Partial-genome evaluation of postweaning feed intake and efficiency of crossbred beef cattle1,2

Abstract

The effects of individual SNP and the variation explained by sets of SNP associated with DMI, metabolic midtest BW, BW gain, and feed efficiency, expressed as phenotypic and genetic residual feed intake, were estimated from BW and the individual feed intake of 1,159 steers on dry lot offered a 3.0 Mcal/kg ration for at least 119 d before slaughter. Parents of these F(1) × F(1) (F(1)(2)) steers were AI-sired F(1) progeny of Angus, Charolais, Gelbvieh, Hereford, Limousin, Red Angus, and Simmental bulls mated to US Meat Animal Research Center Angus, Hereford, and MARC III composite females. Steers were genotyped with the BovineSNP50 BeadChip assay (Illumina Inc., San Diego, CA). Effects of 44,163 SNP having minor allele frequencies >0.05 in the F(1)(2) generation were estimated with a mixed model that included genotype, breed composition, heterosis, age of dam, and slaughter date contemporary groups as fixed effects, and a random additive genetic effect with recorded pedigree relationships among animals. Variance in this population attributable to sets of SNP was estimated with models that partitioned the additive genetic effect into a polygenic component attributable to pedigree relationships and a genotypic component attributable to genotypic relationships. The sets of SNP evaluated were the full set of 44,163 SNP and subsets containing 6 to 40,000 SNP selected according to association with phenotype. Ninety SNP were strongly associated (P < 0.0001) with at least 1 efficiency or component trait; these 90 accounted for 28 to 46% of the total additive genetic variance of each trait. Trait-specific sets containing 96 SNP having the strongest associations with each trait explained 50 to 87% of additive variance for that trait. Expected accuracy of steer breeding values predicted with pedigree and genotypic relationships exceeded the accuracy of their sires predicted without genotypic information, although gains in accuracy were not sufficient to encourage that performance testing be replaced by genotyping and genomic evaluations.

BREEDING AND GENETICS SYMPOSIUM: Networks and pathways to guide genomic selection1–3

“…Bayesian genomic selection (Meuwissen et al, 2001;Habier et al, 2011) can similarly be restricted to a subset of whole-genome SNP. The GREML approach can be extended to include a polygenic component, using pedigree relationships to account for the remainder of the genome (Snelling et al, 2011). Partitioning into genomic and polygenic components may have some advantage for prediction, as breeding values predicted as the sum of polygenic and genomic BLUP (GBLUP) solutions may be more accurate than either whole-genome GBLUP or pedigree BLUP EBV.…”

“…Several expensive-and diffi cultto-measure traits related to animal health, fertility, biological effi ciency, and consumer acceptance may be recorded on intensively phenotyped experimental populations. Genomic predictions can increase EBV accuracy within these populations (Snelling et al, 2011) although extending the genomic predictions to broader livestock industries is limited by lack of relationships with industry populations and lack of phenotypes on industry livestock. For traits where cost, time, and expertise are impediments to developing industry databases with enough relevant phenotypes to support whole-genome selection, efforts to increase the correlations between markers and QTL for these economically important traits may enable genomic selection in industry based on fi ndings from unrelated experimental herds.…”

PHYSIOLOGY AND ENDOCRINOLOGY SYMPOSIUM: How single nucleotide polymorphism chips will advance our knowledge of factors controlling puberty and aid in selecting replacement beef females1,2,3,4

“…Genomic selection (Meuwissen et al, 2001) facilitated by the BovineSNP50 BeadChip (50K; Illumina Inc., San Diego, CA), with more than 54,000 SNP located throughout the genome (Matukumalli et al, 2009), appears to increase accuracy of breeding values predicted for complex traits on young animals with no or few progeny (Van Raden et al, 2009;MacNeil et al, 2010;Snelling et al, 2011). Accuracy of genomic selection is affected by heritability of the trait, number of recorded individuals with genotypes, and effective population size.…”

“…Procedures for partial-genome analysis, initially developed to assess heritability due to SNP selected according to associations with feedlot intake and efficiency (Snelling et al, 2011), were employed to evaluate subsets of BovineHD SNP. Genotypic relationship matrices (M) for each BovineHD subset were com- Raden, 2008), where p i is the B allele frequency for the ith SNP in the set and S is a matrix of differences between individual genotypes (i.e., 0, 1, or 2 copies of the B allele) and the mean genotype (2p i ).…”

“…Ultrasound backfat thickness and LM area were positively correlated with both first service and breeding season pregnancy; the strongest of all genetic correlations with first service conception was between first service conception and backfat thickness. Each of the 50K SNP were tested for association with each trait with a univariate model including a covariate for SNP effects (i.e., 0, 1, or 2 copies of allele B) and a polygenic random animal effect with known pedigree relationships, using procedures initially developed for 50K GWAS of GPE (Snelling et al, 2010(Snelling et al, , 2011.…”