alleles : 235 239
##
## ____________________
## MARKER : MA091
## ____________________
## * Number problematic loci : 0
## * Problematic alleles :
##
## ____________________
## MARKER : MS41
## ____________________
## * Number problematic loci : 0
## * Problematic alleles :
##
## ____________________
## MARKER : MS45
## ____________________
## * Number problematic loci : 0
## * Problematic alleles :
##
## ____________________
## MARKER : MS47
## ____________________
## * Number problematic loci : 5
## * Problematic alleles : 178 174
##
## ____________________
## MARKER : MS53
## ____________________
## * Number problematic loci : 3
## * Problematic alleles : 138
##
## ____________________
## MARKER : MS56
## ____________________
## * Number problematic loci : 0
## * Problematic alleles :
##
## ____________________
## MARKER : MS6
## ____________________
## * Number problematic loci : 8
## * Problematic alleles : 156 168
##
## ____________________
## MARKER : ST10
## ____________________
## * Number problematic loci : 2
## * Problematic alleles : 128 122
Vérification : Nombre d’observations (i.e. donnée pour un marqueur donné) supprimées
Nombre d’observation(s) supprimée(s) : 32
On regarde les informations relatives à ces observations supprimées :
## id_file fsa individual_id marker year_analysis
## 1 3920 <NA> 1009 BIBL18 <NA>
## 2 32943 1327_A15_064.fsa 1327 ST10 2013
## 3 33129 1339_E17_075.fsa 1339 MA002 2013
## 4 34071 1-1393_O06_017.fsa 1393 MA002 <NA>
## 5 35545 S1481_M19_068.fsa 1481 MS53 <NA>
## 6 34228 S1403_C01_013.fsa 1403 BIBL4 <NA>
## 7 34579 S1425_M05_019.fsa 1425 ST10 <NA>
## 8 34775 S1436_C09_045.fsa 1436 MA002 <NA>
## 9 34639 S1428_A24_096.fsa 1428 MA002 <NA>
## 10 35773 S1496_K23_086.fsa 1496 MA002 <NA>
## 11 34292 S1408_K01_005.fsa 1408 MS6 <NA>
## 12 37477 1-1596_M07_020.fsa 1596 MA002 <NA>
## 13 37116 1575_J19_072.fsa 1575 MS53 <NA>
## 14 36111 1516_D05_029.fsa 1516 MS47 <NA>
## 15 36519 1540_D11_046.fsa 1540 MS47 <NA>
## 16 36366 1531_B09_047.fsa 1531 MS47 <NA>
## 17 36483 1538_P09_033.fsa 1538 MS6 <NA>
## 18 41369 1_1623_K01_005.fsa 1623 MS47 <NA>
## 19 41002 2_1624_N01_003.fsa 1624 MA066 <NA>
## 20 41462 1_1624_M01_003.fsa 1624 MS53 <NA>
## 21 41658 1_1637_G05_025.fsa 1637 MS6 <NA>
## 22 41380 1_1635_A05_031.fsa 1635 MS47 <NA>
## 23 41024 2_1647_L07_022.fsa 1647 MA066 <NA>
## 24 41672 1_1651_C09_045.fsa 1651 MS6 <NA>
## 25 40661 1_1652_E09_043.fsa 1652 BIBL4 <NA>
## 26 41684 1_1663_K11_038.fsa 1663 MS6 <NA>
## 27 40675 1_1666_A13_063.fsa 1666 BIBL4 <NA>
## 28 40872 2_1679_N15_052.fsa 1679 MA002 <NA>
## 29 41704 1_1683_E17_075.fsa 1683 MS6 <NA>
## 30 41066 2_1689_B19_080.fsa 1689 MA066 <NA>
## 31 41711 1_1690_C19_078.fsa 1690 MS6 <NA>
## 32 41712 1_1691_E19_076.fsa 1691 MS6 <NA>Les FSA précisés ne sont disponibles nul part. Il s’agit probablement de test d’ajustement. De plus, les individual ID correspondent environ à 2015, donc probablement lors de la passation de Aurélie à Coraline ?
4.1.4 SUPPRESSION MARQUEUR MA001
Le marqueur n’est pas présent dans les analyses de parenté des papiers de Aurélie. On le supprime de la liste.
# REMOVE MA0001 NON USED IN PARENTAGE ANALYSIS
microsatellite_data <- microsatellite_data[!microsatellite_data$marker == "MA001",]
pg <- pg[!pg$marker == "MA001",]
4.1.5 VERIFICATIONS FINALES
Vérification : Tous les allèles présents dans la BDD sont bien présents dans les allèles de références d’Aurélie Cohas (2008)
Nombre d’allèle(s) non-présent(s) : 0
Vérification : Tous les allèles des données lues sur Genemapper sont bien présents dans les allèles de références d’Aurélie Cohas (2008)
Nombre d’allèle(s) non-présent(s) : 0
Vérification : Tous les allèles présents de la référence de A. Cohas sont dans mes allèles extraits de mes analyses GM
## BIBL182 BIBL206
## "137" "222"On a les allèles 137 et 222 qui ne sont pas présents, mais qui
correspondent à des allèles qui ne sont plus utilisés car plus présent
dans le bin Genemapper (i.e. contenant les allèles de
références).
Vérification : Après toutes ces modifications, on recompte le nombre de marqueur
## [1] 16## [1] 16
4.1.6 Hauteur de pics et correspondance de la taille des allèles
On vérifie que les hauteurs d’allèle interprétées sont correctes en identifiant les outliers.
# MARKER NAME
marker_name <-
c("BIBL1",
"BIBL18",
"BIBL20",
"BIBL31",
"BIBL4",
"MA002",
"MA018",
"MA066",
"MA091",
"MS41",
"MS45",
"MS47",
"MS53",
"MS56",
"MS6",
"ST10")
# SET MINIMAL HEIGHT
minimal_height <- 100 # seuil à ajuster
# NUMNER OF READ BELOW THRESHOLD
sum(pg$hauteur_1 < minimal_height | pg$hauteur_2 < minimal_height, na.rm = T)## [1] 8# bdd
sum(microsatellite_data$hauteur_1 < minimal_height | microsatellite_data$hauteur_2 < minimal_height, na.rm = T)## [1] 39# REPARTITION PARMI LES MARQUEURS
pg |>
group_by(year_analysis, marker) |>
summarise(n_low = sum(hauteur_1 < minimal_height | hauteur_2 < minimal_height, na.rm = T)) |>
filter(n_low != 0)## # A tibble: 4 × 3
## # Groups: year_analysis [3]
## year_analysis marker n_low
## <chr> <chr> <int>
## 1 2017 MA066 4
## 2 2023_1 MA066 1
## 3 2023_2 BIBL20 2
## 4 2023_2 MA066 1# BDD
microsatellite_data |>
group_by(marker) |>
summarise(n_low = sum(hauteur_1 < minimal_height | hauteur_2 < minimal_height, na.rm = T)) |>
filter(n_low != 0) |>
as.data.frame()## marker n_low
## 1 BIBL20 7
## 2 BIBL4 1
## 3 MA018 7
## 4 MA066 10
## 5 MS41 8
## 6 MS47 3
## 7 MS53 1
## 8 ST10 2
On enlève les lectures avec une hauteur de pic de moins de 100.
# REMOVE READS BELOW THRESHOLD
pg <- pg |>
mutate(allele_1 = case_when(
!is.na(allele_1) &
!is.na(hauteur_1) &
((hauteur_1 < minimal_height)|
(hauteur_2 < minimal_height)) ~ NA,
.default = allele_1
)
) |>
mutate(
# propagation du NA si new_value = NA pour l’un des deux
allele_1 = if_else(
(is.na(allele_1) | is.na(allele_2)),
NA,
allele_1
),
allele_2 = if_else(
(is.na(allele_1) | is.na(allele_2)),
NA,
allele_2
)
)
# BDD
# REMOVE READ BELOW THRESHOLD
microsatellite_data <- microsatellite_data |>
mutate(allele_1 = case_when(
!is.na(allele_1) &
!is.na(hauteur_1) &
((hauteur_1 < minimal_height)|
(hauteur_2 < minimal_height)) ~ NA,
.default = allele_1
),
alle_2 = case_when(
!is.na(allele_2) &
!is.na(hauteur_2) &
((hauteur_1 < minimal_height)|
(hauteur_2 < minimal_height)) ~ NA,
.default = allele_2
)
) |>
mutate(
# propagation du NA si new_value = NA pour l’un des deux
allele_1 = if_else(
(is.na(allele_1) | is.na(allele_2)),
NA,
allele_1
),
allele_2 = if_else(
(is.na(allele_1) | is.na(allele_2)),
NA,
allele_2
)
)
4.1.6.1 Longueur attendue et observée
On vérifie que la distribution des longueurs des allèles matchent bien avec les allèles de références, au décalage près. Cela permet de vérifier les erreurs (e.g. un allèle rouge dans une zone bleue).
D’abord, on regarde la distribution globale pour voir si on a pas compté comme même allèle deux allèles différents. A noter que ce décalage doit exister au sein d’une même analyse, car les décalages à l’échelle “globale” peuvent simplement représenter différents décalages et pas nécessairement des erreurs.
4.1.6.1.1 Mes lectures
On corrige les erreurs “grossières” d’assignation repérées directement dans les données brutes.
On remarque que MA066 est problématique car deux allèles visiblement différents sont considérés comme étant 233. On regarde comment c’est dans la BDD et on ajuste…Du coup je supprime la barre d’en bas qui ne correspond pas.
# FONCTION DE CORRECTION
#' ... NOTE : si on précise "NA" à new_value, alors les deux allèles
#' ... prendront NA comme nouvelle valeur
corriger_allele <- function(
data = microsatellite_data,
marker_temp, # marqueur concerné
allele_value, # valeur actuelle de l'allèle
new_value, # valeur corrigée
longueur_min, # intervalle inférieure
longueur_max # intervalle supérieure
) {
data %>%
mutate(
# correction individuelle
allele_1 = if_else(
marker == marker_temp &
!is.na(allele_1) &
!is.na(longueur_1) &
allele_1 %in% as.character(allele_value) &
longueur_1 > longueur_min &
longueur_1 < longueur_max,
as.character(new_value),
allele_1
),
allele_2 = if_else(
marker == marker_temp &
allele_2 %in% as.character(allele_value) &
!is.na(allele_2) &
!is.na(longueur_2) &
longueur_2 > longueur_min &
longueur_2 < longueur_max,
as.character(new_value),
allele_2
)
) %>%
mutate(
# propagation du NA si new_value = NA pour l’un des deux
allele_1 = if_else(
marker == marker_temp &
(is.na(allele_1) | is.na(allele_2)),
NA_character_,
allele_1
),
allele_2 = if_else(
marker == marker_temp &
(is.na(allele_1) | is.na(allele_2)),
NA_character_,
allele_2
)
)
}# MA066
pg <- corriger_allele(data = pg, marker_temp = "MA066", allele_value = 233, new_value = NA, longueur_min = 234, longueur_max = 235.5)
# MS41
pg <- corriger_allele(data = pg, marker_temp = "MS41", allele_value = 186, new_value = NA, longueur_min = 187.93, longueur_max = 187.95)
pg <- corriger_allele(data = pg, marker_temp = "MS41", allele_value = 186, new_value = NA, longueur_min = 186.85, longueur_max = 186.87)
pg <- corriger_allele(data = pg, marker_temp = "MS41", allele_value = 184, new_value = NA, longueur_min = 184.77, longueur_max = 184.79)
# MA066
pg <- corriger_allele(data = pg, marker_temp = "MA066", allele_value = 233, new_value = NA, longueur_min = 236.27, longueur_max = 236.29)
# MA018
pg <- corriger_allele(data = pg, marker_temp = "MA018", allele_value = 296, new_value = NA, longueur_min = 295.40, longueur_max = 295.47)
# BIBL31
pg <- corriger_allele(data = pg, marker_temp = "BIBL31", allele_value = 161, new_value = NA, longueur_min = 161.24, longueur_max = 161.46)
pg <- corriger_allele(data = pg, marker_temp = "BIBL31", allele_value = 161, new_value = NA, longueur_min = 162.28, longueur_max = 162.30)
pg <- corriger_allele(data = pg, marker_temp = "BIBL31", allele_value = 159, new_value = NA, longueur_min = 159.39, longueur_max = 159.41)4.1.6.1.2 BDD
On corrige le reste de la BDD pour lesquelles les données brutes Genemapper sont disponibles.
Là il y a pas mal d’erreurs… Je corrige.
Je ne sais pas trop quoi faire avec
MA066, on remarque qu’il y a une grosse bizzarerie avec des allèles 233 en dessous des allèles 231… A voir si on les reclasses tous ou non. Je modulerai ce choix selon les erreurs avec les mères, en post-hoc. Pour le moment, j’ai choisi de supprimer tous ceux en dessous de 231.
# ST10
microsatellite_data <- corriger_allele(marker_temp = "ST10", allele_value = 118, new_value = 134, longueur_min = 134, longueur_max = 140)
microsatellite_data <- corriger_allele(marker_temp = "ST10", allele_value = 120, new_value = NA, longueur_min = 120, longueur_max = 125)
microsatellite_data <- corriger_allele(marker_temp = "ST10", allele_value = 130, new_value = 118, longueur_min = 115, longueur_max = 140)
microsatellite_data <- corriger_allele(marker_temp = "ST10", allele_value = 118, new_value = NA, longueur_min = 120, longueur_max = 140)
microsatellite_data <- corriger_allele(marker_temp = "ST10", allele_value = 136, new_value = NA, longueur_min = 137, longueur_max = 140)
# MS6
microsatellite_data <- corriger_allele(marker_temp = "MS6", allele_value = 160, new_value = 158, longueur_min = 155, longueur_max = 159)
microsatellite_data <- corriger_allele(marker_temp = "MS6", allele_value = 158, new_value = NA, longueur_min = 155, longueur_max = 157.8)
# MS56
microsatellite_data <- corriger_allele(marker_temp = "MS56", allele_value = 110, new_value = 108, longueur_min = 108, longueur_max = 110)
microsatellite_data <- corriger_allele(marker_temp = "MS56", allele_value = 108, new_value = NA, longueur_min = 109.61, longueur_max = 110)
microsatellite_data <- corriger_allele(marker_temp = "MS56", allele_value = 106, new_value = NA, longueur_min = 107.8, longueur_max = 108)
# MS53
microsatellite_data <- corriger_allele(marker_temp = "MS53", allele_value = 142, new_value = 145, longueur_min = 144, longueur_max = 145)
microsatellite_data <- corriger_allele(marker_temp = "MS53", allele_value = 140, new_value = NA, longueur_min = 136, longueur_max = 140.5)
microsatellite_data <- corriger_allele(marker_temp = "MS53", allele_value = 142, new_value = NA, longueur_min = 142, longueur_max = 143)
# MS47
microsatellite_data <- corriger_allele(marker_temp = "MS47", allele_value = 186, new_value = NA, longueur_min = 185, longueur_max = 186)
microsatellite_data <- corriger_allele(marker_temp = "MS47", allele_value = 184, new_value = NA, longueur_min = 182, longueur_max = 184)
microsatellite_data <- corriger_allele(marker_temp = "MS47", allele_value = 180, new_value = NA, longueur_min = 182, longueur_max = 184)
microsatellite_data <- corriger_allele(marker_temp = "MS47", allele_value = 180, new_value = NA, longueur_min = 175, longueur_max = 180)
microsatellite_data <- corriger_allele(marker_temp = "MS47", allele_value = 176, new_value = NA, longueur_min = 177.5, longueur_max = 180)
# MS45
microsatellite_data <- corriger_allele(marker_temp = "MS45", allele_value = 107, new_value = NA, longueur_min = 113, longueur_max = 114)
microsatellite_data <- corriger_allele(marker_temp = "MS45", allele_value = 107, new_value = 109, longueur_min = 109, longueur_max = 110)
microsatellite_data <- corriger_allele(marker_temp = "MS45", allele_value = 109, new_value = NA, longueur_min = 110.35, longueur_max = 111)
microsatellite_data <- corriger_allele(marker_temp = "MS45", allele_value = 111, new_value = NA, longueur_min = 111, longueur_max = 111.7)
# MS41
microsatellite_data <- corriger_allele(marker_temp = "MS41", allele_value = 184, new_value = NA, longueur_min = 185.6, longueur_max = 186)
# MA091
microsatellite_data <- corriger_allele(marker_temp = "MA091", allele_value = 159, new_value = 175, longueur_min = 175, longueur_max = 176)
microsatellite_data <- corriger_allele(marker_temp = "MA091", allele_value = 159, new_value = 173, longueur_min = 173, longueur_max = 175)
# MA066
microsatellite_data <- corriger_allele(marker_temp = "MA066", allele_value = 241, new_value = 231, longueur_min = 233.5, longueur_max = 235)
microsatellite_data <- corriger_allele(marker_temp = "MA066", allele_value = 241, new_value = NA, longueur_min = 241, longueur_max = 242)
microsatellite_data <- corriger_allele(marker_temp = "MA066", allele_value = 231, new_value = NA, longueur_min = 200, longueur_max = 233)
microsatellite_data <- corriger_allele(marker_temp = "MA066", allele_value = 233, new_value = NA, longueur_min = 232, longueur_max = 234)
# MA018
microsatellite_data <- corriger_allele(marker_temp = "MA018", allele_value = 298, new_value = NA, longueur_min = 298, longueur_max = 303)
microsatellite_data <- corriger_allele(marker_temp = "MA018", allele_value = 298, new_value = NA, longueur_min = 296, longueur_max = 296.3)
microsatellite_data <- corriger_allele(marker_temp = "MA018", allele_value = 296, new_value = NA, longueur_min = 294, longueur_max = 294.7)
microsatellite_data <- corriger_allele(marker_temp = "MA018", allele_value = 296, new_value = NA, longueur_min = 295.3, longueur_max = 296)
# MA002
microsatellite_data <- corriger_allele(marker_temp = "MA002", allele_value = 279, new_value = 281, longueur_min = 283, longueur_max = 283.6)
microsatellite_data <- corriger_allele(marker_temp = "MA002", allele_value = 279, new_value = 271, longueur_min = 271, longueur_max = 275)
microsatellite_data <- corriger_allele(marker_temp = "MA002", allele_value = 279, new_value = NA, longueur_min = 281.3, longueur_max = 282)
microsatellite_data <- corriger_allele(marker_temp = "MA002", allele_value = 281, new_value = NA, longueur_min = 283.05, longueur_max = 284)
microsatellite_data <- corriger_allele(marker_temp = "MA002", allele_value = 271, new_value = NA, longueur_min = 274.7, longueur_max = 275)
# BIBL4
microsatellite_data <- corriger_allele(marker_temp = "BIBL4", allele_value = 188, new_value = 175, longueur_min = 175, longueur_max = 176)
microsatellite_data <- corriger_allele(marker_temp = "BIBL4", allele_value = 190, new_value = 188, longueur_min = 188, longueur_max = 190)
microsatellite_data <- corriger_allele(marker_temp = "BIBL4", allele_value = c(188, 190, 192), new_value = NA, longueur_min = 179, longueur_max = 188)
# BIBL31
#'... Marqueur compliqué... Beaucoup de variance dans les longueurs
microsatellite_data <- corriger_allele(marker_temp = "BIBL31", allele_value = 161, new_value = 157, longueur_min = 157, longueur_max = 158)
microsatellite_data <- corriger_allele(marker_temp = "BIBL31", allele_value = 159, new_value = 161, longueur_min = 161, longueur_max = 162.3)
# BIBL20
microsatellite_data <- corriger_allele(marker_temp = "BIBL20", allele_value = 216, new_value = NA, longueur_min = 215.8, longueur_max = 216)
microsatellite_data <- corriger_allele(marker_temp = "BIBL20", allele_value = 208, new_value = NA, longueur_min = 206, longueur_max = 206.5)
# BIBL18
microsatellite_data <- corriger_allele(marker_temp = "BIBL18", allele_value = 149, new_value = 145, longueur_min = 145, longueur_max = 147)
microsatellite_data <- corriger_allele(marker_temp = "BIBL18", allele_value = 149, new_value = NA, longueur_min = 149, longueur_max = 149.85) # Pas sûr de les enlever ceux là, mais décalage justifiable
# BIBL1
microsatellite_data <- corriger_allele(marker_temp = "BIBL1", allele_value = 101, new_value = 107, longueur_min = 107, longueur_max = 109)
microsatellite_data <- corriger_allele(marker_temp = "BIBL1", allele_value = 103, new_value = 101, longueur_min = 101, longueur_max = 103)
microsatellite_data <- corriger_allele(marker_temp = "BIBL1", allele_value = 97, new_value = 95, longueur_min = 97, longueur_max = 98)
microsatellite_data <- corriger_allele(marker_temp = "BIBL1", allele_value = 101, new_value = NA, longueur_min = 103, longueur_max = 104)
microsatellite_data <- corriger_allele(marker_temp = "BIBL1", allele_value = 97, new_value = NA, longueur_min = 99.6, longueur_max = 101)
microsatellite_data <- corriger_allele(marker_temp = "BIBL1", allele_value = c(107, 109), new_value = NA, longueur_min = 108.95, longueur_max = 109.5)
microsatellite_data <- corriger_allele(marker_temp = "BIBL1", allele_value = 101, new_value = NA, longueur_min = 101, longueur_max = 102.2)
microsatellite_data <- corriger_allele(marker_temp = "BIBL1", allele_value = 107, new_value = NA, longueur_min = 107, longueur_max = 107.79)
# sum(is.na(a$allele_1))
# sum(is.na(microsatellite_data$allele_1))
On reverifie que tout est ok ! :
Le marqueur BIBL31 a énormément de variabilité, je vais le plot analyse par analyse pour voir si
4.2 Genotype re-test
Certains individus ont été génotypé plusieurs fois, et les résultats des deux analyses sont parfois différents. Pour choisir quelle analyse prendre, je vais d’abord voir, NA non-inclus, si les résultats par marqueurs test/retest sont congruents. En cas de mismatch, j’ai ensuite réalisé des filtres censés retenir l’allèle le plus vraisembable par marqueur.
4.2.1 Merge des données BDD et des données Genemapper
On merge les deux dataframes car certains individus ont pu être re-testés dans le batch que j’ai analysé.
# artificially add unique id_file to pg
pg$id_file <- paste0("000", 1:nrow(pg))
# ne garder que les colonnes en commun mais en conservant year_analysis
pg <- pg[colnames(pg) %in% colnames(microsatellite_data)]
# reorder columns
pg <- pg[colnames(microsatellite_data)[colnames(microsatellite_data) %in% colnames(pg)]]
# merging
pg_all <- rbind(pg, microsatellite_data[colnames(microsatellite_data)[colnames(microsatellite_data) %in% colnames(pg)]])
4.2.2 Individus re-testés de puis le début de suivi
index_temp_retest <- which(duplicated(paste0(pg_all$individual_id, pg_all$marker))) # row with duplicated marker x individual id
id_temp_retest <- unique(pg_all[index_temp_retest, "individual_id"])
Vérification : Nombre d’individu retesté
Nombre d’individu re-testé : 71.
