Statistical Methods for Finding Missing Heritability

寻找缺失遗传力的统计方法

• 批准号: 8212023
• 负责人: MOMIAO XIONG
• 金额: $ 36.15万
• 依托单位: UNIVERSITY OF TEXAS HLTH SCI CTR HOUSTON
• 依托单位国家: 美国
• 财政年份: 2011
• 资助国家: 美国
• 起止时间: 2011-01-10 至 2014-12-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8212023
• 关键词: Accounting; Alleles; Cardiovascular system; Complex; Computer software; Data; Data Set; Diagnostic; Dimensions; Disease; Environmental Risk Factor; Foundations; Framingham Heart Study; Funding; Genes; Genetic; Genetic Variation; Genome; Goals; Grant; Heritability; Intervention; Link; Measures; Methods; Modeling; National Heart, Lung, and Blood Institute; Pathway Analysis; Phenotype; Predisposition; Process; Psoriasis; Rheumatoid Arthritis; Risk; Source; Statistical Methods; Techniques; Testing; United States National Institutes of Health; Variant; base; data reduction; disorder risk; gene environment interaction; gene interaction; genetic analysis; genetic association; genetic variant; genome sequencing; genome wide association study; next generation; novel; public health relevance; simulation; statistics; tool development

DESCRIPTION (provided by applicant): Genome-wide association studies (GWAS) have become the primary approach for dissecting the genetic basis of complex diseases and are a powerful approach for detecting common alleles that influence disease risk. To date, hundreds of putative disease gene loci have been identified in GWAS. Despite this progress, these newly discovered loci typically account for only a small fraction of disease heritability. This raises new questions about where and how we can find the remaining genetic variation contributing to the susceptibility of complex and common diseases. Potential sources of missing heritability are (1) the contribution of rare variants, (2) gene-gene and gene-environment interaction, (3) combination of multiple SNPs, each with small genetic effect, but collectively conferring large risk, (4) structural variation. Current statistical methods for genetic analysis are well suited for detecting common variants, but new models and methods of analysis are needed for revealing the sources of missing disease heritability. To this end, the goals of this proposal are to develop novel and powerful statistical methods for studying rare variants and gene-gene interactions in the context of next-generation sequencing and GWAS data. Specifically, the methods we will develop will provide a unified analytical framework for testing associations with both common and rare alleles as well as their interaction with genetic and environmental factors. We will also develop graphical models and other statistical methods for co-association and interaction network analysis. The power of these methods will be rigorously analyzed by theoretical and simulation approaches, and will be applied to existing GWAS data sets (psoriasis and rheumatoid arthritis) and next generation sequencing data of extreme cardiovascular phenotypes funded by NIH grant 1RC2 HL02419-01. PUBLIC HEALTH RELEVANCE: This project aims to develop novel and powerful statistical methods for genetic association and interaction analysis of next-generation sequencing data and finding missing heritability unexplained by the current GWAS. Application of these methods to the sequence data will facilitate to identify entire spectrum of genetic variations that influence diseases and provide potential valuable tools for the development of diagnostic and interventional strategies.

描述（由申请人提供）：全基因组关联研究（GWAS）已成为剖析复杂疾病遗传基础的主要方法，并且是检测影响疾病风险的常见等位基因的有力方法。迄今为止，已经在GWAS中鉴定了数百个推定的疾病基因位点。尽管取得了这些进展，但这些新发现的基因座通常只占疾病遗传性的一小部分。这就提出了新的问题，即我们在哪里以及如何找到导致复杂和常见疾病易感性的剩余遗传变异。缺失遗传力的潜在来源是（1）罕见变异的贡献，（2）基因-基因和基因-环境相互作用，（3）多个SNP的组合，每个SNP具有较小的遗传效应，但共同赋予较大的风险，（4）结构变异。目前遗传分析的统计方法非常适合检测常见变异，但需要新的模型和分析方法来揭示缺失疾病遗传力的来源。为此，该提案的目标是开发新的和强大的统计方法，用于在下一代测序和GWAS数据的背景下研究罕见变异和基因-基因相互作用。具体来说，我们将开发的方法将提供一个统一的分析框架，用于测试与常见和罕见等位基因的关联，以及它们与遗传和环境因素的相互作用。我们还将开发图形模型和其他统计方法，用于联合和交互网络分析。这些方法的能力将通过理论和模拟方法进行严格分析，并将应用于现有的GWAS数据集（银屑病和类风湿性关节炎）和由NIH资助1 RC 2 HL 02419 -01资助的极端心血管表型的下一代测序数据。 公共卫生相关性：该项目旨在开发新的和强大的统计方法，用于下一代测序数据的遗传关联和相互作用分析，并发现当前GWAS无法解释的缺失遗传力。将这些方法应用于序列数据将有助于识别影响疾病的整个遗传变异谱，并为诊断和干预策略的发展提供潜在的有价值的工具。