Abstract
Genetic mapping studies of quantitative traits typically focus on detecting loci that contribute additively to trait variation. Genetic interactions are often proposed as a contributing factor to trait variation, but the relative contribution of interactions to trait variation is a subject of debate. Here we use a very large cross between two yeast strains to accurately estimate the fraction of phenotypic variance due to pairwise QTL–QTL interactions for 20 quantitative traits. We find that this fraction is 9% on average, substantially less than the contribution of additive QTL (43%). Statistically significant QTL–QTL pairs typically have small individual effect sizes, but collectively explain 40% of the pairwise interaction variance. We show that pairwise interaction variance is largely explained by pairs of loci at least one of which has a significant additive effect. These results refine our understanding of the genetic architecture of quantitative traits and help guide future mapping studies.
Highlights
Genetic mapping studies of quantitative traits typically focus on detecting loci that contribute additively to trait variation
We find that the contribution of quantitative trait loci (QTL)–QTL interactions to phenotypic variance is typically less than a quarter of the contribution of additive effects
We used simulations (Methods section) to demonstrate that the model can accurately estimate the contributions of additive QTL and QTL–QTL interactions to trait variation over an extensive range of genetic architectures (Supplementary Fig. 2 and Supplementary Data 1)
Summary
Genetic mapping studies of quantitative traits typically focus on detecting loci that contribute additively to trait variation. We previously generated a panel of 1,008 recombinant offspring (‘segregants’) from a cross between two strains of yeast: a widely used laboratory strain (BY) and an isolate from a vineyard (RM)[6] Using this panel, we estimated the contribution of additive genetic factors to phenotypic variation (narrow-sense or additive heritability) for 46 traits and resolved most of this contribution (on average 87%) to specific genome-wide significant quantitative trait loci (QTL). With 1,008 segregants, we were able to detect only a small number of significant QTL–QTL interactions that, in aggregate, explained little of the estimated interaction variance We address these limitations by studying an expanded panel of 4,390 segregants obtained from the same cross. These results provide a picture of the genetic contributions to quantitative traits at an unprecedented resolution
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