Abstract
A fundamental assumption in quantitative genetics is that traits are controlled by many loci of small effect. Using genomic data, this assumption can be tested using chromosome partitioning analyses, where the proportion of genetic variance for a trait explained by each chromosome (h2c), is regressed on its size. However, as h2c‐estimates are necessarily positive (censoring) and the variance increases with chromosome size (heteroscedasticity), two fundamental assumptions of ordinary least squares (OLS) regression are violated. Using simulated and empirical data we demonstrate that these violations lead to incorrect inference of genetic architecture. The degree of bias depends mainly on the number of chromosomes and their size distribution and is therefore specific to the species; using published data across many different species we estimate that not accounting for this effect overall resulted in 28% false positives. We introduce a new and computationally efficient resampling method that corrects for inflation caused by heteroscedasticity and censoring and that works under a large range of dataset sizes and genetic architectures in empirical datasets. Our new method substantially improves the robustness of inferences from chromosome partitioning analyses.
Highlights
Chromosome partitioning analyses, where the proportion of genetic variance for a trait explained by each chromosome (h2c) is regressed on its size, is a common way to test for a polygenic basis of traits
Using simulated and empirical data we demonstrate that these violations lead to incorrect inference of genetic architecture that depends on the number and size distribution of chromosomes, with 28% of published results being false positives
Using simulated data we show that heteroscedasticity in combination with the fact that h2c-estimates are constrained to be positive leads to considerable P value inflation in chromosome partitioning analyses that use ordinary least squares (OLS) regressions between h2c and chromosome size, something that can result in misleading inferences about the genetic architecture of traits
Summary
Chromosome partitioning analyses, where the proportion of genetic variance for a trait explained by each chromosome (h2c) is regressed on its size, is a common way to test for a polygenic basis of traits. SNPs that reach statistical significance at a genomewide level typically only account for a small amount of the total narrow-sense heritability (h2) This has fueled many discussions of “missing” or “hidden” heritability in GWAS studies (Manolio et al 2009; Eichler et al 2010; Yang et al 2013). Evolution Letters published by Wiley Periodicals, Inc. on behalf of Society for the Study of Evolution (SSE) and European Society for Evolutionary Biology (ESEB)
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