Leur ID est :
## [1] 137 832 849 850 864 888 904 1005 1021 1061 1278 1279 1296 1297 1300
## [16] 1303 1311 1312 1316 1319 1327 1336 1345 1352 1355 1359 1361 1375 1376 1381
## [31] 1382 1390 1399 1405 1455 1470 1481 1520 1527 1551 1575 1598 1714 1715 1716
## [46] 1717 1718 1733 1763 1770 1778 1830 1845 1853 1859 1894 1897 1899 1945 1946
## [61] 1947 1952 1957 1960 1961 1990 1994 1999 2046 2124 2138
Vérification : Répartition des re-test par analyse Genemapper
##
## 2013 2015 2017 2020 2022 2023_1 2023_2 2023_3 <NA>
## 44 4 38 8 28 22 12 22 65
4.2.3 Mismatch test-retest
4.2.3.1 Etape 1 : congruence des tests-retests
On extrait les individus pour lesquels on a un mismatch test-retest i.e. où au moins un des test-retest ne donne pas le même résultat que les autres.
mismatch_retest_id <- c() # vector to stock individuals with different test/re-test results
list_error <- list()
mismatch_marker <- c() # store mismatched marker
mismatch_analysis <- c() # store analysis that Id did that failed
# FOR LOOP TO EXTRACT MISMATCHED TEST RETEST PER INDIVIDUAL
for(i in id_temp_retest){
data_temp <- pg_all[pg_all$individual_id == i, ]
data_temp$haplotype <- paste0(data_temp$marker, "_", data_temp$allele_1, "_", data_temp$allele_2)
# check if at least one mismatch test/re-test is present
test <-
data_temp |>
filter(!is.na(allele_1) | !is.na(allele_2)) |>
group_by(marker) |>
mutate(error = length(unique(haplotype)) != 1) |>
arrange(marker) |>
as.data.frame() |>
filter(error)
# count longest sequence
nb_na_seq <-
data_temp |>
group_by(fsa) |>
reframe(seq_non_na = n() - sum(is.na(allele_1))) # longest profil sequence without NAs (i.e. most reliable)
test <-
test |>
right_join(y = nb_na_seq, by = "fsa") |>
filter(error)
# count number of duplicated test
haplotype_count <-
test |>
filter(!is.na(allele_1)) |>
dplyr::count(haplotype)
test <-
test |>
right_join(y = haplotype_count, by = "haplotype") |>
filter(error)
colnames(test)[ncol(test)] <- "n_test"
# output
if(any(test$error)){
# store id
pb_id <- unique(test$individual_id)
pb_marker <- unlist(unique(test$marker))
pb_analysis <- unlist(unique(test$year_analysis))
mismatch_retest_id <- c(mismatch_retest_id, pb_id)
mismatch_marker <- c(mismatch_marker, pb_marker)
mismatch_analysis <- c(mismatch_analysis, pb_analysis)
# store data_temp to manually check results
list_error[[pb_id]] <- test
}
}
# CONCATENATE RESULTS
df_error <- do.call(rbind, list_error)
# write.xlsx(df_error, file = "test_retest_error_to_check.xlsx")
Vérification : Nombre d’individu avec mismatch test-retest
Nombre d’individu avec au moins un mismatch test-retest :
8 (11% des individus re-testés).
Leur ID est :
## [1] 137 832 1327 1763 1830 1845 1947 1952
Vérification : Nombre d’observation (individu*marqueur) avec mismatch test-retest
Nombre de lecture avec au moins un mismatch test-retest : 77
Vérification : Répartition des mismatchs test-retest par marqueur
## mismatch_marker
## BIBL1 BIBL18 BIBL20 BIBL31 BIBL4 MA002 MA018 MA066 MA091 MS41 MS45
## 3 3 2 3 3 2 3 1 2 1 2
## MS47 MS53 MS56 MS6 ST10
## 3 4 1 2 3Les mismatchs test-retests se trouvent surtout sur le marqueur
ST10 (dans l’analyse 2023 PK5 [2023_3]).
Mais ce marqueur a été supprimé pour cette analyse.
Vérification : Bilan erreur
## ID marker A1 A2 analysis
## 1 1763 MS53 140 140 2017
## 2 1763 MS53 140 142 2017
## 3 1947 BIBL31 161 161 2022
## 4 1947 BIBL31 159 163 2023_1
## 5 1947 MA002 279 281 2022
## 6 1947 MA002 279 279 2023_1
## 7 1947 MS47 180 182 2022
## 8 1947 MS47 180 180 2023_1
## 9 1947 MS53 142 142 2022
## 10 1947 MS53 140 142 2023_1
## 11 1947 MS6 158 160 2022
## 12 1947 MS6 158 158 2023_1
## 13 1947 ST10 116 132 2022
## 14 1947 ST10 116 116 2023_1
## 15 1952 BIBL1 103 103 2022
## 16 1952 BIBL1 101 101 2023_1
## 17 1830 BIBL18 147 147 2023_1
## 18 1830 BIBL18 147 147 2023_2
## 19 1830 BIBL18 143 143 2023_3
## 20 1845 BIBL4 190 190 2022
## 21 1845 BIBL4 188 190 2023_2
## 22 1845 MA018 298 298 2022
## 23 1845 MA018 296 298 2023_2
## 24 137 BIBL1 97 101 <NA>
## 25 137 BIBL1 101 101 <NA>
## 26 137 BIBL18 147 147 <NA>
## 27 137 BIBL18 143 147 <NA>
## 28 137 BIBL31 157 157 <NA>
## 29 137 BIBL31 157 161 <NA>
## 30 137 BIBL4 175 190 <NA>
## 31 137 BIBL4 188 190 <NA>
## 32 137 MA002 279 279 <NA>
## 33 137 MA002 279 281 <NA>
## 34 137 MA018 298 298 <NA>
## 35 137 MA018 296 298 <NA>
## 36 137 MA091 167 175 <NA>
## 37 137 MA091 175 175 <NA>
## 38 137 MS45 109 111 <NA>
## 39 137 MS45 107 111 <NA>
## 40 137 MS47 180 180 <NA>
## 41 137 MS47 186 186 <NA>
## 42 137 MS53 142 142 <NA>
## 43 137 MS53 132 140 <NA>
## 44 137 MS56 106 108 <NA>
## 45 137 MS56 108 108 <NA>
## 46 137 ST10 118 136 <NA>
## 47 137 ST10 118 134 <NA>
## 48 832 BIBL1 101 101 <NA>
## 49 832 BIBL1 97 101 <NA>
## 50 832 BIBL18 143 145 <NA>
## 51 832 BIBL18 147 149 <NA>
## 52 832 BIBL20 216 218 <NA>
## 53 832 BIBL20 218 218 <NA>
## 54 832 BIBL31 157 157 <NA>
## 55 832 BIBL31 159 161 <NA>
## 56 832 BIBL4 190 190 <NA>
## 57 832 BIBL4 188 190 <NA>
## 58 832 MA018 296 298 <NA>
## 59 832 MA018 298 298 <NA>
## 60 832 MA066 231 241 <NA>
## 61 832 MA066 231 233 <NA>
## 62 832 MA091 173 175 <NA>
## 63 832 MA091 175 175 <NA>
## 64 832 MS41 184 186 <NA>
## 65 832 MS41 186 186 <NA>
## 66 832 MS45 109 109 <NA>
## 67 832 MS45 111 111 <NA>
## 68 832 MS47 186 186 <NA>
## 69 832 MS47 188 188 <NA>
## 70 832 MS53 140 140 <NA>
## 71 832 MS53 142 142 <NA>
## 72 832 MS6 158 158 <NA>
## 73 832 MS6 142 160 <NA>
## 74 832 ST10 116 118 <NA>
## 75 832 ST10 116 134 <NA>
## 76 1327 BIBL20 208 218 <NA>
## 77 1327 BIBL20 208 208 2013
4.2.3.2 Etape 2
L’idée est maintenant de déterminer quelle analyse test/retest conserver. On sélectionne la meilleure analyse par individu par marqueur selon les critères suivants (par ordre d’importance)
- Qualité de la séquence - Allèles correspondant au profil avec la plus longue séquence sans NAs
- Haplotype le plus fréquent - Si séquence sans NA égale, prendre l’haplotype le plus fréquent
- Analyse la plus récente - Si toutes les fréquences sont égales, prendre la séquence la plus récente
index <- match(df_error$individual_id, pg_all$individual_id)
df_error$year_analysis <- pg_all$year_analysis[index]
df_error$year_analysis[df_error$year_analysis %in% c("2023_1", "2023_2", "2023_3")] <- 2023 # pour tirer au plus récent
to_remove <- c()
for(ind_temp in unique(df_error$individual_id)){
marker_pb <- unique(df_error[df_error$individual_id == ind_temp, "marker"])
for(marker_temp in marker_pb){
df_filtered <- df_error[df_error$individual_id == ind_temp & df_error$marker == marker_temp, ]
# a. Garder séquence(s) la/les plus longue(s)
df_temp <- df_filtered |>
filter(seq_non_na == max(seq_non_na, na.rm = TRUE))
# b. Si plusieurs séquences équivalentes, garder haplotype le plus fréquent
if(nrow(df_temp) > 1){
freq_tab <- table(df_temp$haplotype)
max_freq <- max(freq_tab)
best_haplos <- names(freq_tab[freq_tab == max_freq])
df_temp <- df_temp |>
filter(haplotype %in% best_haplos)
}
# c. Si encore ex-aequo, garder la plus récente analyse
if(nrow(df_temp) > 1 & !all(is.na(df_temp$year_analysis))){
df_temp <- df_temp |>
filter(year_analysis == max(year_analysis, na.rm = TRUE))
}
# Sélection finale : garder le premier (au cas d’égalité stricte)
df_keep <- df_temp[1, ]
# Marquer tous les autres comme "à supprimer"
ids_remove <- setdiff(df_filtered$id_file, df_keep$id_file)
to_remove <- c(to_remove, ids_remove)
}
}
# On supprime les lignes avec les données problématiques ou redondantes
pg_all <- pg_all[!pg_all$id_file %in% to_remove, ]Le problème de cette méthode est que on va crée des chimères de
profils génétiques issus de plusieurs génotypage (i.e. fichiers
.fsa). Je me demande si on ne devrait pas prendre la
séquence non-NA la plus longue uniquement. A réfléchir.
4.2.4 Match test-retest
Pour les individus sans mismatch test/retest, on supprime simplement les doublons. En cas de NA pour un marqueur donné, on conserve la donnée non-NA.
for(i in id_temp_retest){ # [!id_temp_retest %in% mismatch_retest_id]
data_temp <- pg_all[pg_all$individual_id %in% i, ]
id_file_initial <- data_temp$id_file
# on optimise les données gardées i.e. si NAs dans test et pas NAs dans retest, on garde retest
id_file_to_keep <-
data_temp |>
group_by(marker) %>%
# Si le marker a au moins une ligne complète, supprimer les NAs
filter(
!(is.na(allele_1) & is.na(allele_2) & any(!is.na(allele_1) | !is.na(allele_2)))
) %>%
filter(!duplicated(marker)) |> # on enlève les doublons car on sait qu'ils sont identiques (test précédent)
ungroup() |>
dplyr::select(id_file) |>
as.data.frame() |>
unlist()
# remove the non wanted id file
id_file_to_remove <- id_file_initial[!id_file_initial %in% id_file_to_keep]
# filter dataframe
pg_all <- pg_all[!pg_all$id_file %in% id_file_to_remove, ]
}Même problème que plus haut, cette méthode est que on va crée des
chimères de profils génétiques issus de plusieurs génotypage
(i.e. fichier .fsa).
Vérification : Après cette méthode, on s’assure qu’on a plus d’individus avec plusieurs données pour un marqueur donné
Nombre d’individu(s) dupliqué(s) : 0
4.3 Non-genotyped individuals
On calcule le nombre d’individu n’ayant pas été génotypés. A noter qu’Aurélie COHAS m’a indiqué que les individus non-génotypés datant d’avant 2015 ne peuvent simplement pas l’être… Leur ADN est trop âbimé. Certains individus ont eu jusqu’à 10 génotypages, qui n’a jamais rien donné.
Nombre d’individu(s) non-génotypé(s) : 118 / 2042 (5.78%)
Vérification : Répartition des non-génotypés dans les années
##
## 1990 1991 1992 1996 1997 2000 2003 2011 2012 2013 2014 2016 2017 2018 2019
## 16 11 6 8 2 1 13 1 7 6 1 1 41 1 3La plupart des individus non-génotypés sont capturés pour la première fois en 2017, suivi de 1990 et 2003. D’après Corinne, les individus de 2017 non-génotypés correspondent à des oublis.
Vérification : Informations sur ces individus de 2017
Les ID de ces individus capturés en 2017 sont :
## [1] 1793 1794 1795 1797 1798 1799 1800 1801 1803 1805 1806 1807 1808 1809 1810
## [16] 1811 1812 1813 1815 1817 1818 1819 1820 1821 1822 1823 1824 1825 1826 1829
## [31] 1831 1832 1833 1834 1836 1837 1838 1839 1840 1841 1842La plupart (37) sont des marmottons (i.e. date d’émergence en 2017 et date de première capture en 2017) et 4 sont des individus capturés adultes.
Vérification : Nombre d’individus parents sociaux non-génotypés
Ces individus correspondraient aux individus prioritaires pour les analyses car ils apporteraient de l’information de parentée sur toutes leurs portées.
Nombre d’individu(s) non-génotypé(s) parent(s) : 7.
Leur ID sont :
## [1] 6 45 55 1605 1818 1834 1842
Vérification : Répartition des parents non-génotypés selon l’année de capture
##
## 1990 1991 1992 2014 2017
## 1 1 1 1 3La plupart sont des parents de 2017… et 3 d’entre eux sont de très vieux individus (1990, 1991 et 1992).
5 FORMATAGE DES DONNEES GENOTYPES POUR CERVUS
On formatte les données comment dans CERVUS pour avoir un tableau
avec en ligne les individus, en colonne leur profil génétique au loci
donné (i.e. 2 par allèles e.g. BIBL1a /
BIBL1b) et des 0 à la place des NA. Ce tableau
correspondra au fichier genotype files de l’analyse
CERVUS.
5.1 Formatage
# FORMAT DF
pg_cervus <- pg_all |>
arrange(individual_id, desc(id_file)) |>
filter(!duplicated(paste0(marker, individual_id))) |>
#formatting for cervus : ID / SEXE / marker_a / marker_b
pivot_wider(id_cols = c(individual_id, sexe, annee_emergence),
names_from = marker,
values_from = c(allele_1, allele_2),
names_sep = "",
names_vary = "slowest"
)
pg_cervus <- as.data.frame(pg_cervus)
# RENAME COL FOR CERVUS
former_colnames <- colnames(pg_cervus)[4:ncol(pg_cervus)]
suffixe <- rep(x = c("a", "b"), length(former_colnames)/2)
new_names <- paste0(gsub(pattern = "allele_1|allele_2", replacement = "", x = former_colnames), suffixe)
colnames(pg_cervus)[4:ncol(pg_cervus)] <- new_names
# REPLACE NA PER 0 (cf Cervus tutorial)
pg_cervus[4:ncol(pg_cervus)] <- apply(pg_cervus[4:ncol(pg_cervus)], 2, function(x) replace_na(as.numeric(as.character(x)), 0))
# REMOVE BLANK SPACE
pg_cervus[] <- lapply(pg_cervus, function(x) {
if (is.character(x)) gsub(" ", "", x) else x
}) # pg[] permet de garder la structure en data.frame
# TYPE OF VARIABLES
pg_cervus[4:ncol(pg_cervus)] <- sapply(X = pg_cervus[,4:ncol(pg_cervus)], as.character)
pg_cervus$individual_id <- as.character(pg_cervus$individual_id) # IMPORTANT
head(pg_cervus)## individual_id sexe annee_emergence MA066a MA066b MA018a MA018b MA002a MA002b
## 1 100 m NA 231 241 296 298 271 279
## 2 1000 f 2006 231 231 298 298 281 281
## 3 1001 f 2006 231 241 298 298 279 281
## 4 1002 f 2006 231 231 298 298 279 281
## 5 1003 m 2006 231 231 298 298 281 281
## 6 1004 f 2006 231 231 298 298 281 281
## BIBL4a BIBL4b BIBL31a BIBL31b BIBL20a BIBL20b BIBL18a BIBL18b BIBL1a BIBL1b
## 1 175 188 157 159 208 208 137 149 97 101
## 2 190 192 157 157 208 216 143 145 95 101
## 3 188 190 157 159 208 216 143 145 101 109
## 4 188 190 157 159 208 216 145 147 101 109
## 5 188 190 157 159 208 218 143 145 101 107
## 6 188 190 157 159 208 218 145 149 101 107
## ST10a ST10b MS56a MS56b MS6a MS6b MS53a MS53b MS47a MS47b MS45a MS45b MS41a
## 1 130 134 106 108 158 158 140 140 182 184 109 109 186
## 2 116 118 108 108 158 158 140 142 186 186 107 109 186
## 3 116 132 106 108 158 158 140 140 176 182 109 109 186
## 4 116 118 106 108 158 158 132 142 176 182 109 109 186
## 5 118 130 106 108 158 158 140 140 186 186 107 109 184
## 6 118 130 108 108 158 158 132 140 182 186 109 109 184
## MS41b MA091a MA091b
## 1 186 167 175
## 2 186 175 175
## 3 186 159 175
## 4 186 159 175
## 5 186 175 175
## 6 184 173 175
Vérification : Présence de NA au lieu de 0 dans les allèles ?
Nombre de NA dans le tableau : 0
Vérification : Respect de l’ordre des allèles ?
Nombre de cas où allèle 1 < allèle 2 : 0
Vérification : Nombre de profils ne contenant que des 0s
Nombre d’individus sans données : 0
Vérification : Longueur des séquences non-NA
## Fréquence de la longueur de séquence non-NA
## 16 15 14 13 12 11 10 9 8 7 6 5 4 3
## 0.63 0.18 0.08 0.04 0.03 0.02 0.01 0.01 0.00 0.00 0.00 0.00 0.00 0.0093% des profils génétiques ont plus de 13 / 16 marqueurs non-NA.
On considerera un mauvais génotypage si l’individu a moins de 13 marqueurs disponibles pour comparaison.
## [1] 7Nombre d’individu avec moins de 13 marqueurs à comparer : 136 (7%)
Leur ID est :
## [1] 66 240 373 541 542 553 612 649 819 860 864 906 910 915 1099
## [16] 1220 1327 1345 1404 1424 1432 1434 1446 1452 1470 1484 1503 1506 1513 1550
## [31] 1551 1554 1568 1573 1575 1587 1596 1624 1629 1631 1635 1642 1645 1650 1652
## [46] 1653 1654 1688 1691 1697 1698 1700 1702 1719 1723 1731 1738 1747 1765 1768
## [61] 1769 1784 1796 1802 1843 1846 1848 1854 1855 1856 1857 1862 1869 1872 1877
## [76] 1878 1884 1890 1898 1903 1907 1909 1922 1930 1931 1933 1934 1936 1938 1940
## [91] 1941 1943 1948 1949 1950 1951 1956 1966 1967 1995 2005 2017 2068 2075 2101
## [106] 2102 2103 2104 2105 2106 2107 2108 2112 2115 2116 2125 2126 2133 2148 2156
## [121] 2163 2164 2167 2182 2190 2198 2199 2200 2202 2206 2207 2208 2209 2210 2211
## [136] 2212
Vérification : Informations sur la répartition des mauvais profils
## YEAR OF ANALYSIS
## 2015 2017 2020 2022 2023_1 2023_2 2023_3 <NA>
## 14 9 25 16 2 21 11 38## year_analysis marker nb_NA
## 1 2013 BIBL1 0
## 2 2013 BIBL18 2
## 3 2013 BIBL20 0
## 4 2013 BIBL31 1
## 5 2013 BIBL4 1
## 6 2013 MA002 5
## 7 2013 MA018 6
## 8 2013 MA066 6
## 9 2013 MA091 0
## 10 2013 MS41 2
## 11 2013 MS45 2
## 12 2013 MS47 4
## 13 2013 MS53 0
## 14 2013 MS56 3
## 15 2013 MS6 1
## 16 2013 ST10 56
## 17 2015 BIBL1 2
## 18 2015 BIBL18 3
## 19 2015 BIBL20 9
## 20 2015 BIBL31 1
## 21 2015 BIBL4 19
## 22 2015 MA018 5
## 23 2015 MA066 3
## 24 2015 MA091 1
## 25 2015 MS41 8
## 26 2015 MS45 1
## 27 2015 MS47 6
## 28 2015 MS53 12
## 29 2015 MS56 0
## 30 2015 MS6 2
## 31 2015 ST10 26
## 32 2017 BIBL1 6
## 33 2017 BIBL18 2
## 34 2017 BIBL20 5
## 35 2017 BIBL31 7
## 36 2017 BIBL4 26
## 37 2017 MA002 9
## 38 2017 MA018 3
## 39 2017 MA066 10
## 40 2017 MA091 3
## 41 2017 MS41 4
## 42 2017 MS45 1
## 43 2017 MS47 13
## 44 2017 MS53 4
## 45 2017 MS56 2
## 46 2017 MS6 7
## 47 2017 ST10 8
## 48 2020 BIBL1 83
## 49 2020 BIBL18 9
## 50 2020 BIBL20 22
## 51 2020 BIBL31 37
## 52 2020 BIBL4 8
## 53 2020 MA002 9
## 54 2020 MA018 7
## 55 2020 MA066 10
## 56 2020 MA091 6
## 57 2020 MS41 2
## 58 2020 MS45 3
## 59 2020 MS47 11
## 60 2020 MS53 7
## 61 2020 MS56 4
## 62 2020 MS6 5
## 63 2020 ST10 7
## 64 2022 BIBL1 32
## 65 2022 BIBL18 14
## 66 2022 BIBL20 12
## 67 2022 BIBL31 29
## 68 2022 BIBL4 11
## 69 2022 MA002 15
## 70 2022 MA018 4
## 71 2022 MA066 9
## 72 2022 MA091 14
## 73 2022 MS41 2
## 74 2022 MS45 8
## 75 2022 MS47 12
## 76 2022 MS53 13
## 77 2022 MS56 5
## 78 2022 MS6 3
## 79 2022 ST10 7
## 80 2023_1 BIBL1 2
## 81 2023_1 BIBL18 1
## 82 2023_1 BIBL20 2
## 83 2023_1 BIBL31 3
## 84 2023_1 BIBL4 1
## 85 2023_1 MA002 6
## 86 2023_1 MA018 1
## 87 2023_1 MA066 3
## 88 2023_1 MA091 2
## 89 2023_1 MS41 0
## 90 2023_1 MS45 1
## 91 2023_1 MS47 1
## 92 2023_1 MS53 1
## 93 2023_1 MS56 0
## 94 2023_1 MS6 1
## 95 2023_1 ST10 13
## 96 2023_2 BIBL1 43
## 97 2023_2 BIBL18 0
## 98 2023_2 BIBL20 39
## 99 2023_2 BIBL31 18
## 100 2023_2 BIBL4 0
## 101 2023_2 MA002 8
## 102 2023_2 MA018 3
## 103 2023_2 MA066 2
## 104 2023_2 MA091 30
## 105 2023_2 MS41 2
## 106 2023_2 MS45 0
## 107 2023_2 MS47 29
## 108 2023_2 MS53 0
## 109 2023_2 MS56 0
## 110 2023_2 MS6 15
## 111 2023_2 ST10 5
## 112 2023_3 BIBL1 1
## 113 2023_3 BIBL18 8
## 114 2023_3 BIBL20 3
## 115 2023_3 BIBL31 38
## 116 2023_3 BIBL4 0
## 117 2023_3 MA002 0
## 118 2023_3 MA018 9
## 119 2023_3 MA066 15
## 120 2023_3 MA091 0
## 121 2023_3 MS41 7
## 122 2023_3 MS45 0
## 123 2023_3 MS47 0
## 124 2023_3 MS53 0
## 125 2023_3 MS56 0
## 126 2023_3 MS6 0
## 127 2023_3 ST10 38
## 128 <NA> BIBL1 24
## 129 <NA> BIBL18 19
## 130 <NA> BIBL20 50
## 131 <NA> BIBL31 17
## 132 <NA> BIBL4 22
## 133 <NA> MA002 58
## 134 <NA> MA018 46
## 135 <NA> MA066 34
## 136 <NA> MA091 16
## 137 <NA> MS41 22
## 138 <NA> MS45 11
## 139 <NA> MS47 41
## 140 <NA> MS53 22
## 141 <NA> MS56 25
## 142 <NA> MS6 34
## 143 <NA> ST10 127On a beaucoup de génotypage qui n’ont pas bien fonctionnés… notamment
sur l’analyse de 2020 et 2022 qui sont assez
catastrophiques.
