Abstract
How variants with different frequencies contribute to trait variation is a central question in genetics. We use a unique model system to disentangle the contributions of common and rare variants to quantitative traits. We generated ~14,000 progeny from crosses among 16 diverse yeast strains and identified thousands of quantitative trait loci (QTLs) for 38 traits. We combined our results with sequencing data for 1011 yeast isolates to show that rare variants make a disproportionate contribution to trait variation. Evolutionary analyses revealed that this contribution is driven by rare variants that arose recently, and that negative selection has shaped the relationship between variant frequency and effect size. We leveraged the structure of the crosses to resolve hundreds of QTLs to single genes. These results refine our understanding of trait variation at the population level and suggest that studies of rare variants are a fertile ground for discovery of genetic effects.
Highlights
A detailed understanding of the sources of heritable variation is a central goal of modern genetics
To investigate the genetic basis of quantitative traits in the yeast population, we selected 16 highly diverse S. cerevisiae strains that capture much of the known genetic diversity of this species
They contain both alleles at 82% of biallelic SNPs and small indels observed at minor allele frequency >5% in a collection of 1011 S. cerevisiae strains (Peter et al, 2018)
Summary
A detailed understanding of the sources of heritable variation is a central goal of modern genetics. One key question is the relative contribution of DNA sequence variants with different allele frequencies in a population to trait variation. GWAS by design only test common DNA sequence variants; recent studies underscore the likely importance of the contribution of rare variants to heritable variation (Wainschtein et al, 2019). Purifying selection against variants that negatively affect fitness is expected to keep them at low frequencies in a population, resulting in a predicted inverse relationship between effect sizes and allele frequencies for variants that influence fitness-related traits (Gibson, 2012; Goldstein et al, 2013; Kryukov et al, 2007; Pritchard, 2001)
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