Genome-wide association studies (GWAS) allows to dissect the genetic basis of complex traits at the population level. However, despite the extensive number of trait-associated loci found, they often fail to explain a large part of the observed phenotypic variance. One potential source of this discrepancy could be the preponderance of undetected low-frequency genetic variants in natural populations. To increase the allele frequency of those variants and assess their phenotypic effects at the population level, we generated a diallel panel consisting of 3,025 hybrids, derived from pairwise crosses between a subset of natural isolates from a completely sequenced 1,011 Saccharomyces cerevisiae population. We examined each hybrid across a large number of growth traits, resulting in a total of 148,225 cross/trait combinations. Parental versus hybrid regression analysis showed that while most phenotypic variance is explained by additivity, a significant proportion (29%) is governed by non-additive effects. This is confirmed by the fact that a majority of complete dominance is observed in 25% of the traits. By performing GWAS on the diallel panel, we detected 1,723 significantly associated genetic variants, with 16.3% of them being low-frequency variants in the initial population. These variants, which would not be detected using classical GWAS, explain 21% of the phenotypic variance on average. Altogether, our results demonstrate that low-frequency variants should be accounted for as they contribute to a large part of the phenotypic variation observed in a population.

Extensive impact of low-frequency variants on the phenotypic landscape at population-scale