5.2 Save
# # SAVE
# dir <- "C:/Users/quittet/Documents/marmot-quantitative-genetics/PEDIGREE_GENETIQUE/CERVUS/"
# write.table(pg_cervus, file = paste0(dir, "ANALYSIS_0_BDD_CERVUS/genotype.txt"), row.names = F, sep = "\t") # analyse 0
# write.table(pg_cervus, file = paste0(dir, "ANALYSIS_1_BDD_CERVUS/genotype.txt"), row.names = F, sep = "\t") # analyse 1
# write.table(pg_cervus, file = paste0(dir, "ANALYSIS_2_BDD_CERVUS/genotype.txt"), row.names = F, sep = "\t") # analyse 2
5.3 Individus restant à analyser : “mal-génotypés”
On ajoute les individus avec moins de 13 marqueurs non-NA aux ID à re-tester avec Genoscreen
index_temp <- seq_non_na < 13
id_bad_genotyped <- unique(pg_cervus$individual_id[index_temp])
# get id
df_id_to_analyse[(nrow(df_id_to_analyse)+1):(nrow(df_id_to_analyse) + length(id_bad_genotyped)), "individual_id"] <- id_bad_genotyped
6 IDENTIFICATION DES INDIVIDUS A TESTER AVEC CERVUS
Les individus sur lesquels réalisés l’analyse de parenté sont les individus répondant aux critères suivants :
- Génotypé entre 2016 et 2022
- Capturé marmotton (i.e. date d’émergence connu)
- Sans mère génétique précisé dans la BDD
#' extract id of all new tested individuals
id_tested_2016_2022 <- unique(pg$individual_id)
# ajout année de capture la plus ancienne
#' ... note : les dates de captures les plus anciennes ont déjà été filtré
#' ... dans ìndividu
index_temp <- match(pg_cervus$individual_id, individu$individual_id)
pg_cervus$oldest_capture <- individu$annee_capture[index_temp]
pg_cervus <- pg_cervus[c(1,2,3,ncol(pg_cervus), 4:(ncol(pg_cervus) - 1))] # sort columns
# EXTRAIRE LES ID DES INDIVIDUS A TESTER AVEC CERVUS
#' ... (*) qui ont été génotypé entre 2016 et 2022
#' ... (*) mère sociale connue
#' ... (*) sans parents génétiques dans la BDD
id_pups_to_test <- pg_cervus[pg_cervus$individual_id %in% id_tested_2016_2022 & # génotypé en 2016 - 2022
!is.na(pg_cervus$annee_emergence) & # date d'émergence connue
!pg_cervus$individual_id %in% parentee_genetique$individual_id[!is.na(parentee_genetique$mother)], # sans mère génétiques dans la BDD
"individual_id"]Nombre d’individu(s) à tester avec CERVUS : 438
Vérification : Nombre de mismatch test-retest parmi les individus à analyser
Nombre d’individu ayant au moins une donnée issu d’un retest : 5.
Leur ID est :
## [1] 1763 1830 1845 1947 1952
Vérification : Informations sur ces individus re-testés
## individual_id annee_emergence sexe annee_capture genemapper_analysis
## 844 1763 2016 f 2016 2017
## 918 1830 2017 f 2017 2023_1
## 934 1845 NA m 2018 2022
## 1047 1947 2019 m 2019 2022
## 1053 1952 2019 m 2019 2022Les retests ont surtout eu lieu sur des marmottons en 2017 et 2022.
7 RAW PARENTAGE COMPARISON
L’idée ici est de tester si la mère a toujours au moins un de ses allèles en commun avec le marmotton. On utilise pas CERVUS ici.
7.1 Mother test
On teste ici uniquement avec la mère. On regarde si au moins une allèle de l’offspring par locus est présent chez la mère. L’idée étant qu’on connait l’identité de la mère, qui est la femelle dominante. Donc sauf cas rarissime, on ne devrait pas avoir d’erreur.
Vérification : Tous les individus à tester sont présents dans le pédigrée
Nombre d’individu à tester non-présent dans le pédigrée : 1.
Les ID sont :
## [1] "2077"
Seul l’individu 2077 n’est pas présent. Cela est du au
fait que c’est un individu qui a perdu sa bague et qui est
1967 en réalité, après comparaison de son génotype (voir
fichier famille). On corrige :
pg_cervus <- pg_cervus[!pg_cervus$individual_id == 1967, ] # on conserve les data de 1967 qui ont plus de marqueur
pg_cervus$individual_id[pg_cervus$individual_id == 2077] <- "1967" # on renomme
id_pups_to_test <- id_pups_to_test[!id_pups_to_test == "2077"] # on supprime 2077 des pups id à tester
7.1.2 Comparaison des génotypes
On trie le pédigrée selon les critères cités plus haut :
- Mère sociale a été génotypée
- Individus génotypés entre 2016 et 2022
- Mère sociale connue
- Individu avec plus de 13 marqueurs non-NA
# ped_tested <- pedigree_data[pedigree_data$individual_id %in% id_pups_to_test & # id génotypé entre 2016-2022
# !is.na(pedigree_data$mother) & # mère sociale connue
# !pedigree_data$mother %in% id_non_genotyped, ] # mère génotypée
#
ped_tested <- pedigree_data[!is.na(pedigree_data$mother) &
# mère sociale connue!pedigree_data$individual_id %in% id_non_genotyped &
!pedigree_data$mother %in% id_non_genotyped &
# mère génotypée
pedigree_data$individual_id %in% pg_cervus$individual_id &
!pedigree_data$individual_id %in% parentee_genetique$individual_id, ] # individu dont la parentée génétique n'a pas été testé
Vérification : Nombre d’individu testé effectivement
Nombre d’individu(s) répondant à tous les critères de filtre : 424
7.1.3 Test
La fonction match_genotype() permet de comparer si
l’offspring et la mère partagent au moins un allèle à chaque
marqueur.
res_match <- apply(X = ped_tested[1:2], MARGIN = 1, FUN = function(x) match_genotype(x)$mother_match) Associations correctes : 376 / 424 (89%)
7.1.4 Exploration des mismatchs
A noter que l’ensemble des parentées sociales ont été re-vérifiées à partir du fichier famille quand il y avait au moins un mismatch…
# GET MISMATCH INDIVIDUAL INFORMATION
index_temp <- as.numeric(which(!res_match))
index_temp <- match(ped_tested$individual_id, individu$individual_id) # match cohort
ped_tested["annee_emergence"] <- individu[index_temp, "annee_emergence"]
ped_tested[["match_mother"]] <- res_match
ped_tested["n_loci_compared"] <- apply(X = ped_tested[1:2], MARGIN = 1, FUN = function(x) match_genotype(x)$n)
ped_tested["n_error"] <- apply(X = ped_tested[1:2], MARGIN = 1, FUN = function(x) match_genotype(x)$nb_mismatch_mother_offspring)
# LENGTH OF THE SEQ FOR OFFSPRING
length_seq <- apply(pg_cervus[5:ncol(pg_cervus)], 1, function(x) sum(x!=0)/2)
index_temp <- match(ped_tested$individual_id, pg_cervus$individual_id)
ped_tested$length_seq <- length_seq[index_temp] # offspring sequence
index_temp <- match(ped_tested$mother, pg_cervus$individual_id)
ped_tested$length_seq_mother <- length_seq[index_temp] # mother sequence
7.1.4.1 Nombre d’erreur et nombre de loci comparé
On calcule ici le nombre de cas où un individu a un mismatch avec sa mère. On précise également le nombre de loci comparable entre les deux (en excluant les NA).
## Nombre de marqueurs avec mismatch
## 0 1 2 3 7 14 15 16 <NA>
## 376 34 4 1 1 2 1 5 0## Nombre de loci comparé
## 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 <NA>
## 1 1 2 1 10 5 3 14 20 32 47 72 96 74 46 0
L’immense majorité des mismatchs est situé seulement sur un marqueur. 5 individus ont 2 mismatchs avec leur mère et un individu a 4 mismatchs.
7.1.4.2 Nombre de mère unique avec mismatch
Nombre de mère unique avec au moins un mismatch avec leur offspring : 16.
Leur ID est :
## [1] 1134 1182 1298 1315 1408 1432 1433 1520 1560 1564 1569 1635 1657
et les erreurs sont réparties comme suit :
##
## 1134 1182 1298 1315 1408 1432 1433 1520
## 2 2 1 6 2 1 1 1
## 1560 1564 1569 1635 1657 dummy_891 dummy_945 dummy_973
## 6 1 1 9 6 1 4 4Les mères 1315, 1560, 1657 et 1635 ont respectivement un mismatch avec 6,6,6 et 9 de leur offspring. A regarder de plus près. Il est possible que ces mères ne soient pas les mères sociales ou qu’elles aient une erreur de génotypage…
La qualité de leur génotypage :
ped_tested[!ped_tested$match_mother, c("individual_id", "length_seq", "mother", "length_seq_mother", "n_loci_compared"),] |>
arrange(mother)## individual_id length_seq mother length_seq_mother n_loci_compared
## 1 1758 16 1134 16 16
## 2 1757 16 1134 16 16
## 3 1701 14 1182 16 14
## 4 1698 11 1182 16 11
## 5 1862 11 1298 15 10
## 6 2133 11 1315 15 10
## 7 2072 16 1315 15 15
## 8 2071 15 1315 15 14
## 9 2003 16 1315 15 15
## 10 1770 16 1315 15 15
## 11 1769 12 1315 15 11
## 12 1792 15 1408 14 13
## 13 1731 10 1408 14 9
## 14 1835 13 1432 9 8
## 15 1738 11 1433 15 10
## 16 1956 12 1520 16 12
## 17 1918 13 1560 15 12
## 18 1917 15 1560 15 14
## 19 1916 14 1560 15 13
## 20 1856 11 1560 15 10
## 21 1855 11 1560 15 11
## 22 1854 12 1560 15 11
## 23 1948 10 1564 15 9
## 24 2075 12 1569 13 9
## 25 1827 14 1635 6 4
## 26 1928 15 1635 6 6
## 27 1895 15 1635 6 6
## 28 1931 12 1635 6 6
## 29 1930 12 1635 6 6
## 30 1929 15 1635 6 6
## 31 1894 16 1635 6 6
## 32 1893 14 1635 6 5
## 33 1886 13 1635 6 6
## 34 2125 11 1657 14 10
## 35 2121 16 1657 14 14
## 36 2064 16 1657 14 14
## 37 2062 16 1657 14 14
## 38 1952 16 1657 14 14
## 39 1947 16 1657 14 14
## 40 1909 7 dummy_891 NA 7
## 41 2030 16 dummy_945 NA 16
## 42 2029 16 dummy_945 NA 16
## 43 2028 16 dummy_945 NA 16
## 44 2027 16 dummy_945 NA 16
## 45 2147 14 dummy_973 NA 14
## 46 2146 15 dummy_973 NA 15
## 47 2140 14 dummy_973 NA 14
## 48 2139 16 dummy_973 NA 16Une partie des mismatchs est due à des individus (offspring ou parents) mal génotypés. La mère 1635 est clairement à re-génotyper (seulement 6 marqueurs non-NA)
7.1.4.3 Répartition des individus ayant au moins un mismatch avec leur mère dans les analyses
##
## 2015 2017 2020 2022 2023_1 2023_2 2023_3 <NA>
## 2 6 16 7 9 7 1 0
7.1.4.4 Répartition mismatchs dans les années d’analyses
## ANALYSIS OF THE MOTHER
## ANALYSIS OF OFFSPRING 2013 2015 2023_1 <NA>
## 2015 0 0 0 2
## 2017 2 0 0 4
## 2020 1 8 0 7
## 2022 1 2 1 3
## 2023_1 2 2 0 5
## 2023_2 1 2 0 4
## 2023_3 0 1 0 0
## <NA> 0 0 0 0
7.1.4.5 Répartition des erreurs parmi les marqueurs
## Analysis of offspring
## 2015 2017 2020 2022 2023_1 2023_2 2023_3 <NA>
## BIBL1 0 0 0 0 4 4 0 0
## BIBL18 0 0 0 0 5 4 0 0
## BIBL20 1 0 1 2 4 4 0 0
## BIBL31 1 3 6 4 8 5 0 0
## BIBL4 0 2 1 0 5 4 0 0
## MA002 0 0 1 1 4 4 0 0
## MA018 0 0 0 0 4 4 0 0
## MA066 0 0 0 0 4 5 0 0
## MA091 0 0 1 0 4 4 0 0
## MS41 0 1 8 1 4 4 1 0
## MS45 0 0 0 0 4 4 0 0
## MS47 0 0 1 1 5 1 0 0
## MS53 1 1 1 0 4 4 0 0
## MS56 0 0 1 0 4 4 0 0
## MS6 0 0 1 0 4 3 0 0
## ST10 0 0 0 0 4 4 0 0
## <NA> 0 0 0 0 0 0 0 0## Analysis of mother
## 2013 2015 2023_1 <NA>
## BIBL1 0 0 0 8
## BIBL18 0 0 0 9
## BIBL20 0 0 1 11
## BIBL31 6 5 0 16
## BIBL4 0 0 0 12
## MA002 1 0 0 9
## MA018 0 0 0 8
## MA066 0 1 0 8
## MA091 0 0 0 9
## MS41 0 9 0 10
## MS45 0 0 0 8
## MS47 0 0 0 8
## MS53 0 0 0 11
## MS56 0 0 0 9
## MS6 0 0 0 8
## ST10 0 0 0 8
## <NA> 0 0 0 0## , , NA = BIBL1
##
## Mother
## Offspring 2013 2015 2023_1 <NA>
## 2015 0 0 0 0
## 2017 0 0 0 0
## 2020 0 0 0 0
## 2022 0 0 0 0
## 2023_1 0 0 0 4
## 2023_2 0 0 0 4
## 2023_3 0 0 0 0
## <NA> 0 0 0 0
##
## , , NA = BIBL18
##
## Mother
## Offspring 2013 2015 2023_1 <NA>
## 2015 0 0 0 0
## 2017 0 0 0 0
## 2020 0 0 0 0
## 2022 0 0 0 0
## 2023_1 0 0 0 5
## 2023_2 0 0 0 4
## 2023_3 0 0 0 0
## <NA> 0 0 0 0
##
## , , NA = BIBL20
##
## Mother
## Offspring 2013 2015 2023_1 <NA>
## 2015 0 0 0 1
## 2017 0 0 0 0
## 2020 0 0 0 1
## 2022 0 0 1 1
## 2023_1 0 0 0 4
## 2023_2 0 0 0 4
## 2023_3 0 0 0 0
## <NA> 0 0 0 0
##
## , , NA = BIBL31
##
## Mother
## Offspring 2013 2015 2023_1 <NA>
## 2015 0 0 0 1
## 2017 2 0 0 1
## 2020 0 0 0 6
## 2022 1 2 0 1
## 2023_1 2 2 0 4
## 2023_2 1 1 0 3
## 2023_3 0 0 0 0
## <NA> 0 0 0 0
##
## , , NA = BIBL4
##
## Mother
## Offspring 2013 2015 2023_1 <NA>
## 2015 0 0 0 0
## 2017 0 0 0 2
## 2020 0 0 0 1
## 2022 0 0 0 0
## 2023_1 0 0 0 5
## 2023_2 0 0 0 4
## 2023_3 0 0 0 0
## <NA> 0 0 0 0
##
## , , NA = MA002
##
## Mother
## Offspring 2013 2015 2023_1 <NA>
## 2015 0 0 0 0
## 2017 0 0 0 0
## 2020 1 0 0 0
## 2022 0 0 0 1
## 2023_1 0 0 0 4
## 2023_2 0 0 0 4
## 2023_3 0 0 0 0
## <NA> 0 0 0 0
##
## , , NA = MA018
##
## Mother
## Offspring 2013 2015 2023_1 <NA>
## 2015 0 0 0 0
## 2017 0 0 0 0
## 2020 0 0 0 0
## 2022 0 0 0 0
## 2023_1 0 0 0 4
## 2023_2 0 0 0 4
## 2023_3 0 0 0 0
## <NA> 0 0 0 0
##
## , , NA = MA066
##
## Mother
## Offspring 2013 2015 2023_1 <NA>
## 2015 0 0 0 0
## 2017 0 0 0 0
## 2020 0 0 0 0
## 2022 0 0 0 0
## 2023_1 0 0 0 4
## 2023_2 0 1 0 4
## 2023_3 0 0 0 0
## <NA> 0 0 0 0
##
## , , NA = MA091
##
## Mother
## Offspring 2013 2015 2023_1 <NA>
## 2015 0 0 0 0
## 2017 0 0 0 0
## 2020 0 0 0 1
## 2022 0 0 0 0
## 2023_1 0 0 0 4
## 2023_2 0 0 0 4
## 2023_3 0 0 0 0
## <NA> 0 0 0 0
##
## , , NA = MS41
##
## Mother
## Offspring 2013 2015 2023_1 <NA>
## 2015 0 0 0 0
## 2017 0 0 0 1
## 2020 0 8 0 0
## 2022 0 0 0 1
## 2023_1 0 0 0 4
## 2023_2 0 0 0 4
## 2023_3 0 1 0 0
## <NA> 0 0 0 0
##
## , , NA = MS45
##
## Mother
## Offspring 2013 2015 2023_1 <NA>
## 2015 0 0 0 0
## 2017 0 0 0 0
## 2020 0 0 0 0
## 2022 0 0 0 0
## 2023_1 0 0 0 4
## 2023_2 0 0 0 4
## 2023_3 0 0 0 0
## <NA> 0 0 0 0
##
## , , NA = MS47
##
## Mother
## Offspring 2013 2015 2023_1 <NA>
## 2015 0 0 0 0
## 2017 0 0 0 0
## 2020 0 0 0 1
## 2022 0 0 0 1
## 2023_1 0 0 0 5
## 2023_2 0 0 0 1
## 2023_3 0 0 0 0
## <NA> 0 0 0 0
##
## , , NA = MS53
##
## Mother
## Offspring 2013 2015 2023_1 <NA>
## 2015 0 0 0 1
## 2017 0 0 0 1
## 2020 0 0 0 1
## 2022 0 0 0 0
## 2023_1 0 0 0 4
## 2023_2 0 0 0 4
## 2023_3 0 0 0 0
## <NA> 0 0 0 0
##
## , , NA = MS56
##
## Mother
## Offspring 2013 2015 2023_1 <NA>
## 2015 0 0 0 0
## 2017 0 0 0 0
## 2020 0 0 0 1
## 2022 0 0 0 0
## 2023_1 0 0 0 4
## 2023_2 0 0 0 4
## 2023_3 0 0 0 0
## <NA> 0 0 0 0
##
## , , NA = MS6
##
## Mother
## Offspring 2013 2015 2023_1 <NA>
## 2015 0 0 0 0
## 2017 0 0 0 0
## 2020 0 0 0 1
## 2022 0 0 0 0
## 2023_1 0 0 0 4
## 2023_2 0 0 0 3
## 2023_3 0 0 0 0
## <NA> 0 0 0 0
##
## , , NA = ST10
##
## Mother
## Offspring 2013 2015 2023_1 <NA>
## 2015 0 0 0 0
## 2017 0 0 0 0
## 2020 0 0 0 0
## 2022 0 0 0 0
## 2023_1 0 0 0 4
## 2023_2 0 0 0 4
## 2023_3 0 0 0 0
## <NA> 0 0 0 0
##
## , , NA = NA
##
## Mother
## Offspring 2013 2015 2023_1 <NA>
## 2015 0 0 0 0
## 2017 0 0 0 0
## 2020 0 0 0 0
## 2022 0 0 0 0
## 2023_1 0 0 0 0
## 2023_2 0 0 0 0
## 2023_3 0 0 0 0
## <NA> 0 0 0 0
On construit le tableau d’erreur en complétant les informations (e.g. haplotype du père) et en ajoutant le nombre d’offspring avec qui la mère a des mismatchs afin de déterminer si l’erreur vient du génotypage de la mère ou de l’offspring.
# TOTAL NUMBER OF ERROR BETWEEN MOTHER AND OFFSPRINGS
# on match le nombre d'erreur total avec la mère de l'offspring
index_temp <- match(df_error_mother$individual_id, ped_tested$individual_id)
df_error_mother$n_error_total <- ped_tested$n_error[index_temp]
df_error_mother$cohort <- ped_tested$cohort[index_temp]
# AJOUT DE L'HAPLOTYPE DU PERE
#' ... Certaines erreurs viennent d'un décalage avec la mère. On regarde l'haplotype
#' ... du père pour voir de qui vient le décalage (de moi ou de la bdd)...
index_temp <- match(df_error_mother$individual_id, pedigree_data$individual_id)
df_error_mother$father <- pedigree_data[index_temp, "father"]
# fonction pour ajouter l'haplotype du père au DF
#' ... l'idée est de détecter de qui vient le décalage à l'allèle donné.
