Dominance on the Human Genome and Non-additive Polygenic Models for Predicting Complex Traits

点击翻译按钮获取中文摘要

Project Abstract Dominance is one of the most fundamental concepts in genetics and has many key implications in population genetics, as it ultimately determines how selection manifests in a population. However, despite its unarguable importance, dominance is also one of the least characterized quantities in genetics, especially in humans, with the major challenge being current methods cannot distinguish dominance from the fitness effect of genomic variants. This proposed K99/R00 work will systematically address this longstanding problem from a dual- perspectives, by inferring dominance in humans and quantitatively model its role in shaping the phenotypes of complex traits and diseases. Specifically, in Aim1, I will develop a powerful machine learning-based method to infer the realistic distribution of dominance on the human genome in megabase-scale, leveraging archaic introgressed ancestry in non-African populations that is sensitive to dominance variation in genomic regions. In Aim 2, I will develop non-additive polygenic models accounting for dominance in full genomic regions to identify complex traits profiled in UK Biobank that deviate from additive models, improve the accuracy of phenotype and disease risk predictions, and contribute to an in-depth understanding of complex trait biology. Finally, in Aim 3 (R00 phase), I will extend these approaches to infer dominance variation in worldwide populations and investigate how dominance, combined with selection and admixture, determines complex trait phenotypes in diverse human populations. The mentored phase of this work will take place at the Department of Ecology and Evolutionary Biology at UCLA, where Dr. Zhang will have access to rich training opportunities and be supported by active scientific communities, including numerous seminar series, journal clubs, and networking activities. Dr. Kirk Lohmueller (primary mentor) and Dr. Sriram Sankararaman (co-mentor) will train Dr. Zhang in computational and statistical methods in population genetics, machine learning applications, and large-scale disease association data analysis. The research trainings, collaborations, and professional development during the K99 phase will assist Dr. Zhang in becoming an independent investigator in human population genetics.