Imputation and Analysis of Rare Variants in Admixed Populations

描述（由申请人提供）： 项目描述：全基因组关联研究（GWAS）已经为广泛的人类性状确定了>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平行测序技术来研究不太常见（次要等位基因频率[MAF] 0.5- 5%）和罕见（MAF<0.5%）的变体，以下简称为罕见变体。与此同时，多种族GWAS被认为在基因发现和精细定位方面可能更强大，正受到遗传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。这些混合人群提供了一个独特的机会，基因定位，因为人们可以利用混合连锁不平衡（LD），以寻找潜在的疾病，在人群中的患病率显着不同的基因。然而，几乎没有方法学工作存在的混合人口，可以容纳后GWAS数据。方法工作至少在三个主要领域滞后。首先，有几个，如果有的话，基因型插补方法，是专门为混合样本，可以适应不断增加的公共资源，以及研究样本中的基因分型和测序数据的典型混合物。插补将继续发挥重要作用，因为测序对于大型GWAS样本收集仍然成本高昂。第二，还没有发表的工作在混合人群中的罕见变异插补的实际问题。第三，尽管最近有丰富的文献在相对同质的人群中进行罕见变异关联分析的统计方法，但该领域需要能够有效分析混合样本中罕见变异的方法，特别是使用插补或部分插补数据的方法。在本申请中，我们提出以下目的来填补上述空白：1）。发展基于隐马尔可夫模型和奇异值分解的混合群体单倍型间插补方法; 2）.评估混合人群中罕见变异插补的质量并提供实用指南; 3）。开发一种稳健的统计检验，用于分析混合群体中的罕见变异;以及4）。开发、分发和支持本项目中开发的方法的免费软件包。

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.