Signatures of negative selection in the genetic architecture of human complex traits

Abstract

We develop a Bayesian mixed linear model that simultaneously estimates single-nucleotide polymorphism (SNP)-based heritability, polygenicity (proportion of SNPs with nonzero effects), and the relationship between SNP effect size and minor allele frequency for complex traits in conventionally unrelated individuals using genome-wide SNP data. We apply the method to 28 complex traits in the UK Biobank data (N = 126,752) and show that on average, 6% of SNPs have nonzero effects, which in total explain 22% of phenotypic variance. We detect significant (P < 0.05/28) signatures of natural selection in the genetic architecture of 23 traits, including reproductive, cardiovascular, and anthropometric traits, as well as educational attainment. The significant estimates of the relationship between effect size and minor allele frequency in complex traits are consistent with a model of negative (or purifying) selection, as confirmed by forward simulation. We conclude that negative selection acts pervasively on the genetic variants associated with human complex traits.

Assessing the potential for assisted gene flow using past introduction of Norway spruce in southern Sweden: Local adaptation and genetic basis of quantitative traits in trees

“…By tightly controlling environmental variance, they are ideal systems for investigations on the genetic basis of phenotypic traits. Re‐sequencing technologies being continuously developed and becoming more affordable, it will soon be possible to sequence a number of individuals large enough to apply statistical methods currently limited to humans and a handful of model species, allowing investigating, for instance, the strength and direction of selection acting on a trait of interest (Guo, Yang, & Visscher, ; Zeng et al, and references therein). Finally, our study showed that comparing traits that followed different geographic gradients could help to better comprehend and address the confounding effect of population structure on GWAS.…”

Beyond SNP Heritability: Polygenicity and Discoverability of Phenotypes Estimated with a Univariate Gaussian Mixture Model

“…Consistent with the work of others [11,15], we assume the causal SNPs are distributed randomly throughout the genome (an assumption that can be relaxed when explicitly considering different SNP categories, but that in the main is consistent with the additive variation explained by a given part of the genome being proportional to the length of DNA [22]). In a Bayesian approach, we assume that the parameter β for a SNP has a distribution (in that specific sense, this is similar to a random effects model), representing subjective information on β, not a distribution across tangible populations [23].…”

“…The effects of population structure [10], combined with high polygenicity and linkage disequilibrium (LD), leading to spurious degrees of SNP association, or inflation, considerably complicate matters, and are also areas of much focus [11,12,13]. Despite these challenges, there have been recent significant advances in the development of mathematical models of polygenic architecture based on GWAS [14,15]. One of the advantages of these models is that they can be used for power estimation in human phenotypes, enabling prediction of the capabilities of future GWAS.…”

An evolutionary compass for detecting signals of polygenic selection and mutational bias

“…; Zeng et al. ; Simons et al. ), and have not incorporated mutational bias (i.e., the propensity for new mutations to be preferentially trait‐increasing or preferentially trait‐decreasing).…”

“…While these studies have argued that polygenic selection is likely to be an important determinant of variation in traits such as height and skin pigmentation (Berg and Coop 2014), important questions remain about the evolutionary mechanisms that drive complex trait variation. In particular, most previous studies of selection on human complex traits have focused either on polygenic adaptation (Turchin et al 2012;Berg and Coop 2014;Field et al 2016;Berg et al 2017;Edge and Coop 2018;Racimo et al 2018) or stabilizing/negative selection Zeng et al 2018;Simons et al 2018), and have not incorporated mutational bias (i.e., the propensity for new mutations to be preferentially trait-increasing or preferentially trait-decreasing). To detect polygenic adaptation, studies have relied on genotype data from multiple populations to probe the frequency and linkage properties of trait-associated alleles as compared to a null based on genome-wide single nucleotide polymorphisms (Turchin et al 2012;Berg and Coop 2014;Racimo et al 2018), or have used haplotype-based statistics and dense sequence data from a single population (Field et al 2016).…”