library(tidyverse)
library(janitor)
library(knitr)
library(kableExtra)
library(scales)
library(gridExtra)

# COLORBLIND-FRIENDLY PALETTE (Tol's bright qualitative palette)
gen_colors <- c("G3" = "#0077BB", "G4" = "#EE7733", "G5" = "#009988")
batch_colors <- c("0" = "#0077BB", "3" = "#33BBEE", "4" = "#009988", 
                  "5" = "#EE7733", "6" = "#CC3311", "7" = "#EE3377")

# Inbreeding category colors (colorblind-friendly, neutral tones)
inbreeding_colors <- c(
  "Non-inbred (F = 0%)" = "#009988",
  "Below 2nd cousin (F < 3.125%)" = "#33BBEE",
  "2nd cousin level (F < 6.25%)" = "#0077BB",
  "1st cousin level (F < 12.5%)" = "#EE7733",
  "Half-sib level (F < 25%)" = "#AA7744",
  "Full-sib level (F ≥ 25%)" = "#7744AA"
)

# Custom theme
theme_tilili <- function() {
  theme_minimal(base_size = 12) +
    theme(
      plot.title = element_text(face = "bold", size = 14, hjust = 0, color = "#234e52"),
      plot.subtitle = element_text(size = 11, hjust = 0, color = "#4a5568"),
      legend.position = "bottom",
      panel.grid.minor = element_blank(),
      axis.title = element_text(face = "bold", size = 11),
      strip.text = element_text(face = "bold", size = 11)
    )
}
theme_set(theme_tilili())

# Base directory
base_dir <- "C:/Users/SAlemu/OneDrive - CGIAR/Documents/Poultryaudit/Dataset"

# Body weight data and WOMBAT results
wombat_base <- file.path(base_dir, "Analysis_with_wombat2")
tilili_pheno_file <- file.path(base_dir, "AllG-345.csv")
tilili_wombat_dir <- file.path(wombat_base, "Tilili", "Model1_Animal_YearMonth")

# Egg production data
egg_weight_file <- file.path(wombat_base, "TililiAEW", "Masterfile_AEW.csv")
egg_number_file <- file.path(wombat_base, "Tililieggnum", "Masterfile_egg.csv")

1 Executive Summary

This report presents a comprehensive genetic evaluation of the Tilili local Ethiopian chicken ecotype, including:

Body Weight Genetic Parameters - Heritability and variance components for BW at 16 weeks
Inbreeding Analysis - Detailed classification based on biological relationship levels
Egg Weight Analysis - Weekly means and trends across generations (Weeks 31-61)
Egg Number Analysis - Weekly means and trends across generations (Weeks 31-61)

🐔 About the Tilili Line

Tilili is a local Ethiopian chicken ecotype included in ILRI’s breeding program to improve productivity while maintaining adaptation to smallholder scavenging conditions. The breed is managed under a semi-scavenging system with 80% restricted feeding during the outdoor phase (weeks 9-16) to simulate smallholder farming conditions.

Attribute	Details
Origin	Local Ethiopian ecotype
Target Environment	Smallholder semi-scavenging systems
Housing	Phase 1: Indoor (Wk 0-9), Phase 2: Free-range (Wk 9-16)
Feeding	80% restricted feeding during outdoor phase
Selection Traits	Body weight, egg production, adaptability

2 Part 1: Body Weight Genetic Parameters

# Load Tilili phenotype data
tilili_raw <- read.csv(tilili_pheno_file, stringsAsFactors = FALSE, fileEncoding = "Latin1")
colnames(tilili_raw) <- trimws(colnames(tilili_raw))

tilili_data <- data.frame(
  ID = as.character(tilili_raw$ChickenID),
  Sire_ID = as.character(tilili_raw$SireID),
  Dam_ID = as.character(tilili_raw$DamId),
  Sex = as.character(tilili_raw$Sex),
  Batch = as.character(tilili_raw$Batch),
  Generation = as.character(tilili_raw$Generation),
  BW_16wk = as.numeric(tilili_raw$Week16),
  stringsAsFactors = FALSE
)

# Clean parent IDs
tilili_data$Sire_ID[tilili_data$Sire_ID %in% c("", "0", "NA", "na") | is.na(tilili_data$Sire_ID)] <- NA
tilili_data$Dam_ID[tilili_data$Dam_ID %in% c("", "0", "NA", "na") | is.na(tilili_data$Dam_ID)] <- NA

# Summary statistics
n_total <- nrow(tilili_data)
n_with_bw <- sum(!is.na(tilili_data$BW_16wk))
mean_bw <- round(mean(tilili_data$BW_16wk, na.rm = TRUE), 0)
sd_bw <- round(sd(tilili_data$BW_16wk, na.rm = TRUE), 0)
cv_bw <- round(sd_bw / mean_bw * 100, 1)

male_bw <- round(mean(tilili_data$BW_16wk[tilili_data$Sex == "M"], na.rm = TRUE), 0)
female_bw <- round(mean(tilili_data$BW_16wk[tilili_data$Sex == "F"], na.rm = TRUE), 0)
dimorphism <- round((male_bw - female_bw) / female_bw * 100, 1)

gen_counts <- table(tilili_data$Generation)

# Extract WOMBAT variance component results
sum_est_file <- file.path(tilili_wombat_dir, "SumEstimates.out")
ebv_file <- file.path(tilili_wombat_dir, "RnSoln_animal.dat")
id_map_file <- file.path(wombat_base, "Tilili", "ID_mapping.csv")

# Initialize
Va <- NA; Va_SE <- NA; Ve <- NA; Ve_SE <- NA; Vp <- NA; h2 <- NA; h2_SE <- NA; N_rec <- NA
tilili_vc <- NULL
tilili_ebv <- NULL

if (file.exists(sum_est_file)) {
  sum_lines <- readLines(sum_est_file)
  
  # Extract Va and h2
  covs_a_idx <- grep("COVS A 1 1.*vrat", sum_lines)
  if (length(covs_a_idx) > 0) {
    covs_a_line <- sum_lines[covs_a_idx[1]]
    nums <- as.numeric(unlist(regmatches(covs_a_line, gregexpr("[0-9]+\\.?[0-9]*", covs_a_line))))
    if (length(nums) >= 7) { Va <- nums[4]; Va_SE <- nums[5]; h2 <- nums[6]; h2_SE <- nums[7] }
  }
  
