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Leveraging human evolutionary history to improve our understanding of complex disease architecture

利用人类进化史来提高我们对复杂疾病结构的理解

基本信息

批准号：
10752744
负责人：
Syed Arslan Abbas Zaidi
金额：
$ 24.9万
依托单位：
UNIVERSITY OF MINNESOTA
依托单位国家：
美国
项目类别：
财政年份：
2021
资助国家：
美国
起止时间：
2021-08-01 至 2026-01-31
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10752744
关键词：
Address Admixture Advisory Committees African American population Aging Apoptosis Architecture Asian population Award Cell Respiration Communities Complex Computer Simulation Consanguinity DNA Data Demographic Impact Demography Developing Countries Diagnosis Disease Electronic Health Record Ethnic Population Ethnography Genes Genetic Genetic Epistasis Genetic Research Genetic Risk Genetic Structures Genetic study Genome Goals Haplogroup Heart Diseases Human Individual Learning Skill Medicine Metabolism Methods Mission Mitochondria Mitochondrial DNA Modeling National Institute of General Medical Sciences Non-Insulin-Dependent Diabetes Mellitus Nuclear Pakistan Pattern Pennsylvania Performance Persons Phenotype Play Population Population Heterogeneity Premature Birth Prevention Recording of previous events Research Residual state Resources Risk Role Scientist Shapes South Asian Statistical Bias Statistical Methods Structure Testing Training Uncertainty United States National Institutes of Health Universities Variant Veterans biobank causal variant clinical application computerized tools disorder risk ethnic diversity genetic architecture genetic predictors genetic risk factor genome wide association study human disease improved mortality novel strategies phenome polygenic risk score precision medicine programs rare variant risk prediction simulation success trait

项目摘要

Title: Leveraging human evolutionary history to improve our fundamental understanding of complex disease architecture Abstract: The overarching goal of this research is to improve the applicability of genetic risk predictions within and across human populations by leveraging recent advances in our understanding of human evolutionary history. In Aim 1, I will carry out empirically-guided simulations to investigate how fine-scale substructure in large genome-wide association studies (GWAS; e.g. UK Biobank) biases our inference of complex trait architecture and polygenic risk score prediction. I will leverage these findings to develop statistical and computational tools to correct for such biases. In Aim 2, I will investigate whether recent admixture in humans generates incompatibilities between mitochondrial and nuclear DNA in African Americans. To test this, I will analyze genetic and electronic health record data from the ethnically diverse Penn Medicine Biobank to test whether mito-nuclear discordance—degree of ancestry divergence between mitochondrial and nuclear genomes—is associated with the risk of diseases common among African Americans. Additionally, I will test for selection against mito-nuclear incompatibilities in recently admixed populations. In Aim 3, I will investigate how the practice of endogamy and consanguinity among Pakistanis shapes their disease risk architecture. I will further evaluate the ability and limitations of currently used GWAS methods, which are typically modeled after outbred populations, to infer disease architecture given the complex population structure in Pakistanis. I will improve upon these methods, thereby making GWAS more widely applicable to a diverse set of people. Each of my three aims is independent, yet together they will lead to improvements in diagnosis, treatment, and prevention of human diseases—the overarching mission of the NIGMS. I will learn the skills needed to accomplish these aims with the help of my advisory committee, comprising of Drs. Iain Mathieson, Sarah Tishkoff, Doug Wallace, and Marilyn Ritchie, who are world-class leaders in genetics research. With the training plan that I have outlined and the resources at the University of Pennsylvania, I am confident that the K99 award will help me achieve my goal of becoming an independent scientist in the field of statistical genetics.

题目：利用人类进化史来提高我们的基本认识