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.

项目摘要 多年对各种复杂人类特征的遗传研究已牵涉到几种遗传 变体是两种或多种疾病/特征的风险因素，包括看似无关的特征。一个 最近对> 4100个全基因组关联研究（GWAS）的500个特征的系统评估（GWAS） 已经揭示了与这些特征相关的90％的变体至少影响两个特征。 遗传区域或基因座的这种现象赋予了多个特征的风险 多效性。发现多效性的模式对于全面理解 人类疾病和特征的生物学机制（例如，了解遗传变异如何引导 特征变化和特征相关性，以及特征如何彼此之间的因果关系）， 从长远来看可以产生翻译影响（例如，指导分子靶标或 有助于预测药物开发中的副作用）。而研究的科学意义 多效性或遗传重叠是识别常见的统计方法的良好理解 仍缺乏特征之间的遗传基础。对于在A下采样的特征尤其如此 基于家庭的设计。在AIM 1中，解决多效性的方法论挑战， 我们提出了一种新颖的创新统计方法，用于识别常见的遗传变异 影响两个可能相关的特征。在AIM 2中，我们非试图将我们的方法扩展到AIM 1中 确定影响两个独立特征的稀有遗传变异。我们仅使用聚合级别 基因型 - 表型关联结果（或GWAS摘要统计），因此有助于保护 人类受试者数据并促进全球合作。提出的方法，同时 在该地区实质上胜过当前方法的潜力将更加一般，并且 与现有研究工作不同的方式不同：适用于相关特征（目标 1;例如与疾病相关的内表型和一个分子性状，或两个不同的特征） 在独立的个体集中测量的特征（目标1，2；例如单独的病例对照研究 关于两种疾病），要共享一些样本的特征（AIM 1；例如，病例对照研究，共享 对照），研究可能不会随机采样或无关的个人的设计（目的 1，2;例如案例三重奏设计），对于罕见的变体（AIM 2），我们的开放访问工具将允许 世界各地的基因组学研究人员即使在资源中也很容易采用和应用我们的方法 环境差（目标1、2）。这些目标的成功实施将为广泛提供 具有新颖，有力和可扩展方法的科学界以及有据可查的免费 软件，以统计研究复杂人类疾病之间的多效性问题/ 仅使用GWAS摘要统计数据，整个等位基因频谱的特征。