  # Extract Ve
  covs_z_idx <- grep("COVS Z 1 1.*vrat", sum_lines)
  if (length(covs_z_idx) > 0) {
    covs_z_line <- sum_lines[covs_z_idx[1]]
    nums_z <- as.numeric(unlist(regmatches(covs_z_line, gregexpr("[0-9]+\\.?[0-9]*", covs_z_line))))
    if (length(nums_z) >= 5) { Ve <- nums_z[4]; Ve_SE <- nums_z[5] }
  }
  
  # Extract Vp
  covs_t_idx <- grep("COVS T 1 1", sum_lines)
  if (length(covs_t_idx) > 0) {
    covs_t_line <- sum_lines[covs_t_idx[1]]
    nums_t <- as.numeric(unlist(regmatches(covs_t_line, gregexpr("[0-9]+\\.?[0-9]*", covs_t_line))))
    if (length(nums_t) >= 5) { Vp <- nums_t[4] }
  }
  
  # Extract N records
  rec_idx <- grep("No. of records", sum_lines)
  if (length(rec_idx) > 0) {
    rec_line <- sum_lines[rec_idx[1]]
    nums_rec <- as.numeric(unlist(regmatches(rec_line, gregexpr("[0-9]+", rec_line))))
    N_rec <- nums_rec[1]
  }
  
  if (!is.na(h2)) {
    tilili_vc <- data.frame(
      Line = "Tilili", N = ifelse(!is.na(N_rec), N_rec, NA),
      Va = round(Va, 1), Va_SE = round(Va_SE, 1),
      Ve = round(Ve, 1), Ve_SE = round(Ve_SE, 1),
      Vp = round(Vp, 1), h2 = round(h2, 3), h2_SE = round(h2_SE, 3)
    )
  }
}

# Extract EBV results
if (file.exists(ebv_file) && file.exists(id_map_file)) {
  ebv_lines <- readLines(ebv_file)
  header_idx <- grep("Run_No", ebv_lines)
  
  if (length(header_idx) > 0) {
    data_start <- header_idx[1] + 1
    data_lines <- ebv_lines[data_start:length(ebv_lines)]
    data_lines <- data_lines[nchar(trimws(data_lines)) > 0]
    
    ebv_data <- read.table(text = data_lines, header = FALSE, fill = TRUE)
    
    if (ncol(ebv_data) >= 7) {
      colnames(ebv_data) <- c("Run_No", "ID_num", "Trait", "EBV", "SE_EBV", "Accuracy", "Inbreeding_pct")
    } else {
      colnames(ebv_data) <- c("Run_No", "ID_num", "Trait", "EBV", "Inbreeding_pct")[1:ncol(ebv_data)]
    }
    
    id_map <- read.csv(id_map_file, stringsAsFactors = FALSE)
    ebv_merged <- merge(ebv_data, id_map, by.x = "ID_num", by.y = "ID_num", all.x = TRUE)
    
    tilili_ebv <- data.frame(
      ID_original = as.character(ebv_merged$ID_orig),
      ID_wombat = ebv_merged$ID_num, 
      EBV = ebv_merged$EBV,
      SE_EBV = if ("SE_EBV" %in% colnames(ebv_merged)) ebv_merged$SE_EBV else NA,
      Accuracy = if ("Accuracy" %in% colnames(ebv_merged)) ebv_merged$Accuracy else NA,
      Inbreeding_pct = if ("Inbreeding_pct" %in% colnames(ebv_merged)) ebv_merged$Inbreeding_pct else NA
    )
    
    # Add biologically meaningful inbreeding categories
    tilili_ebv <- tilili_ebv %>%
      mutate(
        Inbreeding_Category = case_when(
          is.na(Inbreeding_pct) ~ NA_character_,
          Inbreeding_pct == 0 ~ "Non-inbred (F = 0%)",
          Inbreeding_pct < 3.125 ~ "Below 2nd cousin (F < 3.125%)",
          Inbreeding_pct < 6.25 ~ "2nd cousin level (F < 6.25%)",
          Inbreeding_pct < 12.5 ~ "1st cousin level (F < 12.5%)",
          Inbreeding_pct < 25 ~ "Half-sib level (F < 25%)",
          TRUE ~ "Full-sib level (F ≥ 25%)"
        ),
        Inbreeding_Category = factor(Inbreeding_Category, levels = c(
          "Non-inbred (F = 0%)",
          "Below 2nd cousin (F < 3.125%)",
          "2nd cousin level (F < 6.25%)",
          "1st cousin level (F < 12.5%)",
          "Half-sib level (F < 25%)",
          "Full-sib level (F ≥ 25%)"
        ))
      )
  }
}

# Calculate inbreeding summary statistics
if (!is.null(tilili_ebv)) {
  n_non_inbred <- sum(tilili_ebv$Inbreeding_pct == 0, na.rm = TRUE)
  n_negligible <- sum(tilili_ebv$Inbreeding_pct > 0 & tilili_ebv$Inbreeding_pct < 3.125, na.rm = TRUE)
  n_low <- sum(tilili_ebv$Inbreeding_pct >= 3.125 & tilili_ebv$Inbreeding_pct < 6.25, na.rm = TRUE)
  n_moderate <- sum(tilili_ebv$Inbreeding_pct >= 6.25 & tilili_ebv$Inbreeding_pct < 12.5, na.rm = TRUE)
  n_high <- sum(tilili_ebv$Inbreeding_pct >= 12.5 & tilili_ebv$Inbreeding_pct < 25, na.rm = TRUE)
  n_severe <- sum(tilili_ebv$Inbreeding_pct >= 25, na.rm = TRUE)
  n_concern <- n_moderate + n_high + n_severe  # Animals requiring attention
}

2.1 Population Overview

5,906

Total Birds

3,783

With BW_16wk

1073g

Mean Weight

0.196

Heritability

Generations

2.2 Phenotypic Summary

data.frame(
  Metric = c("Total Birds", "Birds with BW_16wk", "Mean BW_16wk (g)", "SD BW_16wk (g)", 
             "CV (%)", "Min BW_16wk (g)", "Max BW_16wk (g)",
             "Male BW_16wk (g)", "Female BW_16wk (g)", "Sexual Dimorphism (%)"),
  Value = c(n_total, n_with_bw, mean_bw, sd_bw, cv_bw,
            round(min(tilili_data$BW_16wk, na.rm = TRUE), 0),
            round(max(tilili_data$BW_16wk, na.rm = TRUE), 0),
            male_bw, female_bw, dimorphism)
) %>%
  kable(align = c("l", "r")) %>%
  kable_styling(bootstrap_options = c("striped", "hover"), full_width = FALSE) %>%
  row_spec(0, bold = TRUE, background = "#319795", color = "white")

