Imputation and Analysis of Rare Variants in Admixed Populations

描述(由申请人提供)： 项目简介：全基因组关联研究(GWAS)已经确定了广泛人类特征的4000个遗传位点，但仍有很大一部分遗传力无法解释。在后Gwas时代，遗传学家正在利用大规模并行测序技术来研究不太常见的(次要等位基因频率[MAF]0.5-5%)和稀有(MAF&lt；0.5%)变异，以下简称为稀有变异。与此同时，被认为在基因发现和精细定位方面潜在更强大的多民族GWA正受到基因学界越来越多的关注。在多种族人口中，非洲裔美国人和西班牙裔美国人等混合人口特别有吸引力，因为他们占美国人口的20%以上。这些混合群体为基因作图提供了独特的机会，因为人们可以利用混合连锁不平衡(LD)来寻找疾病的潜在基因，这些疾病在不同群体中的患病率存在显著差异。然而，对于能够适应GWA后数据的混合人口，方法学工作很少。方法论工作至少在三个主要领域滞后。首先，几乎没有(如果有的话)为混合样本量身定做的基因定位方法，能够适应不断增加的公共资源，以及研究样本中典型的基因分型和测序数据的混合。归罪将继续发挥重要作用，因为测序对于GWAS的大量样本收集来说，成本仍然是令人望而却步的。其次，关于混合种群中罕见的变异归因的实际问题，还没有发表过任何研究。第三，尽管最近有丰富的统计方法用于在相对同质的群体中进行稀有变异关联分析，但该领域需要能够有效地分析混合样本中的稀有变异的方法，特别是在输入或部分输入数据的情况下。在本申请中，我们提出了以下目的来填补上述空白：1)。开发高效的基于隐马尔可夫模型和奇异值分解的混合种群单倍型到单倍型推算方法；2)。评估稀有变异在混合种群中的质量并提供实用指南；3)开发一种稳健的统计测试，用于分析混合种群中的稀有变异；以及4)。为本项目中开发的方法开发、分发和支持免费提供的软件包。 公共卫生相关性： 与公共健康相关的全基因组关联研究已经确定了4000个人类特征的遗传位点，但仍保留了很大比例的遗传性 无法解释。在后Gwas时代，遗传学家正在利用大规模并行测序技术来研究不太常见的(次要等位基因频率[MAF]0.5-5%)和稀有(MAF&lt；0.5%)变异，以下简称为稀有变异。与此同时，被认为在基因发现和精细定位方面潜在更强大的多民族GWA正受到基因学界越来越多的关注。在多种族人口中，非洲裔美国人和西班牙裔美国人等混合人口特别有吸引力，因为他们占美国人口的20%以上。这些混合群体为基因作图提供了独特的机会，因为人们可以利用混合连锁不平衡(LD)来寻找疾病的潜在基因，这些疾病在不同群体中的患病率存在显著差异。然而，对于能够适应GWA后数据的混合人口，方法学工作很少。在这项应用中，我们将填补混合群体中稀有变异遗传分析的方法学和实践空白。

DESCRIPTION (provided by applicant): Project Description: Genomewide association studies (GWAS) have identified >4000 genetic loci for a wide range of human traits, but still leaving a large proportion of heritability unexplained. In the post-GWAS era, geneticists are exploiting massively parallel sequencing technologies to study less common (minor allele frequency [MAF] 0.5- 5%) and rare (MAF<0.5%) variants, hereafter together referred to as rare variants for brevity. In the meantime, multiethnic GWAS, recognized as potentially more powerful for gene discovery and fine mapping, are receiving increasing attention from the genetics community. Among the multiethnic populations, admixed populations such as African Americans and Hispanic Americans are particularly attractive because they comprise more than 20% of the US population. These admixed populations offer a unique opportunity for gene mapping because one can utilize admixture linkage disequilibrium (LD) to search for genes underlying diseases that differ strikingly in prevalences across populations. However, little methodological work exists for admixed populations that can accommodate post-GWAS data. The methodological work lags in at least three major areas. First, there are few, if any, genotype imputation methods that are tailored to admixed samples, can accommodate the ever increasing public resources, and the typical mixture of genotyping and sequencing data among the study samples. Imputation will continue to play an essential role as sequencing will remain cost prohibitive for large GWAS collections of samples. Second, there has been no published work on practical issues regarding rare variant imputation in admixed populations. Third, despite the recent rich literature of statistical methods for rare variant association analysis in relatively homogenous populations, the field needs methods that can efficiently analyze rare variants in admixed samples, particularly with imputed or partially imputed data. In this application, we propose the following aims to fill in the above gaps: 1). Develop efficient hidden Markov model and Singular Value Decomposition based methods for haplotype-to-haplotype imputation in admixed populations; 2). Assess quality of and provide practical guidelines on rare variants imputation in admixed populations; 3). Develop a robust statistical test for the analysis of rare variants in admixed populations; and 4). Develop, distribute and support freely available software packages for the methods developed in this project. PUBLIC HEALTH RELEVANCE: Public Health Relevance Genomewide association studies (GWAS) have identified >4000 genetic loci for a wide range of human traits, but still leaving a large proportion of heritability unexplained. In the post-GWAS era, geneticists are exploiting massively parallel sequencing technologies to study less common (minor allele frequency [MAF] 0.5- 5%) and rare (MAF<0.5%) variants, hereafter together referred to as rare variants for brevity. In the meantime, multiethnic GWAS, recognized as potentially more powerful for gene discovery and fine mapping, are receiving increasing attention from the genetics community. Among the multiethnic populations, admixed populations such as African Americans and Hispanic Americans are particularly attractive because they comprise more than 20% of the US population. These admixed populations offer a unique opportunity for gene mapping because one can utilize admixture linkage disequilibrium (LD) to search for genes underlying diseases that differ strikingly in prevalences across populations. However, little methodological work exists for admixed populations that can accommodate post-GWAS data. In this application, we will fill in methodological and practical gaps in the genetic analysis of rare variants in admixed populations