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）。我们今天只了解拷贝数变异（CNV）的最简单形式——简单的删除和重复。多等位基因拷贝数变异（multi-allelic copy-number variation, mCNV）是基因组结构变异的一种大的、功能上重要的但尚未被鉴定的形式，它涉及基因和其他功能元件，其中三个或更多的分离等位基因产生了每个二倍体人类基因组的大范围拷贝数（如2到10）。广泛使用的分析方法难以对mCNVs进行评估，并且无法在用于研究人类遗传复杂表型的基因组尺度分子或统计方法中进行评估。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，一种计算方法（基于现有全基因组序列数据的分析）和一种分子方法（基于数字计数微滴的PCR）来准确分析参考人群中的mCNVs（目标1）。通过在包含基因型、等位基因频率、遗传和单倍型信息的统计框架中分析这些数据，我们将把mCNV等位基因放入由HapMap和1000基因组创建的单倍型图谱中，并使mCNV能够最大程度地进行基因型插入（目标2）。我们将深入表征10个生物医学上重要的mcnv位点，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数据集的统计推算，对mCNVs进行廉价的全基因组关联的计算机试验研究（目标4）。这项工作的成功完成将有助于发现疾病风险与基因剂量之间的关系，有助于揭示人类疾病的分子病因学。

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. PUBLIC HEALTH RELEVANCE: Variation in the human genome influences risk of disease and can be used to find the genes underlying each disease, leading to new ideas for therapies. Many genes can exist in very different numbers of copies (such as 0 to 12) in different peoples' genomes; this form of variation is today not understood well. Our work will help to understand this form of genome variation and enable many human geneticists to find specific genes that relate to each disease.