Complex genetic models for simple traits and simple models for complex traits

We attempt to combine statistical genetics with evolutionary and functional genomics to understand the relationship between genotype and phenotype. The effect of even simple Mendelian mutations may depend on genetic background and environment. For example, an appreciable fraction of disease-causing alleles are fixed in the genomes of other species, suggesting a role for genomic context. We developed a model of genetic interactions that predicts most of these to be simple pairwise compensations. Functional testing of this model on known and one newly discovered human disease genes revealed discrete cis amino acid residues that, although benign on their own, could rescue the human mutations in vivo. For genetically complex phenotypes, the degree of polygenicity, the distribution of effect sizes and the underlying biology all remain unknown. With the help of evolutionary models, we demonstrate that not all plausible genetic architectures are compatible with the observed phenotypic distributions. We use genetics of gene expression and other endophenotypes to reveal biology behind genetic association signals.