title: Using single-step genomic best linear unbiased predictor to enhance the mitigation of seasonal losses due to heat stress in pigs.
abstract: The purposes of this study were to analyze the impact of seasonal losses due to heat stress in pigs from different breeds raised in different environments and to evaluate the accuracy improvement from adding genomic information to genetic evaluations. Data were available for 2 different swine populations: purebred Duroc animals raised in Texas and North Carolina and commercial crosses of Duroc and F females (Landrace × Large White) raised in Missouri and North Carolina; pedigrees provided links for animals from different states. Pedigree information was available for 553,442 animals, of which 8,232 pure breeds were genotyped. Traits were BW at 170 d for purebred animals and HCW for crossbred animals. Analyses were done with an animal model as either single- or 2-trait models using phenotypes measured in different states as separate traits. Additionally, reaction norm models were fitted for 1 or 2 traits using heat load index as a covariable. Heat load was calculated as temperature-humidity index greater than 70 and was averaged over 30 d prior to data collection. Variance components were estimated with average information REML, and EBV and genomic EBV (GEBV) with BLUP or single-step genomic BLUP (ssGBLUP). Validation was assessed for 146 genotyped sires with progeny in the last generation. Accuracy was calculated as a correlation between EBV and GEBV using reduced data (all animals, except the last generation) and using complete data. Heritability estimates for purebred animals were similar across states (varying from 0.23 to 0.26), and reaction norm models did not show evidence of a heat stress effect. Genetic correlations between states for heat loads were always strong (>0.91). For crossbred animals, no differences in heritability were found in single- or 2-trait analysis (from 0.17 to 0.18), and genetic correlations between states were moderate (0.43). In the reaction norm for crossbreeds, heritabilities ranged from 0.15 to 0.30 and genetic correlations between heat loads were as weak as 0.36, with heat load ranging from 0 to 12. Accuracies with ssGBLUP were, on average, 25% greater than with BLUP. Accuracies were greater in 2-trait reaction norm models and at extreme heat load values. Impacts of seasonality are evident only for crossbred animals. Genomic information can help producers mitigate heat stress in swine by identifying superior sires that are more resistant to heat stress.
authors.forename: B O
authors.initials: BO
authors.lastname: Fragomeni
authors.forename: D A L
authors.initials: DA
authors.lastname: Lourenco
authors.forename: S
authors.initials: S
authors.lastname: Tsuruta
authors.forename: H L
authors.initials: HL
authors.lastname: Bradford
authors.forename: K A
authors.initials: KA
authors.lastname: Gray
authors.forename: Y
authors.initials: Y
authors.lastname: Huang
authors.forename: I
authors.initials: I
authors.lastname: Misztal
mesh_terms: D000818:Animals; D001947:Breeding; D005260:Female; D059647:Gene-Environment Interaction; D023281:Genomics; D005838:Genotype; D018869:Heat-Shock Response; D006358:Hot Temperature; D006813:Humidity; D016014:Linear Models; D008297:Male; D008923:Missouri; D009657:North Carolina; D010375:Pedigree; D010641:Phenotype; D012044:Regression Analysis; D012621:Seasons; D013552:Swine / Q000254:growth & development / Q000502:physiology*; D013781:Texas