Rapid Evolution of Genomic Architecture and Multi-omics Traits

摘要

Abstract The evolution of phenotypic traits is important both for our understanding of evolutionary theory, but also for genetic epidemiology and statistical genetics. Through this proposal, I will use large scale sequencing and multi-omics profiling to test the rapidness of trait evolution. To test this hypothesis, I will advance our understanding of rare variation and mutation, fine-scale population structure, and multi-omics traits and disease. Current Projects 1) Native American evolution and health. In a collaboration I started with the Peruvian National Institute of Health, we have sequence 150 predominantly Native American ancestry individuals from Peru, recently published in PNAS and now are evaluating the global evolutionary dynamics of the Fatty Acid Desaturase (FADS) gene cluster, which is critical to poly-unsaturated fatty acid regulation. 2) Rare variants in TOPMed. Within the Trans-Omics for Precision Medicine (TOPMed) project, I developed a new means of evaluating different annotation categories of rare variation between closely related cohorts. I find that functional variation (e.g. non-sense) are also more susceptible to population structure. 3) Mutation by ancestry. In two projects, I test for differences in mutational patterns by ancestry. In the first, I demonstrate that cancer cell lines have differences in somatic mutation rates by ancestry. In the second, I show that Amish individuals have on average 3 less de novo mutations than non-Founder Europeans. Future Projects 1) Rare variants and study design. Expanding from our analysis in current project 3, we will extend this methodology to compare variation not by categories, but for some continuous values for in silico predictors of deleteriousness and for a wider range of methodologies. 2) Rare variant IBD. We will develop a new method to identify small segments that are identical-by- descent (IBD) by leveraging rare variation. This will be critical in how we model the genomic relationship matrix for association models. 3) Mutation rate variation by ancestry. Building from current project 3, we will use the de novo mutation counts we identify in trios across TOPMed as a phenotypic outcome for a genome-wide association analysis. Preliminary findings show some promising results that we will follow-up using molecular assays in yeast. 4) Evolutionary systems biology of rapidly changing traits. Using this program, we will develop an Approximate Bayesian Computation (ABC) framework to identify complex systems biology models of disease traits mediated by molecular phenotypes.