It were selected 20 meat quality and carcass traits recorded in 960 individuals, which were genotyping for 42428 SNP. For the statistical analysis was fitted a linear mixed model, including fixed effects of \(sex\), \(slaughter group\) and \(carcass\ weight\) and the additive and dominance random effects.

\(\mathbf{Y= X \beta + Zu + Zd +e}\)

The additive genomic relationship matrix was (\(\mathbf{G_{a}}\)) computed as in \(VanRaden\ (2008)\) and dominance genomic relationship matrix was \(\mathbf{G_{d}}\) computed with the method proposed by \(Vitezica\ et\ al.\ (2013)\).

load("~/Documents/Tesis_Maestria/validation/MSU_PRP/Data_Master/geno_matrix_msuprp.Rdata")
dim(genomatrix)

## [1]  1015 42081

genomatrix[1:5,1:5]

##      MARC0044150 ASGA0000014 H3GA0000026 ASGA0000021 ALGA0000009
## 1001           1           1           1           1           1
## 1002           1           1           1           1           1
## 1003           1           1           1           1           1
## 1004           1           1           1           1           1
## 1006           0           1           2           2           2

#head(colnames(genomatrix))
#head(rownames(genomatrix))
Gvitezica<-Gmatrix(genomatrix, method="Vitezica")

## Initial data: 
##  Number of Individuals: 1015 
##  Number of Markers: 42081 
## 
## Missing data check: 
##  Total SNPs: 42081 
##   0 SNPs dropped due to missing data threshold of 1 
##  Total of: 42081  SNPs 
## MAF check: 
##  No SNPs with MAF below 0 
## Monomorphic check: 
##  No monomorphic SNPs 
## Summary check: 
##  Initial:  42081 SNPs 
##  Final:  42081  SNPs ( 0  SNPs removed) 
##  
## Completed! Time = 155.005  seconds

dim(Gvitezica)

## [1] 1015 1015

Gvitezica[1:5,1:5]

##             1001       1002         1003        1004         1006
## 1001 0.874904537 0.10410675  0.152648805  0.13359638  0.007286798
## 1002 0.104106747 0.84330218  0.082749995  0.03922358  0.043686681
## 1003 0.152648805 0.08275000  0.858641254  0.17930445 -0.007187304
## 1004 0.133596383 0.03922358  0.179304448  0.90453765 -0.098926850
## 1006 0.007286798 0.04368668 -0.007187304 -0.09892685  1.110226699

load("~/Documents/Tesis_Maestria/validation/MSU_PRP/Data_Master/G_msuprp.Rdata")
load("~/Documents/Tesis_Maestria/validation/MSU_PRP/Data_Master/msuprphenofile.Rdata")

cont_struc2<-rbind(
  c("last_lum.1",y~sex + car_wt + slgdt_cd),c("dress_ptg.1",y~ + car_wt + slgdt_cd),
  c("car_length.1",y~sex + car_wt + slgdt_cd),c("belly.1",y~sex + car_wt + slgdt_cd),
  c("juiciness.1",y~sex + car_wt + slgdt_cd),c("WBS.1",y~sex + car_wt + slgdt_cd),
  c("ph_24h.1",y~sex + car_wt + slgdt_cd),c("driploss.1",y~sex + car_wt + slgdt_cd),
  c("protein.1",y~sex + car_wt + slgdt_cd),c("cook_yield.1",y~sex + car_wt + slgdt_cd),
  c("bf10_22wk.1",y~sex + car_wt + slgdt_cd),c("lma_22wk.1",y~ sex + car_wt + slgdt_cd), ##======== new traits
  c("ham.1",y ~ sex + car_wt + slgdt_cd),c("loin.1",y ~ sex + car_wt + slgdt_cd),
  c("fat.1",y~ sex + car_wt + slgdt_cd),c("moisture.1",y ~ sex + car_wt + slgdt_cd), 
  c( "tenderness.1", ~ sex + car_wt + slgdt_cd),c("boston.1", ~ sex + car_wt + slgdt_cd),
  c( "spareribs.1", ~ sex + car_wt + slgdt_cd),c("picnic.1", ~ sex + car_wt + slgdt_cd)
)

gb.meatraits<-list()
for (i in 1:nrow(cont_struc2)) {
  gb.meatraits[[i]]<-gblup(cont_struc2[i,][[1]],pheno.msuprp,
                           c(cont_struc2[i,][[2]],~Gvitezica),G, vdata=list(Gvitezica=Gvitezica),
                           pos=c(T,T,T))
  names(gb.meatraits)[i]<-cont_struc2[i]
  #print(i)
}

