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

Abstract

Understanding genotype-by-environment interactions (G × E) is crucial to understand environmental adaptation in mammals and improve the sustainability of agricultural production. Here, we present an extensive study investigating the interaction of genome-wide SNP markers with a vast assortment of environmental variables and searching for SNPs controlling phenotypic variance (vQTL) using a large beef cattle dataset. We showed that G × E contribute 10.1%, 3.8%, and 2.8% of the phenotypic variance of birth weight, weaning weight, and yearling weight, respectively. G × E genome-wide association analysis (GWAA) detected a large number of G × E loci affecting growth traits, which the traditional GWAA did not detect, showing that functional loci may have non-additive genetic effects regardless of differences in genotypic means. Further, variance-heterogeneity GWAA detected loci enriched with G × E effects without requiring prior knowledge of the interacting environmental factors. Functional annotation and pathway analysis of G × E genes revealed biological mechanisms by which cattle respond to changes in their environment, such as neurotransmitter activity, hypoxia-induced processes, keratinization, hormone, thermogenic and immune pathways. We unraveled the relevance and complexity of the genetic basis of G × E underlying growth traits, providing new insights into how different environmental conditions interact with specific genes influencing adaptation and productivity in beef cattle and potentially across mammals.

Figure 1

A flowchart overview of the entire study. Each topic is discussed in detail in the corresponding sections. N sample size, CG contemporary group, BW birth weight, WW weaning weight, YW yearling weight, GWAA genome-wide association analysis, G × E genotype-by-environment interactions, vGWAA variance-heterogeneity GWAA.

Figure 2

Genotype-by-environment interaction genome-wide association analyses (G × E GWAA) for growth traits in Simmental cattle. (a) Mean temperature average annual over the most recent three full decades covering the conterminous United States. (b) Manhattan plot of G × E GWAA of mean temperature for birth weight. (c) Elevation of the conterminous United States. (d) Manhattan plot of G × E GWAA of elevation for weaning weight. (e) Manhattan plot of G × E GWAA of elevation for yearling weight. (f) Boundaries for ecoregion assignments in the United States; (top panel) United States partitioned into nine ecoregions based on similar topographic and environmental conditions; (bottom panel) location of beef farms for which data was retrieved. (g) Manhattan plot of G × E GWAA of Forested Mountains ecoregion for birth weight. (h) Manhattan plot of G × E GWAA of Forested Mountains ecoregion for weaning weight. (i) Manhattan plot of G × E GWAA of Forested Mountains ecoregion for yearling weight. In Manhattan plots, horizontal red line indicates a significant threshold (P < 1e−5). Environmental continuous variables were drawn from the PRISM climate dataset (http://prism.oregonstate.edu). The United States was partitioned into nine regions using k-means clustering. Maps were plotted using the maps R package (version 3.1, https://cran.r-project.org/web/packages/maps/), using public domain data from the US Department of Commerce, Census Bureau.

Figure 3

The absolute values of the significant G × E SNP effects and their allele frequency using (a) univariate models with continuous environmental variables, (b) univariate models with ecoregion, (c) multivariate models with continuous environmental variables, and (d) multivariate models with ecoregion. Figure made with R version 3.6.3. (https://www.r-project.org/).