Genome-wide association and genotype by environment interactions for growth traits in U.S. Gelbvieh cattle

Abstract

BackgroundSingle nucleotide polymorphism (SNP) arrays have facilitated discovery of genetic markers associated with complex traits in domestic cattle; thereby enabling modern breeding and selection programs. Genome-wide association analyses (GWAA) for growth traits were conducted on 10,837 geographically diverse U.S. Gelbvieh cattle using a union set of 856,527 imputed SNPs. Birth weight (BW), weaning weight (WW), and yearling weight (YW) were analyzed using GEMMA and EMMAX (via imputed genotypes). Genotype-by-environment (GxE) interactions were also investigated.ResultsGEMMA and EMMAX produced moderate marker-based heritability estimates that were similar for BW (0.36–0.37, SE = 0.02–0.06), WW (0.27–0.29, SE = 0.01), and YW (0.39–0.41, SE = 0.01–0.02). GWAA using 856K imputed SNPs (GEMMA; EMMAX) revealed common positional candidate genes underlying pleiotropic QTL for Gelbvieh growth traits on BTA6, BTA7, BTA14, and BTA20. The estimated proportion of phenotypic variance explained (PVE) by the lead SNP defining these QTL (EMMAX) was larger and most similar for BW and YW, and smaller for WW. Collectively, GWAAs (GEMMA; EMMAX) produced a highly concordant set of BW, WW, and YW QTL that met a nominal significance level (P ≤ 1e-05), with prioritization of common positional candidate genes; including genes previously associated with stature, feed efficiency, and growth traits (i.e., PLAG1, NCAPG, LCORL, ARRDC3, STC2). Genotype-by-environment QTL were not consistent among traits at the nominal significance threshold (P ≤ 1e-05); although some shared QTL were apparent at less stringent significance thresholds (i.e., P ≤ 2e-05).ConclusionsPleiotropic QTL for growth traits were detected on BTA6, BTA7, BTA14, and BTA20 for U.S. Gelbvieh beef cattle. Seven QTL detected for Gelbvieh growth traits were also recently detected for feed efficiency and growth traits in U.S. Angus, SimAngus, and Hereford cattle. Marker-based heritability estimates and the detection of pleiotropic QTL segregating in multiple breeds support the implementation of multiple-breed genomic selection.

Powerful detection of polygenic selection and evidence of environmental adaptation in US beef cattle

“…We expect that the many significant GPSM regions will be tagging allele frequency changes driven by selection. That said, we observe relatively few of the regions identified by GPSM overlap with major growth QTL identified using identical Gelbvieh and Simmental datasets 55,56 . Most of the major growth-related QTL still segregating in these breeds are effectively unchanged over the course of our sampled datasets.…”

“…Previous work in cattle has identified loci that are involved in genotype-by-environment (GxE) interactions. GxE variants have different effects in different environments 55,56,176,177 . These approaches are different, but complementary to our envGWAS analysis.…”

Extensive genome-wide association analyses identify genotype-by-environment interactions of growth traits in Simmental cattle

“…The DNA samples were genotyped with various low density assays and were imputed to the combination of Illumina BovineHD (Illumina, San Diego, CA) and the GeneSeek Genomic Profiler F250 (GeneSeek, Lincoln, NE) according to Rowan et al (2019). The genotype filtering was performed to remove non-autosomal variants, as well as both variants and individuals with call rate less than 0.90, using PLINK 1.9 (Purcell et al, 2007).…”

“…minimum, mean, maximum and mean dew point temperatures; elevation; precipitation; and minimum and maximum vapor pressure deficit) separately; and using the ecoregions as environmental factors, where each ecoregion was compared against the total dataset using 0 and 1 dummy coding. In the GxE GWA analyses, for each SNP in turn, GEMMA fits a linear mixed model that controls both the SNP main effect and environmental main effect, while tested for the interaction effect and controlling for population stratification, evaluating the alternative hypothesis (H1: ≠ 0) against the null hypothesis (H0: = 0) for each interaction, therefore the resulting P-values correspond to the significance of the GxE interaction (Smith et al, 2019). This model can be described as:…”

“…Studies have reported the existence of GxE interaction for growth traits in beef cattle. Potential genes involved in GxE interaction when comparing US geographic regions were identified by Smith et al (2019) using Gelbvieh cattle. These studies suggest that the same trait may be modeled by different sets of genes across divergent environments (Falconer & Mackay, 1996).…”