View Program - 2022 Population, Evolutionary, and Quantitative Genetics Conference

Understanding the genetic basis of complex phenotypes through genome-wide association (GWA) studies is a central pursuit of human medical genetics. GWA methods are widely and successfully used, but they face challenges. Many of these challenges are related to the fact that variants are tested for association with a phenotype independently, whereas in reality variants at different sites are correlated because of their shared evolutionary history. One way to explicitly model this shared history is through the ancestral recombination graph (ARG), which can be thought of as a series of local coalescent trees. Recent computational and methodological breakthroughs have made it feasible to estimate accurate ARGs from biobank samples. Here, we explore the potential of a generalized ARG-based approach to association mapping. In terms of a local tree in the ARG, standard GWAS can be understood as testing a branch on which a typed mutation occurred for association with the phenotype. We propose a framework in which an arbitrary number of branches can be tested for association simultaneously. Standard GWAS, Identity-By-Descent Mapping, and some methods for local heritability estimation can be viewed as special cases under our generalized framework. Our methods may be especially beneficial for finding associations for phenotypes with multiple causal alleles in the same locus (allelic heterogeneity), and the ARG may also efficiently capture untyped variation. Another potential benefit of our framework is that it can reduce the number of tests performed compared with GWAS, alleviating the multiple testing burden. We illustrate the methods using simulations under realistic demographies and further explore how other tasks related to association mapping, such as fine mapping, can be viewed in terms of the ARG. We expect that by framing association mapping in terms of the ARG, we can increase statistical power to detect associations, and that our investigations may provide intuition about the benefits of using the ARG in population genomic methods in general.

ARG-based Association Mapping