Metric	Value
Total Birds	5906.0
Birds with BW_16wk	3783.0
Mean BW_16wk (g)	1073.0
SD BW_16wk (g)	303.0
CV (%)	28.2
Min BW_16wk (g)	110.0
Max BW_16wk (g)	2210.0
Male BW_16wk (g)	1210.0
Female BW_16wk (g)	956.0
Sexual Dimorphism (%)	26.6

2.3 Population Structure by Generation

tilili_data %>%
  group_by(Generation) %>%
  summarise(
    N = n(),
    N_BW = sum(!is.na(BW_16wk)),
    Mean_BW = round(mean(BW_16wk, na.rm = TRUE), 0),
    SD_BW = round(sd(BW_16wk, na.rm = TRUE), 0),
    N_Sires = n_distinct(Sire_ID[!is.na(Sire_ID)]),
    N_Dams = n_distinct(Dam_ID[!is.na(Dam_ID)]),
    .groups = "drop"
  ) %>%
  mutate(Generation = paste0("G", Generation)) %>%
  kable(col.names = c("Generation", "N Total", "N with BW", "Mean BW (g)", "SD (g)", "N Sires", "N Dams"),
        align = c("l", rep("r", 6))) %>%
  kable_styling(bootstrap_options = c("striped", "hover"), full_width = TRUE) %>%
  row_spec(0, bold = TRUE, background = "#319795", color = "white")

Generation	N Total	N with BW	Mean BW (g)	SD (g)	N Sires	N Dams
G3	722	458	1188	343	48	212
G4	4112	2537	1014	279	44	289
G5	1072	788	1198	295	16	87

tilili_data %>%
  filter(!is.na(BW_16wk)) %>%
  mutate(Generation = paste0("G", Generation)) %>%
  ggplot(aes(x = Generation, y = BW_16wk, fill = Generation)) +
  geom_boxplot(alpha = 0.8, outlier.shape = 21) +
  geom_jitter(alpha = 0.2, width = 0.2, size = 0.8) +
  scale_fill_manual(values = gen_colors) +
  labs(title = "Tilili: Body Weight at 16 Weeks by Generation",
       x = "Generation", y = "Body Weight (g)") +
  theme_tilili() + theme(legend.position = "none")

2.4 Variance Components and Heritability

ℹ️ Analysis Method

Genetic parameters were estimated using WOMBAT software with an animal model:

Model: y = Xb + Za + e

Fixed effects (b): Sex, Batch, Year, Month
Random effect (a): Additive genetic effect (animal)
Trait: Body weight at 16 weeks (BW_16wk)

if (!is.null(tilili_vc)) {
  tilili_vc %>%
    kable(col.names = c("Line", "N", "Va", "SE(Va)", "Ve", "SE(Ve)", "Vp", "h²", "SE(h²)"),
          align = c("l", rep("r", 8))) %>%
    kable_styling(bootstrap_options = c("striped", "hover"), full_width = TRUE) %>%
    row_spec(0, bold = TRUE, background = "#319795", color = "white")
} else {
  cat("Variance component results not available.")
}

Line	N	Va	SE(Va)	Ve	SE(Ve)	Vp	h²	SE(h²)
Tilili	3710	12914.1	2674.6	52893.5	2018.5	65807.5	0.196	0.037

2.5 EBV Summary Statistics

if (!is.null(tilili_ebv) && nrow(tilili_ebv) > 0) {
  data.frame(
    Metric = c("N Animals with EBV", "Mean EBV (g)", "SD EBV (g)", 
               "Min EBV (g)", "Max EBV (g)", "EBV Range (g)"),
    Value = c(nrow(tilili_ebv),
              round(mean(tilili_ebv$EBV, na.rm = TRUE), 1),
              round(sd(tilili_ebv$EBV, na.rm = TRUE), 1),
              round(min(tilili_ebv$EBV, na.rm = TRUE), 1),
              round(max(tilili_ebv$EBV, na.rm = TRUE), 1),
              round(max(tilili_ebv$EBV, na.rm = TRUE) - min(tilili_ebv$EBV, na.rm = TRUE), 1))
  ) %>%
    kable(align = c("l", "r")) %>%
    kable_styling(bootstrap_options = c("striped", "hover"), full_width = FALSE) %>%
    row_spec(0, bold = TRUE, background = "#319795", color = "white")
}

Metric	Value
N Animals with EBV	3882.0
Mean EBV (g)	43.7
SD EBV (g)	74.8
Min EBV (g)	-182.3
Max EBV (g)	312.6
EBV Range (g)	494.9

if (!is.null(tilili_ebv) && nrow(tilili_ebv) > 0) {
  ggplot(tilili_ebv, aes(x = EBV)) +
    geom_histogram(bins = 30, fill = "#319795", color = "white", alpha = 0.8) +
    geom_vline(xintercept = 0, linetype = "dashed", color = "black", linewidth = 1) +
    geom_vline(xintercept = mean(tilili_ebv$EBV, na.rm = TRUE), 
               linetype = "solid", color = "#EE7733", linewidth = 1) +
    labs(title = "Tilili: EBV Distribution for Body Weight at 16 Weeks",
         subtitle = "Black dashed = 0 | Orange solid = Mean EBV",
         x = "EBV (g)", y = "Count") +
    theme_tilili()
}

2.6 Top 10 Breeding Animals (by EBV)

if (!is.null(tilili_ebv) && nrow(tilili_ebv) > 0) {
  tilili_ebv %>%
    arrange(desc(EBV)) %>%
    head(10) %>%
    mutate(
      Rank = 1:10,
      EBV = round(EBV, 1),
      SE_EBV = ifelse(is.na(SE_EBV), "-", round(SE_EBV, 1)),
      Accuracy = ifelse(is.na(Accuracy), "-", round(Accuracy, 2)),
      Inbreeding_pct = ifelse(is.na(Inbreeding_pct), "-", round(Inbreeding_pct, 2)),
      Inbreeding_Category = as.character(Inbreeding_Category)
    ) %>%
    select(Rank, ID_original, EBV, SE_EBV, Accuracy, Inbreeding_pct, Inbreeding_Category) %>%
    kable(col.names = c("Rank", "Animal ID", "EBV (g)", "SE", "Accuracy", "F (%)", "Inbreeding Level"),
          align = c("c", "l", rep("r", 4), "l")) %>%
    kable_styling(bootstrap_options = c("striped", "hover"), full_width = TRUE) %>%
    row_spec(0, bold = TRUE, background = "#276749", color = "white") %>%
    row_spec(1, bold = TRUE, background = "#c6f6d5")
}

