Genomic architecture of phenotypic extremes in a wild cervid

S J Anderson,A B A Shafer,J H Richard,S D Côté

doi:10.1186/s12864-022-08333-x

Abstract

Identifying the genes underlying fitness-related traits such as body size and male ornamentation can provide tools for conservation and management and are often subject to various selective pressures. Here we performed high-depth whole genome re-sequencing of pools of individuals representing the phenotypic extremes for antler and body size in white-tailed deer (Odocoileus virginianus). Samples were selected from a tissue repository containing phenotypic data for 4,466 male white-tailed deer from Anticosti Island, Quebec, with four pools representing the extreme phenotypes for antler and body size after controlling for age. Our results revealed a largely homogenous population but detected highly divergent windows between pools for both traits, with the mean allele frequency difference of 14% for and 13% for antler and body SNPs in outlier windows, respectively. Genes in outlier antler windows were enriched for pathways associated with cell death and protein metabolism and some of the most differentiated windows included genes associated with oncogenic pathways and reproduction, processes consistent with antler evolution and growth. Genes associated with body size were more nuanced, suggestive of a highly complex trait. Overall, this study revealed the complex genomic make-up of both antler morphology and body size in free-ranging white-tailed deer and identified target loci for additional analyses.

Highlights

Characterizing the genomic architecture underlying phenotypes in natural populations provides insights into the evolution of quantitative traits [1]
Phenotypes We selected the top individuals at the tail ends of the distribution for measurements used in our antler and body size rankings which are representative of the extreme phenotypes
There were no significant differences in mean age between the pools (Fig. 1b) and individual rankings between antler and body size were weakly correlated (Pearson r = 0.33, p

Summary

Introduction

Characterizing the genomic architecture underlying phenotypes in natural populations provides insights into the evolution of quantitative traits [1]. Some quantitative traits are correlated with metrics of fitness and are important because they might directly influence population viability [2]. This relationship between genomic architecture and quantitative traits, is not easy to empirically identify [3], and often has unclear and unpredictable responses to selection [4]. [5,6,7,8,9,10]) This sampling methodology of so-called extreme phenotypes aims to maximize the additive genetic variance for the sampled trait, increasing the power to detect quantitative trait loci or QTL [5]. Population history (i.e. degree of linkage disequilibrium) has a profound effect on identifying QTL, essentially making the ability to identify linked or causative loci easier in small (isolated) populations, and challenging in large populations with high effective population size (Ne), and by proxy high recombination rates (see [12])

Objectives

Methods

Results

Conclusion