Statistical methods for inferring recent human demographic and evolutionary history

 描述：推断最近人类人口统计和进化历史的统计方法。候选人：我的主要研究目标是研究人类群体内和群体间遗传变异的分布，塑造它们的进化力量，以及它们的功能和表型后果。我有一个很好的记录进行原创性和创造性的研究，证明了我出色的出版记录。在复旦大学攻读博士期间，我的研究兴趣主要集中在常见的遗传变异，包括人类拷贝数变异的突变热点研究。在华盛顿大学（UW）博士后培训的前三年，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，最近才出现，并且富含有害等位基因;而个体携带的大多数变异是常见的，影响很弱。培训：我组建了一支出色的指导团队，包括约书亚·阿基博士、布莱恩·布朗宁博士和莎朗·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拥有深厚的背景。许多培训资源可在UW，包括科学讲座，多学科课程，和广泛的职业发展计划。我将与我的导师团队密切合作，并参加这些活动，以扩大我的科学知识，加强我的独立研究能力，并磨练在口头陈述，赠款写作，学术求职和实验室管理的职业发展技能。研究人员：下一代测序研究提供了全面研究罕见变异的能力，为推断人类近代历史提供了一个潜在的途径，这在以前是不可能的。描述个体间的血统认同（IBD）模式是群体遗传学推断的一个强有力的框架，尽管需要根据测序数据的独特特征开发新的方法。在这个建议中，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，我将开发一种统计算法来准确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6，515名美国人的精细规模人口结构模式（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（目标2）]，并通过检测不同模型下最近或正在进行的适应的特征，包括生物途径或网络上的多基因选择（目标3），研究人口统计学历史和自然选择如何塑造遗传变异的模式。这些方法将应用于来自约8，000个地理上不同的个体的测序数据。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R 01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。

 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.