Rank	Animal ID	EBV (g)	SE	Accuracy	F (%)	Inbreeding Level
1	6233	312.6	85.3	0.74	25.76	Full-sib level (F ≥ 25%)
2	6235	295.0	85.3	0.74	25.76	Full-sib level (F ≥ 25%)
3	2481	273.2	85.3	0.74	25.76	Full-sib level (F ≥ 25%)
4	2485	267.3	85.3	0.74	25.76	Full-sib level (F ≥ 25%)
5	1941	265.6	85.3	0.74	25.76	Full-sib level (F ≥ 25%)
6	1370	257.4	85.3	0.74	25.76	Full-sib level (F ≥ 25%)
7	1371	255.7	85.3	0.74	25.76	Full-sib level (F ≥ 25%)
8	257	255.7	63.7	0.83	0.98	Below 2nd cousin (F < 3.125%)
9	1054	254.2	85.3	0.74	25.76	Full-sib level (F ≥ 25%)
10	1055	253.2	85.3	0.74	25.76	Full-sib level (F ≥ 25%)

3 Part 2: Inbreeding Analysis

🧬 How WOMBAT Computes Inbreeding Coefficients

WOMBAT calculates inbreeding coefficients (F) using the Meuwissen & Luo (1992) algorithm, which is a computationally efficient recursive method based on the pedigree’s additive genetic relationship matrix (A-matrix).

Inbreeding Coefficient Formula:

F_i = ½ × a_sd

Where:

F_i = Inbreeding coefficient of animal i
a_sd = Additive genetic relationship between the sire (s) and dam (d) of animal i

Interpretation:

If parents are unrelated: a_sd = 0, therefore F = 0%
If parents are half-siblings: a_sd = 0.25, therefore F = 12.5%
If parents are full-siblings: a_sd = 0.50, therefore F = 25%
If parents are parent-offspring: a_sd = 0.50, therefore F = 25%

Algorithm Details:

The Meuwissen & Luo algorithm processes the pedigree in chronological order (from oldest to youngest animals) and recursively computes:

The diagonal elements of the A-matrix (which include inbreeding)
The inbreeding coefficient for each animal based on the relationship between its parents

Reference: Meuwissen, T.H.E. & Luo, Z. (1992). Computing inbreeding coefficients in large populations. Genetics Selection Evolution, 24: 305-313.

3.1 Biologically Meaningful Inbreeding Categories

Rather than simply classifying animals as “inbred” vs “non-inbred” at any F > 0%, we use biologically meaningful thresholds based on equivalent relationship levels:

data.frame(
  Category = c("Non-inbred", "Below 2nd cousin", "2nd cousin level", "1st cousin level", "Half-sib level", "Full-sib level"),
  F_Range = c("F = 0%", "0% < F < 3.125%", "3.125% ≤ F < 6.25%", "6.25% ≤ F < 12.5%", "12.5% ≤ F < 25%", "F ≥ 25%"),
  Equivalent_Mating = c("Unrelated parents", "More distant than 2nd cousins", "Second cousin mating", "First cousin mating", "Half-sibling mating", "Full-sibling or parent-offspring"),
  Management = c("Ideal for breeding", "Suitable for breeding", "Acceptable", "Use with caution", "Limit use", "Avoid as parents"),
  Expected_Depression = c("0%", "< 1%", "1-2%", "2-4%", "4-8%", "> 8%")
) %>%
  kable(col.names = c("Category", "F Range", "Equivalent Mating", "Management", "Expected Inbreeding Depression"),
        align = c("l", "l", "l", "l", "c")) %>%
  kable_styling(bootstrap_options = c("striped", "hover"), full_width = TRUE) %>%
  row_spec(0, bold = TRUE, background = "#319795", color = "white") %>%
  row_spec(4, background = "#fff3e0") %>%
  row_spec(5, background = "#ffe0b2") %>%
  row_spec(6, background = "#e1bee7")

Category	F Range	Equivalent Mating	Management	Expected Inbreeding Depression
Non-inbred	F = 0%	Unrelated parents	Ideal for breeding	0%
Below 2nd cousin	0% < F < 3.125%	More distant than 2nd cousins	Suitable for breeding	< 1%
2nd cousin level	3.125% ≤ F < 6.25%	Second cousin mating	Acceptable	1-2%
1st cousin level	6.25% ≤ F < 12.5%	First cousin mating	Use with caution	2-4%
Half-sib level	12.5% ≤ F < 25%	Half-sibling mating	Limit use	4-8%
Full-sib level	F ≥ 25%	Full-sibling or parent-offspring	Avoid as parents	> 8%

3.2 Population Inbreeding Distribution

if (!is.null(tilili_ebv) && nrow(tilili_ebv) > 0) {
  # Calculate summary by category
  inbreeding_summary <- tilili_ebv %>%
    filter(!is.na(Inbreeding_Category)) %>%
    count(Inbreeding_Category) %>%
    mutate(
      Percent = round(n / sum(n) * 100, 1),
      Cumulative_Pct = round(cumsum(n) / sum(n) * 100, 1)
    )
  
  # Display summary table
  inbreeding_summary %>%
    kable(col.names = c("Inbreeding Category", "N Animals", "Percent (%)", "Cumulative (%)"),
          align = c("l", rep("r", 3))) %>%
    kable_styling(bootstrap_options = c("striped", "hover"), full_width = FALSE) %>%
    row_spec(0, bold = TRUE, background = "#319795", color = "white") %>%
    row_spec(which(inbreeding_summary$Inbreeding_Category == "1st cousin level (F < 12.5%)"), background = "#fff3e0") %>%
    row_spec(which(inbreeding_summary$Inbreeding_Category == "Half-sib level (F < 25%)"), background = "#ffe0b2") %>%
    row_spec(which(inbreeding_summary$Inbreeding_Category == "Full-sib level (F ≥ 25%)"), background = "#e1bee7")
}

