Population genetics of deleterious polymorphism in human populations

描述(申请人提供)：尽管有许多单核苷酸多态(SNPs)与疾病的关联研究，但大多数常见疾病的遗传基础尚不清楚。一个可能的原因是，致病的SNPs在进化上是不利的，因此，大量个别罕见的突变会导致疾病。用目前的实验室和统计技术很难研究罕见的基因变异与常见疾病之间的关系。为了设计稀有变异的优化研究，需要额外的群体遗传模型来预测稀有和常见SNP之间的相关性或连锁不平衡(LD)。以前开发的有害变异的种群遗传模型不能令人满意地做到这一点。这项拟议研究的目标是开发和测试关于弱有害SNPs之间的LD程度的预测。新的模型将包括在多个地点的选择和现实的人口统计学模型，这两个模型都对预测LD模式至关重要。由于对人类人口历史的准确估计对于提供新的模型是重要的，该项目的第一个目标将开发从基因组数据估计人口参数的新方法。具体地说，有效利用全基因组重测序数据的马尔可夫链蒙特卡罗方法将允许从复杂的种群遗传模型中进行精确的参数估计。这种方法将应用于1000基因组计划产生的人类群体的数据。利用这些参数估计，第二个目标将开发现实的群体遗传模型，该模型可以预测弱有害等位基因周围的LD程度。这将使用简单人口模型的合并理论进行分析。更复杂的模型将需要模拟。第三个目标将使用来自第三个1000基因组试点项目的数据来经验性地评估不同人群中预测的有害等位基因周围的LD模式。这项工作将对评估和设计稀有变异和疾病风险的研究至关重要。癌症、糖尿病和心脏病等常见疾病给公共卫生带来了难以想象的负担。虽然这些疾病在一定程度上都是由遗传风险因素引起的，但在许多情况下，涉及的具体基因仍然难以捉摸。这项研究将提供关于人类进化史的新见解，这对于设计和解释遗传变异和常见疾病风险的研究至关重要。

DESCRIPTION (provided by applicant): Despite many association studies of single nucleotide polymorphisms (SNPs) and disease, the genetic basis of most common disease is poorly understood. One possible reason for this is that disease-causing SNPs are evolutionarily disadvantageous, and as a result, a large number of individually rare mutations cause disease. It is difficult to study the relationship between rare genetic variants and common disease using current laboratory and statistical techniques. In order to design optimal studies of rare variants, additional population genetic models, which provide predictions of the correlation, or linkage disequilibrium (LD), between rare and common SNPs are required. Previously developed population genetic models of deleterious variation do not satisfactorily do this. The goal of the proposed research is to develop and test predictions as to the extent of LD between weakly deleterious SNPs. The new models will incorporate selection at multiple sites and realistic models of human demography, both of which are critical for prediction of LD patterns. Since accurate estimates of human demographic history are important for informing the new models, the first Aim of the project will develop novel methods to estimate demographic parameters from genomic data. Specifically, a Markov Chain Monte Carlo approach that efficiently uses genome-wide resequencing data will allow precise parameter estimates from complex population genetic models. This method will be applied to data from human populations generated from the 1000 Genomes Project. Using these parameter estimates, the second Aim will develop realistic population genetic models that can predict the extent of LD around weakly deleterious alleles. This will be done analytically using coalescent theory for simple demographic models. More complex models will require simulation. The third Aim will use data from the third 1000 Genomes Pilot Project to empirically assess patterns of LD around predicted deleterious alleles in different human populations. This work will be critically important for evaluating and designing studies of rare variants and disease risk. Common diseases such as cancer, diabetes and heart disease represent an incredible public health burden. While these diseases are all caused, in part, by genetic risk factors, in many cases, the specific genes involved remain elusive. This research will provide novel insights regarding human evolutionary history which are critical to the design and interpretation studies of genetic variation and risk to common disease.