Polygenic Adaptation across Space

Rapid advances in sequencing are making an unprecedented amount of genomic data available from diverse organisms. However, theoretical and statistical frameworks for analysing sequences lag far behind, yet are crucial for identifying functionally important regions of the genome and predicting their contributions to key traits, e.g., disease risk in humans, yield in crops and environmental maladaptation in endangered populations. While numerous tools of genomic prediction, notably Genome-Wide Association Studies (GWAS), have been developed in recent years, a common and unsolved challenge is to disentangle the effects of different evolutionary processes on genomic variation. In particular, genetic differences between groups of individuals may reflect either natural selection (typically on highly polygenic traits) or spatial population structure (i.e., limited exchange of individuals and genes between geographically distant groups) or both. Moreover, both selection and population structure generate correlations along the genome, making it difficult to pinpoint individual genetic variants that affect traits. To address these challenges, we will develop new theory (based on mathematical analysis and computation) to understand polygenic adaptation across space, focusing on how sequence variation is shaped by: (i) the genetic architecture of selected traits i.e., the numbers, effects and genomic distribution of variants influencing a trait (Aim 1), and (ii) and the spatial distribution of populations (Aim 2). We will build upon this to investigate if/how selection and population structure can be disentangled in GWAS, and how selection affects the power of GWAS and the portability of GWAS findings across different populations (Aim 3). Such theory is essential and timely- both for understanding fundamental evolutionary questions as well as harnessing the full potential of genomics and GWAS for personalised medicine, agriculture and conservation.