Inbreeding Category	N Animals	Percent (%)	Cumulative (%)
Non-inbred (F = 0%)	408	10.5	10.5
Below 2nd cousin (F < 3.125%)	2233	57.5	68.0
2nd cousin level (F < 6.25%)	647	16.7	84.7
1st cousin level (F < 12.5%)	394	10.1	94.8
Half-sib level (F < 25%)	176	4.5	99.4
Full-sib level (F ≥ 25%)	24	0.6	100.0

if (!is.null(tilili_ebv) && nrow(tilili_ebv) > 0) {
  inbreeding_summary %>%
    ggplot(aes(x = Inbreeding_Category, y = n, fill = Inbreeding_Category)) +
    geom_bar(stat = "identity", alpha = 0.9) +
    geom_text(aes(label = paste0(n, "\n(", Percent, "%)")), 
              vjust = -0.3, size = 3.5, fontface = "bold") +
    scale_fill_manual(values = inbreeding_colors) +
    labs(title = "Tilili: Distribution of Animals by Inbreeding Category",
         subtitle = "Based on biologically meaningful thresholds (relationship levels)",
         x = "Inbreeding Category", y = "Number of Animals") +
    theme_tilili() +
    theme(legend.position = "none",
          axis.text.x = element_text(angle = 25, hjust = 1, size = 10)) +
    ylim(0, max(inbreeding_summary$n) * 1.15)
}

3.3 Inbreeding Coefficient Distribution

if (!is.null(tilili_ebv) && nrow(tilili_ebv) > 0) {
  # Filter to animals with F > 0 for better visualization
  inbred_animals <- tilili_ebv %>% filter(Inbreeding_pct > 0)
  
  if (nrow(inbred_animals) > 0) {
    ggplot(inbred_animals, aes(x = Inbreeding_pct, fill = Inbreeding_Category)) +
      geom_histogram(bins = 30, color = "white", alpha = 0.85) +
      geom_vline(xintercept = c(3.125, 6.25, 12.5, 25), 
                 linetype = "dashed", color = "gray40", linewidth = 0.7) +
      annotate("text", x = 3.125, y = Inf, label = "2nd Cousin", 
               vjust = 2, hjust = -0.1, size = 3, color = "gray30") +
      annotate("text", x = 6.25, y = Inf, label = "1st Cousin", 
               vjust = 2, hjust = -0.1, size = 3, color = "gray30") +
      annotate("text", x = 12.5, y = Inf, label = "Half-sib", 
               vjust = 2, hjust = -0.1, size = 3, color = "gray30") +
      annotate("text", x = 25, y = Inf, label = "Full-sib", 
               vjust = 2, hjust = -0.1, size = 3, color = "gray30") +
      scale_fill_manual(values = inbreeding_colors) +
      labs(title = "Tilili: Inbreeding Coefficient Distribution (F > 0% only)",
           subtitle = "Vertical lines indicate relationship thresholds",
           x = "Inbreeding Coefficient (%)", y = "Number of Animals",
           fill = "Category") +
      theme_tilili() +
      theme(legend.position = "right")
  } else {
    cat("No inbred animals (F > 0%) found in the population.")
  }
}

3.4 Detailed Inbreeding Statistics

if (!is.null(tilili_ebv) && nrow(tilili_ebv) > 0) {
  # Calculate detailed statistics
  F_stats <- tilili_ebv %>%
    filter(!is.na(Inbreeding_pct)) %>%
    summarise(
      N_Total = n(),
      N_Non_Inbred = sum(Inbreeding_pct == 0),
      Pct_Non_Inbred = round(sum(Inbreeding_pct == 0) / n() * 100, 1),
      N_Inbred = sum(Inbreeding_pct > 0),
      Pct_Inbred = round(sum(Inbreeding_pct > 0) / n() * 100, 1),
      Mean_F_All = round(mean(Inbreeding_pct), 3),
      Mean_F_Inbred = round(mean(Inbreeding_pct[Inbreeding_pct > 0]), 3),
      SD_F_Inbred = round(sd(Inbreeding_pct[Inbreeding_pct > 0]), 3),
      Max_F = round(max(Inbreeding_pct), 3),
      N_Concern = sum(Inbreeding_pct >= 6.25)
    )
  
  data.frame(
    Metric = c("Total animals evaluated",
               "Non-inbred animals (F = 0%)",
               "Percentage non-inbred",
               "Inbred animals (F > 0%)",
               "Percentage inbred",
               "Mean F (all animals)",
               "Mean F (inbred animals only)",
               "SD of F (inbred animals)",
               "Maximum F observed",
               "Animals requiring attention (F ≥ 6.25%)"),
    Value = c(F_stats$N_Total,
              F_stats$N_Non_Inbred,
              paste0(F_stats$Pct_Non_Inbred, "%"),
              F_stats$N_Inbred,
              paste0(F_stats$Pct_Inbred, "%"),
              paste0(F_stats$Mean_F_All, "%"),
              paste0(F_stats$Mean_F_Inbred, "%"),
              paste0(F_stats$SD_F_Inbred, "%"),
              paste0(F_stats$Max_F, "%"),
              F_stats$N_Concern)
  ) %>%
    kable(align = c("l", "r")) %>%
    kable_styling(bootstrap_options = c("striped", "hover"), full_width = FALSE) %>%
    row_spec(0, bold = TRUE, background = "#319795", color = "white") %>%
    row_spec(10, bold = TRUE, background = "#fffaf0")
}

Metric	Value
Total animals evaluated	3882
Non-inbred animals (F = 0%)	408
Percentage non-inbred	10.5%
Inbred animals (F > 0%)	3474
Percentage inbred	89.5%
Mean F (all animals)	3.034%
Mean F (inbred animals only)	3.39%
SD of F (inbred animals)	3.921%
Maximum F observed	25.757%
Animals requiring attention (F ≥ 6.25%)	594

3.5 Animals with Elevated Inbreeding (F ≥ 6.25%)

if (!is.null(tilili_ebv) && nrow(tilili_ebv) > 0) {
  high_inbreeding <- tilili_ebv %>%
    filter(Inbreeding_pct >= 6.25) %>%
    arrange(desc(Inbreeding_pct)) %>%
    mutate(
      EBV = round(EBV, 1),
      Inbreeding_pct = round(Inbreeding_pct, 2)
    ) %>%
    select(ID_original, EBV, Inbreeding_pct, Inbreeding_Category)
  
  if (nrow(high_inbreeding) > 0) {
    high_inbreeding %>%
      head(20) %>%
      kable(col.names = c("Animal ID", "EBV (g)", "Inbreeding (%)", "Relationship Level"),
            align = c("l", "r", "r", "l")) %>%
      kable_styling(bootstrap_options = c("striped", "hover"), full_width = FALSE) %>%
      row_spec(0, bold = TRUE, background = "#805ad5", color = "white") %>%
      row_spec(which(high_inbreeding$Inbreeding_Category[1:min(20, nrow(high_inbreeding))] == "Full-sib level (F ≥ 25%)"), 
               background = "#e1bee7") %>%
      row_spec(which(high_inbreeding$Inbreeding_Category[1:min(20, nrow(high_inbreeding))] == "Half-sib level (F < 25%)"), 
               background = "#ffe0b2")
  } else {
    cat("No animals with F ≥ 6.25% (first cousin level or higher) found.")
  }
}