## Warning: solution lies close to zero for some positive variance components, their standard errors may not be valid
## Warning: solution lies close to zero for some positive variance components, their standard errors may not be valid
## Warning: solution lies close to zero for some positive variance components, their standard errors may not be valid
## Warning: solution lies close to zero for some positive variance components, their standard errors may not be valid
## Warning: solution lies close to zero for some positive variance components, their standard errors may not be valid
## Warning: solution lies close to zero for some positive variance components, their standard errors may not be valid
## Warning: solution lies close to zero for some positive variance components, their standard errors may not be valid
## Warning: solution lies close to zero for some positive variance components, their standard errors may not be valid
## Warning: solution lies close to zero for some positive variance components, their standard errors may not be valid

gbdomeff.meatraits<-gb.meatraits
rm(gb.meatraits)

# additive variance
hG<-lapply(gbdomeff.meatraits, function(x)varcomp(x)[1,])%>%
  map_df(as_tibble)%>%mutate(trait=names(gbdomeff.meatraits))%>%
  select(trait,Estimate, StdError, prop.var, se)
# Dominance variance
hd<-lapply(gbdomeff.meatraits, function(x)varcomp(x)[2,])%>%
  map_df(as_tibble)%>%mutate(trait=names(gbdomeff.meatraits))%>%
  select(trait,Estimate, StdError, prop.var, se)
# Error variance
he<-lapply(gbdomeff.meatraits, function(x)varcomp(x)[3,])%>%
  map_df(as_tibble)%>%mutate(trait=names(gbdomeff.meatraits))%>%
  select(trait,Estimate, StdError, prop.var, se)
# table
res<-hG%>%left_join(hd, by = c("trait"))%>%left_join(he, by = c("trait"))
colnames(res)<-c("trait","variance","se","h","se","variance","se","h","se",
                 "variance","se","h","se")

kable(res)%>%
  kable_styling(bootstrap_options = c("striped", "hover", "condensed"),full_width = F, 
               position = "center",font_size = 14) %>%
  add_header_above(c(" ","Additive" = 4, "Dominance" = 4, "Error"=4), italic = T,
                   bold = T)

