The diversity of human populations has been shaped by past admixture and natural selection, influencing the frequency and distribution of genetic variants linked to traits and diseases. Understanding these processes is key to identifying novel genotype-phenotype associations and their relevance to modern health. Using UK Biobank data, we will develop and assess a streamlined and computationally efficient bioinformatics pipeline to investigate the impact of ancestry specific markers derived from ancient modern human populations (e.g. Hunter-gatherer-related, Farmer-related, etc) and archaic populations (Neanderthal, Denisovan, and unidentified lineages) on genetic variation and its association with specific phenotypes and selection signatures in modern populations. By comparing newly identified variants from our data resource covering underrepresented ancient and present-day African, Eurasian, and Asia-Pacific populations with those in the UK Biobank, we aim to uncover ancestry-specific genetic factors linked to specific diseases and traits. This cross-population analysis will clarify how genetic diversity has been shaped over time and reveal biologically significant variants that may contribute to disease risk. Through advanced computational methods, statistical modelling and evolutionary genomics, our research will provide deeper insights into human adaptation, the genetic architecture of health, and potential applications for precision medicine.
Specifically, we aim to address the following questions:
1) How have ancestry-specific genetic markers from ancient and archaic populations influenced the distribution of disease- and trait-associated variants in modern humans?
2) What are the signatures of recent natural selection on these ancestry-derived variants, and how do they affect health-related traits today?
3) Can integrating ancient and underrepresented population data improve detection and interpretation of genotype-phenotype associations?