Statistical methods for identifying pleiotropy between complex human traits

识别复杂人类特征之间多效性的统计方法

• 批准号: 10646535
• 负责人: Debashree Ray
• 金额: $ 24.56万
• 依托单位: JOHNS HOPKINS UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-06-23 至 2025-05-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10646535
• 关键词: Address; Adopted; Area; Autoimmune; Benchmarking; Biological; Biology; Calibration; Case/Control Studies; Collaborations; Communities; Complex; Computer software; Coronary Arteriosclerosis; Craniofacial Abnormalities; Crohn' s disease; Data; Data Set; Detection; Development; Diagnosis; Disease; Ensure; Etiology; Evaluation; Family; Frequencies; Gene Frequency; Genetic; Genetic Predisposition to Disease; Genetic Research; Genetic Variation; Genomics; Genotype; Health; Human; Human Genome; Individual; Intervention; Investigation; Knowledge; Male Pattern Baldness; Malignant Neoplasms; Malignant neoplasm of prostate; Mathematics; Measures; Methodology; Methods; Modeling; Molecular; Molecular Target; Non-Insulin-Dependent Diabetes Mellitus; Normalcy; Parents; Parkinson Disease; Pattern; Phenotype; Population; Public Health Applications Research; Research; Research Design; Research Personnel; Resource-limited setting; Risk; Risk Factors; Sample Size; Sampling; Signal Transduction; Span 60; Statistical Distributions; Statistical Methods; Testing; Therapeutic; Tonsillectomy; Ulcerative Colitis; Variant; case control; design; drug development; early onset; endophenotype; genetic architecture; genetic testing; genetic variant; genome wide association study; genome-wide; human disease; human subject protection; innovation; large scale simulation; novel; open source; pleiotropism; rare variant; side effect; statistics; theories; tool; trait; translational impact; user friendly software; user-friendly

PROJECT SUMMARY Years of genetic research on various complex human traits have implicated several genetic variants as risk factors for two or more diseases/traits, including seemingly unrelated traits. A recent systematic evaluation of >500 traits from >4100 genome-wide association studies (GWAS) has revealed that 90% of the variants associated with these traits influence at least two traits. This phenomenon where a genetic region or locus confers risk to more than one trait is known as pleiotropy. Discovering patterns of pleiotropy is crucial for a comprehensive understanding of biological mechanisms of human diseases and traits (e.g. understand how genetic variation leads to trait variation and inter-trait correlations, and how traits may be causally related to each other), and can have translational impact in the long run (e.g. guide identification of molecular targets or help predict side-effects in drug development). While the scientific significance of studying pleiotropy or genetic overlap is well-understood, statistical methods for identifying common genetic basis between traits are still lacking. This is especially true for traits sampled under a family-based design. To address methodological challenges in investigating pleiotropy, in Aim 1, we propose a novel, innovative statistical method for identifying common genetic variants influencing two possibly correlated traits. In Aim 2, we non-trivially extend our method in Aim 1 to identify rare genetic variants influencing two independent traits. We use only aggregate-level genotype-phenotype association results (or GWAS summary statistics), thus helping protect human subjects data and facilitating global collaborations. The proposed methods, while having the potential to substantially outperform current approaches in the area, will be more general and distinct from existing research efforts in the following ways: applicability to correlated traits (Aim 1; e.g. a disease-related endophenotype and a molecular trait, or two different -omics traits), to traits measured on independent sets of individuals (Aims 1, 2; e.g. separate case-control studies on two diseases), to traits sharing some samples (Aim 1; e.g. case-control studies with shared controls), to study designs where individuals may not be randomly sampled or unrelated (Aims 1, 2; e.g. case-parent trio design), to rare variants (Aim 2), and our open-access tools will allow genomics researchers across the world to readily adopt and apply our methods even in resource- poor environments (Aims 1, 2). Successful implementation of these aims will provide the broader scientific community with novel, powerful and scalable methods, along with well-documented free software, to statistically investigate questions of pleiotropy between complex human diseases/ traits across the entire allele frequency spectrum using GWAS summary statistics only.

项目概要 多年对各种复杂人类特征的基因研究表明，一些遗传因素与 变异作为两种或多种疾病/性状的危险因素，包括看似不相关的性状。一个 最近对超过 4100 个全基因组关联研究 (GWAS) 中的超过 500 个性状进行了系统评估 研究表明，与这些性状相关的 90% 的变异至少影响两个性状。 这种遗传区域或基因座赋予多个性状风险的现象被称为 多效性。发现多效性模式对于全面理解 人类疾病和性状的生物学机制（例如了解遗传变异如何导致 性状变异和性状间相关性，以及性状之间如何因果关系）， 从长远来看可以产生转化影响（例如指导分子靶标的识别或 帮助预测药物开发中的副作用）。虽然研究的科学意义 多效性或遗传重叠是众所周知的，用于识别常见基因的统计方法 性状之间的遗传基础仍然缺乏。对于在以下条件下采样的性状尤其如此 基于家庭的设计。为了解决研究多效性的方法学挑战，在目标 1 中， 我们提出了一种新颖的、创新的统计方法来识别常见的遗传变异 影响两个可能相关的特征。在目标 2 中，我们将目标 1 中的方法扩展为 识别影响两个独立性状的罕见遗传变异。我们只使用聚合级别 基因型-表型关联结果（或 GWAS 摘要统计），从而帮助保护 人类受试者数据并促进全球合作。所提出的方法，同时具有 显着优于该领域当前方法的潜力将更加普遍和 与现有的研究工作在以下方面有所不同： 对相关性状的适用性（目标 1;例如与疾病相关的内表型和分子特征，或两个不同的组学特征）， 对独立个体进行测量的特征（目标 1、2；例如单独的病例对照研究 两种疾病），共享一些样本的特征（目标 1；例如，具有共享样本的病例对照研究） 控制），研究个体可能不是随机抽样或不相关的设计（目标 1, 2;例如案例父三重奏设计），到罕见的变体（目标 2），我们的开放获取工具将允许 世界各地的基因组学研究人员即使在资源匮乏的领域也能轻松采用和应用我们的方法 恶劣的环境（目标 1、2）。这些目标的成功实施将提供更广泛的 科学界提供新颖、强大且可扩展的方法，以及有据可查的免费方法 软件，统计研究复杂人类疾病/之间的多效性问题 仅使用 GWAS 摘要统计来分析整个等位基因频谱的特征。