ped.msuprp<-as.data.frame(MSUPRP_meat$pedigree)
head(ped.msuprp)## ID Par1 Par2 gener sex
## 1 6070 0 0 0 0
## 2 6071 0 0 0 0
## 3 6086 0 0 0 0
## 4 6088 0 0 0 0
## 5 6092 0 0 0 0
## 6 6323 0 0 0 1# males and females in the pedigree
table(ped.msuprp$sex)##
## 0 1
## 518 519# generations in the pedigree
table(ped.msuprp$gener)##
## 0 1 2
## 20 57 960# individuals with genotype and phenotype
length(intersect(rownames(G), rownames(pheno.msuprp)))## [1] 940length(intersect(ped.msuprp$ID, rownames(pheno.msuprp)))## [1] 960It were selected 12 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 described in Cariró 2017, in which included fixed effects of sex, slaughter group and carcass weight, with exception for the number of ribs trait, that only had sex as fixed effect, the genomic relationship matrix (G) was computed as VanRadem 2008.
load("gbMeatratisMsuPRP.Rdata")
lapply(gb.meatraits, function(x)varcomp(x))## $last_lum.1
## Estimate StdError prop.var se
## G 11.94571 2.109141 0.4079896 0.05732552
## In 17.33374 1.178952 0.5920104 0.06185818
##
## $dress_ptg.1
## Estimate StdError prop.var se
## G 0.8023418 0.1777845 0.2824343 0.05422069
## In 2.0384664 0.1276088 0.7175657 0.06414282
##
## $car_length.1
## Estimate StdError prop.var se
## G 1.914497 0.3014001 0.4848341 0.05739534
## In 2.034271 0.1461378 0.5151659 0.05816738
##
## $num_ribs.1
## Estimate StdError prop.var se
## G 0.2767628 0.06142882 0.3580153 0.06718035
## In 0.4962848 0.03999038 0.6419847 0.07587294
##
## $belly.1
## Estimate StdError prop.var se
## G 0.02476174 0.006784861 0.1982324 0.04957400
## In 0.10015092 0.005952166 0.8017676 0.06240203
##
## $juiciness.1
## Estimate StdError prop.var se
## G 0.02367782 0.01209957 0.07286584 0.03631702
## In 0.30127313 0.01644872 0.92713416 0.05148086
##
## $WBS.1
## Estimate StdError prop.var se
## G 0.1185178 0.02584294 0.2925219 0.05496538
## In 0.2866409 0.01820819 0.7074781 0.06459903
##
## $ph_24h.1
## Estimate StdError prop.var se
## G 0.002886079 0.0008285234 0.1868492 0.04932895
## In 0.012559954 0.0007510999 0.8131508 0.06273813
##
## $driploss.1
## Estimate StdError prop.var se
## G 0.3073669 0.06849498 0.2803005 0.05420036
## In 0.7891952 0.04941001 0.7196995 0.06422381
##
## $protein.1
## Estimate StdError prop.var se
## G 0.4460666 0.08317114 0.3783189 0.05738021
## In 0.7330089 0.04926695 0.6216811 0.06334544
##
## $cook_yield.1
## Estimate StdError prop.var se
## G 2.378149 0.4996554 0.3111316 0.05563172
## In 5.265398 0.3380695 0.6888684 0.06450203
##
## $bf10_22wk
## Estimate StdError prop.var se
## G 7.53506 1.388605 0.3808797 0.05692426
## In 12.24825 0.816722 0.6191203 0.06270037lapply(gb.meatraits, function(x)dim(x$model$G))## $last_lum.1
## [1] 929 929
##
## $dress_ptg.1
## [1] 931 931
##
## $car_length.1
## [1] 930 930
##
## $num_ribs.1
## [1] 655 655
##
## $belly.1
## [1] 930 930
##
## $juiciness.1
## [1] 925 925
##
## $WBS.1
## [1] 920 920
##
## $ph_24h.1
## [1] 910 910
##
## $driploss.1
## [1] 929 929
##
## $protein.1
## [1] 918 918
##
## $cook_yield.1
## [1] 921 921
##
## $bf10_22wk
## [1] 931 931load("gbAmat_MsuPRPmeat.Rdata")
lapply(gbAmat.meatraits, function(x)varcomp(x))## $last_lum.1
