Multi-allelic copy number variation of the human genome

描述（由申请人提供）：人类基因组表现出广泛的拷贝数变异（CNV）。我们今天只知道拷贝数变异（CNV）的最简单形式-简单的缺失和重复。基因组结构变异的一种大型、功能重要且尚未表征的形式是多等位基因拷贝数变异（mCNV），涉及基因和其他功能元件，其中三个或更多个分离的等位基因产生广泛的拷贝数（例如2至10）每个二倍体人类基因组。mCNV对广泛使用的分析方法是难治的，并且在用于研究人类遗传复杂表型的基因组规模的分子或统计方法中未进行评估。在这项工作中，我们将开发方法和支持数据集，使mCNVs能够被常规和严格地分析与人类表型变异的关系。我们将使用两种新方法准确分析参考人群中的mCNVs，一种是计算方法（基于对可用全基因组序列数据的分析），另一种是分子方法（基于数字计数微滴中的PCR），用于准确分析队列中的mCNVs（目标1）。通过在一个统计框架中分析这些数据，该框架整合了有关基因型、等位基因频率、遗传和单倍型的信息，我们将把mCNV等位基因置于HapMap和1000 Genomes创建的单倍型图谱上，并使mCNV尽可能地可用于基因型插补（目的2）。我们将深入描述10个生物医学重要位点的mCNVs，以了解这些多态性在群体遗传学水平，突变率和历史，以及与临床表型的关系（目标3）。最后，我们将基于对现有GWAS数据集的统计插补，对mCNVs进行廉价的计算机全基因组关联研究（目标4）。这项工作的成功完成将导致发现疾病风险和基因剂量之间的关系，有助于揭示人类疾病的分子病因。

DESCRIPTION (provided by applicant): The human genome exhibits extensive copy number variation (CNV). We today understand only the simplest form of copy number variation (CNV) - simple deletions and duplications. A large, functionally important and still-uncharacterized form of genome structural variation is multi-allelic copy-number variation (mCNV), involving genes and other functional elements for which three or more segregating alleles give rise to a wide range of copy numbers (such as 2 to 10) per diploid human genome. mCNVs have been refractory to widely used analysis methods and are not assessed in the genome-scale molecular or statistical approaches used to study genetically complex phenotypes in humans. In this work, we will develop approaches and supporting data sets that enable mCNVs to be routinely and rigorously analyzed for relationship to variation in human phenotypes. We will accurately analyze mCNVs in reference populations, using two new approaches, one computational (based on analysis of available whole-genome sequence data) and one molecular (based on PCR in digitally counted microdroplets) for accurately analyzing mCNVs in cohorts (Aim 1). By analyzing these data in a statistical framework that incorporates information about genotypes, allele frequencies, inheritance, and haplotypes, we will place mCNV alleles onto the haplotype maps created by HapMap and 1000 Genomes, and render mCNVs accessible to genotype imputation to the fullest extent possible (Aim 2). We will deeply characterize mCNVs at ten biomedically important loci, to understand these polymorphisms at the levels of population genetics, mutational rates and histories, and relationships to clinical phenotypes (Aim 3). Finally, we will pilot inexpensive in silico genome-wide association studies for mCNVs based on statistical imputation into existing GWAS data sets (Aim 4). The successful completion of this work will lead to the discovery of relationships between disease risk and gene dosage, helping to reveal the molecular etiology of human disease.