Statistical methods for inferring recent human demographic and evolutionary history

 描述：推断现代人类人口和进化史的统计方法。候选人：我的主要研究目标是研究遗传变异在人类种群内部和之间的分布，塑造它们的进化力量，以及它们的功能和表型后果。我在进行原创和创造性研究方面有很好的记录，我出色的出版记录证明了这一点。在复旦大学接受博士培训期间，我的研究兴趣集中在常见的基因变异上，包括研究人类拷贝数变异的突变热点。在华盛顿大学的头三年博士后培训期间，我在NHLBI外显子组测序项目中发挥了主导作用，并表明人类群体中的大多数蛋白质编码变异是罕见的、最近出现的，并富含有害的等位基因；而大多数个人携带的变异是常见的，影响很小。培训：我组建了一支杰出的指导团队，其中包括约书亚·阿基博士、布莱恩·布朗宁博士和莎伦·布朗宁博士，他们在人类遗传学、基因组学和统计遗传学方面都有很强的背景。威斯康星大学提供大量的培训资源，包括科学讲座、多学科课程和广泛的职业发展计划。我将与我的指导团队密切合作，参与这些活动，以扩大我的科学知识，加强我的独立研究能力，并在口头陈述、撰写补助金、学术求职和实验室管理方面磨练职业发展技能。研究：下一代测序研究提供了全面研究罕见变异的能力，提供了一种潜在的途径来推断以前不可能的近代人类历史。描绘个体之间的逐种身份(IBD)模式是群体遗传学推断的一个强大框架，尽管需要开发新的方法来适应测序数据的独特特征。在这项提案中，我将建立一个基于IBD的框架，以推断最近的人口历史。简而言之，在指导期间，我将开发一个统计算法来准确地检测外显子组数据中的IB，并开发基于网络的方法来表征IBD图。这些方法将用于评估6515名美国人的精细种群结构模式(目标1)。在独立期间，[我将通过比较IBD区域内外有害变异的分布情况[我将调查人口历史和自然选择如何塑造遗传变异模式(目标2)]，并通过检测不同模式下最近或正在进行的适应的特征，包括生物路径或网络上的多基因选择(目标3)。这些方法将应用于对来自不同地理位置的约8,000人的数据进行排序。拟议的框架旨在为一个独立的研究计划奠定基础，该计划有可能为R01提案产生新的研究，包括基于IBD的新统计数据，用于绘制导致疾病的罕见变异图。

 DESCRIPTION: 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.