Structurally complex genome loci in human populations and human phenotypes

人群和人类表型中结构复杂的基因组位点

基本信息

批准号：
10211665
负责人：
Steven Andrew McCarroll
金额：
$ 72.25万
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 2026-05-31
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10211665
关键词：
Alleles Biological Blood Code Collection Companions Complex Computer software Copy Number Polymorphism DNA DNA Sequence Data Data Analyses Data Set Disease Exons Gene Conversion Gene Dosage Gene Frequency Gene Proteins Genes Genetic Polymorphism Genetic Variation Genetic study Genome Genotype Goals Haplotypes Homologous Gene Human Human Biology Human Genetics Human Genome Maps Methods Minisatellite Repeats Mutation Nucleotides Pattern Phase Phenotype Population Property Regulatory Element Research Risk SNP array Scientist Series Shapes Sjogren&apos s Syndrome Structure Tertiary Protein Structure Testing Variant Work base biobank disorder risk exome genetic analysis genetic approach genetic association genome analysis genome wide association study homologous recombination innovation novel phenotypic data protein structure trait whole genome

项目摘要

SUMMARY/ABSTRACT Structurally complex loci (SCLs) are hotspots of genome dynamism whose relationship to human phenotypic variation is unknown. SCLs have multiple segments of duplicated DNA sequence which can contain or flank genes, exons, or regulatory elements; these repeated sequences recombine with one another to generate new alleles by non-allelic homologous recombination and gene conversion, creating many functionally distinct alleles with different gene dosages and/or protein structures. Human genetics does not yet know the alleles that are present at most SCLs, nor their relationship to human phenotypic variation. Genetic variation at SCLs tends to arise from many alleles, to be hard to assemble, and to have complex relationships to nearby SNPs and SNP haplotypes. Yet SCLs provide a real opportunity to relate phenotypes to allelic series of functional alleles with interpretable effects on gene dosage or protein domain structure. In this work, we will develop ways to ascertain how SCLs at loci across the genome are comprised of allelic series and relate to a diverse set of human phenotypes. To do this, we will combine data from many forms of genome analysis – definitive long-read data (n ~102 and growing), whole-genome and whole-exome sequence data (104-105) and SNP array data (105-107) with companion phenotype data. In Aim 1, we will develop methods to reveal the full spectrum of variation at SCLs. We will (a) identify variable DNA features and the ways in which these features vary and co-distribute across thousands of people of diverse ancestries, and (b) find the underlying alleles and allele frequencies that explain this population-scale variation. In Aim 2, we will enable powerful genotype-phenotype analyses that leverage vast existing SNP data sets; we will do this by creating large panels of reference haplotypes of SCL alleles and surrounding SNPs, and advancing methods for imputing SCL alleles into SNP data. In Aim 3, we will advance approaches for genetic association analysis and fine-mapping at SCLs, and explore the functional consequences of SCLs on quantitative traits and disease risk. We aspire to make and enable many more discoveries about how allelic series at structurally complex loci shape human phenotypes.

摘要/摘要结构复杂的局部（SCL）是基因组动态的热点，其与人类表型的关系变异是未知的。 SCL具有多个重复的DNA序列的段，可以包含或侧翼基因，外显子或调节元素；这些重复的序列相互重组以生成新的非平行性同源重组和基因转换的等位基因，创造了许多功能上不同的具有不同基因剂量和/或蛋白质结构的等位基因。人类遗传学尚不了解大多数SCL中存在的等位基因，也不知道它们与人类的关系表型变异。 SCL的遗传变异往往源于许多等位基因，很难组装，并且与附近的SNP和SNP单倍型有复杂的关系。但是SCL为相关提供了真正的机会对等位基因等位基因等位基因等位基因的表型，对基因剂量或蛋白质结构域具有可解释的作用结构。在这项工作中，我们将开发方法来确定Allic跨基因组的SCLS如何包括系列，与潜水员的一组人类表型有关。为此，我们将结合多种形式的数据基因组分析 - 确定的长阅读数据（N〜102及增长），全基因组和全身序列带有伴随表型数据的数据（104-105）和SNP阵列数据（105-107）。在AIM 1中，我们将开发方法来揭示SCLS的全部变化范围。我们将（a）确定变量 DNA功能以及这些功能在成千上万的人中变化和共同分布的方式潜水员的祖先，（b）找到解释这种种群规模的基本等位基因和等位基因频率变化。在AIM 2中，我们将启用强大的基因型 - 表型分析，以利用大量现有SNP数据集；将通过创建SCL等位基因和周围SNP的参考单倍型的大型参考单倍型来做到这一点，将SCL等位基因归为SNP数据的方法。在AIM 3中，我们将推进SCLS遗传关联分析和精细映射的方法，并探索 SCLS对定量性状和疾病风险的功能后果。我们渴望创造并启动更多有关在结构复杂基因座形状的等位基因系列的发现人类表型。