get_father_haplo <- function(i){
father_temp <- df_error_mother[i, c("father")]
marker_temp <- df_error_mother[i, c("marker")]
df_temp <- df_error_mother[i, c("marker", "off_haplo", "mother_haplo")]
haplo_father <- unlist(pg_cervus[pg_cervus$individual_id == father_temp, grepl(paste0("\\b", marker_temp, "\\b"), gsub("a|b", "", colnames(pg_cervus)))])
haplo_father <- paste0(haplo_father[1], "/", haplo_father[2])
df_temp$haplo_father <- haplo_father
if(length(haplo_father) > 0){
return(df_temp)
} else{
return("Father non-génotypé")
}
}
df_error_mother$father_haplo <- NA
for(i in 1:nrow(df_error_mother)){
df_error_mother[i, "father_haplo"] <- get_father_haplo(i)[, "haplo_father"]
}
# AJOUT DU NOMBRE DE MIMSTACH TOTAL AVEC LES DESCENDANTS DE TOUTE LA PORTEE
#' A UNE ANNEE DONEEE
ref_litter_error <-
ped_tested |>
group_by(mother, cohort) |>
summarise(n_correct = sum(match_mother), n_incorrect = sum(!match_mother)) |>
as.data.frame()
# add to df_error_mother
index <- match(paste0(df_error_mother$mother_id, df_error_mother$cohort), paste0(ref_litter_error$mother, ref_litter_error$cohort))
df_error_mother <- cbind(df_error_mother, ref_litter_error[index, c("n_correct", "n_incorrect")])
# NOMBRE DE MISMATCH ENTRE LA MERE ET L'ENSEMBLE DE SES DESCENDANTS
ref_litter_error <-
ped_tested |>
group_by(mother) |>
summarise(n_correct_tot = sum(match_mother), n_incorrect_tot = sum(!match_mother)) |>
as.data.frame()
# add to df_error_mother
index <- match(df_error_mother$mother_id, ref_litter_error$mother)
df_error_mother <- cbind(df_error_mother, ref_litter_error[index, c("n_correct_tot", "n_incorrect_tot")])
# ADD TERRITORY OF BIRTH
index <- match(df_error_mother$individual_id, pheno$individual_id)
df_error_mother$territoire <- pheno$territoire[index]
df_error_mother$annee_emergence <- pheno$annee_emergence[index]
# LENGTH OF COMPARED SEQUENCE
index <- match(df_error_mother$individual_id, ped_tested$individual_id)
df_error_mother <- cbind(df_error_mother, ped_tested[index, c("n_loci_compared", "length_seq", "length_seq_mother")])
# ADD REPRODUCTIVE STATUS
index_temp <- match(paste0(df_error_mother$mother_id, df_error_mother$annee_emergence), paste0(statut_individu$individu_id, statut_individu$annee_capture))
df_error_mother$statut_reproducteur <- statut_individu$statut_reproducteur[index_temp]
df_error_mother[c("individual_id", "mother_id", "statut_reproducteur")] |>
arrange(mother_id)## individual_id mother_id statut_reproducteur
## 1 1758 1134 <NA>
## 2 1758 1134 <NA>
## 3 1757 1134 <NA>
## 4 1701 1182 <NA>
## 5 1701 1182 <NA>
## 6 1698 1182 <NA>
## 7 1862 1298 allaitante
## 8 2133 1315 allaitante
## 9 2072 1315 <NA>
## 10 2071 1315 <NA>
## 11 2003 1315 allaitante
## 12 1770 1315 allaitante
## 13 1769 1315 allaitante
## 14 1792 1408 <NA>
## 15 1731 1408 <NA>
## 16 1835 1432 allaitante
## 17 1835 1432 allaitante
## 18 1738 1433 allaitante
## 19 1956 1520 allaitante
## 20 1918 1560 allaitante
## 21 1917 1560 allaitante
## 22 1916 1560 allaitante
## 23 1856 1560 allaitante
## 24 1855 1560 allaitante
## 25 1854 1560 allaitante
## 26 1948 1564 allaitante
## 27 1948 1564 allaitante
## 28 2075 1569 <NA>
## 29 2075 1569 <NA>
## 30 2075 1569 <NA>
## 31 1827 1635 allaitante
## 32 1928 1635 allaitante
## 33 1895 1635 allaitante
## 34 1931 1635 allaitante
## 35 1930 1635 allaitante
## 36 1929 1635 allaitante
## 37 1894 1635 allaitante
## 38 1893 1635 allaitante
## 39 1886 1635 allaitante
## 40 2125 1657 gestante
## 41 2121 1657 gestante
## 42 2064 1657 <NA>
## 43 2062 1657 <NA>
## 44 1952 1657 allaitante
## 45 1947 1657 allaitante
## 46 1909 dummy_891 <NA>
## 47 1909 dummy_891 <NA>
## 48 1909 dummy_891 <NA>
## 49 1909 dummy_891 <NA>
## 50 1909 dummy_891 <NA>
## 51 1909 dummy_891 <NA>
## 52 1909 dummy_891 <NA>
## 53 2030 dummy_945 <NA>
## 54 2030 dummy_945 <NA>
## 55 2030 dummy_945 <NA>
## 56 2030 dummy_945 <NA>
## 57 2030 dummy_945 <NA>
## 58 2030 dummy_945 <NA>
## 59 2030 dummy_945 <NA>
## 60 2030 dummy_945 <NA>
## 61 2030 dummy_945 <NA>
## 62 2030 dummy_945 <NA>
## 63 2030 dummy_945 <NA>
## 64 2030 dummy_945 <NA>
## 65 2030 dummy_945 <NA>
## 66 2030 dummy_945 <NA>
## 67 2030 dummy_945 <NA>
## 68 2030 dummy_945 <NA>
## 69 2029 dummy_945 <NA>
## 70 2029 dummy_945 <NA>
## 71 2029 dummy_945 <NA>
## 72 2029 dummy_945 <NA>
## 73 2029 dummy_945 <NA>
## 74 2029 dummy_945 <NA>
## 75 2029 dummy_945 <NA>
## 76 2029 dummy_945 <NA>
## 77 2029 dummy_945 <NA>
## 78 2029 dummy_945 <NA>
## 79 2029 dummy_945 <NA>
## 80 2029 dummy_945 <NA>
## 81 2029 dummy_945 <NA>
## 82 2029 dummy_945 <NA>
## 83 2029 dummy_945 <NA>
## 84 2029 dummy_945 <NA>
## 85 2028 dummy_945 <NA>
## 86 2028 dummy_945 <NA>
## 87 2028 dummy_945 <NA>
## 88 2028 dummy_945 <NA>
## 89 2028 dummy_945 <NA>
## 90 2028 dummy_945 <NA>
## 91 2028 dummy_945 <NA>
## 92 2028 dummy_945 <NA>
## 93 2028 dummy_945 <NA>
## 94 2028 dummy_945 <NA>
## 95 2028 dummy_945 <NA>
## 96 2028 dummy_945 <NA>
## 97 2028 dummy_945 <NA>
## 98 2028 dummy_945 <NA>
## 99 2028 dummy_945 <NA>
## 100 2028 dummy_945 <NA>
## 101 2027 dummy_945 <NA>
## 102 2027 dummy_945 <NA>
## 103 2027 dummy_945 <NA>
## 104 2027 dummy_945 <NA>
## 105 2027 dummy_945 <NA>
## 106 2027 dummy_945 <NA>
## 107 2027 dummy_945 <NA>
## 108 2027 dummy_945 <NA>
## 109 2027 dummy_945 <NA>
## 110 2027 dummy_945 <NA>
## 111 2027 dummy_945 <NA>
## 112 2027 dummy_945 <NA>
## 113 2027 dummy_945 <NA>
## 114 2027 dummy_945 <NA>
## 115 2027 dummy_945 <NA>
## 116 2027 dummy_945 <NA>
## 117 2147 dummy_973 <NA>
## 118 2147 dummy_973 <NA>
## 119 2147 dummy_973 <NA>
## 120 2147 dummy_973 <NA>
## 121 2147 dummy_973 <NA>
## 122 2147 dummy_973 <NA>
## 123 2147 dummy_973 <NA>
## 124 2147 dummy_973 <NA>
## 125 2147 dummy_973 <NA>
## 126 2147 dummy_973 <NA>
## 127 2147 dummy_973 <NA>
## 128 2147 dummy_973 <NA>
## 129 2147 dummy_973 <NA>
## 130 2147 dummy_973 <NA>
## 131 2146 dummy_973 <NA>
## 132 2146 dummy_973 <NA>
## 133 2146 dummy_973 <NA>
## 134 2146 dummy_973 <NA>
## 135 2146 dummy_973 <NA>
## 136 2146 dummy_973 <NA>
## 137 2146 dummy_973 <NA>
## 138 2146 dummy_973 <NA>
## 139 2146 dummy_973 <NA>
## 140 2146 dummy_973 <NA>
## 141 2146 dummy_973 <NA>
## 142 2146 dummy_973 <NA>
## 143 2146 dummy_973 <NA>
## 144 2146 dummy_973 <NA>
## 145 2146 dummy_973 <NA>
## 146 2140 dummy_973 <NA>
## 147 2140 dummy_973 <NA>
## 148 2140 dummy_973 <NA>
## 149 2140 dummy_973 <NA>
## 150 2140 dummy_973 <NA>
## 151 2140 dummy_973 <NA>
## 152 2140 dummy_973 <NA>
## 153 2140 dummy_973 <NA>
## 154 2140 dummy_973 <NA>
## 155 2140 dummy_973 <NA>
## 156 2140 dummy_973 <NA>
## 157 2140 dummy_973 <NA>
## 158 2140 dummy_973 <NA>
## 159 2140 dummy_973 <NA>
## 160 2139 dummy_973 <NA>
## 161 2139 dummy_973 <NA>
## 162 2139 dummy_973 <NA>
## 163 2139 dummy_973 <NA>
## 164 2139 dummy_973 <NA>
## 165 2139 dummy_973 <NA>
## 166 2139 dummy_973 <NA>
## 167 2139 dummy_973 <NA>
## 168 2139 dummy_973 <NA>
## 169 2139 dummy_973 <NA>
## 170 2139 dummy_973 <NA>
## 171 2139 dummy_973 <NA>
## 172 2139 dummy_973 <NA>
## 173 2139 dummy_973 <NA>
## 174 2139 dummy_973 <NA>
## 175 2139 dummy_973 <NA># FILTER LES INDIVIDUS DEPUIS 2013 TO 2023
# df_error_mother <- df_error_mother[!is.na(df_error_mother$year_analysis), ]
# WE REMOVE THE CASE ALREADY CORRECTED IN BDD
#' ... Donc les cas où la parentée sociale est différente de la parentée génétique
index <- match(df_error_mother$individual_id, parentee_genetique$individual_id)
df_error_mother$match_gen <- ifelse(df_error_mother$mother_id == parentee_genetique$mother[index], T, F)
df_error_mother <-
df_error_mother |>
filter(match_gen | is.na(match_gen))
# PRINT RESULTS
df_error_mother <-
df_error_mother |>
arrange(mother_id, marker)
# write.xlsx(df_error_mother, file = paste0("df_error_mother_mother_", format(Sys.time(), format = "%d_%m_%H_%M%_%S"), ".xlsx"), row.names = FALSE)7.1.4.6 Correction des erreurs
# CHECK LES ERREURS ENTRE PORTEE ET MERES (à faire tourner à la main)
check_mother_siblings_mismatch <- function(mother_id, father_id, list_markers) {
# Identifier les enfants du couple
siblings <- pedigree_data |>
filter(mother == mother_id & father == father_id) |>
pull(individual_id)
# Construire les noms des colonnes d’allèles
alleles_names <- paste0(rep(list_markers, each = 2), c("a", "b"))
# Extraire les génotypes
df_geno <- pg_cervus[
pg_cervus$individual_id %in% siblings,
c("individual_id", alleles_names)
]
# Construire une colonne "marker" pour chaque marqueur
for(i in seq_along(list_markers)){
idx <- (i - 1) * 2 + 1 # 1,3,5,...
a1 <- alleles_names[idx]
a2 <- alleles_names[idx + 1]
df_geno[[list_markers[i]]] <- paste0(df_geno[[a1]], "/", df_geno[[a2]])
}
return(df_geno[c("individual_id", list_markers)])
}
# GET SIBLINGS GENOTYPE
check_mother_siblings_mismatch(
mother_id = 1087,
father_id = 1067,
list_markers = c("BIBL31")
)## individual_id BIBL31
## 332 1300 161/161
## 333 1301 157/163
## 347 1314 161/161
## 348 1315 161/161
## 396 1360 157/163
## 397 1361 157/163
## 398 1362 161/161
## 422 1385 161/161
## 493 1451 161/161
## 494 1452 157/163
## 497 1455 157/163
## 498 1456 161/161
## 552 1507 157/163
## 553 1508 157/163
## 554 1509 161/161
## 556 1510 157/163## $mother_match
## [1] FALSE
##
## $genotype_offspring
## MA066a MA066b MA018a MA018b MA002a MA002b BIBL4a BIBL4b BIBL31a BIBL31b
## 554 231 231 298 298 279 281 190 190 161 161
## BIBL20a BIBL20b BIBL18a BIBL18b BIBL1a BIBL1b MS56a MS56b MS6a MS6b MS53a
## 554 208 216 147 147 95 97 106 108 158 158 142
## MS53b MS47a MS47b MS45a MS45b MS41a MS41b MA091a MA091b
## 554 142 184 186 107 109 186 186 169 175
##
## $genotype_mother
## MA066a MA066b MA018a MA018b MA002a MA002b BIBL4a BIBL4b BIBL31a BIBL31b
## 348 231 231 298 298 279 281 190 190 161 161
## BIBL20a BIBL20b BIBL18a BIBL18b BIBL1a BIBL1b MS56a MS56b MS6a MS6b MS53a
## 348 216 216 143 145 97 107 108 108 158 158 140
## MS53b MS47a MS47b MS45a MS45b MS41a MS41b MA091a MA091b
## 348 142 184 186 107 107 184 186 169 175
##
## $n
## [1] 15
##
## $nb_mismatch_mother_offspring
## [1] 1
##
## $offspring_id_mismatch
## [1] 1509
##
## $mother_id_mismatch
## [1] 1315
##
## $df_error
## individual_id marker mother_id off_haplo mother_haplo
## 1 1509 BIBL18 1315 147/147 143/145## individual_id mother father father_lod father_delta annee_emergence sexe
## 3691 1455 1087 1067 4.829543 2.777754 2013 f
Erreur issu du génotypage de l’offspring
Pour identifier si le problème vient de l’offspring ou d’une autre
raison, je croise plusieurs informations : si la femelle dominante est
allaitante, comment le ou les mismatchs sont répartis intra et
inter-portée, le nombre d’autres femelles dominantes, le cas échéant je
teste les génotypes avec ces autres femelles en cas d’erreur etc…Toutes
les “enquêtes” sont disponibles dans le fichier .xlsx
df_error_mother.
# FUNCTION TO CORRECT GENOTYPE EASILY
correction_function <- function(id, marker, new_haplotype){
# change pg_cervus
pg_cervus[pg_cervus$individual_id %in% id, grepl(paste0("\\b", marker, "\\b"), x = gsub("a|b", "", colnames(pg_cervus)))] <<- new_haplotype
}
##############################################################################
# #
# ERREURS #
# #
##############################################################################
# Erreur offspring // mère 204
correction_function(id = c("811"), marker = "BIBL20", new_haplotype = "0")
# La mère 533 est inconnue (déjà corrigée dans pédigrée génétique)
#' ... On les enlève du jeu de données
id_to_remove <- ped_tested[ped_tested$mother == 533, "individual_id"]
pg_cervus <- pg_cervus[!pg_cervus$individual_id %in% id_to_remove, ]
ped_tested <- ped_tested[!ped_tested$mother == 533, ]
# Erreur offspring // mère 636
correction_function(id = c("1567"), marker = "MS53", new_haplotype = "0")
# Erreur offspring // mère 942
correction_function(id = c("1330", "1329", "1396", "1501", "1603", "1602", "1601"), marker = "BIBL31", new_haplotype = "0")
# Erreur pas claire // mère 988
correction_function(id = c("1629"), marker = "MS45", new_haplotype = "0")
# Erreur offspring // mère 1050
correction_function(id = c("1294", "1386", "1364"), marker = "BIBL31", new_haplotype = "0")
# Erreur offspring // mère 1087
correction_function(id = c("1315" ,"1300" ,"1314" ,"1385" ,"1362" ,"1456" ,"1451" ,"1509"), marker = "BIBL31", new_haplotype = "0")
# Erreur offspring // mère 1112 : l'offsrping 1335 est le seul de sa portée avec erreurs (n = 4), sûrement pas le bon individu génotypé... Je mets "NA" pour tout son génotype
pg_cervus <- pg_cervus[!pg_cervus$individual_id == 1335, ]
ped_tested <- ped_tested[!ped_tested$individual_id == 1335, ]
# Erreur offspring // mère 1120
correction_function(id = c("1381", "1383", "1384"), marker = "BIBL31", new_haplotype = "0")
# Erreur offspring // mère 1134
correction_function(id = c("1656"), marker = "BIBL18", new_haplotype = "0")
correction_function(id = c("1656"), marker = "MA066", new_haplotype = "0")
# Erreur offspring // mère 1182 2012 à 2014
correction_function(id = c("1368", "1369", "1495", "1560"), marker = "BIBL31", new_haplotype = "0")
# Erreur offspring // mère 1182 2015, bizarre, mais voir fichier.xlsx df_error_mother
correction_function(id = c("1698"), marker = "MS53", new_haplotype = "0")
correction_function(id = c("1701"), marker = "BIBL20", new_haplotype = "0")
correction_function(id = c("1701"), marker = "BIBL31", new_haplotype = "0")
# Erreur offspring // mère 1199, L'allèle est 186/186 (car 1635 est aussi mère et a le même mismatch avec toute ses portées)
correction_function(id = c("1635"), marker = "MS41", new_haplotype = "186")
# Erreur offspring // mère 1231
correction_function(id = c("1517"), marker = "BIBL31", new_haplotype = "0")
# Erreur offspring // mère 1292
correction_function(id = c("1641"), marker = "BIBL31", new_haplotype = "0")
# Erreur offspring // mère 1297
correction_function(id = c("1536", "1533", "1532"), marker = "BIBL31", new_haplotype = "0")
# Erreur offspring // mère 1298
correction_function(id = c("1862"), marker = "MA002", new_haplotype = "0")
# Erreur offspring // mère 1321
correction_function(id = c("1679"), marker = "BIBL20", new_haplotype = "0")
# Erreur offspring // mère 1360
correction_function(id = c("1669"), marker = "BIBL31", new_haplotype = "0")
# Erreur offspring // mère 1408
correction_function(id = c("1691"), marker = "BIBL31", new_haplotype = "0")
correction_function(id = c("1731", "1792"), marker = "BIBL31", new_haplotype = "0")
############################################################
# A RETESTER QUAND LES AUTRES DE LA PORTEE SERONT GENOTYPEES
# Erreur offspring // mère 1432
correction_function(id = c("1835"), marker = c("BIBL20"), new_haplotype = "0")
correction_function(id = c("1835"), marker = c("MS47"), new_haplotype = "0")
############################################################################
# A RETESTER APRES AVOIR DEMANDE A REB CE QU'IL S'EST PASSE SUR LE TERRITOIRE
#' ... Car 1540 comme mère est suggérée, mais je dois savoir dans quel sens ça va car seule une partie de la portée est coloriée dnas le livret famille
correction_function(id = c("1738"), marker = c("MS41"), new_haplotype = "0")
# Erreur offspring // mère 1437
correction_function(id = c("1580", "1655"), marker = "BIBL31", new_haplotype = "0")
# Erreur offspring // mère 1520
correction_function(id = c("1956"), marker = "BIBL20", new_haplotype = "0")
# Erreur offspring // mère 1564
correction_function(id = c("1948"), marker = "MS41", new_haplotype = "0")
correction_function(id = c("1948"), marker = "MA002", new_haplotype = "0")
# Erreur 2075 : probablement pas le bon individu. Je le supprime complètement.
pg_cervus <- pg_cervus[!pg_cervus$individual_id == 2075, ]
ped_tested <- ped_tested[!ped_tested$individual_id == 2075, ]
# Erreur offspring // mère 1657 : cohorte 2019/2020
correction_function(id = c("1947", "1952", "2062", "2064"), marker = "BIBL31", new_haplotype = "0")
#############################################################################
# PAS CLAIR, DANS LE DOUTE JE METS NAs A TOUT LE MONDE, A VOIR AVEC REB
#' ... Car 1540 comme mère est suggérée, mais je dois savoir dans quel sens ça va car seule une partie de la portée est coloriée dnas le livret famille
# Erreur offspring // mère 1657
correction_function(id = c("2121", "2125", "1657"), marker = c("BIBL31"), new_haplotype = "0")
correction_function(id = c("2125"), marker = "MA066", new_haplotype = "0")
Correction de l’identité des mères en cas de vrai erreur d’attribution
# En 2016, 1443 est la vraie mère de la portée, et non 1134.
ped_tested[ped_tested$mother == 1134 & ped_tested$cohort == 2016 & !is.na(ped_tested$mother) & !is.na(ped_tested$cohort), "mother"] <- "1443"
# La mère 1182 en 2015 n'est probablement pas la bonne, je la mets NAs et pour le moment la supprime du pédigrée
id_to_remove <- ped_tested[ped_tested$mother == 1182 & ped_tested$cohort == 2015 & !is.na(ped_tested$mother) & !is.na(ped_tested$cohort), "individual_id"]
ped_tested <- ped_tested[!ped_tested$individual_id %in% id_to_remove, ]Vérification : On s’assure qu’il n’y a plus de mismatchs offsprings / mother
res_match_temp <- apply(X = ped_tested[1:2],
MARGIN = 1,
FUN = function(x) match_genotype(x)$mother_match) Nombre de mismatch mère / offspring : 9
7.2 Father test
On teste ici les pères sociaux. On va comparer les allèles par locus restant après comparaison avec la mère. C’est à dire e.g. que si l’enfant est 144/155 à un locus donné et que la mère est 144/146, alors on regardera si le père a l’allèle 155 à ce même locus. Si on a au moins un mismatch, on traitera l’individu commep potentiel EPY.
7.2.2 Comparaison des génotypes
On enlève les individus pour lesquels le père n’a pas été génotypé
Vérification : Nombre d’individu total testé effectivement
Nombre d’individu(s) répondant à tous les critères de filtre : 408
On teste le match entre le profil de le père et des offsprings à
l’aide de la fonction match_genotype() disponible dans le
code CERVUS_fonctions.r. L’idée est de récupérer les
potentiels EPY qui seront l’ensemble des individus ayant au moins un
mismatch avec leur père social. Tous ces potentiels EPY seront analysés
avec CERVUS.
7.2.3 Test
Pour le moment, on ignore individus ayant des pères inconnus et
les individus ayant plus d’un mismatch avec leur mère présent dans
l’objet df_error_significant_mismatch$mother_id
Ici on doit prendre en compte le fait que on va comparer pour chaque marqueur : (1) le ou les allèles de l’offspring conditionnellement à la comparaison avec la mère avec ceux du père, mais aussi (2) tout en prenant en compte le fait que la première méthode cache une part de variation entre l’offspring et le père, car si la mère a beaucoup de NA dans ses haplotypes et pas le père, alors on perd de l’info. Pour résoudre ce problème on va aussi comparer pour tous les loci disponibles si au moins un des allèles de l’offspring se trouve chez le père. On réalise ensuite l’union des résultats (e.g. si (1) donne match == TRUE et (2) donne match == FAUX, alors on précisera FAUX).
A noter que je pourrai optimiser la fonction
match_genotpye()pour faire les deux d’un coup, mais par gain de temps je ne vais pas le faire et doubler les analyses à chaque fois pour avoir ces deux formes de mismatches..
# Mismatch father/offspring with alleles leftover for comparison after mother/offspring comparison
res_match_mother_conditionnal <- apply(
X = ped_tested[1:3],
MARGIN = 1,
FUN = function(x)
match_genotype(x)$father_match
)
# Test all father/offspring loci i.e. if at least one locus allele(s) of the offspring is found in father locus
res_match_father_alone <- apply(
X = ped_tested[c(1, 3)],
MARGIN = 1,
FUN = function(x)
match_genotype(x)$mother_match
)
# Union of both mismatch
res_match <- res_match_mother_conditionnal & res_match_father_aloneAssociations correctes : 324 / 408 (79%)
7.2.4 Exploration des mismatchs
A noter que l’ensemble des parentées sociales ont été re-vérifiées à partir du fichier famille quand il y avait au moins un mismatch…
# GET MISMATCH INDIVIDUAL INFORMATION
index_temp <- as.numeric(which(!res_match))
index_temp <- match(ped_tested$individual_id, individu$individual_id) # match cohort
ped_tested[["match_father"]] <- res_match
# GET NUMBER OF MISMATCH
# On extrait les erreurs liés à la comparaison du génotype conditionnellement à la mère et par comparaison directe offspring / father. On fait ensuite l'union entre les deux.
df_error_mother_conditional <- apply(X = ped_tested[!is.na(ped_tested$father), 1:3], MARGIN = 1, FUN = function(x) match_genotype(x)$df_error)
df_error_father_only <- apply(X = ped_tested[!is.na(ped_tested$father), c(1,3)], MARGIN = 1, FUN = function(x) match_genotype(x)$df_error)
# Formatage
df_error_mother_conditional <- do.call(rbind, df_error_mother_conditional)
df_error_father_only <- do.call(rbind, df_error_father_only)
colnames(df_error_father_only)[colnames(df_error_father_only) %in% c("mother_id", "mother_haplo")] <- c("father_id", "father_haplo")
df_error_father <- rbind(df_error_mother_conditional, df_error_father_only)
df_error_father <- df_error_father[!duplicated(paste0(df_error_father$individual_id, df_error_father$marker, df_error_father$father_id)), ] # on elève les intersections entre les deux "types" de mismatchs
rownames(df_error_father) <- 1:nrow(df_error_father)
7.2.4.1 Nombre d’erreur et nombre de loci comparé
On calcule ici le nombre de cas où un individu a un mismatch avec son père On précise également le nombre de loci comparable entre les deux (en excluant les NA).
## Nombre de marqueurs avec mismatch
## 0 1 2 3 4 5 7 13 <NA>
## 331 46 16 5 2 3 4 1 0## Nombre de loci comparé
## 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 <NA>
## 1 1 2 1 11 6 4 16 16 31 48 82 75 70 44 0
Comme attendu, on observe beaucoup plus de mismatch entre les pères et les offsprings qu’entre les mères et les offsprings.
7.2.4.2 Nombre de père unique avec mismatch
Nombre de père unique avec au moins un mismatch avec leur offspring : 25.