Animal ID	EBV (g)	Inbreeding (%)	Relationship Level
1056	189.8	25.76	Full-sib level (F ≥ 25%)
6235	295.0	25.76	Full-sib level (F ≥ 25%)
6233	312.6	25.76	Full-sib level (F ≥ 25%)
6232	243.3	25.76	Full-sib level (F ≥ 25%)
6189	229.6	25.76	Full-sib level (F ≥ 25%)
6014	186.1	25.76	Full-sib level (F ≥ 25%)
4642	170.7	25.76	Full-sib level (F ≥ 25%)
4422	252.4	25.76	Full-sib level (F ≥ 25%)
4421	223.9	25.76	Full-sib level (F ≥ 25%)
2485	267.3	25.76	Full-sib level (F ≥ 25%)
2484	187.8	25.76	Full-sib level (F ≥ 25%)
2483	217.3	25.76	Full-sib level (F ≥ 25%)
2482	239.0	25.76	Full-sib level (F ≥ 25%)
2481	273.2	25.76	Full-sib level (F ≥ 25%)
1942	241.4	25.76	Full-sib level (F ≥ 25%)
1941	265.6	25.76	Full-sib level (F ≥ 25%)
1371	255.7	25.76	Full-sib level (F ≥ 25%)
1370	257.4	25.76	Full-sib level (F ≥ 25%)
1362	222.5	25.76	Full-sib level (F ≥ 25%)
1055	253.2	25.76	Full-sib level (F ≥ 25%)

📋 Inbreeding Management Guidelines

Based on the inbreeding analysis:

Prioritize non-inbred or distantly related animals (F < 3.125%) for breeding decisions
Limit use of half-sib level animals (F ≥ 12.5%) as breeding candidates
Avoid using full-sib level animals (F ≥ 25%) as parents to prevent inbreeding depression
Monitor effective population size (Ne) to prevent future inbreeding accumulation
Implement Optimal Contribution Selection (OCS) to balance genetic gain and genetic diversity

4 Part 3: Egg Weight Analysis

# Load and process egg weight data
df_egg_weight_raw <- read_csv(egg_weight_file, show_col_types = FALSE) %>%
  clean_names()

week_cols_ew <- names(df_egg_weight_raw)[grepl("^week_", names(df_egg_weight_raw))]

df_egg_weight_long <- df_egg_weight_raw %>%
  select(bird_id = chicken_id, sire_id, dam_id, generation = generation3, batch, pen, all_of(week_cols_ew)) %>%
  pivot_longer(cols = all_of(week_cols_ew), names_to = "week", values_to = "egg_weight") %>%
  mutate(
    week = as.numeric(str_extract(week, "\\d+")),
    egg_weight = na_if(egg_weight, 0),
    egg_weight = if_else(!is.na(egg_weight) & egg_weight > 100, NA_real_, egg_weight),
    generation = factor(generation, levels = sort(unique(generation)), labels = paste0("G", sort(unique(generation)))),
    batch = factor(batch)
  )

n_birds_ew <- n_distinct(df_egg_weight_long$bird_id)
n_records_ew <- sum(!is.na(df_egg_weight_long$egg_weight))
mean_ew <- round(mean(df_egg_weight_long$egg_weight, na.rm = TRUE), 1)
sd_ew <- round(sd(df_egg_weight_long$egg_weight, na.rm = TRUE), 1)
n_gen_ew <- n_distinct(df_egg_weight_long$generation)

170

Birds

4,389

Valid Records

53.1g

Mean Weight

Generations

31-61

Week Range

4.1 Weekly Means by Generation

weekly_gen_ew <- df_egg_weight_long %>%
  group_by(generation, week) %>%
  summarise(n = sum(!is.na(egg_weight)), mean = mean(egg_weight, na.rm = TRUE),
            sd = sd(egg_weight, na.rm = TRUE), se = sd / sqrt(n), .groups = "drop") %>%
  filter(n > 0)

weekly_gen_ew %>%
  group_by(generation) %>%
  summarise(weeks_with_data = n(), total_observations = sum(n),
            mean_egg_weight = round(mean(mean, na.rm = TRUE), 1), .groups = "drop") %>%
  kable(col.names = c("Generation", "Weeks with Data", "Total Observations", "Overall Mean (g)"),
        align = c("l", rep("r", 3))) %>%
  kable_styling(bootstrap_options = c("striped", "hover"), full_width = FALSE) %>%
  row_spec(0, bold = TRUE, background = "#319795", color = "white")

Generation	Weeks with Data	Total Observations	Overall Mean (g)
G3	31	2069	52.4
G4	31	2320	53.8

ggplot(weekly_gen_ew, aes(x = week, y = mean, color = generation, group = generation)) +
  geom_line(linewidth = 1.0) +
  geom_point(size = 2.0) +
  geom_errorbar(aes(ymin = mean - se, ymax = mean + se), width = 0.3, alpha = 0.5) +
  scale_color_manual(values = gen_colors) +
  scale_x_continuous(breaks = seq(31, 61, by = 2)) +
  labs(title = "Tilili: Mean Egg Weight by Week of Age",
       subtitle = "Comparison by Generation | Error bars: ± 1 SE",
       x = "Week of Age", y = "Mean Egg Weight (g)", color = "Generation",
       caption = "Note: Zeros treated as missing | Outliers (>100g) removed") +
  theme_tilili()

4.2 Overall Generation Summary - Egg Weight

df_egg_weight_long %>%
  group_by(generation) %>%
  summarise(n_birds = n_distinct(bird_id), n_records = sum(!is.na(egg_weight)),
            mean_egg_weight = round(mean(egg_weight, na.rm = TRUE), 1),
            sd_egg_weight = round(sd(egg_weight, na.rm = TRUE), 1),
            min_egg_weight = round(min(egg_weight, na.rm = TRUE), 1),
            max_egg_weight = round(max(egg_weight, na.rm = TRUE), 1), .groups = "drop") %>%
  kable(col.names = c("Generation", "N Birds", "N Records", "Mean (g)", "SD (g)", "Min (g)", "Max (g)"),
        align = c("l", rep("r", 6))) %>%
  kable_styling(bootstrap_options = c("striped", "hover"), full_width = TRUE) %>%
  row_spec(0, bold = TRUE, background = "#319795", color = "white")

