Accurate and robust inference of mutational bias across complex traits and diseases

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PROJECT SUMMARY/ABSTRACT A long-standing question in human genetics is why common diseases continue to persist in the population despite potential fitness consequences. Naively, we would expect selection to remove disease from the population, suggesting that it must be maintained by a countervailing force. A natural null hypothesis is that disease is maintained in the population at least partially by a mutational bias towards the disease state. To date, the degree of mutational across complex diseases remains unexplored. However, over the past decade and a half genome-wide association studies (GWAS) have identified genetic loci associated with complex diseases providing insight into the underlying genetic architecture of complex diseases. With the development of the right tools, this large influx of GWAS data will allow us to measure mutational bias across traits. Here I propose to develop methods that accurately and robustly infer mutational bias and apply them to GWAS datasets. Population genetic theory indicates that the average frequency of causal risk alleles is a sensitive measure of mutational bias. In Aim 1, I will first demonstrate that calculating the average frequency of associated risk alleles, as identified in GWAS, is also an accurate measure of mutational bias. I will then conduct a broad screen for evidence of mutational bias across the 7,221 phenotypes available in the Pan-UK Biobank. In Aim 2, I will first demonstrate that population stratification in GWAS summary statistics can potentially mimic signals of mutational bias and then develop a method to correct for the effects of stratification using the evolutionary status of an allele as an instrumental variable. Finally, in Aim 3 I will extend cutting-edge LD based statistical methods for estimating and partitioning heritability to provide the first genome-wide estimates of the degree of mutational bias across complex traits and within functional genomic categories. Together my results will provide insight into the evolutionary forces driving complex trait evolution and generate novel methods with application beyond my direct research question. A better understanding of the evolutionary mechanisms driving disease etiology will not only help answer the question of disease persistence but also uncover biological processes driving disease susceptibility.