Human genetics research has made enormous progress in identifying the genetic basis of complex traits and disease. An emerging consensus is that, since the effect of any individual variant on disease risk is typically small, we need to take a ?polygenic? approach to interpreting this information. That is, we need to consider the cumulative effect of many variants in order to make useful prediction about individual disease risk, and to make robust inference about evolution. The biggest limitation of current data is that most genomic studies have been carried out in populations of European ancestry and their results may not be applicable in non-European ancestry populations. This limits the utility of genomic medicine and risks exacerbating existing global and national health disparities. Ideally, we would carry out large-scale genomic studies in diverse populations across the world. However this is technically and economically difficult and, in some cases, impossible. Instead, we aim to use the information that has already been collected from studies of European-ancestry populations, and develop statistical and population genetic methods that allow us to use this information in populations of non-European ancestry. We will infer ancestry-specific effects and apply those to make prediction in populations of admixed ancestry. Using local ancestry inference we will test for differences in trait architecture and genetic effects between populations. In parallel, we aim to understand the biological and evolutionary basis for differences in genetic effects and complex trait distributions between populations. Such differences can occur because of differences in population structure or because of differences in demographic history and selective pressures.
We aim to use both simulations and data to understand the interaction between these forces and quantify their effect on complex traits and genetic effects. By tracking complex trait evolution using ancient DNA, we add a temporal dimension to our analysis, allowing us to identify changes over time in the genetic variation underlying complex traits. As well as providing a direct window into human history and evolution, this approach also helps to explain differences in complex traits and disease risk among present-day human populations. In summary, this research program will extend our knowledge of the genetic basis of human complex traits. It will increase our understanding of human history and evolution and help to explain present- day trait distributions, to interpret the results of genome-wide association studies and to allow polygenic risk prediction to be used in non-European ancestry populations.
Information about the genetic basis of common traits is largely restricted to populations of European ancestry. We will develop statistical and population genetic methods to translate this information across populations so that we can make polygenic risk predictions in non-European ancestry populations. By combining information from genome-wide association studies and ancient DNA, we will track the evolution of complex traits over time to understand the evolutionary basis of present-day human phenotypic variation.