Generation	N Birds	N Records	Mean (g)	SD (g)	Min (g)	Max (g)
G3	85	2069	52.3	4.6	39	69
G4	85	2320	53.8	4.6	35	74

4.3 Weekly Means by Batch

weekly_batch_ew <- df_egg_weight_long %>%
  group_by(batch, week) %>%
  summarise(n = sum(!is.na(egg_weight)), mean = mean(egg_weight, na.rm = TRUE), .groups = "drop") %>%
  filter(n > 0)

ggplot(weekly_batch_ew, aes(x = week, y = mean, color = batch, group = batch)) +
  geom_line(linewidth = 0.8) +
  geom_point(size = 1.5, alpha = 0.7) +
  scale_color_manual(values = batch_colors) +
  scale_x_continuous(breaks = seq(31, 61, by = 5)) +
  labs(title = "Tilili: Mean Egg Weight by Week of Age",
       subtitle = "Comparison by Batch",
       x = "Week of Age", y = "Mean Egg Weight (g)", color = "Batch") +
  theme_tilili()

5 Part 4: Egg Number Analysis

# Load and process egg number data
df_egg_number_raw <- read_csv(egg_number_file, show_col_types = FALSE) %>%
  clean_names()

week_cols_en <- names(df_egg_number_raw)[grepl("^week_", names(df_egg_number_raw))]

df_egg_number_long <- df_egg_number_raw %>%
  select(bird_id = birds_id, sire_id, dam_id, generation, batch, pen, all_of(week_cols_en)) %>%
  pivot_longer(cols = all_of(week_cols_en), names_to = "week", values_to = "egg_number") %>%
  mutate(
    week = as.numeric(str_extract(week, "\\d+")),
    egg_number = na_if(egg_number, 0),
    generation = factor(generation, levels = sort(unique(generation)), labels = paste0("G", sort(unique(generation)))),
    batch = factor(batch)
  )

n_birds_en <- n_distinct(df_egg_number_long$bird_id)
n_records_en <- sum(!is.na(df_egg_number_long$egg_number))
mean_en <- round(mean(df_egg_number_long$egg_number, na.rm = TRUE), 2)
sd_en <- round(sd(df_egg_number_long$egg_number, na.rm = TRUE), 2)
n_gen_en <- n_distinct(df_egg_number_long$generation)

180

Birds

4,892

Valid Records

4.33

Mean Eggs/Week

Generations

31-61

Week Range

5.1 Weekly Means by Generation

weekly_gen_en <- df_egg_number_long %>%
  group_by(generation, week) %>%
  summarise(n = sum(!is.na(egg_number)), mean = mean(egg_number, na.rm = TRUE),
            sd = sd(egg_number, na.rm = TRUE), se = sd / sqrt(n), .groups = "drop") %>%
  filter(n > 0)

weekly_gen_en %>%
  group_by(generation) %>%
  summarise(weeks_with_data = n(), total_observations = sum(n),
            mean_egg_number = round(mean(mean, na.rm = TRUE), 2), .groups = "drop") %>%
  kable(col.names = c("Generation", "Weeks with Data", "Total Observations", "Overall Mean"),
        align = c("l", rep("r", 3))) %>%
  kable_styling(bootstrap_options = c("striped", "hover"), full_width = FALSE) %>%
  row_spec(0, bold = TRUE, background = "#319795", color = "white")

Generation	Weeks with Data	Total Observations	Overall Mean
G3	31	2228	4.34
G4	31	2664	4.27

ggplot(weekly_gen_en, aes(x = week, y = mean, color = generation, group = generation)) +
  geom_line(linewidth = 1.0) +
  geom_point(size = 2.0) +
  geom_errorbar(aes(ymin = mean - se, ymax = mean + se), width = 0.3, alpha = 0.5) +
  scale_color_manual(values = gen_colors) +
  scale_x_continuous(breaks = seq(31, 61, by = 2)) +
  labs(title = "Tilili: Mean Egg Number by Week of Age",
       subtitle = "Comparison by Generation | Error bars: ± 1 SE",
       x = "Week of Age", y = "Mean Egg Number (eggs/week)", color = "Generation",
       caption = "Note: Zeros treated as missing") +
  theme_tilili()

5.2 Overall Generation Summary - Egg Number

df_egg_number_long %>%
  group_by(generation) %>%
  summarise(n_birds = n_distinct(bird_id), n_records = sum(!is.na(egg_number)),
            mean_egg_number = round(mean(egg_number, na.rm = TRUE), 2),
            sd_egg_number = round(sd(egg_number, na.rm = TRUE), 2),
            min_egg_number = round(min(egg_number, na.rm = TRUE), 2),
            max_egg_number = round(max(egg_number, na.rm = TRUE), 2), .groups = "drop") %>%
  kable(col.names = c("Generation", "N Birds", "N Records", "Mean", "SD", "Min", "Max"),
        align = c("l", rep("r", 6))) %>%
  kable_styling(bootstrap_options = c("striped", "hover"), full_width = TRUE) %>%
  row_spec(0, bold = TRUE, background = "#319795", color = "white")

Generation	N Birds	N Records	Mean	SD	Min	Max
G3	90	2228	4.37	1.67	1.0	7
G4	90	2664	4.29	1.67	0.2	7

5.3 Weekly Means by Batch

weekly_batch_en <- df_egg_number_long %>%
  group_by(batch, week) %>%
  summarise(n = sum(!is.na(egg_number)), mean = mean(egg_number, na.rm = TRUE), .groups = "drop") %>%
  filter(n > 0)

ggplot(weekly_batch_en, aes(x = week, y = mean, color = batch, group = batch)) +
  geom_line(linewidth = 0.8) +
  geom_point(size = 1.5, alpha = 0.7) +
  scale_color_manual(values = batch_colors) +
  scale_x_continuous(breaks = seq(31, 61, by = 5)) +
  labs(title = "Tilili: Mean Egg Number by Week of Age",
       subtitle = "Comparison by Batch",
       x = "Week of Age", y = "Mean Egg Number (eggs/week)", color = "Batch") +
  theme_tilili()