## Estimate StdError prop.var se
## G 7.363331 2.838081 0.2550024 0.09128279
## In 21.512202 1.954270 0.7449976 0.11605547
##
## $dress_ptg.1
## Estimate StdError prop.var se
## G 0.5553926 0.2410794 0.1929825 0.07891576
## In 2.3225511 0.1826059 0.8070175 0.10499977
##
## $car_length.1
## Estimate StdError prop.var se
## G 1.522296 0.4999720 0.378358 0.1138272
## In 2.501131 0.3063584 0.621642 0.1300656
##
## $num_ribs.1
## Estimate StdError prop.var se
## G 0.4070451 0.12895893 0.4969527 0.1463337
## In 0.4120372 0.07639819 0.5030473 0.1468772
##
## $belly.1
## Estimate StdError prop.var se
## G 0.0165701 0.008517949 0.1335745 0.06578841
## In 0.1074813 0.007371443 0.8664255 0.09115774
##
## $juiciness.1
## Estimate StdError prop.var se
## G 0.01960876 0.01529044 0.05957097 0.04560395
## In 0.30955757 0.01790241 0.94042903 0.06677040
##
## $WBS.1
## Estimate StdError prop.var se
## G 0.1052577 0.04032519 0.2613151 0.09290947
## In 0.2975422 0.02761103 0.7386849 0.11757911
##
## $ph_24h.1
## Estimate StdError prop.var se
## G 0.002342879 0.0011431682 0.1518095 0.07058800
## In 0.013090144 0.0009454277 0.8481905 0.09661863
##
## $driploss.1
## Estimate StdError prop.var se
## G 0.5053551 0.15921453 0.4218718 0.1217951
## In 0.6925326 0.09495791 0.5781282 0.1331435
##
## $protein.1
## Estimate StdError prop.var se
## G 0.2227889 0.09692893 0.1947251 0.0797670
## In 0.9213313 0.07317803 0.8052749 0.1059874
##
## $cook_yield.1
## Estimate StdError prop.var se
## G 2.728623 0.9283688 0.3492584 0.1090476
## In 5.083996 0.5823687 0.6507416 0.1280995
##
## $bf10_22wk
## Estimate StdError prop.var se
## G 2.83468 1.392122 0.1477185 0.06917347
## In 16.35506 1.156531 0.8522815 0.09484762lapply(gbAmat.meatraits, function(x)dim(x$model$G))## $last_lum.1
## [1] 947 947
##
## $dress_ptg.1
## [1] 949 949
##
## $car_length.1
## [1] 948 948
##
## $num_ribs.1
## [1] 670 670
##
## $belly.1
## [1] 948 948
##
## $juiciness.1
## [1] 943 943
##
## $WBS.1
## [1] 936 936
##
## $ph_24h.1
## [1] 928 928
##
## $driploss.1
## [1] 947 947
##
## $protein.1
## [1] 936 936
##
## $cook_yield.1
## [1] 937 937
##
## $bf10_22wk
## [1] 949 949print(h2)## h^2 G matrix A matrix
## last_lum.1 0.40 0.250
## dress_ptg.1 0.28 0.190
## car_length.1 0.48 0.380
## num_ribs.1 0.35 0.490
## belly.1 0.19 0.130
## juiciness.1 0.07 0.059
## WBS.1 0.29 0.260
## ph_24h.1 0.18 0.150
## driploss.1 0.28 0.420
## protein.1 0.37 0.190
## cook_yield.1 0.31 0.350
## bf10_22wk 0.38 0.140