	Additive				Dominance				Error
trait	variance	se	h	se	variance	se	h	se	variance	se	h	se
last_lum.1	11.5757165	2.0952584	0.3952326	0.0619165	1.1256536	1.3545514	0.0384335	0.0462073	16.5869961	1.5685600	0.5663339	0.0782534
dress_ptg.1	0.7819981	0.1780865	0.2712238	0.0562611	0.0000056	0.1303447	0.0000019	0.0452080	2.1012166	0.1692199	0.7287742	0.0863552
car_length.1	1.8290545	0.2967271	0.4659999	0.0624384	0.0661043	0.1575841	0.0168418	0.0401385	2.0298519	0.1939803	0.5171583	0.0720551
belly.1	0.0224656	0.0066551	0.1788988	0.0507988	0.0141215	0.0076877	0.1124530	0.0609548	0.0889901	0.0080084	0.7086482	0.0929250
juiciness.1	0.0181911	0.0116438	0.0556336	0.0353135	0.0372191	0.0210125	0.1138266	0.0638070	0.2715705	0.0221838	0.8305398	0.0978724
WBS.1	0.1055807	0.0249016	0.2619154	0.0573434	0.0354425	0.0229176	0.0879226	0.0567173	0.2620869	0.0245121	0.6501620	0.0886759
ph_24h.1	0.0027057	0.0008239	0.1741834	0.0509571	0.0016162	0.0009590	0.1040499	0.0614804	0.0112115	0.0010115	0.7217668	0.0949740
driploss.1	0.2909966	0.0678184	0.2644305	0.0571409	0.1006135	0.0623351	0.0914281	0.0565045	0.7088554	0.0662484	0.6441414	0.0877328
protein.1	0.4498552	0.0838309	0.3805322	0.0586275	0.0002244	0.0201112	0.0001898	0.0170165	0.7320942	0.0640745	0.6192780	0.0691395
cook_yield.1	2.3992393	0.5038151	0.3132153	0.0587863	0.0005317	0.3397988	0.0000694	0.0443598	5.2602622	0.4392184	0.6867153	0.0847026
bf10_22wk.1	7.3850016	1.3931006	0.3693591	0.0619021	2.1038506	1.0782630	0.1052236	0.0539078	10.5052466	1.0967949	0.5254174	0.0785525
lma_22wk.1	5.0305502	0.9249311	0.3850263	0.0613363	0.3542076	0.5851594	0.0271102	0.0447608	7.6807148	0.7038796	0.5878635	0.0789288
ham.1	0.0966666	0.0153221	0.4849492	0.0622453	0.0000000	0.0074521	0.0000000	0.0373849	0.1026669	0.0095391	0.5150508	0.0706655
loin.1	0.0715079	0.0159297	0.2836316	0.0584854	0.0356542	0.0151831	0.1414203	0.0601222	0.1449533	0.0148648	0.5749482	0.0846908
fat.1	0.8915997	0.1353985	0.5136022	0.0644857	0.1716871	0.0826080	0.0988996	0.0477261	0.6726866	0.0820397	0.3874982	0.0651345
moisture.1	0.7012585	0.1327946	0.3717722	0.0618945	0.0935176	0.0921570	0.0495784	0.0488070	1.0914823	0.1044396	0.5786494	0.0807249
tenderness.1	0.1010140	0.0223137	0.2873905	0.0574184	0.0000000	0.0158215	0.0000000	0.0450130	0.2504731	0.0204982	0.7126095	0.0859851
boston.1	0.0173932	0.0043108	0.2377872	0.0547841	0.0023812	0.0037131	0.0325539	0.0506982	0.0533716	0.0044584	0.7296589	0.0892447
spareribs.1	0.0056260	0.0010561	0.3730575	0.0606631	0.0000000	0.0006326	0.0000001	0.0419500	0.0094548	0.0008160	0.6269423	0.0801249
picnic.1	0.0118596	0.0038481	0.1512639	0.0463930	0.0000271	0.0024236	0.0003453	0.0309173	0.0665167	0.0049817	0.8483908	0.0778800

likelihood ratio test

Trait 1: Lastlumb trait

gb1<-gb.meatraits$last_lum.1
varcomp(gb1)

##    Estimate StdError  prop.var         se
## G  11.69689 2.090714 0.4002318 0.05708301
## In 17.52840 1.179894 0.5997682 0.06192753

# likelihood additive animal model
gb1$llik

## [1] -1866.318

# likelihood  dominance animal model
gbdomeff.meatraits$last_lum.1$llik

## [1] -1865.9

# LRT
-2*((-1866.318)-(-1865.9))

## [1] 0.836

# p-value
pchisq(0.836,df = 1,lower.tail = F)

## [1] 0.3605433

Trait 2: BF10

gb2<-gb.meatraits$bf10_22wk.1
varcomp(gb2)

##     Estimate  StdError  prop.var         se
## G   7.445604 1.3822399 0.3764104 0.05670196
## In 12.334942 0.8161379 0.6235896 0.06261859

# likelihood additive animal model
gb2$llik

## [1] -1703.51

# likelihood  dominance animal model
gbdomeff.meatraits$bf10_22wk.1$llik

## [1] -1700.825

# LRT
-2*((-1703.51)-(-1700.825))

## [1] 5.37

# p-value
pchisq(5.37,df = 1,lower.tail = F)

## [1] 0.02048598

Dominance effects in meat quality traits

Belcy Karine Angarita Barajas

12/13/2021

likelihood ratio test

Trait 1: Lastlumb trait

Trait 2: BF10