Leur ID est :
## [1] 1091 1099 1258 1309 1326 1342 1404 1409 1417 1440 1442 1487 1500 1526 1561
## [16] 1618 1672 1729 1845 1850 1859 1867 1895
7.2.4.3 Répartition des individus ayant au moins un mismatch avec leur père dans les analyses
##
## 2017 2020 2022 2023_1 2023_2 2023_3
## 13 25 16 14 10 6
7.2.4.4 Répartition des individus potentiellement EPY dans les anciens individus avec un mismatch avec la mère
Nombre d’individus potentiellement EPY ayant eu un mismatch avec leur mère avant correction : 27
## individual_id marker father_id off_haplo
## 3 1827 MS45 1309 107/111
## 8 2198 BIBL18 1867 143/143
## 9 2147 MA066 1672 231/231
## 10 2147 MS6 1672 158/158
## 11 2147 MS53 1672 142/142
## 12 2147 MA091 1672 175/175
## 17 2146 MS53 1672 142/142
## 26 2139 MA018 1672 298/298
## 27 2139 MA002 1672 271/271
## 28 2139 BIBL4 1672 190/190
## 29 2139 BIBL31 1672 157/157
## 30 2139 BIBL20 1672 218/218
## 31 2139 BIBL18 1672 149/149
## 37 2139 MS41 1672 186/186
## 38 2139 MA091 1672 175/175
## 41 2132 MA002 1895 271/279
## 43 2125 MS41 1409 184/184
## 44 2122 MS41 1409 184/184
## 49 2098 ST10 1859 118/130
## 50 2097 MA002 1859 271/279
## 51 2097 ST10 1859 120/130
## 52 2089 MS53 1729 142/142
## 55 2088 MS53 1729 142/142
## 61 2030 MA066 1672 231/231
## 62 2030 MA018 1672 298/298
## 63 2030 BIBL31 1672 157/157
## 68 2029 MA066 1672 231/231
## 87 2010 BIBL20 1618 218/218
## 92 1981 MS53 1500 140/142
## 93 1974 BIBL20 1845 208/216
## 94 1973 BIBL20 1258 208/216
## 95 1956 MA066 1561 231/241
## 96 1956 MS53 1561 132/140
## 101 1950 MS53 1440 132/140
## 110 1921 MS45 1526 107/107
## 111 1920 MS45 1526 107/107
## 112 1918 MS45 1091 107/109
## 113 1916 BIBL18 1091 149/149
## 114 1909 BIBL4 1442 175/175
## 115 1909 MS56 1442 108/108
## 121 1877 MS56 1561 106/108
## 122 1876 MS56 1409 108/108
## 125 1874 MS41 1409 184/184
## 127 1857 MA002 1091 271/281
## 128 1856 MA066 1091 241/241
## 133 1851 MS45 1526 107/111
## 134 1804 BIBL31 1326 157/163
## 135 1802 BIBL31 1326 157/157
## 136 1796 MA002 1326 279/281
## 139 1792 BIBL18 1417 143/147
## 145 1786 MA002 1404 271/281
## 146 1786 BIBL18 1404 145/147
## 147 1786 MS56 1404 104/108
## 152 1783 MS45 1342 107/111
## 171 2215 MA091 dummy_1023 175/175
## 176 2214 BIBL20 dummy_1023 218/218
## 177 2214 BIBL18 dummy_1023 149/149
## 178 2214 BIBL1 dummy_1023 101/107
## 179 2214 MS56 dummy_1023 106/108
## 180 2214 MS6 dummy_1023 158/158
## 185 2214 MA091 dummy_1023 175/175
## 194 2213 MS6 dummy_1023 142/158
## 195 2213 MS53 dummy_1023 140/142
## 196 2213 MS47 dummy_1023 180/186
## 197 2213 MS45 dummy_1023 107/109
## 198 2213 MS41 dummy_1023 184/186
## 199 2213 MA091 dummy_1023 159/175
## 205 1766 BIBL20 dummy_825 208/208
## 206 1766 BIBL18 dummy_825 145/147
## 209 1766 MS56 dummy_825 106/108
## 211 1766 MS53 dummy_825 140/142
## 212 1766 MS47 dummy_825 184/186
## 217 1765 MA002 dummy_825 279/281
## 218 1765 BIBL4 dummy_825 188/190
## 219 1765 BIBL31 dummy_825 159/161
## 220 1765 BIBL20 dummy_825 208/218
## 223 1765 ST10 dummy_825 118/130
## 229 1764 MA002 dummy_825 279/281
## 230 1764 BIBL4 dummy_825 188/190
## 231 1764 BIBL31 dummy_825 157/159
## 236 1764 MS56 dummy_825 106/106
## 255 1763 MS45 dummy_825 107/109
## father_haplo
## 3 107/107
## 8 145/149
## 9 231/241
## 10 158/158
## 11 142/142
## 12 173/175
## 17 142/142
## 26 296/298
## 27 271/279
## 28 188/190
## 29 157/163
## 30 216/218
## 31 143/149
## 37 184/186
## 38 173/175
## 41 279/279
## 43 186/186
## 44 186/186
## 49 116/116
## 50 281/281
## 51 116/116
## 52 132/140
## 55 132/140
## 61 231/241
## 62 296/298
## 63 157/163
## 68 231/241
## 87 208/220
## 92 142/144
## 93 208/208
## 94 216/220
## 95 231/231
## 96 140/142
## 101 142/142
## 110 109/109
## 111 109/109
## 112 107/107
## 113 143/147
## 114 175/190
## 115 108/108
## 121 108/108
## 122 106/106
## 125 186/186
## 127 279/279
## 128 231/231
## 133 109/109
## 134 161/161
## 135 161/161
## 136 271/279
## 139 143/145
## 145 271/279
## 146 143/149
## 147 108/108
## 152 109/111
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NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, \nNA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, \nNA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA)/c(NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, \nNA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, \nNA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, \nNA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, \nNA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, \nNA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, \nNA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, \nNA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, \nNA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, \nNA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, \nNA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, \nNA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, \nNA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, \nNA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, \nNA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 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7.2.4.5 Répartition des pères avec au moins un mismatch avec leur offspring les années d’analyses
## ANALYSIS OF THE FATHER
## ANALYSIS OF OFFSPRING 2013 2017 2020 2022 2023_2 2023_3 <NA>
## 2017 2 0 0 0 0 0 11
## 2020 0 0 4 0 0 6 15
## 2022 3 1 0 1 0 6 5
## 2023_1 0 4 3 0 4 0 3
## 2023_2 0 0 2 0 4 0 4
## 2023_3 0 0 1 0 0 1 4
## <NA> 0 0 0 0 0 0 0
7.2.4.6 Répartition des erreurs parmi les marqueurs
##
## BIBL1 BIBL18 BIBL20 BIBL31 BIBL4 MA002 MA018 MA066 MA091 MS41 MS45
## 9 17 12 17 12 27 11 11 18 19 28
## MS47 MS53 MS56 MS6 ST10
## 15 23 13 14 11## Analysis of offspring
## 2017 2020 2022 2023_1 2023_2 2023_3 <NA>
## BIBL1 5 0 0 1 0 3 0
## BIBL18 6 1 2 2 2 4 0
## BIBL20 4 1 3 0 1 3 0
## BIBL31 6 0 4 5 2 0 0
## BIBL4 4 1 0 2 2 3 0
## MA002 8 5 2 4 5 3 0
## MA018 4 0 0 2 2 3 0
## MA066 1 3 1 2 1 3 0
## MA091 4 2 3 2 4 3 0
## MS41 6 1 2 2 5 3 0
## MS45 5 12 2 3 2 4 0
## MS47 2 2 3 3 1 4 0
## MS53 4 0 5 7 4 3 0
## MS56 5 3 2 0 0 3 0
## MS6 3 1 0 4 3 3 0
## ST10 4 2 1 3 1 0 0
## <NA> 0 0 0 0 0 0 0## Analysis of mother
## 2013 2017 2020 2022 2023_2 2023_3 <NA>
## BIBL1 0 0 0 0 1 0 8
## BIBL18 0 1 3 0 2 0 11
## BIBL20 0 0 0 1 1 1 9
## BIBL31 4 1 0 0 6 0 6
## BIBL4 0 0 0 0 4 0 8
## MA002 1 1 6 0 5 0 14
## MA018 0 0 0 0 4 0 7
## MA066 0 0 0 0 3 1 7
## MA091 0 3 1 0 3 1 10
## MS41 0 0 0 0 4 0 15
## MS45 1 2 0 0 3 6 16
## MS47 0 1 0 0 1 1 12
## MS53 0 3 1 0 8 3 8
## MS56 0 0 0 0 0 3 10
## MS6 0 0 0 0 7 0 7
## ST10 0 1 4 0 2 0 4
## <NA> 0 0 0 0 0 0 0## , , NA = BIBL1
##
## Father
## Offspring 2013 2017 2020 2022 2023_2 2023_3 <NA>
## 2017 0 0 0 0 0 0 5
## 2020 0 0 0 0 0 0 0
## 2022 0 0 0 0 0 0 0
## 2023_1 0 0 0 0 1 0 0
## 2023_2 0 0 0 0 0 0 0
## 2023_3 0 0 0 0 0 0 3
## <NA> 0 0 0 0 0 0 0
##
## , , NA = BIBL18
##
## Father
## Offspring 2013 2017 2020 2022 2023_2 2023_3 <NA>
## 2017 0 0 0 0 0 0 6
## 2020 0 0 0 0 0 0 1
## 2022 0 1 0 0 0 0 1
## 2023_1 0 0 2 0 0 0 0
## 2023_2 0 0 0 0 2 0 0
## 2023_3 0 0 1 0 0 0 3
## <NA> 0 0 0 0 0 0 0
##
## , , NA = BIBL20
##
## Father
## Offspring 2013 2017 2020 2022 2023_2 2023_3 <NA>
## 2017 0 0 0 0 0 0 4
## 2020 0 0 0 0 0 0 1
## 2022 0 0 0 1 0 1 1
## 2023_1 0 0 0 0 0 0 0
## 2023_2 0 0 0 0 1 0 0
## 2023_3 0 0 0 0 0 0 3
## <NA> 0 0 0 0 0 0 0
##
## , , NA = BIBL31
##
## Father
## Offspring 2013 2017 2020 2022 2023_2 2023_3 <NA>
## 2017 1 0 0 0 0 0 5
## 2020 0 0 0 0 0 0 0
## 2022 3 0 0 0 0 0 1
## 2023_1 0 1 0 0 4 0 0
## 2023_2 0 0 0 0 2 0 0
## 2023_3 0 0 0 0 0 0 0
## <NA> 0 0 0 0 0 0 0
##
## , , NA = BIBL4
##
## Father
## Offspring 2013 2017 2020 2022 2023_2 2023_3 <NA>
## 2017 0 0 0 0 0 0 4
## 2020 0 0 0 0 0 0 1
## 2022 0 0 0 0 0 0 0
## 2023_1 0 0 0 0 2 0 0
## 2023_2 0 0 0 0 2 0 0
## 2023_3 0 0 0 0 0 0 3
## <NA> 0 0 0 0 0 0 0
##
## , , NA = MA002
##
## Father
## Offspring 2013 2017 2020 2022 2023_2 2023_3 <NA>
## 2017 0 0 0 0 0 0 8
## 2020 0 0 2 0 0 0 3
## 2022 1 1 0 0 0 0 0
## 2023_1 0 0 2 0 2 0 0
## 2023_2 0 0 2 0 3 0 0
## 2023_3 0 0 0 0 0 0 3
## <NA> 0 0 0 0 0 0 0
##
## , , NA = MA018
##
## Father
## Offspring 2013 2017 2020 2022 2023_2 2023_3 <NA>
## 2017 0 0 0 0 0 0 4
## 2020 0 0 0 0 0 0 0
## 2022 0 0 0 0 0 0 0
## 2023_1 0 0 0 0 2 0 0
## 2023_2 0 0 0 0 2 0 0
## 2023_3 0 0 0 0 0 0 3
## <NA> 0 0 0 0 0 0 0
##
## , , NA = MA066
##
## Father
## Offspring 2013 2017 2020 2022 2023_2 2023_3 <NA>
## 2017 0 0 0 0 0 0 1
## 2020 0 0 0 0 0 0 3
## 2022 0 0 0 0 0 1 0
## 2023_1 0 0 0 0 2 0 0
## 2023_2 0 0 0 0 1 0 0
## 2023_3 0 0 0 0 0 0 3
## <NA> 0 0 0 0 0 0 0
##
## , , NA = MA091
##
## Father
## Offspring 2013 2017 2020 2022 2023_2 2023_3 <NA>
## 2017 0 0 0 0 0 0 4
## 2020 0 0 1 0 0 0 1
## 2022 0 1 0 0 0 1 1
## 2023_1 0 2 0 0 0 0 0
## 2023_2 0 0 0 0 3 0 1
## 2023_3 0 0 0 0 0 0 3
## <NA> 0 0 0 0 0 0 0
##
## , , NA = MS41
##
## Father
## Offspring 2013 2017 2020 2022 2023_2 2023_3 <NA>
## 2017 0 0 0 0 0 0 6
## 2020 0 0 0 0 0 0 1
## 2022 0 0 0 0 0 0 2
## 2023_1 0 0 0 0 2 0 0
## 2023_2 0 0 0 0 2 0 3
## 2023_3 0 0 0 0 0 0 3
## <NA> 0 0 0 0 0 0 0
##
## , , NA = MS45
##
## Father
## Offspring 2013 2017 2020 2022 2023_2 2023_3 <NA>
## 2017 1 0 0 0 0 0 4
## 2020 0 0 0 0 0 5 7
## 2022 0 0 0 0 0 0 2
## 2023_1 0 2 0 0 1 0 0
## 2023_2 0 0 0 0 2 0 0
## 2023_3 0 0 0 0 0 1 3
## <NA> 0 0 0 0 0 0 0
##
## , , NA = MS47
##
## Father
## Offspring 2013 2017 2020 2022 2023_2 2023_3 <NA>
## 2017 0 0 0 0 0 0 2
## 2020 0 0 0 0 0 0 2
## 2022 0 1 0 0 0 1 1
## 2023_1 0 0 0 0 0 0 3
## 2023_2 0 0 0 0 1 0 0
## 2023_3 0 0 0 0 0 0 4
## <NA> 0 0 0 0 0 0 0
##
## , , NA = MS53
##
## Father
## Offspring 2013 2017 2020 2022 2023_2 2023_3 <NA>
## 2017 0 0 0 0 0 0 4
## 2020 0 0 0 0 0 0 0
## 2022 0 1 0 0 0 3 1
## 2023_1 0 2 1 0 4 0 0
## 2023_2 0 0 0 0 4 0 0
## 2023_3 0 0 0 0 0 0 3
## <NA> 0 0 0 0 0 0 0
##
## , , NA = MS56
##
## Father
## Offspring 2013 2017 2020 2022 2023_2 2023_3 <NA>
## 2017 0 0 0 0 0 0 5
## 2020 0 0 0 0 0 1 2
## 2022 0 0 0 0 0 2 0
## 2023_1 0 0 0 0 0 0 0
## 2023_2 0 0 0 0 0 0 0
## 2023_3 0 0 0 0 0 0 3
## <NA> 0 0 0 0 0 0 0
##
## , , NA = MS6
##
## Father
## Offspring 2013 2017 2020 2022 2023_2 2023_3 <NA>
## 2017 0 0 0 0 0 0 3
## 2020 0 0 0 0 0 0 1
## 2022 0 0 0 0 0 0 0
## 2023_1 0 0 0 0 4 0 0
## 2023_2 0 0 0 0 3 0 0
## 2023_3 0 0 0 0 0 0 3
## <NA> 0 0 0 0 0 0 0
##
## , , NA = ST10
##
## Father
## Offspring 2013 2017 2020 2022 2023_2 2023_3 <NA>
## 2017 0 0 0 0 0 0 4
## 2020 0 0 2 0 0 0 0
## 2022 0 1 0 0 0 0 0
## 2023_1 0 0 2 0 1 0 0
## 2023_2 0 0 0 0 1 0 0
## 2023_3 0 0 0 0 0 0 0
## <NA> 0 0 0 0 0 0 0
##
## , , NA = NA
##
## Father
## Offspring 2013 2017 2020 2022 2023_2 2023_3 <NA>
## 2017 0 0 0 0 0 0 0
## 2020 0 0 0 0 0 0 0
## 2022 0 0 0 0 0 0 0
## 2023_1 0 0 0 0 0 0 0
## 2023_2 0 0 0 0 0 0 0
## 2023_3 0 0 0 0 0 0 0
## <NA> 0 0 0 0 0 0 0Enormément de mismatch sur le marqueur ST10
avec les pères.. suivi de MS45, MA002 et
BIBL31.
7.2.5 Check des erreurs…
# TOTAL NUMBER OF ERROR BETWEEN FATHER AND OFFSPRINGS
# on match le nombre d'erreur total avec la mère de l'offspring
index_temp <- match(df_error_father$individual_id, ped_tested$individual_id)
df_error_father$n_error_total <- ped_tested$n_error[index_temp]
df_error_father$cohort <- ped_tested$cohort[index_temp]
# AJOUT DE L'HAPLOTYPE DU PERE
#' ... Certaines erreurs viennent d'un décalage avec la mère On regarde l'haplotype
#' ... de la mère pour voir de qui vient le décalage (de moi ou de la bdd)...
index_temp <- match(df_error_father$individual_id, pedigree_data$individual_id)
df_error_father$mother <- pedigree_data[index_temp, "mother"]
# fonction pour ajouter l'haplotype du père au DF
#' ... l'idée est de détecter de qui vient le décalage à l'allèle donné.