6 Part 5: Combined Trait Summary

6.1 Combined Generation Trends

combined_weekly <- bind_rows(
  weekly_gen_ew %>% select(generation, week, mean, se) %>% mutate(trait = "Egg Weight (g)"),
  weekly_gen_en %>% select(generation, week, mean, se) %>% mutate(trait = "Egg Number")
)

ggplot(combined_weekly, aes(x = week, y = mean, color = generation, group = generation)) +
  geom_line(linewidth = 1.0) +
  geom_point(size = 1.5) +
  geom_ribbon(aes(ymin = mean - se, ymax = mean + se, fill = generation), alpha = 0.2, color = NA) +
  facet_wrap(~ trait, scales = "free_y", ncol = 1) +
  scale_color_manual(values = gen_colors) +
  scale_fill_manual(values = gen_colors) +
  scale_x_continuous(breaks = seq(31, 61, by = 5)) +
  labs(title = "Tilili: Egg Production Traits by Week of Age",
       subtitle = "Generation Comparison | Shaded area: ± 1 SE",
       x = "Week of Age", y = "Mean Value", color = "Generation", fill = "Generation") +
  theme_tilili() +
  theme(strip.text = element_text(face = "bold", size = 12))

6.2 Overall Trait Summary Table

data.frame(
  Trait = c("Body Weight (16 wk)", "Egg Weight (g)", "Egg Number"),
  N_Birds = c(n_with_bw, n_birds_ew, n_birds_en),
  N_Records = c(n_with_bw, n_records_ew, n_records_en),
  Overall_Mean = c(mean_bw, mean_ew, mean_en),
  Overall_SD = c(sd_bw, sd_ew, sd_en),
  Heritability = c(ifelse(!is.na(h2), h2, "N/A"), "TBD", "TBD")
) %>%
  kable(col.names = c("Trait", "N Birds", "N Records", "Mean", "SD", "h²"),
        align = c("l", rep("r", 5))) %>%
  kable_styling(bootstrap_options = c("striped", "hover"), full_width = TRUE) %>%
  row_spec(0, bold = TRUE, background = "#319795", color = "white")

Trait	N Birds	N Records	Mean	SD	h²
Body Weight (16 wk)	3783	3783	1073.00	303.00	0.196
Egg Weight (g)	170	4389	53.10	4.60	TBD
Egg Number	180	4892	4.33	1.67	TBD

7 Conclusions and Recommendations

✅ Key Findings

Body Weight (16 weeks):

Mean BW_16wk: 1073g (SD: 303g, CV: 28.2%)
Heritability: 0.196 ± 0.037 (low to moderate)
Sexual dimorphism: 26.6% (males heavier than females)
Generations evaluated: G3, G4, G5 with 3,783 phenotyped birds
EBV range: 494.9g indicating genetic variation for selection

Inbreeding Status:

Non-inbred animals (F = 0%): 408 birds
Below 2nd cousin level (F < 3.125%): 2233 birds
Animals at 1st cousin level or higher (F ≥ 6.25%): 594 birds
Mean inbreeding (all animals): 3.03%

Egg Weight:

Mean: 53.1g (SD: 4.6g) across laying period (weeks 31-61)
Generations: G3 and G4 with 170 birds and 4,389 valid records
G4 shows slightly higher mean egg weight than G3, suggesting positive genetic trend

Egg Number:

Mean: 4.33 eggs/week (SD: 1.67)
Generations: G3 and G4 with 180 birds and 4,892 valid records
Both generations show similar production levels across the 31-week recording period

⚠️ Data Notes

Zero values: Treated as missing data per specification (birds not laying, dead, or not recorded)
Outliers in egg weight: Values >100g removed as likely data entry errors
Body weight coverage: G4 has largest sample size (4,112 birds) providing robust estimates
Inbreeding computation: WOMBAT uses Meuwissen & Luo (1992) algorithm based on pedigree A-matrix
Inbreeding categories: Biologically meaningful thresholds based on relationship levels (e.g., half-sib level = 12.5%, full-sib level = 25%)

📋 Recommendations

Estimate genetic parameters for egg traits - Run WOMBAT repeatability model analysis for egg weight and egg number to enable genetic evaluation
Develop selection index - Combine body weight (h² = 0.196) with egg production traits for balanced genetic improvement
Manage inbreeding actively:
- Prioritize animals with F < 3.125% for breeding
- Limit use of half-sib level animals (F ≥ 12.5%) as parents
- Avoid breeding full-sib level animals (F ≥ 25%)
- Implement Optimal Contribution Selection (OCS) to balance genetic gain and diversity
Continue phenotype recording - Expand egg production recording to G5 birds as they reach laying age
Use top EBV animals judiciously - Top breeding candidates identified (EBV up to 312.6g) but verify inbreeding category before use
Cohort-based evaluations - Conduct within-batch genetic evaluations for timely selection decisions

Tilili Poultry Breeding Program Evaluation

Body Weight Genetic Parameters & Egg Production Trait Analysis

Setegn Worku Alemu, PhD | Quantitative Geneticist

January 07, 2026

1 Executive Summary

🐔 About the Tilili Line

2 Part 1: Body Weight Genetic Parameters

2.1 Population Overview

2.2 Phenotypic Summary

2.3 Population Structure by Generation

2.4 Variance Components and Heritability

ℹ️ Analysis Method

2.5 EBV Summary Statistics

2.6 Top 10 Breeding Animals (by EBV)

3 Part 2: Inbreeding Analysis

🧬 How WOMBAT Computes Inbreeding Coefficients

3.1 Biologically Meaningful Inbreeding Categories

3.2 Population Inbreeding Distribution

3.3 Inbreeding Coefficient Distribution

3.4 Detailed Inbreeding Statistics

3.5 Animals with Elevated Inbreeding (F ≥ 6.25%)

📋 Inbreeding Management Guidelines

4 Part 3: Egg Weight Analysis

4.1 Weekly Means by Generation

4.2 Overall Generation Summary - Egg Weight

4.3 Weekly Means by Batch

5 Part 4: Egg Number Analysis

5.1 Weekly Means by Generation

5.2 Overall Generation Summary - Egg Number

5.3 Weekly Means by Batch

6 Part 5: Combined Trait Summary

6.1 Combined Generation Trends

6.2 Overall Trait Summary Table

7 Conclusions and Recommendations

✅ Key Findings

⚠️ Data Notes

📋 Recommendations