Multi-allelic copy number variation of the human genome

人类基因组的多等位基因拷贝数变异

基本信息

批准号：
8532954
负责人：
Steven Andrew McCarroll
金额：
$ 47.75万
依托单位：
HARVARD MEDICAL SCHOOL
依托单位国家：
美国
项目类别：
财政年份：
2012
资助国家：
美国
起止时间：
2012-08-18 至 2016-05-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/8532954
关键词：
Affect Alleles Autoimmune Diseases Base Pairing Biological Assay Clinical Complex Computer Simulation Computing Methodologies Copy Number Polymorphism Data Data Analyses Data Quality Data Set Development Diploidy Disease Disease Association Dose Elements Etiology Exhibits Frequencies Gene Dosage Gene Frequency Genes Genetic Genetic Polymorphism Genome Genotype Goals Haplotypes Hematological Disease Heritability Human Human Genome Individual Lead Maps Mental disorders Methods Molecular Mutation Noise Phenotype Population Population Genetics Property Reading Recording of previous events Refractory Resources Sampling Signal Transduction Statistical Methods Structure Technology Variant Work base clinical phenotype cohort disorder risk genetic analysis genome sequencing genome wide association study human disease molecular scale nanolitre novel novel strategies tool

项目摘要

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.

描述（由申请人提供）：人类基因组表现出广泛的拷贝数变化（CNV）。今天，我们仅了解最简单的拷贝数变化（CNV） - 简单的删除和重复。基因组结构变异的大型，功能上重要且仍在纯净的形式是多相关的拷贝数变化（MCNV），涉及基因和其他功能元素，每个二氯化人体基因组的基因和其他三个或多个隔离等位基因都会产生大量的拷贝数（例如2至10）。 MCNV已对广泛使用的分析方法难治性，在基因组规模的分子或统计方法中未评估，用于研究人类的遗传复杂表型。在这项工作中，我们将开发方法和支持数据集，使MCNV常规，严格地分析与人类表型变化的关系。我们将使用两种新方法，一种计算（基于可用全基因组序列数据的分析）和一个分子（基于数字计数的微芯片中的PCR）准确地分析参考群体中的MCNV，以准确分析同源物中的MCNV（AIM 1）。通过在统计框架中分析这些数据，该数据结合了有关基因型，等位基因频率，遗传和单倍型的信息，我们将将MCNV等位基因放置在HAPMAP和1000个基因组创建的单倍型图上，并使MCNV可用于基因型符合基因型，以使基因型完全插入最大程度的程度（AIM 2）。我们将在十个生物医学重要的基因座上深入了解MCNV，以了解这些多态性，以种群遗传学，突变率和历史以及与临床表型的关系的水平（AIM 3）。最后，我们将基于统计归因于现有的GWAS数据集（AIM 4）的统计归因于MCNV的硅全基因组关联研究（AIM 4）。这项工作的成功完成将导致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。