get_father_haplo <- function(i){
father_temp <- df_error_father[i, c("father_id")]
marker_temp <- df_error_father[i, c("marker")]
df_temp <- df_error_father[i, c("marker", "off_haplo", "father_haplo")]
haplo_father <- unlist(pg_cervus[pg_cervus$individual_id == father_temp, grepl(paste0("\\b", marker_temp, "\\b"), gsub("a|b", "", colnames(pg_cervus)))])
haplo_father <- paste0(haplo_father[1], "/", haplo_father[2])
df_temp$haplo_father <- haplo_father
if(length(haplo_father) > 0){
return(df_temp)
} else{
return("Father non-génotypée")
}
}
df_error_father$father_haplo <- NA
for(i in 1:nrow(df_error_father)){
df_error_father[i, "father_haplo"] <- get_father_haplo(i)[, "haplo_father"]
}
# AJOUT DU NOMBRE DE MIMSTACH TOTAL AVEC LES DESCENDANTS DE TOUTE LA PORTEE
#' A UNE ANNEE DONEEE
ref_litter_error <-
ped_tested |>
group_by(father, cohort) |>
summarise(n_correct = sum(match_father), n_incorrect = sum(!match_father)) |>
as.data.frame()
# add to df_error_father
index <- match(paste0(df_error_father$mother_id, df_error_father$cohort), paste0(ref_litter_error$mother, ref_litter_error$cohort))
df_error_father <- cbind(df_error_father, ref_litter_error[index, c("n_correct", "n_incorrect")])
# NOMBRE DE MISMATCH ENTRE LE PERE ET L'ENSEMBLE DE SES DESCENDANTS
ref_litter_error <-
ped_tested |>
group_by(father) |>
summarise(n_correct_tot = sum(match_father), n_incorrect_tot = sum(!match_father)) |>
as.data.frame()
# add to df_error_father
index <- match(df_error_father$father_id, ref_litter_error$father)
df_error_father <- cbind(df_error_father, ref_litter_error[index, c("n_correct_tot", "n_incorrect_tot")])
# ADD TERRITORY OF BIRTH
index <- match(df_error_father$individual_id, pheno$individual_id)
df_error_father$territoire <- pheno$territoire[index]
df_error_father$annee_emergence <- pheno$annee_emergence[index]
# LENGTH OF COMPARED SEQUENCE
index <- match(df_error_father$individual_id, ped_tested$individual_id)
df_error_father <- cbind(df_error_father, ped_tested[index, c("n_loci_compared", "length_seq", "length_seq_father")])
# ADD REPRODUCTIVE STATUS
index_temp <- match(paste0(df_error_father$father_id, df_error_father$annee_emergence), paste0(statut_individu$individu_id, statut_individu$annee_capture))
df_error_father$statut_reproducteur <- statut_individu$statut_reproducteur[index_temp]
df_error_father[c("individual_id", "father_id", "statut_reproducteur")] |>
arrange(father_id)## individual_id father_id statut_reproducteur
## 1 1919 1091 scrotal
## 2 1919 1091 scrotal
## 3 1918 1091 scrotal
## 4 1916 1091 scrotal
## 5 1857 1091 scrotal
## 6 1856 1091 scrotal
## 7 1856 1091 scrotal
## 8 1855 1091 scrotal
## 9 1854 1091 scrotal
## 10 1854 1091 scrotal
## 11 1923 1099 scrotal
## 12 1923 1099 scrotal
## 13 1922 1099 scrotal
## 14 1973 1258 scrotal
## 15 1827 1309 scrotal
## 16 1895 1309 scrotal
## 17 1931 1309 scrotal
## 18 1930 1309 scrotal
## 19 1894 1309 scrotal
## 20 1886 1309 scrotal
## 21 1804 1326 <NA>
## 22 1802 1326 <NA>
## 23 1796 1326 <NA>
## 24 1796 1326 <NA>
## 25 1751 1326 <NA>
## 26 1783 1342 scrotal
## 27 1787 1404 <NA>
## 28 1787 1404 <NA>
## 29 1786 1404 <NA>
## 30 1786 1404 <NA>
## 31 1786 1404 <NA>
## 32 1785 1404 <NA>
## 33 1785 1404 <NA>
## 34 1784 1404 <NA>
## 35 1784 1404 <NA>
## 36 1945 1409 scrotal
## 37 2208 1409 <NA>
## 38 2126 1409 scrotal
## 39 2125 1409 scrotal
## 40 2122 1409 scrotal
## 41 2064 1409 <NA>
## 42 2062 1409 <NA>
## 43 1952 1409 scrotal
## 44 1947 1409 scrotal
## 45 1876 1409 scrotal
## 46 1876 1409 scrotal
## 47 1874 1409 scrotal
## 48 1874 1409 scrotal
## 49 1716 1409 scrotal
## 50 1716 1409 scrotal
## 51 1713 1409 scrotal
## 52 1712 1409 scrotal
## 53 1792 1417 scrotal
## 54 1792 1417 scrotal
## 55 1792 1417 scrotal
## 56 1792 1417 scrotal
## 57 1792 1417 scrotal
## 58 1792 1417 scrotal
## 59 1949 1440 scrotal
## 60 1951 1440 scrotal
## 61 1950 1440 scrotal
## 62 1950 1440 scrotal
## 63 1909 1442 scrotal
## 64 1909 1442 scrotal
## 65 1909 1442 scrotal
## 66 2135 1487 scrotal
## 67 1981 1500 scrotal
## 68 1852 1526 scrotal
## 69 2026 1526 scrotal
## 70 1921 1526 scrotal
## 71 1920 1526 scrotal
## 72 1858 1526 scrotal
## 73 1851 1526 scrotal
## 74 1920 1526 scrotal
## 75 1956 1561 scrotal
## 76 1956 1561 scrotal
## 77 1956 1561 scrotal
## 78 1877 1561 scrotal
## 79 2010 1618 scrotal
## 80 2010 1618 scrotal
## 81 2010 1618 scrotal
## 82 2010 1618 scrotal
## 83 2010 1618 scrotal
## 84 1940 1618 scrotal
## 85 1940 1618 scrotal
## 86 2147 1672 scrotal
## 87 2147 1672 scrotal
## 88 2147 1672 scrotal
## 89 2147 1672 scrotal
## 90 2146 1672 scrotal
## 91 2146 1672 scrotal
## 92 2146 1672 scrotal
## 93 2146 1672 scrotal
## 94 2146 1672 scrotal
## 95 2146 1672 scrotal
## 96 2146 1672 scrotal
## 97 2140 1672 scrotal
## 98 2140 1672 scrotal
## 99 2140 1672 scrotal
## 100 2140 1672 scrotal
## 101 2140 1672 scrotal
## 102 2139 1672 scrotal
## 103 2139 1672 scrotal
## 104 2139 1672 scrotal
## 105 2139 1672 scrotal
## 106 2139 1672 scrotal
## 107 2139 1672 scrotal
## 108 2139 1672 scrotal
## 109 2139 1672 scrotal
## 110 2139 1672 scrotal
## 111 2139 1672 scrotal
## 112 2139 1672 scrotal
## 113 2139 1672 scrotal
## 114 2139 1672 scrotal
## 115 2030 1672 <NA>
## 116 2030 1672 <NA>
## 117 2030 1672 <NA>
## 118 2030 1672 <NA>
## 119 2030 1672 <NA>
## 120 2030 1672 <NA>
## 121 2030 1672 <NA>
## 122 2029 1672 <NA>
## 123 2029 1672 <NA>
## 124 2029 1672 <NA>
## 125 2029 1672 <NA>
## 126 2029 1672 <NA>
## 127 2029 1672 <NA>
## 128 2029 1672 <NA>
## 129 2028 1672 <NA>
## 130 2028 1672 <NA>
## 131 2028 1672 <NA>
## 132 2028 1672 <NA>
## 133 2027 1672 <NA>
## 134 2027 1672 <NA>
## 135 2027 1672 <NA>
## 136 2027 1672 <NA>
## 137 2027 1672 <NA>
## 138 2027 1672 <NA>
## 139 2027 1672 <NA>
## 140 2089 1729 scrotal
## 141 2089 1729 scrotal
## 142 2089 1729 scrotal
## 143 2088 1729 scrotal
## 144 2085 1729 scrotal
## 145 2085 1729 scrotal
## 146 2084 1729 scrotal
## 147 1974 1845 <NA>
## 148 1888 1850 scrotal
## 149 1887 1850 scrotal
## 150 2099 1859 scrotal
## 151 2099 1859 scrotal
## 152 2098 1859 scrotal
## 153 2098 1859 scrotal
## 154 2098 1859 scrotal
## 155 2097 1859 scrotal
## 156 2097 1859 scrotal
## 157 2198 1867 scrotal
## 158 2137 1895 scrotal
## 159 2132 1895 scrotal
## 160 2215 dummy_1023 <NA>
## 161 2215 dummy_1023 <NA>
## 162 2215 dummy_1023 <NA>
## 163 2215 dummy_1023 <NA>
## 164 2215 dummy_1023 <NA>
## 165 2215 dummy_1023 <NA>
## 166 2215 dummy_1023 <NA>
## 167 2215 dummy_1023 <NA>
## 168 2215 dummy_1023 <NA>
## 169 2215 dummy_1023 <NA>
## 170 2215 dummy_1023 <NA>
## 171 2215 dummy_1023 <NA>
## 172 2215 dummy_1023 <NA>
## 173 2215 dummy_1023 <NA>
## 174 2214 dummy_1023 <NA>
## 175 2214 dummy_1023 <NA>
## 176 2214 dummy_1023 <NA>
## 177 2214 dummy_1023 <NA>
## 178 2214 dummy_1023 <NA>
## 179 2214 dummy_1023 <NA>
## 180 2214 dummy_1023 <NA>
## 181 2214 dummy_1023 <NA>
## 182 2214 dummy_1023 <NA>
## 183 2214 dummy_1023 <NA>
## 184 2214 dummy_1023 <NA>
## 185 2214 dummy_1023 <NA>
## 186 2214 dummy_1023 <NA>
## 187 2214 dummy_1023 <NA>
## 188 2213 dummy_1023 <NA>
## 189 2213 dummy_1023 <NA>
## 190 2213 dummy_1023 <NA>
## 191 2213 dummy_1023 <NA>
## 192 2213 dummy_1023 <NA>
## 193 2213 dummy_1023 <NA>
## 194 2213 dummy_1023 <NA>
## 195 2213 dummy_1023 <NA>
## 196 2213 dummy_1023 <NA>
## 197 2213 dummy_1023 <NA>
## 198 2213 dummy_1023 <NA>
## 199 2213 dummy_1023 <NA>
## 200 2213 dummy_1023 <NA>
## 201 2213 dummy_1023 <NA>
## 202 1766 dummy_825 <NA>
## 203 1766 dummy_825 <NA>
## 204 1766 dummy_825 <NA>
## 205 1766 dummy_825 <NA>
## 206 1766 dummy_825 <NA>
## 207 1766 dummy_825 <NA>
## 208 1766 dummy_825 <NA>
## 209 1766 dummy_825 <NA>
## 210 1766 dummy_825 <NA>
## 211 1766 dummy_825 <NA>
## 212 1766 dummy_825 <NA>
## 213 1766 dummy_825 <NA>
## 214 1766 dummy_825 <NA>
## 215 1766 dummy_825 <NA>
## 216 1765 dummy_825 <NA>
## 217 1765 dummy_825 <NA>
## 218 1765 dummy_825 <NA>
## 219 1765 dummy_825 <NA>
## 220 1765 dummy_825 <NA>
## 221 1765 dummy_825 <NA>
## 222 1765 dummy_825 <NA>
## 223 1765 dummy_825 <NA>
## 224 1765 dummy_825 <NA>
## 225 1765 dummy_825 <NA>
## 226 1765 dummy_825 <NA>
## 227 1765 dummy_825 <NA>
## 228 1764 dummy_825 <NA>
## 229 1764 dummy_825 <NA>
## 230 1764 dummy_825 <NA>
## 231 1764 dummy_825 <NA>
## 232 1764 dummy_825 <NA>
## 233 1764 dummy_825 <NA>
## 234 1764 dummy_825 <NA>
## 235 1764 dummy_825 <NA>
## 236 1764 dummy_825 <NA>
## 237 1764 dummy_825 <NA>
## 238 1764 dummy_825 <NA>
## 239 1764 dummy_825 <NA>
## 240 1764 dummy_825 <NA>
## 241 1764 dummy_825 <NA>
## 242 1764 dummy_825 <NA>
## 243 1763 dummy_825 <NA>
## 244 1763 dummy_825 <NA>
## 245 1763 dummy_825 <NA>
## 246 1763 dummy_825 <NA>
## 247 1763 dummy_825 <NA>
## 248 1763 dummy_825 <NA>
## 249 1763 dummy_825 <NA>
## 250 1763 dummy_825 <NA>
## 251 1763 dummy_825 <NA>
## 252 1763 dummy_825 <NA>
## 253 1763 dummy_825 <NA>
## 254 1763 dummy_825 <NA>
## 255 1763 dummy_825 <NA>
## 256 1763 dummy_825 <NA>
## 257 1763 dummy_825 <NA># FILTER LES INDIVIDUS DEPUIS 2013 TO 2023
# df_error_father <- df_error_father[!is.na(df_error_father$year_analysis), ]
# WE REMOVE THE CASE ALREADY CORRECTED IN BDD
#' ... Donc les cas où la parentée sociale est différente de la parentée génétique
index <- match(df_error_father$individual_id, parentee_genetique$individual_id)
df_error_father$match_gen <- ifelse(df_error_father$father_id == parentee_genetique$father[index], T, F)
df_error_father <-
df_error_father |>
filter(match_gen | is.na(match_gen))
# PRINT RESULTS
df_error_father <-
df_error_father |>
arrange(father_id, individual_id, cohort, marker)
write.xlsx(df_error_father, file = paste0("df_error_father_", format(Sys.time(), format = "%d_%m_%H_%M%_%S"), ".xlsx"), row.names = FALSE)## individual_id marker father_id off_haplo father_haplo year_analysis_father
## 1 1854 MA002 1091 271/281 279/279 <NA>
## 2 1854 MA066 1091 241/241 231/231 <NA>
## 3 1855 MA002 1091 271/281 279/279 <NA>
## 4 1856 MA066 1091 241/241 231/231 <NA>
## 5 1856 MS45 1091 107/109 107/107 <NA>
## 6 1857 MA002 1091 271/281 279/279 <NA>
## 7 1916 BIBL18 1091 149/149 143/147 <NA>
## 8 1918 MS45 1091 107/109 107/107 <NA>
## 9 1919 MA066 1091 241/241 231/231 <NA>
## 10 1919 MS45 1091 107/109 107/107 <NA>
## 11 1922 ST10 1099 118/134 120/134 2020
## 12 1923 MA091 1099 173/173 169/175 2020
## 13 1923 ST10 1099 116/116 120/134 2020
## 14 1973 BIBL20 1258 208/216 216/220 2023_3
## 15 1827 MS45 1309 107/111 107/107 2023_3
## 16 1886 MS45 1309 107/111 107/107 2023_3
## 17 1894 MS45 1309 107/111 107/107 2023_3
## 18 1895 MS45 1309 107/111 107/107 2023_3
## 19 1930 MS45 1309 107/111 107/107 2023_3
## 20 1931 MS45 1309 107/111 107/107 2023_3
## 21 1751 BIBL31 1326 157/163 161/161 2013
## 22 1796 BIBL31 1326 157/159 161/161 2013
## 23 1796 MA002 1326 279/281 271/279 2013
## 24 1802 BIBL31 1326 157/157 161/161 2013
## 25 1804 BIBL31 1326 157/163 161/161 2013
## 26 1783 MS45 1342 107/111 109/111 2013
## 27 1784 BIBL4 1404 188/190 190/190 <NA>
## 28 1784 MA002 1404 271/283 271/279 <NA>
## 29 1785 MA002 1404 271/283 271/279 <NA>
## 30 1785 MS53 1404 140/140 132/142 <NA>
## 31 1786 BIBL18 1404 145/147 143/149 <NA>
## 32 1786 MA002 1404 271/281 271/279 <NA>
## 33 1786 MS56 1404 104/108 108/108 <NA>
## 34 1787 BIBL18 1404 143/145 143/149 <NA>
## 35 1787 MA002 1404 271/283 271/279 <NA>
## 36 1712 BIBL1 1409 95/97 101/107 <NA>
## 37 1713 MS41 1409 184/184 186/186 <NA>
## 38 1716 BIBL31 1409 157/163 157/161 <NA>
## 39 1716 MS41 1409 184/184 186/186 <NA>
## 40 1874 MS41 1409 184/184 186/186 <NA>
## 41 1874 MS47 1409 180/184 180/180 <NA>
## 42 1876 MS47 1409 180/184 180/180 <NA>
## 43 1876 MS56 1409 108/108 106/106 <NA>
## 44 1945 MS47 1409 180/184 180/180 <NA>
## 45 1947 MS41 1409 184/184 186/186 <NA>
## 46 1952 MS41 1409 184/184 186/186 <NA>
## 47 2062 MS47 1409 180/184 180/180 <NA>
## 48 2064 MS47 1409 180/184 180/180 <NA>
## 49 2122 MS41 1409 184/184 186/186 <NA>
## 50 2125 MS41 1409 184/184 186/186 <NA>
## 51 2126 MS41 1409 184/184 186/186 <NA>
## 52 2208 MS47 1409 180/184 180/180 <NA>
## 53 1792 BIBL18 1417 143/147 143/145 2017
## 54 1792 MA002 1417 279/281 271/283 2017
## 55 1792 MA091 1417 175/177 173/175 2017
## 56 1792 MS47 1417 182/186 176/184 2017
## 57 1792 MS53 1417 132/140 140/142 2017
## 58 1792 ST10 1417 118/132 130/136 2017
## 59 1949 MS56 1440 108/108 106/106 2023_3
## 60 1950 MS53 1440 132/140 142/142 2023_3
## 61 1950 MS56 1440 108/108 106/106 2023_3
## 62 1951 MS47 1440 184/186 180/180 2023_3
## 63 1909 BIBL4 1442 175/175 175/190 <NA>
## 64 1909 MS56 1442 108/108 108/108 <NA>
## 65 1909 MS6 1442 158/158 158/158 <NA>
## 66 2135 MA091 1487 169/173 175/175 <NA>
## 67 1981 MS53 1500 140/142 142/144 2023_3
## 68 1851 MS45 1526 107/111 109/109 <NA>
## 69 1852 MS45 1526 107/111 109/109 <NA>
## 70 1858 MS45 1526 107/107 109/109 <NA>
## 71 1920 BIBL31 1526 157/157 159/161 <NA>
## 72 1920 MS45 1526 107/107 109/109 <NA>
## 73 1921 MS45 1526 107/107 109/109 <NA>
## 74 2026 MS47 1526 186/188 182/186 <NA>
## 75 1877 MS56 1561 106/108 108/108 2023_3
## 76 1956 MA066 1561 231/241 231/231 2023_3
## 77 1956 MA091 1561 175/177 167/167 2023_3
## 78 1956 MS53 1561 132/140 140/142 2023_3
## 79 1940 BIBL20 1618 218/218 208/220 <NA>
## 80 1940 MA091 1618 175/175 173/179 <NA>
## 81 2010 BIBL18 1618 147/147 143/149 <NA>
## 82 2010 BIBL20 1618 218/218 208/220 <NA>
## 83 2010 MA091 1618 175/175 173/179 <NA>
## 84 2010 MS45 1618 107/107 109/109 <NA>
## 85 2010 MS53 1618 142/142 140/140 <NA>
## 86 2027 BIBL31 1672 157/157 157/163 2023_2
## 87 2027 BIBL4 1672 190/190 188/190 2023_2
## 88 2027 MA002 1672 271/271 271/279 2023_2
## 89 2027 MA018 1672 298/298 296/298 2023_2
## 90 2027 MS41 1672 186/186 184/186 2023_2
## 91 2027 MS53 1672 142/142 142/142 2023_2
## 92 2027 MS6 1672 158/158 158/158 2023_2
## 93 2028 BIBL31 1672 157/157 157/163 2023_2
## 94 2028 MS53 1672 142/142 142/142 2023_2
## 95 2028 MS6 1672 158/158 158/158 2023_2
## 96 2028 ST10 1672 120/120 120/132 2023_2
## 97 2029 BIBL31 1672 157/157 157/163 2023_2
## 98 2029 BIBL4 1672 190/190 188/190 2023_2
## 99 2029 MA002 1672 271/271 271/279 2023_2
## 100 2029 MA066 1672 231/231 231/241 2023_2
## 101 2029 MS45 1672 109/109 107/109 2023_2
## 102 2029 MS53 1672 142/142 142/142 2023_2
## 103 2029 MS6 1672 158/158 158/158 2023_2
## 104 2030 BIBL1 1672 97/97 107/107 2023_2
## 105 2030 BIBL31 1672 157/157 157/163 2023_2
## 106 2030 MA018 1672 298/298 296/298 2023_2
## 107 2030 MA066 1672 231/231 231/241 2023_2
## 108 2030 MS41 1672 186/186 184/186 2023_2
## 109 2030 MS53 1672 142/142 142/142 2023_2
## 110 2030 MS6 1672 158/158 158/158 2023_2
## 111 2139 BIBL18 1672 149/149 143/149 2023_2
## 112 2139 BIBL20 1672 218/218 216/218 2023_2
## 113 2139 BIBL31 1672 157/157 157/163 2023_2
## 114 2139 BIBL4 1672 190/190 188/190 2023_2
## 115 2139 MA002 1672 271/271 271/279 2023_2
## 116 2139 MA018 1672 298/298 296/298 2023_2
## 117 2139 MA091 1672 175/175 173/175 2023_2
## 118 2139 MS41 1672 186/186 184/186 2023_2
## 119 2139 MS45 1672 107/107 107/109 2023_2
## 120 2139 MS47 1672 180/180 180/180 2023_2
## 121 2139 MS53 1672 142/142 142/142 2023_2
## 122 2139 MS6 1672 158/158 158/158 2023_2
## 123 2139 ST10 1672 132/132 120/132 2023_2
## 124 2140 BIBL18 1672 149/149 143/149 2023_2
## 125 2140 BIBL4 1672 190/190 188/190 2023_2
## 126 2140 MA002 1672 271/271 271/279 2023_2
## 127 2140 MS45 1672 109/109 107/109 2023_2
## 128 2140 MS53 1672 142/142 142/142 2023_2
## 129 2146 BIBL31 1672 157/157 157/163 2023_2
## 130 2146 MA002 1672 271/271 271/279 2023_2
## 131 2146 MA018 1672 298/298 296/298 2023_2
## 132 2146 MA091 1672 175/175 173/175 2023_2
## 133 2146 MS41 1672 186/186 184/186 2023_2
## 134 2146 MS53 1672 142/142 142/142 2023_2
## 135 2146 MS6 1672 158/158 158/158 2023_2
## 136 2147 MA066 1672 231/231 231/241 2023_2
## 137 2147 MA091 1672 175/175 173/175 2023_2
## 138 2147 MS53 1672 142/142 142/142 2023_2
## 139 2147 MS6 1672 158/158 158/158 2023_2
## 140 2084 BIBL31 1729 157/157 159/161 2017
## 141 2085 MA091 1729 167/167 175/175 2017
## 142 2085 MS45 1729 107/111 109/109 2017
## 143 2088 MS53 1729 142/142 132/140 2017
## 144 2089 MA091 1729 167/167 175/175 2017
## 145 2089 MS45 1729 107/109 109/109 2017
## 146 2089 MS53 1729 142/142 132/140 2017
## 147 1974 BIBL20 1845 208/216 208/208 2022
## 148 1887 MA002 1850 279/281 271/279 2020
## 149 1888 MA002 1850 279/281 271/279 2020
## 150 2097 MA002 1859 271/279 281/281 2020
## 151 2097 ST10 1859 120/130 116/116 2020
## 152 2098 BIBL18 1859 145/145 143/149 2020
## 153 2098 MA002 1859 271/279 281/281 2020
## 154 2098 ST10 1859 118/130 116/116 2020
## 155 2099 BIBL18 1859 145/145 143/149 2020
## 156 2099 MS53 1859 132/142 140/142 2020
## 157 2198 BIBL18 1867 143/143 145/149 2020
## 158 2132 MA002 1895 271/279 279/279 2020
## 159 2137 MA002 1895 271/279 279/279 2020
## 160 2213 BIBL1 dummy_1023 101/107 NA/NA <NA>
## 161 2213 BIBL18 dummy_1023 145/149 NA/NA <NA>
## 162 2213 BIBL20 dummy_1023 218/218 NA/NA <NA>
## 163 2213 BIBL4 dummy_1023 188/190 NA/NA <NA>
## 164 2213 MA002 dummy_1023 281/281 NA/NA <NA>
## 165 2213 MA018 dummy_1023 298/298 NA/NA <NA>
## 166 2213 MA066 dummy_1023 231/231 NA/NA <NA>
## 167 2213 MA091 dummy_1023 159/175 NA/NA <NA>
## 168 2213 MS41 dummy_1023 184/186 NA/NA <NA>
## 169 2213 MS45 dummy_1023 107/109 NA/NA <NA>
## 170 2213 MS47 dummy_1023 180/186 NA/NA <NA>
## 171 2213 MS53 dummy_1023 140/142 NA/NA <NA>
## 172 2213 MS56 dummy_1023 106/108 NA/NA <NA>
## 173 2213 MS6 dummy_1023 142/158 NA/NA <NA>
## 174 2214 BIBL1 dummy_1023 101/107 NA/NA <NA>
## 175 2214 BIBL18 dummy_1023 149/149 NA/NA <NA>
## 176 2214 BIBL20 dummy_1023 218/218 NA/NA <NA>
## 177 2214 BIBL4 dummy_1023 188/190 NA/NA <NA>
## 178 2214 MA002 dummy_1023 279/281 NA/NA <NA>
## 179 2214 MA018 dummy_1023 298/298 NA/NA <NA>
## 180 2214 MA066 dummy_1023 231/231 NA/NA <NA>
## 181 2214 MA091 dummy_1023 175/175 NA/NA <NA>
## 182 2214 MS41 dummy_1023 184/186 NA/NA <NA>
## 183 2214 MS45 dummy_1023 109/109 NA/NA <NA>
## 184 2214 MS47 dummy_1023 184/186 NA/NA <NA>
## 185 2214 MS53 dummy_1023 140/142 NA/NA <NA>
## 186 2214 MS56 dummy_1023 106/108 NA/NA <NA>
## 187 2214 MS6 dummy_1023 158/158 NA/NA <NA>
## 188 2215 BIBL1 dummy_1023 101/107 NA/NA <NA>
## 189 2215 BIBL18 dummy_1023 147/149 NA/NA <NA>
## 190 2215 BIBL20 dummy_1023 208/218 NA/NA <NA>
## 191 2215 BIBL4 dummy_1023 188/190 NA/NA <NA>
## 192 2215 MA002 dummy_1023 279/281 NA/NA <NA>
## 193 2215 MA018 dummy_1023 298/298 NA/NA <NA>
## 194 2215 MA066 dummy_1023 231/231 NA/NA <NA>
## 195 2215 MA091 dummy_1023 175/175 NA/NA <NA>
## 196 2215 MS41 dummy_1023 184/186 NA/NA <NA>
## 197 2215 MS45 dummy_1023 107/109 NA/NA <NA>
## 198 2215 MS47 dummy_1023 184/186 NA/NA <NA>
## 199 2215 MS53 dummy_1023 140/142 NA/NA <NA>
## 200 2215 MS56 dummy_1023 106/108 NA/NA <NA>
## 201 2215 MS6 dummy_1023 158/158 NA/NA <NA>
## 202 1763 BIBL1 dummy_825 101/107 NA/NA <NA>
## 203 1763 BIBL18 dummy_825 143/147 NA/NA <NA>
## 204 1763 BIBL20 dummy_825 208/218 NA/NA <NA>
## 205 1763 BIBL31 dummy_825 159/159 NA/NA <NA>
## 206 1763 BIBL4 dummy_825 188/190 NA/NA <NA>
## 207 1763 MA002 dummy_825 265/281 NA/NA <NA>
## 208 1763 MA018 dummy_825 298/298 NA/NA <NA>
## 209 1763 MA066 dummy_825 231/233 NA/NA <NA>
## 210 1763 MA091 dummy_825 159/179 NA/NA <NA>
## 211 1763 MS41 dummy_825 184/186 NA/NA <NA>
## 212 1763 MS45 dummy_825 107/109 NA/NA <NA>
## 213 1763 MS53 dummy_825 140/140 NA/NA <NA>
## 214 1763 MS56 dummy_825 106/108 NA/NA <NA>
## 215 1763 MS6 dummy_825 142/160 NA/NA <NA>
## 216 1763 ST10 dummy_825 118/118 NA/NA <NA>
## 217 1764 BIBL1 dummy_825 101/107 NA/NA <NA>
## 218 1764 BIBL18 dummy_825 143/145 NA/NA <NA>
## 219 1764 BIBL20 dummy_825 208/208 NA/NA <NA>
## 220 1764 BIBL31 dummy_825 157/159 NA/NA <NA>
## 221 1764 BIBL4 dummy_825 188/190 NA/NA <NA>
## 222 1764 MA002 dummy_825 279/281 NA/NA <NA>
## 223 1764 MA018 dummy_825 298/298 NA/NA <NA>
## 224 1764 MA091 dummy_825 167/175 NA/NA <NA>
## 225 1764 MS41 dummy_825 184/186 NA/NA <NA>
## 226 1764 MS45 dummy_825 107/109 NA/NA <NA>
## 227 1764 MS47 dummy_825 184/186 NA/NA <NA>
## 228 1764 MS53 dummy_825 140/140 NA/NA <NA>
## 229 1764 MS56 dummy_825 106/106 NA/NA <NA>
## 230 1764 MS6 dummy_825 158/158 NA/NA <NA>
## 231 1764 ST10 dummy_825 118/118 NA/NA <NA>
## 232 1765 BIBL1 dummy_825 101/107 NA/NA <NA>
## 233 1765 BIBL18 dummy_825 147/147 NA/NA <NA>
## 234 1765 BIBL20 dummy_825 208/218 NA/NA <NA>
## 235 1765 BIBL31 dummy_825 159/161 NA/NA <NA>
## 236 1765 BIBL4 dummy_825 188/190 NA/NA <NA>
## 237 1765 MA002 dummy_825 279/281 NA/NA <NA>
## 238 1765 MA018 dummy_825 298/298 NA/NA <NA>
## 239 1765 MA091 dummy_825 167/179 NA/NA <NA>
## 240 1765 MS41 dummy_825 184/184 NA/NA <NA>
## 241 1765 MS45 dummy_825 109/109 NA/NA <NA>
## 242 1765 MS56 dummy_825 106/108 NA/NA <NA>
## 243 1765 ST10 dummy_825 118/130 NA/NA <NA>
## 244 1766 BIBL1 dummy_825 101/107 NA/NA <NA>
## 245 1766 BIBL18 dummy_825 145/147 NA/NA <NA>
## 246 1766 BIBL20 dummy_825 208/208 NA/NA <NA>
## 247 1766 BIBL31 dummy_825 159/159 NA/NA <NA>
## 248 1766 MA002 dummy_825 279/281 NA/NA <NA>
## 249 1766 MA018 dummy_825 298/298 NA/NA <NA>
## 250 1766 MA091 dummy_825 159/175 NA/NA <NA>
## 251 1766 MS41 dummy_825 184/184 NA/NA <NA>
## 252 1766 MS45 dummy_825 107/109 NA/NA <NA>
## 253 1766 MS47 dummy_825 184/186 NA/NA <NA>
## 254 1766 MS53 dummy_825 140/142 NA/NA <NA>
## 255 1766 MS56 dummy_825 106/108 NA/NA <NA>
## 256 1766 MS6 dummy_825 142/160 NA/NA <NA>
## 257 1766 ST10 dummy_825 118/130 NA/NA <NA>
## year_analysis n_error_total cohort mother n_correct n_incorrect
## 1 2020 2 2018 1560 0 4
## 2 2020 2 2018 1560 0 4
## 3 2020 1 2018 1560 0 4
## 4 2020 2 2018 1560 0 4
## 5 2020 2 2018 1560 0 4
## 6 2020 1 2018 1560 0 4
## 7 2020 1 2019 1560 1 3
## 8 2020 1 2019 1560 1 3
## 9 2020 2 2019 1560 1 3
## 10 2020 2 2019 1560 1 3
## 11 2020 1 2019 1553 1 3
## 12 2020 2 2019 1553 1 3
## 13 2020 2 2019 1553 1 3
## 14 2022 1 2019 1229 1 3
## 15 2023_3 1 2017 1635 1 0
## 16 2020 1 2018 1635 0 4
## 17 2020 1 2018 1635 0 4
## 18 2020 1 2018 1635 0 4
## 19 2020 1 2019 1635 1 3
## 20 2020 1 2019 1635 1 3
## 21 2017 1 2016 1437 2 0
## 22 2022 2 2016 1437 2 0
## 23 2022 2 2016 1437 2 0
## 24 2022 1 2017 1437 1 0
## 25 2022 1 2017 1437 1 0
## 26 2017 1 2016 1433 2 0
## 27 2017 2 2016 1420 2 0
## 28 2017 2 2016 1420 2 0
## 29 2017 2 2016 1420 2 0
## 30 2017 2 2016 1420 2 0
## 31 2017 3 2016 1420 2 0
## 32 2017 3 2016 1420 2 0
## 33 2017 3 2016 1420 2 0
## 34 2017 2 2016 1420 2 0
## 35 2017 2 2016 1420 2 0
## 36 2017 1 2016 1297 2 0
## 37 2017 1 2016 1297 2 0
## 38 2017 2 2016 1297 2 0
## 39 2017 2 2016 1297 2 0
## 40 2020 2 2018 1657 0 4
## 41 2020 2 2018 1657 0 4
## 42 2020 2 2018 1657 0 4
## 43 2020 2 2018 1657 0 4
## 44 2022 1 2019 1657 1 3
## 45 2022 1 2019 1657 1 3
## 46 2022 1 2019 1657 1 3
## 47 2023_1 1 2020 1657 4 0
## 48 2023_1 1 2020 1657 4 0
## 49 2023_2 1 2021 1657 1 3
## 50 2023_2 1 2021 1657 1 3
## 51 2023_2 1 2021 1657 1 3
## 52 2023_3 1 2022 1657 2 1
## 53 2022 5 <NA> 1408 0 1
## 54 2022 5 <NA> 1408 0 1
## 55 2022 5 <NA> 1408 0 1
## 56 2022 5 <NA> 1408 0 1
## 57 2022 5 <NA> 1408 0 1
## 58 2022 5 <NA> 1408 0 1
## 59 2022 1 2019 1564 1 3
## 60 2022 2 2019 1564 1 3
## 61 2022 2 2019 1564 1 3
## 62 2022 1 2019 1564 1 3
## 63 2020 3 2018 dummy_891 0 4
## 64 2020 3 2018 dummy_891 0 4
## 65 2020 3 2018 dummy_891 0 4
## 66 2023_2 1 2021 1315 1 3
## 67 2022 1 2019 1790 1 3
## 68 2020 1 2018 1475 0 4
## 69 2020 1 2018 1475 0 4
## 70 2020 1 2018 1475 0 4
## 71 2022 1 2019 1475 1 3
## 72 2022 1 2019 1475 1 3
## 73 2020 1 2019 1475 1 3
## 74 2023_1 1 2020 1475 4 0
## 75 2020 1 2018 1520 0 4
## 76 2022 3 2019 1520 1 3
## 77 2022 3 2019 1520 1 3
## 78 2022 3 2019 1520 1 3
## 79 2020 2 2019 1443 1 3
## 80 2020 2 2019 1443 1 3
## 81 2022 5 2019 1443 1 3
## 82 2022 5 2019 1443 1 3
## 83 2022 5 2019 1443 1 3
## 84 2022 5 2019 1443 1 3
## 85 2022 5 2019 1443 1 3
## 86 2023_1 7 2020 dummy_945 4 0
## 87 2023_1 7 2020 dummy_945 4 0
## 88 2023_1 7 2020 dummy_945 4 0
## 89 2023_1 7 2020 dummy_945 4 0
## 90 2023_1 7 2020 dummy_945 4 0
## 91 2023_1 7 2020 dummy_945 4 0
## 92 2023_1 7 2020 dummy_945 4 0
## 93 2023_1 4 2020 dummy_945 4 0
## 94 2023_1 4 2020 dummy_945 4 0
## 95 2023_1 4 2020 dummy_945 4 0
## 96 2023_1 4 2020 dummy_945 4 0
## 97 2023_1 7 2020 dummy_945 4 0
## 98 2023_1 7 2020 dummy_945 4 0
## 99 2023_1 7 2020 dummy_945 4 0
## 100 2023_1 7 2020 dummy_945 4 0
## 101 2023_1 7 2020 dummy_945 4 0
## 102 2023_1 7 2020 dummy_945 4 0
## 103 2023_1 7 2020 dummy_945 4 0
## 104 2023_1 7 2020 dummy_945 4 0
## 105 2023_1 7 2020 dummy_945 4 0
## 106 2023_1 7 2020 dummy_945 4 0
## 107 2023_1 7 2020 dummy_945 4 0
## 108 2023_1 7 2020 dummy_945 4 0
## 109 2023_1 7 2020 dummy_945 4 0
## 110 2023_1 7 2020 dummy_945 4 0
## 111 2023_2 13 2021 dummy_973 1 3
## 112 2023_2 13 2021 dummy_973 1 3
## 113 2023_2 13 2021 dummy_973 1 3
## 114 2023_2 13 2021 dummy_973 1 3
## 115 2023_2 13 2021 dummy_973 1 3
## 116 2023_2 13 2021 dummy_973 1 3
## 117 2023_2 13 2021 dummy_973 1 3
## 118 2023_2 13 2021 dummy_973 1 3
## 119 2023_2 13 2021 dummy_973 1 3
## 120 2023_2 13 2021 dummy_973 1 3
## 121 2023_2 13 2021 dummy_973 1 3
## 122 2023_2 13 2021 dummy_973 1 3
## 123 2023_2 13 2021 dummy_973 1 3
## 124 2023_2 5 2021 dummy_973 1 3
## 125 2023_2 5 2021 dummy_973 1 3
## 126 2023_2 5 2021 dummy_973 1 3
## 127 2023_2 5 2021 dummy_973 1 3
## 128 2023_2 5 2021 dummy_973 1 3
## 129 2023_2 7 2021 dummy_973 1 3
## 130 2023_2 7 2021 dummy_973 1 3
## 131 2023_2 7 2021 dummy_973 1 3
## 132 2023_2 7 2021 dummy_973 1 3
## 133 2023_2 7 2021 dummy_973 1 3
## 134 2023_2 7 2021 dummy_973 1 3
## 135 2023_2 7 2021 dummy_973 1 3
## 136 2023_2 4 2021 dummy_973 1 3
## 137 2023_2 4 2021 dummy_973 1 3
## 138 2023_2 4 2021 dummy_973 1 3
## 139 2023_2 4 2021 dummy_973 1 3
## 140 2023_1 1 2021 1759 1 3
## 141 2023_1 2 2021 1759 1 3
## 142 2023_1 2 2021 1759 1 3
## 143 2023_1 1 2021 1759 1 3
## 144 2023_1 3 2021 1759 1 3
## 145 2023_1 3 2021 1759 1 3
## 146 2023_1 3 2021 1759 1 3
## 147 2022 1 2019 1548 1 3
## 148 2020 1 2018 1408 0 4
## 149 2020 1 2018 1408 0 4
## 150 2023_1 2 2021 1935 1 3
## 151 2023_1 2 2021 1935 1 3
## 152 2023_1 3 2021 1935 1 3
## 153 2023_1 3 2021 1935 1 3
## 154 2023_1 3 2021 1935 1 3
## 155 2023_1 2 2021 1935 1 3
## 156 2023_1 2 2021 1935 1 3
## 157 2023_3 1 2022 1897 2 1
## 158 2023_2 1 2021 1863 1 3
## 159 2023_2 1 2021 1863 1 3
## 160 2023_3 0 2022 1899 2 1
## 161 2023_3 0 2022 1899 2 1
## 162 2023_3 0 2022 1899 2 1
## 163 2023_3 0 2022 1899 2 1
## 164 2023_3 0 2022 1899 2 1
## 165 2023_3 0 2022 1899 2 1
## 166 2023_3 0 2022 1899 2 1
## 167 2023_3 0 2022 1899 2 1
## 168 2023_3 0 2022 1899 2 1
## 169 2023_3 0 2022 1899 2 1
## 170 2023_3 0 2022 1899 2 1
## 171 2023_3 0 2022 1899 2 1
## 172 2023_3 0 2022 1899 2 1
## 173 2023_3 0 2022 1899 2 1
## 174 2023_3 0 2022 1899 2 1
## 175 2023_3 0 2022 1899 2 1
## 176 2023_3 0 2022 1899 2 1
## 177 2023_3 0 2022 1899 2 1
## 178 2023_3 0 2022 1899 2 1
## 179 2023_3 0 2022 1899 2 1
## 180 2023_3 0 2022 1899 2 1
## 181 2023_3 0 2022 1899 2 1
## 182 2023_3 0 2022 1899 2 1
## 183 2023_3 0 2022 1899 2 1
## 184 2023_3 0 2022 1899 2 1
## 185 2023_3 0 2022 1899 2 1
## 186 2023_3 0 2022 1899 2 1
## 187 2023_3 0 2022 1899 2 1
## 188 2023_3 0 2022 1899 2 1
## 189 2023_3 0 2022 1899 2 1
## 190 2023_3 0 2022 1899 2 1
## 191 2023_3 0 2022 1899 2 1
## 192 2023_3 0 2022 1899 2 1
## 193 2023_3 0 2022 1899 2 1
## 194 2023_3 0 2022 1899 2 1
## 195 2023_3 0 2022 1899 2 1
## 196 2023_3 0 2022 1899 2 1
## 197 2023_3 0 2022 1899 2 1
## 198 2023_3 0 2022 1899 2 1
## 199 2023_3 0 2022 1899 2 1
## 200 2023_3 0 2022 1899 2 1
## 201 2023_3 0 2022 1899 2 1
## 202 2017 0 2016 1606 2 0
## 203 2017 0 2016 1606 2 0
## 204 2017 0 2016 1606 2 0
## 205 2017 0 2016 1606 2 0
## 206 2017 0 2016 1606 2 0
## 207 2017 0 2016 1606 2 0
## 208 2017 0 2016 1606 2 0
## 209 2017 0 2016 1606 2 0
## 210 2017 0 2016 1606 2 0
## 211 2017 0 2016 1606 2 0
## 212 2017 0 2016 1606 2 0
## 213 2017 0 2016 1606 2 0
## 214 2017 0 2016 1606 2 0
## 215 2017 0 2016 1606 2 0
## 216 2017 0 2016 1606 2 0
## 217 2017 0 2016 1606 2 0
## 218 2017 0 2016 1606 2 0
## 219 2017 0 2016 1606 2 0
## 220 2017 0 2016 1606 2 0
## 221 2017 0 2016 1606 2 0
## 222 2017 0 2016 1606 2 0
## 223 2017 0 2016 1606 2 0
## 224 2017 0 2016 1606 2 0
## 225 2017 0 2016 1606 2 0
## 226 2017 0 2016 1606 2 0
## 227 2017 0 2016 1606 2 0
## 228 2017 0 2016 1606 2 0
## 229 2017 0 2016 1606 2 0
## 230 2017 0 2016 1606 2 0
## 231 2017 0 2016 1606 2 0
## 232 2017 0 2016 1606 2 0
## 233 2017 0 2016 1606 2 0
## 234 2017 0 2016 1606 2 0
## 235 2017 0 2016 1606 2 0
## 236 2017 0 2016 1606 2 0
## 237 2017 0 2016 1606 2 0
## 238 2017 0 2016 1606 2 0
## 239 2017 0 2016 1606 2 0
## 240 2017 0 2016 1606 2 0
## 241 2017 0 2016 1606 2 0
## 242 2017 0 2016 1606 2 0
## 243 2017 0 2016 1606 2 0
## 244 2017 0 2016 1606 2 0
## 245 2017 0 2016 1606 2 0
## 246 2017 0 2016 1606 2 0
## 247 2017 0 2016 1606 2 0
## 248 2017 0 2016 1606 2 0
## 249 2017 0 2016 1606 2 0
## 250 2017 0 2016 1606 2 0
## 251 2017 0 2016 1606 2 0
## 252 2017 0 2016 1606 2 0
## 253 2017 0 2016 1606 2 0
## 254 2017 0 2016 1606 2 0
## 255 2017 0 2016 1606 2 0
## 256 2017 0 2016 1606 2 0
## 257 2017 0 2016 1606 2 0
## n_correct_tot n_incorrect_tot territoire annee_emergence n_loci_compared
## 1 1 7 e 2018 10
## 2 1 7 e 2018 10
## 3 1 7 e 2018 10
## 4 1 7 e 2018 9
## 5 1 7 e 2018 9
## 6 1 7 e 2018 11
## 7 1 7 e 2019 12
## 8 1 7 e 2019 11
## 9 1 7 e 2019 12
## 10 1 7 e 2019 12
## 11 8 2 n 2019 12
## 12 8 2 n 2019 14
## 13 8 2 n 2019 14
## 14 18 1 y 2019 15
## 15 7 6 n2 2017 4
## 16 7 6 n2 2018 6
## 17 7 6 n2 2018 6
## 18 7 6 n2 2018 6
## 19 7 6 n2 2019 6
## 20 7 6 n2 2019 6
## 21 3 4 imilloz 2016 15
## 22 3 4 imilloz 2016 9
## 23 3 4 imilloz 2016 9
## 24 3 4 imilloz 2017 10
## 25 3 4 imilloz 2017 12
## 26 5 1 u 2016 13
## 27 0 4 z 2016 10
## 28 0 4 z 2016 10
## 29 0 4 z 2016 15
## 30 0 4 z 2016 15
## 31 0 4 z 2016 14
## 32 0 4 z 2016 14
## 33 0 4 z 2016 14
## 34 0 4 z 2016 13
## 35 0 4 z 2016 13
## 36 10 14 etalus 2016 15
## 37 10 14 etalus 2016 16
## 38 10 14 etalus 2016 16
## 39 10 14 etalus 2016 16
## 40 10 14 etalus 2018 12
## 41 10 14 etalus 2018 12
## 42 10 14 etalus 2018 12
## 43 10 14 etalus 2018 12
## 44 10 14 eadret 2019 13
## 45 10 14 etalus 2019 13
## 46 10 14 etalus 2019 13
## 47 10 14 etalus 2020 13
## 48 10 14 etalus 2020 13
## 49 10 14 etalus 2021 12
## 50 10 14 etalus 2021 8
## 51 10 14 etalus 2021 8
## 52 10 14 etalus 2022 9
## 53 2 1 w 2016 12
## 54 2 1 w 2016 12
## 55 2 1 w 2016 12
## 56 2 1 w 2016 12
## 57 2 1 w 2016 12
## 58 2 1 w 2016 12
## 59 19 3 t 2019 6
## 60 19 3 n3 2019 4
## 61 19 3 n3 2019 4
## 62 19 3 n3 2019 10
## 63 0 1 u 2018 7
## 64 0 1 u 2018 7
## 65 0 1 u 2018 7
## 66 15 1 h 2021 13
## 67 12 1 y 2019 13
## 68 12 6 g 2018 12
## 69 12 6 g 2018 13
## 70 12 6 g 2018 13
## 71 12 6 g 2019 13
## 72 12 6 g 2019 13
## 73 12 6 g 2019 12
## 74 12 6 g 2020 14
## 75 13 2 c 2018 12
## 76 13 2 c 2019 11
## 77 13 2 c 2019 11
## 78 13 2 c 2019 11
## 79 14 2 ce 2019 11
## 80 14 2 ce 2019 11
## 81 14 2 ce 2019 14
## 82 14 2 ce 2019 14
## 83 14 2 ce 2019 14
## 84 14 2 ce 2019 14
## 85 14 2 ce 2019 14
## 86 0 8 imilloz 2020 16
## 87 0 8 imilloz 2020 16
## 88 0 8 imilloz 2020 16
## 89 0 8 imilloz 2020 16
## 90 0 8 imilloz 2020 16
## 91 0 8 imilloz 2020 16
## 92 0 8 imilloz 2020 16
## 93 0 8 imilloz 2020 16
## 94 0 8 imilloz 2020 16
## 95 0 8 imilloz 2020 16
## 96 0 8 imilloz 2020 16
## 97 0 8 imilloz 2020 16
## 98 0 8 imilloz 2020 16
## 99 0 8 imilloz 2020 16
## 100 0 8 imilloz 2020 16
## 101 0 8 imilloz 2020 16
## 102 0 8 imilloz 2020 16
## 103 0 8 imilloz 2020 16
## 104 0 8 imilloz 2020 16
## 105 0 8 imilloz 2020 16
## 106 0 8 imilloz 2020 16
## 107 0 8 imilloz 2020 16
## 108 0 8 imilloz 2020 16
## 109 0 8 imilloz 2020 16
## 110 0 8 imilloz 2020 16
## 111 0 8 imilloz 2021 16
## 112 0 8 imilloz 2021 16
## 113 0 8 imilloz 2021 16
## 114 0 8 imilloz 2021 16
## 115 0 8 imilloz 2021 16
## 116 0 8 imilloz 2021 16
## 117 0 8 imilloz 2021 16
## 118 0 8 imilloz 2021 16
## 119 0 8 imilloz 2021 16
## 120 0 8 imilloz 2021 16
## 121 0 8 imilloz 2021 16
## 122 0 8 imilloz 2021 16
## 123 0 8 imilloz 2021 16
## 124 0 8 imilloz 2021 14
## 125 0 8 imilloz 2021 14
## 126 0 8 imilloz 2021 14
## 127 0 8 imilloz 2021 14
## 128 0 8 imilloz 2021 14
## 129 0 8 imilloz 2021 15
## 130 0 8 imilloz 2021 15
## 131 0 8 imilloz 2021 15
## 132 0 8 imilloz 2021 15
## 133 0 8 imilloz 2021 15
## 134 0 8 imilloz 2021 15
## 135 0 8 imilloz 2021 15
## 136 0 8 imilloz 2021 14
## 137 0 8 imilloz 2021 14
## 138 0 8 imilloz 2021 14
## 139 0 8 imilloz 2021 14
## 140 3 4 chalet 2021 16
## 141 3 4 chalet 2021 16
## 142 3 4 chalet 2021 16
## 143 3 4 chalet 2021 16
## 144 3 4 chalet 2021 16
## 145 3 4 chalet 2021 16
## 146 3 4 chalet 2021 16
## 147 13 1 l2 2019 14
## 148 1 2 w 2018 12
## 149 1 2 w 2018 12
## 150 5 3 f 2021 15
## 151 5 3 f 2021 15
## 152 5 3 f 2021 15
## 153 5 3 f 2021 15
## 154 5 3 f 2021 15
## 155 5 3 f 2021 15
## 156 5 3 f 2021 15
## 157 7 1 n4 2022 12
## 158 2 2 t 2021 14
## 159 2 2 t 2021 13
## 160 0 3 b2 2022 12
## 161 0 3 b2 2022 12
## 162 0 3 b2 2022 12
## 163 0 3 b2 2022 12
## 164 0 3 b2 2022 12
## 165 0 3 b2 2022 12
## 166 0 3 b2 2022 12
## 167 0 3 b2 2022 12
## 168 0 3 b2 2022 12
## 169 0 3 b2 2022 12
## 170 0 3 b2 2022 12
## 171 0 3 b2 2022 12
## 172 0 3 b2 2022 12
## 173 0 3 b2 2022 12
## 174 0 3 b2 2022 12
## 175 0 3 b2 2022 12
## 176 0 3 b2 2022 12
## 177 0 3 b2 2022 12
## 178 0 3 b2 2022 12
## 179 0 3 b2 2022 12
## 180 0 3 b2 2022 12
## 181 0 3 b2 2022 12
## 182 0 3 b2 2022 12
## 183 0 3 b2 2022 12
## 184 0 3 b2 2022 12
## 185 0 3 b2 2022 12
## 186 0 3 b2 2022 12
## 187 0 3 b2 2022 12
## 188 0 3 b2 2022 12
## 189 0 3 b2 2022 12
## 190 0 3 b2 2022 12
## 191 0 3 b2 2022 12
## 192 0 3 b2 2022 12
## 193 0 3 b2 2022 12
## 194 0 3 b2 2022 12
## 195 0 3 b2 2022 12
## 196 0 3 b2 2022 12
## 197 0 3 b2 2022 12
## 198 0 3 b2 2022 12
## 199 0 3 b2 2022 12
## 200 0 3 b2 2022 12
## 201 0 3 b2 2022 12
## 202 0 4 j 2016 14
## 203 0 4 j 2016 14
## 204 0 4 j 2016 14
## 205 0 4 j 2016 14
## 206 0 4 j 2016 14
## 207 0 4 j 2016 14
## 208 0 4 j 2016 14
## 209 0 4 j 2016 14
## 210 0 4 j 2016 14
## 211 0 4 j 2016 14
## 212 0 4 j 2016 14
## 213 0 4 j 2016 14
## 214 0 4 j 2016 14
## 215 0 4 j 2016 14
## 216 0 4 j 2016 14
## 217 0 4 j 2016 14
## 218 0 4 j 2016 14
## 219 0 4 j 2016 14
## 220 0 4 j 2016 14
## 221 0 4 j 2016 14
## 222 0 4 j 2016 14
## 223 0 4 j 2016 14
## 224 0 4 j 2016 14
## 225 0 4 j 2016 14
## 226 0 4 j 2016 14
## 227 0 4 j 2016 14
## 228 0 4 j 2016 14
## 229 0 4 j 2016 14
## 230 0 4 j 2016 14
## 231 0 4 j 2016 14
## 232 0 4 j 2016 11
## 233 0 4 j 2016 11
## 234 0 4 j 2016 11
## 235 0 4 j 2016 11
## 236 0 4 j 2016 11
## 237 0 4 j 2016 11
## 238 0 4 j 2016 11
## 239 0 4 j 2016 11
## 240 0 4 j 2016 11
## 241 0 4 j 2016 11
## 242 0 4 j 2016 11
## 243 0 4 j 2016 11
## 244 0 4 r 2016 13
## 245 0 4 r 2016 