Abstract
The genetic component of complex disease risk in humans remains largely unexplained. A corollary is that the allelic spectrum of genetic variants contributing to complex disease risk is unknown. Theoretical models that relate population genetic processes to the maintenance of genetic variation for quantitative traits may suggest profitable avenues for future experimental design. Here we use forward simulation to model a genomic region evolving under a balance between recurrent deleterious mutation and Gaussian stabilizing selection. We consider three different genetic models, two different population growth models, and several different methods for identifying genomic regions harboring variants associated with complex disease risk. We demonstrate that the genetic model, relating genotype to phenotype, has a qualitative effect on the genetic architecture of a complex trait. In particular, the variance component partitioning across the allele frequency spectrum and the power of statistical tests is more affected by the assumed genetic model than by population growth. Models with incomplete recessivity most closely match the minor allele frequency distribution of significant hits from empirical genome-wide association studies. Such models show little dominance variance, which is consistent with recent empirical estimates of heritability explained by typed markers. We highlight a particular model of incomplete recessivity that is appealing from first principles. Under that model, deleterious mutations at the same gene partially fail to complement one another. Interestingly this gene-based model predicts considerable levels of unexplained variance associated with within locus epistasis. Our results suggest a need for improvement of statistical tools for region based genetic association and heritability estimation.
