Statistical methods for inferring recent human demographic and evolutionary history. CANDIDATE: My primary research goal is to study the distribution of genetic variation within and between human populations, the evolutionary forces that have shaped them, and their functional and phenotypic consequences. I have a strong track record of performing original and creative research, as demonstrated by my outstanding publication record. During my doctoral training at Fudan University, my research interests focused on common genetic variation, including the study of mutational hotspots of copy number variation in humans. During the first three years of postdoctoral training at the University of Washington (UW), I played a leading role in the NHLBI Exome Sequencing Project and showed that the majority of protein-coding variants in human populations are rare, arose recently, and enriched for deleterious alleles; while most variants carried by individuals are common and with weak effects. TRAINING: I have assembled an exceptional mentoring team, including Dr. Joshua Akey, Dr. Brian Browning, and Dr. Sharon Browning who collectively have strong backgrounds in human genetics, genomics, and statistical genetics. Numerous training resources are available at UW, including scientific lectures, multidisciplinary courses, and extensive career development programs. I will work closely with my mentoring team and participate in these activities to extend my scientific knowledge, strengthen my independent research abilities, and hone career development skills in oral presentation, grant writing, academic job searches, and lab management. RESEARCH: Next-generation sequencing studies provide the ability to comprehensively study rare variation, providing a potential avenue to infer recent human history that was not previously possible. Delineating patterns of Identity-by-Descent (IBD) among individuals is a powerful framework for population genetics inference, although new methods need to be developed tailored to the unique characteristics of sequencing data. In this proposal, I will establish a framework based on IBD to make inferences of recent population history. Briefly, during the mentored period, I will develop a statistical algorithm to accurately detect IB in exome data, and develop network-based approaches to characterize IBD graphs. These methods will be applied to assess patterns of fine-scale population structure in 6,515 U.S. individuals (Aim 1). During the independent period, [I will investigate how demographic history and natural selection shape patterns of genetic variation by comparing the distribution of deleterious variation located within and outside of IBD regions (Aim 2)], and by detecting signatures of recent or ongoing adaptation under different models, including polygenic selection on biological pathways or networks (Aim 3). These methods will be applied to sequencing data from ~8,000 geographically diverse individuals. The proposed framework is intended to lay the groundwork for an independent research program with the potential to generate new research for R01 proposals, including novel IBD based statistics for mapping rare variants that contribute to disease.
Large-scale sequencing has shown that most human variation is rare and arose very recently. These data sets provide an extraordinary opportunity to learn about recent human history. The goal of this proposal is to develop powerful and innovative statistical methods to make inferences about rare variation and recent human history, and will have important implications for disease gene mapping.
| Fu, Wenqing; Ligabue, Alessio; Rogers, Kai J et al. (2017) Human RECQ Helicase Pathogenic Variants, Population Variation and ""Missing"" Diseases. Hum Mutat 38:193-203 |
| Fu, Wenqing; Browning, Sharon R; Browning, Brian L et al. (2016) Robust Inference of Identity by Descent from Exome-Sequencing Data. Am J Hum Genet 99:1106-1116 |
