Association mapping reveals the role of purifying selection in the maintenance of genomic variation in gene expression

Abstract

The evolutionary forces that maintain genetic variation in quantitative traits within populations remain poorly understood. One hypothesis suggests that variation is under purifying selection, resulting in an excess of low-frequency variants and a negative correlation between minor allele frequency and selection coefficients. Here, we test these predictions using the genetic loci associated with total expression variation (eQTLs) and allele-specific expression variation (aseQTLs) mapped within a single population of the plant Capsella grandiflora. In addition to finding eQTLs and aseQTLs for a large fraction of genes, we show that alleles at these loci are rarer than expected and exhibit a negative correlation between phenotypic effect size and frequency. Overall, our results show that the distribution of frequencies and effect sizes of the loci responsible for local expression variation within a single outcrossing population are consistent with the effects of purifying selection.gene expression | population genomics | association mapping G enetic variation for quantitative traits persists within populations despite the expectation that prevalent stabilizing selection will reduce genetic variance (1). One hypothesis suggests that variation is under purifying selection, resulting in an excess of low-frequency variants and a negative correlation between minor allele frequency and selection coefficients (2). Although studies of allele frequency spectra show that purifying selection on functional DNA sequences is prevalent (3-5), little is known about how the genetic variants under selection relate to phenotype, and ultimately, how phenotypic variation is maintained within populations. Association mapping can identify specific loci influencing phenotypes, providing candidates for further analysis of selection (6). In particular, mapping the local regulatory variants that affect gene expression can identify a large number of genetic loci that affect a phenotype. Additionally, mapping the genetic basis of gene expression may answer questions about the basic biology of gene regulation, for example, by testing predictions that conserved noncoding sequences ("CNSs") are constrained because they have regulatory function (7).Early eQTL studies mapped expression divergence between two lines, finding that many genes have local expression QTL (8, 9). These studies have provided insight into selection on eQTLs; for example, a correlation between recombination rate and eQTL density implied that background selection is a dominant force acting on expression variation in Caenorhabditis elegans (10), and a skew toward rare allele frequencies in promoters of genes with eQTLs suggests that purifying selection may act on expression variation (11). However, eQTL studies of populationlevel genetic variation have thus far been limited to a few study systems (12-16) and only one study, in humans, has identified a negative correlation between phenotypic effect size and frequency (15). In addition, human eQTL studies have shown that loci ex...

Standing genetic variation in a tissue-specific enhancer underlies selfing-syndrome evolution in Capsella

“…The population genetic analysis was performed on a dataset including 180 resequenced C. grandiflora individuals from a single population (36), a species-wide sample of 13 C. grandiflora individuals (30), and a C. rubella species-wide sample of 73 individuals. The latter include the sequencing data for 51 C. rubella individuals, which were downloaded from the European Nucleotide Archive (www.ebi.ac.…”

What can genome‐wide association studies tell us about the evolutionary forces maintaining genetic variation for quantitative traits?

“…Data from Josephs et al . (). (c) A classic example of local adaptation, human lactase persistence, is largely controlled by variation at one locus: rs4988235.…”

Quantifying the regulatory effect size of cis-acting genetic variation using allelic fold change

“…Linear regression has also been utilized on log-transformed (Flutre et al 2013;Battle et al 2014Battle et al , 2015 or z-scored expression data ) to derive slope estimates that do not depend on expression levels. Other statistics include the observed difference between genotype classes, such as the mean difference in expression between heterozygous and the more common homozygote class, sometimes with log transformation or scaling by mean (Gutierrez-Arcelus et al 2015;Josephs et al 2015). The proportion of expression variance in the population explained by an eQTL is a widely used statistic that is informative of population variance but not of the molecular effect of an eQTL (Grundberg et al 2012;Wright et al 2014;Kirsten et al 2015).…”