Genome-wide association analyses based on a multiple-trait approach for modeling feed efficiency

Abstract

Genome-wide association (GWA) of feed efficiency (FE) could help target important genomic regions influencing FE. Data provided by an international dairy FE research consortium consisted of phenotypic records on dry matter intakes (DMI), milk energy (MILKE), and metabolic body weight (MBW) on 6,937 cows from 16 stations in 4 counties. Of these cows, 4,916 had genotypes on 57,347 single nucleotide polymorphism (SNP) markers. We compared a GWA analysis based on the more classical residual feed intake (RFI) model with one based on a previously proposed multiple trait (MT) approach for modeling FE using an alternative measure (DMI|MILKE,MBW). Both models were based on a single-step genomic BLUP procedure that allowed the use of phenotypes from both genotyped and nongenotyped cows. Estimated effects for single SNP markers were small and not statistically important but virtually identical for either FE measure (RFI vs. DMI|MILKE,MBW). However, upon further refining this analysis to develop joint tests within nonoverlapping 1-Mb windows, significant associations were detected between either measure of FE with a window on each of Bos taurus autosomes BTA12 and BTA26. There was, as expected, no overlap between detected genomic regions for DMI|MILKE,MBW and genomic regions influencing the energy sink traits (i.e., MILKE and MBW) because of orthogonal relationships clearly defined between the various traits. Conversely, GWA inferences on DMI can be demonstrated to be partly driven by genetic associations between DMI with these same energy sink traits, thereby having clear implications when comparing GWA studies on DMI to GWA studies on FE-like measures such as RFI.

Exact p-values for large-scale single step genome-wide association, with an application for birth weight in American Angus

“…Estimates of SNP effects from single-step methods became available in 2012 [18,47]; however, a measure of significance for SNP estimates has not been available until now in current implementations of single-step GBLUP. Expanding the ideas from the equivalence between GBLUP and GWAS [23–25], Lu et al [27] derived exact GWAS for a single-step GBLUP framework in a relatively small data set. In our study we showed exact p-values calculated for the first time using a large data set including genotypes, phenotypes, and pedigree.…”

“…Fortunately, Lu et al [27] showed that the same logic used in [23–25] can be used in a SSGBLUP context. Simply stated, to obtain a statistical test for the effect of a single marker, we only need estimates of the breeding values of genotyped animals and their sampling distribution, and this can be readily obtained from SSGBLUP.…”

“…Simply stated, to obtain a statistical test for the effect of a single marker, we only need estimates of the breeding values of genotyped animals and their sampling distribution, and this can be readily obtained from SSGBLUP. Unfortunately, the article of Lu et al [27] seems largely unnoticed because it focuses on feed efficiency and uses a small data set (i.e., 7,000 phenotyped animals from which 5,000 are genotyped), which does not show computational tools, challenges and bottlenecks.…”

“…In the present work, we show the implementation of Single Step GWAS (SSGWAS) with exact p-values as in [27], together with an application in a very large beef cattle data set. We describe algorithms and computational procedures along with their bottlenecks.…”

Identification of genetic loci associated with growth traits at weaning in yak through a genome‐wide association study

“…HS6ST3 (heparan sulfate 6-O-sulfotransferase) has been associated with residual feed intake (Yao et al 2013), body weight and size (Wang et al 2018), and the fatty acid profile of beef (de Lemos et al 2018). DOCK1 (dedicator of cytokinesis 1) has been reported as a candidate gene for carcass composition indicator traits (Silva et al 2019), marbling score (Ryu & Lee 2016), feed efficiency (Lu et al 2018) and body weight (Li et al 2018a;Reis Neto et al 2019). We identified nine SNPs corresponding to eight genes associated with yak CG at time of weaning.…”