13
## 246 0 4 r 2016 13
## 247 0 4 r 2016 13
## 248 0 4 r 2016 13
## 249 0 4 r 2016 13
## 250 0 4 r 2016 13
## 251 0 4 r 2016 13
## 252 0 4 r 2016 13
## 253 0 4 r 2016 13
## 254 0 4 r 2016 13
## 255 0 4 r 2016 13
## 256 0 4 r 2016 13
## 257 0 4 r 2016 13
## length_seq length_seq_father statut_reproducteur match_gen
## 1 12 16 scrotal NA
## 2 12 16 scrotal NA
## 3 11 16 scrotal NA
## 4 11 16 scrotal NA
## 5 11 16 scrotal NA
## 6 12 16 scrotal NA
## 7 14 16 scrotal NA
## 8 13 16 scrotal NA
## 9 14 16 scrotal NA
## 10 14 16 scrotal NA
## 11 12 12 scrotal NA
## 12 14 12 scrotal NA
## 13 14 12 scrotal NA
## 14 15 14 scrotal NA
## 15 14 14 scrotal NA
## 16 13 14 scrotal NA
## 17 16 14 scrotal NA
## 18 15 14 scrotal NA
## 19 12 14 scrotal NA
## 20 12 14 scrotal NA
## 21 16 16 <NA> NA
## 22 10 16 <NA> NA
## 23 10 16 <NA> NA
## 24 10 16 <NA> NA
## 25 13 16 <NA> NA
## 26 14 16 scrotal NA
## 27 11 12 <NA> NA
## 28 11 12 <NA> NA
## 29 16 12 <NA> NA
## 30 16 12 <NA> NA
## 31 15 12 <NA> NA
## 32 15 12 <NA> NA
## 33 15 12 <NA> NA
## 34 14 12 <NA> NA
## 35 14 12 <NA> NA
## 36 15 15 scrotal NA
## 37 16 15 scrotal NA
## 38 16 15 scrotal NA
## 39 16 15 scrotal NA
## 40 14 15 scrotal NA
## 41 14 15 scrotal NA
## 42 14 15 scrotal NA
## 43 14 15 scrotal NA
## 44 16 15 scrotal NA
## 45 15 15 scrotal NA
## 46 15 15 scrotal NA
## 47 15 15 <NA> NA
## 48 15 15 <NA> NA
## 49 14 15 scrotal NA
## 50 9 15 scrotal NA
## 51 9 15 scrotal NA
## 52 10 15 <NA> NA
## 53 14 15 scrotal NA
## 54 14 15 scrotal NA
## 55 14 15 scrotal NA
## 56 14 15 scrotal NA
## 57 14 15 scrotal NA
## 58 14 15 scrotal NA
## 59 7 14 scrotal NA
## 60 4 14 scrotal NA
## 61 4 14 scrotal NA
## 62 11 14 scrotal NA
## 63 7 15 scrotal NA
## 64 7 15 scrotal NA
## 65 7 15 scrotal NA
## 66 15 16 scrotal NA
## 67 15 14 scrotal NA
## 68 14 16 scrotal NA
## 69 15 16 scrotal NA
## 70 14 16 scrotal NA
## 71 15 16 scrotal NA
## 72 15 16 scrotal NA
## 73 14 16 scrotal NA
## 74 15 16 scrotal NA
## 75 12 14 scrotal NA
## 76 11 14 scrotal NA
## 77 11 14 scrotal NA
## 78 11 14 scrotal NA
## 79 12 14 scrotal NA
## 80 12 14 scrotal NA
## 81 15 14 scrotal NA
## 82 15 14 scrotal NA
## 83 15 14 scrotal NA
## 84 15 14 scrotal NA
## 85 15 14 scrotal NA
## 86 16 16 <NA> NA
## 87 16 16 <NA> NA
## 88 16 16 <NA> NA
## 89 16 16 <NA> NA
## 90 16 16 <NA> NA
## 91 16 16 <NA> NA
## 92 16 16 <NA> NA
## 93 16 16 <NA> NA
## 94 16 16 <NA> NA
## 95 16 16 <NA> NA
## 96 16 16 <NA> NA
## 97 16 16 <NA> NA
## 98 16 16 <NA> NA
## 99 16 16 <NA> NA
## 100 16 16 <NA> NA
## 101 16 16 <NA> NA
## 102 16 16 <NA> NA
## 103 16 16 <NA> NA
## 104 16 16 <NA> NA
## 105 16 16 <NA> NA
## 106 16 16 <NA> NA
## 107 16 16 <NA> NA
## 108 16 16 <NA> NA
## 109 16 16 <NA> NA
## 110 16 16 <NA> NA
## 111 16 16 scrotal NA
## 112 16 16 scrotal NA
## 113 16 16 scrotal NA
## 114 16 16 scrotal NA
## 115 16 16 scrotal NA
## 116 16 16 scrotal NA
## 117 16 16 scrotal NA
## 118 16 16 scrotal NA
## 119 16 16 scrotal NA
## 120 16 16 scrotal NA
## 121 16 16 scrotal NA
## 122 16 16 scrotal NA
## 123 16 16 scrotal NA
## 124 14 16 scrotal NA
## 125 14 16 scrotal NA
## 126 14 16 scrotal NA
## 127 14 16 scrotal NA
## 128 14 16 scrotal NA
## 129 15 16 scrotal NA
## 130 15 16 scrotal NA
## 131 15 16 scrotal NA
## 132 15 16 scrotal NA
## 133 15 16 scrotal NA
## 134 15 16 scrotal NA
## 135 15 16 scrotal NA
## 136 14 16 scrotal NA
## 137 14 16 scrotal NA
## 138 14 16 scrotal NA
## 139 14 16 scrotal NA
## 140 16 14 scrotal NA
## 141 16 14 scrotal NA
## 142 16 14 scrotal NA
## 143 16 14 scrotal NA
## 144 16 14 scrotal NA
## 145 16 14 scrotal NA
## 146 16 14 scrotal NA
## 147 14 15 <NA> NA
## 148 14 14 scrotal NA
## 149 14 14 scrotal NA
## 150 16 15 scrotal NA
## 151 16 15 scrotal NA
## 152 16 15 scrotal NA
## 153 16 15 scrotal NA
## 154 16 15 scrotal NA
## 155 16 15 scrotal NA
## 156 16 15 scrotal NA
## 157 12 13 scrotal NA
## 158 16 15 scrotal NA
## 159 15 15 scrotal NA
## 160 14 NA <NA> NA
## 161 14 NA <NA> NA
## 162 14 NA <NA> NA
## 163 14 NA <NA> NA
## 164 14 NA <NA> NA
## 165 14 NA <NA> NA
## 166 14 NA <NA> NA
## 167 14 NA <NA> NA
## 168 14 NA <NA> NA
## 169 14 NA <NA> NA
## 170 14 NA <NA> NA
## 171 14 NA <NA> NA
## 172 14 NA <NA> NA
## 173 14 NA <NA> NA
## 174 14 NA <NA> NA
## 175 14 NA <NA> NA
## 176 14 NA <NA> NA
## 177 14 NA <NA> NA
## 178 14 NA <NA> NA
## 179 14 NA <NA> NA
## 180 14 NA <NA> NA
## 181 14 NA <NA> NA
## 182 14 NA <NA> NA
## 183 14 NA <NA> NA
## 184 14 NA <NA> NA
## 185 14 NA <NA> NA
## 186 14 NA <NA> NA
## 187 14 NA <NA> NA
## 188 14 NA <NA> NA
## 189 14 NA <NA> NA
## 190 14 NA <NA> NA
## 191 14 NA <NA> NA
## 192 14 NA <NA> NA
## 193 14 NA <NA> NA
## 194 14 NA <NA> NA
## 195 14 NA <NA> NA
## 196 14 NA <NA> NA
## 197 14 NA <NA> NA
## 198 14 NA <NA> NA
## 199 14 NA <NA> NA
## 200 14 NA <NA> NA
## 201 14 NA <NA> NA
## 202 15 NA <NA> NA
## 203 15 NA <NA> NA
## 204 15 NA <NA> NA
## 205 15 NA <NA> NA
## 206 15 NA <NA> NA
## 207 15 NA <NA> NA
## 208 15 NA <NA> NA
## 209 15 NA <NA> NA
## 210 15 NA <NA> NA
## 211 15 NA <NA> NA
## 212 15 NA <NA> NA
## 213 15 NA <NA> NA
## 214 15 NA <NA> NA
## 215 15 NA <NA> NA
## 216 15 NA <NA> NA
## 217 15 NA <NA> NA
## 218 15 NA <NA> NA
## 219 15 NA <NA> NA
## 220 15 NA <NA> NA
## 221 15 NA <NA> NA
## 222 15 NA <NA> NA
## 223 15 NA <NA> NA
## 224 15 NA <NA> NA
## 225 15 NA <NA> NA
## 226 15 NA <NA> NA
## 227 15 NA <NA> NA
## 228 15 NA <NA> NA
## 229 15 NA <NA> NA
## 230 15 NA <NA> NA
## 231 15 NA <NA> NA
## 232 12 NA <NA> NA
## 233 12 NA <NA> NA
## 234 12 NA <NA> NA
## 235 12 NA <NA> NA
## 236 12 NA <NA> NA
## 237 12 NA <NA> NA
## 238 12 NA <NA> NA
## 239 12 NA <NA> NA
## 240 12 NA <NA> NA
## 241 12 NA <NA> NA
## 242 12 NA <NA> NA
## 243 12 NA <NA> NA
## 244 14 NA <NA> NA
## 245 14 NA <NA> NA
## 246 14 NA <NA> NA
## 247 14 NA <NA> NA
## 248 14 NA <NA> NA
## 249 14 NA <NA> NA
## 250 14 NA <NA> NA
## 251 14 NA <NA> NA
## 252 14 NA <NA> NA
## 253 14 NA <NA> NA
## 254 14 NA <NA> NA
## 255 14 NA <NA> NA
## 256 14 NA <NA> NA
## 257 14 NA <NA> NA
Ces mismatchs sont répartis entre 25 pères.
Leur ID est :
## [1] 1091 1099 1258 1309 1326 1342 1404 1409 1417 1440 1442 1487 1500 1526 1561
## [16] 1618 1672 1729 1845 1850 1859 1867 1895
7.2.6 Individus restant à analyser
On ajoute ces individus potentiellement EPY et leur père aux ID à re-tester avec Genoscreen.
# get epy ID and their father
id_epy <- df_error$individual_id
id_father_epy <- unique(df_error$father_id)
# add epy and father to df
df_id_to_analyse[(nrow(df_id_to_analyse)+1):(nrow(df_id_to_analyse) + length(c(id_epy, id_father_epy))), "individual_id"] <- c(id_epy, id_father_epy)
# complete info
df_id_to_analyse$genotyped <- NA
df_id_to_analyse$length_marker_non_na <- NA
df_id_to_analyse$epy <- NA
# ... id genotyped ?
df_id_to_analyse$genotyped <- ifelse(df_id_to_analyse$individual_id %in% id_non_genotyped, "no", "yes")
# ... length of the non-NA sequence of the genetic profile
seq_non_na <- apply(pg_cervus[5:ncol(pg_cervus)], 1, function(x) sum(x!=0, na.rm = T)/2)
index_temp <- match(df_id_to_analyse$individual_id, pg_cervus$individual_id)
df_id_to_analyse$length_marker_non_na <- seq_non_na[index_temp]
# ... is the individual a potential EPY ?
df_id_to_analyse$epy <- ifelse(df_id_to_analyse$individual_id %in% id_epy | df_id_to_analyse$individual_id %in% id_father_epy, "yes", ifelse(df_id_to_analyse$individual_id %in% id_non_genotyped, "NA", "no"))
# Remove duplicates
df_id_to_analyse <- df_id_to_analyse[!duplicated(df_id_to_analyse$individual_id), ]
# AJOUT DES INDIVIDUS CAPTUREES POUR LA PREMIERE FOIS EN 2023:2025
id_2023_2025 <- individu$individual_id[individu$annee_capture %in% 2023:2025]
df_id_to_analyse[(nrow(df_id_to_analyse)+1):(nrow(df_id_to_analyse) + length(id_2023_2025)), "individual_id"] <- id_2023_2025
df_id_to_analyse[df_id_to_analyse$individual_id %in% id_2023_2025, "genotyped"] <- "no"
# ADD IF INDIVIDUAL IS A PARENT
df_id_to_analyse$parent <- ifelse(df_id_to_analyse$individual_id %in% pedigree_data$mother | df_id_to_analyse$individual_id %in% pedigree_data$father, "yes", "no")
# ADD COHORT AND FIRST CAPTURE PER INDIVIDUAL
index_temp <- match(df_id_to_analyse$individual_id, individu$individual_id)
df_id_to_analyse$annee_emergence <- individu$annee_emergence[index_temp]
df_id_to_analyse$annee_first_capture <- individu$annee_capture[index_temp]
# INDIVIDUS DEJA RETESTE ?
df_id_to_analyse$retest <- ifelse(df_id_to_analyse$individual_id %in% id_temp_retest, "yes", "no")
# SAVE
# df_id_to_analyse <- xlsx::read.xlsx(file = paste0(dir, "/df_id_to_analyse_in_genemapper.xlsx"), sheetIndex = 1)
# write.xlsx(df_id_to_analyse, file = paste0(dir, "/df_id_to_analyse_in_genemapper.xlsx"))Nombre d’individu à tester 506.
C’est trop… Je vais appliquer quelques filtres pour ne conserver que les individus les plus pertinents : (1) les marmottons non-génotypés, (2) les parents non-génotypés, (3) les individus très mal génotypés. Les individus capturés anciens non-génotypés ne sont pas à intégrer car leur ADN est de mauvaise qualité et n’ont pas réussi à l’être correctement (discussion avec Aurélie Cohas).
df_id_to_analyse$to_keep <- NA
# Portées de 2023:2025
df_id_to_analyse[df_id_to_analyse$annee_emergence %in% 2023:2025, "to_keep"] <- 1
sum(df_id_to_analyse$to_keep, na.rm = T)## [1] 221# Portées manquantes de 2017
df_id_to_analyse[df_id_to_analyse$annee_emergence %in% 2017, "to_keep" ] <- 1
sum(df_id_to_analyse$to_keep, na.rm = T)## [1] 260# Parents non-génotypés > 2015
df_id_to_analyse[df_id_to_analyse$annee_first_capture > 2015 &
df_id_to_analyse$retest == "no" &
df_id_to_analyse$parent == "yes" &
df_id_to_analyse$genotyped == "no", "to_keep"] <- 1
sum(df_id_to_analyse$to_keep, na.rm = T)## [1] 261# Parents mal génotypés (< 10 marqueurs)
df_id_to_analyse[df_id_to_analyse$annee_first_capture > 2015 &
df_id_to_analyse$retest == "no" &
df_id_to_analyse$parent == "yes" &
df_id_to_analyse$length_marker_non_na <= 10 &
!is.na(df_id_to_analyse$length_marker_non_na), "to_keep"] <- 1
sum(df_id_to_analyse$to_keep, na.rm = T)## [1] 265# Individus mal génotypés
df_id_to_analyse[df_id_to_analyse$annee_first_capture > 2015 &
df_id_to_analyse$retest == "no" &
df_id_to_analyse$parent == "no" &
df_id_to_analyse$length_marker_non_na < 10 &
!is.na(df_id_to_analyse$length_marker_non_na), "to_keep"] <- 1
sum(df_id_to_analyse$to_keep, na.rm = T)## [1] 278On doit avoir 96*3 = 288 places.
## [1] 278On doit aussi ajouter des individus déjà re-testés avec génotypes bien connus, qui couvrent au maximum la range de chaque marqueur de façon à pouvoir détecter un éventuel décalage dans les données (cf Aurélie Cohas). Je vais ajouter 10 individus de références.
# ADD LENGTH OF THE SEQUENCE
pg_retest <- pg_cervus
pg_retest$length_sequence <- apply(X = pg_retest[5:ncol(pg_retest)], MARGIN = 1, FUN = function(x) sum(x != 0)/2)
# SELECTION DES INDIVIDUS A RETESTER
#' CRITERES : individus retestés + dernière capture récente + absence de NAs + absence de mismatch avec la mère + pas de mismatch
pg_retest <- pg_retest[pg_retest$individual_id %in% id_temp_retest &
pg_retest$oldest_capture >= 2019 &
pg_retest$length_sequence == 16 &
!pg_retest$individual_id %in% df_error_mother$individual_id &
!pg_retest$individual_id %in% df_error$individual_id, ]
nrow(pg_retest) # 10 !## [1] 10# AJOUT DE CES INDIVIDUS DANS LES INDIVIDUS A TESTER
id_to_genotype <- data.frame(individual_id = c(df_id_to_genotype$individual_id, pg_retest$individual_id), oldest_capture = NA)
# AJOUT DATE DERNIERE CAPTURE (pour aider à la recherche des échantillons)
statut_individu_temp <-
statut_individu |>
group_by(individu_id) |>
filter(annee_capture == max(annee_capture)) |>
filter(!duplicated(individu_id))
index_temp <- match(id_to_genotype$individual_id, statut_individu_temp$individu_id)
id_to_genotype$oldest_capture <- statut_individu_temp$annee_capture[index_temp]
id_to_genotype <- id_to_genotype |> arrange(oldest_capture)
#' @VERIFICATION
sum(duplicated(id_to_genotype$individual_id)) # pas de duplication## [1] 0# SAVE
write.xlsx(
id_to_genotype,
file = paste0(dir, "/df_id_to_analyse_in_genemapper_", format(Sys.time(), format = "%d_%m_%H_%M%_%S"), ".xlsx"),
row.names = F,
showNA = F
)
8 ANALYSE CERVUS
8.1 Genotype file
On sauvegarde pg_cervus en format .txt (tabulation) pour
Cervus.
# CONVERSION DES ALLELES EN NOMBRE
df_temp <- pg_cervus[, c(1:2, 5:ncol(pg_cervus))]
df_temp[3:ncol(df_temp)] <- sapply(df_temp[3:ncol(df_temp)], as.numeric)
# SAVE
write.table(df_temp, file = paste0(dir, "/CERVUS ANALYSIS/genotype.txt"), sep = "\t", row.names = FALSE)
save(pg_cervus, file = paste0(dir, "/CERVUS ANALYSIS/pg_cervus.rds"))
8.2 Offspring file
Ce fichier doit contenir l’ID de l’individu à tester, la mère (qu’on traitera comme connue) et \(x\) colonnes contenant les ID des pères candidats.
On a déjà les individus à tester dans CERVUS dans l’objet
ped_tested contenant les ID individuelles et les parents
sociaux des individus génotypés entre 2016 et 2022, de parents
génétiques non-disponibles dans la BDD, de date d’émergence connue et de
mère sociale connue et génotypée.
Nombre d’individu testé : 408
On va constuire 4 offsprings files :
- Un premier pour tester la validité de la méthode avec une uniquement la femelle dominante comme candidate
- Un second avec la mère connue et le mâle dominant comme unique candidat
- Un troisième avec la mère connue et l’ensemble des mâles
sexuellement matures à \(t\) et \(t-1\) comme pères candidats
- Un quatrième avec uniquement les offsprings avec au moins un mismatch avec le mâle dominant et l’ensemble des mâles sexuellement matures à \(t\) et \(t-1\) comme pères candidats
8.2.1 Mère candidat
df_temp <- ped_tested[c("individual_id", "mother")]
df_temp$father <- NA
write.table(df_temp, file = paste0(dir, "/CERVUS ANALYSIS/MOTHER ONLY/offspring_mere.txt"), sep = "\t", row.names = FALSE)
8.2.2 Père seul candidat
df_temp <- ped_tested[c("individual_id", "mother", "father")]
write.table(df_temp, file = paste0(dir, "/CERVUS ANALYSIS/MALE DOMINANT ONLY/offspring_pere_seul.txt"), sep = "\t", row.names = FALSE)
8.2.3 Mâles sexuellement matures candidats
On va considérer comme candidat tous les mâles sexuellement matures présent entre \(t-1\) et \(t+1\), \(t\) étant la naissance du marmotton. On ajoute aussi systématiquement le mâle dominant comme candidat.
# GET MOTHER ID FOR EACH INDIVIDUALS IN CAPTURE MATRIX
index <- match(mat_info_id$individual_id, pedigree_data$individual_id)
mat_info_id["mother"] <- pedigree_data$mother[index]
# GET OLDEST CAPTURE OF INDIVIDUALS TO IDENTIFY FATHER CANDIDATE
mat_info_id["first_capture"] <- pedigree_data$first_capture[index]
# ID TO ANALYSE IN CERVUS
id_to_analyse <- ped_tested$individual_id
# CONSTRUCT OFFSPRING DATAFRAME BASED
offspring_cervus_t <- c()
t_levels <- c()
# we bind the offspring file with associated candidate father per year
for(t in c(2016:2022)){
# OFFSPRING OF YEAR t TO BE TESTED
offspring <- mat_info_id[mat_info_id$cohort == t &
!duplicated(mat_info_id$individual_id), c("individual_id", "mother", "cohort")]
offspring <-
offspring |>
filter(individual_id %in% id_to_analyse)
if(nrow(offspring) == 0) next
# SELECT CANDIDATE FATHERS
candidate_fathers <-
mat_info_id |>
filter(
sexe == "m" & # male individual
(age_capture == "adult" | (as.numeric(as.character(first_capture)) <= t - 2)) & # adult à t
(annee_capture %in% (c(t-1, t, t+1))) # recapturé après t
) |> # captured / observed recently
dplyr::select(individual_id) |>
unlist() |>
unique()
cat(paste0("\nNb of candidate fathers for year ", t, " : ", length(candidate_fathers)))
# OFFSPRING FILE BIND WITH CANDIDATE FATHERS
if(length(offspring) > 0){
offspring_cervus_t.temp <- cbind(offspring, t(candidate_fathers))
# ADD DOMINANT MALE IF NOT PRESENT IN CANDIDATE
id_offspring <- offspring_cervus_t.temp$individual_id
index <- match(id_offspring, pedigree_data$individual_id)
soc_father <- pedigree_data[index, "father"]
col_temp <- c()
for(i in 1:length(id_offspring)){
if(!soc_father[i] %in% offspring_cervus_t.temp[i, 4:ncol(offspring_cervus_t.temp)]){
col_temp <- c(col_temp, soc_father[i])
} else{
col_temp <- c(col_temp, NA)
}
}
if(!all(is.na(col_temp))){
offspring_cervus_t.temp <- cbind(offspring_cervus_t.temp, col_temp)
}
offspring_cervus_t <- bind_rows(offspring_cervus_t, offspring_cervus_t.temp)
t_levels <- c(t_levels, offspring_cervus_t.temp$cohort)
}
}##
## Nb of candidate fathers for year 2016 : 85
## Nb of candidate fathers for year 2017 : 99
## Nb of candidate fathers for year 2018 : 75
## Nb of candidate fathers for year 2019 : 50
## Nb of candidate fathers for year 2020 : 62
## Nb of candidate fathers for year 2021 : 64
## Nb of candidate fathers for year 2022 : 93# SAVE
write.table(offspring_cervus_t, file = paste0(dir, "/CERVUS ANALYSIS/MALES AS CANDIDATES/offspring_male_candidates.txt"), sep = "\t", row.names = FALSE)
8.2.4 EPY potentiels et mâles sexuellement matures candidats
On extrait les individus ayant au moins un mismatch avec le mâle social comme potentiel EPY. Ce sont ces indivius qu’on va tester dans CERVUS.
# FILTER OFFSPRING FILES TO KEEP ONLY INDIVIDUALS WITH AT LEAST ONE MISMATCH WITH FATHER
id_potential_epy <- ped_tested$individual_id[!ped_tested$match_father]
offspring_cervus_t_epy <- offspring_cervus_t[offspring_cervus_t$individual_id %in% id_potential_epy, ]
# SAVE
write.table(offspring_cervus_t_epy, file = paste0(dir, "/CERVUS ANALYSIS/EPY/offspring_epy.txt"), sep = "\t", row.names = FALSE)