The impact of SNP density on quantitative genetic analyses of body size traits in a wild population of Soay sheep

Abstract

Understanding the genetic architecture underpinning quantitative traits in wild populations is pivotal to understanding the processes behind trait evolution. The ‘animal model’ is a popular method for estimating quantitative genetic parameters such as heritability and genetic correlation and involves fitting an estimate of relatedness between individuals in the study population. Genotypes at genome‐wide markers can be used to estimate relatedness; however, relatedness estimates vary with marker density, potentially affecting results. Increasing density of markers is also expected to increase the power to detect quantitative trait loci (QTL). In order to understand how the density of genetic markers affects the results of quantitative genetic analyses, we estimated heritability and performed genome‐wide association studies (GWAS) on five body size traits in an unmanaged population of Soay sheep using two different SNP densities: a dataset of 37,037 genotyped SNPs and an imputed dataset of 417,373 SNPs. Heritability estimates did not differ between the two SNP densities, but the high‐density imputed SNP dataset revealed four new SNP‐trait associations that were not found with the lower density dataset, as well as confirming all previously‐found QTL. We also demonstrated that fitting fixed and random effects in the same step as performing GWAS is a more powerful approach than pre‐correcting for covariates in a separate model.

Investigating pedigree- and SNP-associated components of heritability in a wild population of Soay sheep

“…0.019) between adult foreleg length and adult hindleg length (Bérénos et al 2014 ), inclusion of a thresholded GRM at t = 0.1 is significant for adult foreleg length and not adult hindleg length. However, in a recent genome-wide association study of this population, we found two peaks on chromosomes 7 and 9 that were significantly associated with adult foreleg length but not with adult hindleg length (James et al 2022 ). It is possible that family-associated variation such as epistasis and dominance are acting upon these two regions, resulting in the inclusion of the thresholded GRM being significant for adult foreleg length but not adult hindleg length.…”

“…Genotypes with a probability of <0.99 were excluded, resulting in, 419,281 autosomal SNPs remaining for 8557 individuals (4035 females, 4452 males). We have previously shown that imputation does not affect heritability estimates for these traits in this population (James et al 2022 ).…”

“…A detailed description of trait measurements can be found in Beraldi et al ( 2007 ). Several studies have demonstrated that these traits are under polygenic control (Bérénos et al 2015 ; Ashraf et al 2021 ; Hunter et al 2022 ; James et al 2022 ).…”

“…To deal with multiple measurements per individual for the adult live traits, we used a repeatability model by fitting ID as a random effect to ensure that uncertainty was correctly propagated through all estimations (Mrode 2014 ). Although DISSECT does not currently have the option to automatically analyse repeated measures, it is possible to modify input files to allow for a repeated measures model (see James et al ( 2022 ) for method details).…”

“…A conventional GRM captures identity-by-state, and the resulting estimates are dependent on which SNPs are used to calculate the GRM; if neither the causal SNP nor any SNPs in linkage disequilibrium (LD) with the causal SNP have been genotyped, the h 2 GRM estimate may be underestimated (Yang et al 2015 ). This can be avoided by using high density genotyping arrays or whole genome sequencing, however in some wild populations it has been shown that increasing the number of SNPs above a certain threshold does not affect GRM-based heritability estimates (Bérénos et al 2014 ; Perrier et al 2018 ; James et al 2022 ).…”