Methods for leveraging family-based designs and summary data to elucidate complex trait genetics

利用基于家族的设计和汇总数据来阐明复杂性状遗传学的方法

• 批准号: 10713748
• 负责人: Debashree Ray
• 金额: $ 40.94万
• 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: 2023-08-15 至 2028-05-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10713748
• 关键词: Address; Admixture; Adult; Attention; Biology; Child Health; Complex; Complex Genetic Trait; Data; Data Science; Databases; Disease; Ethics; Explosion; Family; Gene Frequency; Genes; Genetic; Genetic study; Genomics; Genotype; Health; Human; Hybrids; Individual; Investigation; Literature; Low Prevalence; Mathematics; Mentors; Methodology; Methods; Molecular; Outcome; Parents; Participant; Play; Policy Making; Population Study; Positioning Attribute; Public Health Applications Research; Public Health Schools; Race; Rare Diseases; Reproducibility; Research; Research Personnel; Resource-limited setting; Role; Sample Size; Statistical Methods; Student recruitment; Training; biobank; causal variant; design; ethnic diversity; experience; gene environment interaction; gene interaction; genetic epidemiology; genome wide association study; graduate student; improved; innovation; multi-ethnic; multidisciplinary; novel; population based; population stratification; programs; rare variant; skills; statistics; tool; trait

ABSTRACT To better understand genetic basis of complex human traits, two fundamentally different and complementary designs employed in genome-wide association studies (GWAS) are population-based and family-based designs. With the advent of biobanks and large-scale biomedical databases, recent years have seen an explosion in genetic studies of adult traits/diseases, and consequently, a rapid advancement in methodology for population- based designs that these biobanks depend on. In contrast, methods for family-based designs have received little to no attention although they play an important role in the investigation of genetic basis of low-prevalence/rare disorders and of child health outcomes. Analysis methods based on family-based designs can protect against population stratification and admixture (thus allowing for racial/ethnic diversity among participants), and can be more powerful than a population-based study of similar sample size. Another consequence of large-scale biobanks is the public availability of aggregate-level genotype-trait association results (or GWAS summary statistics) for a wide spectrum of complex human traits, including molecular traits that are intermediate between genotype and a disease-related trait. Methods that can leverage GWAS summary statistics to understand biology underlying diseases are in high demand since they are nearly as efficient and avoid logistical/ethical concerns related to sharing individual-level data. In this application, I propose a research program of developing novel statistical methods and open-access tools for genetic epidemiology studies, with a particular focus on family-based designs. Some of these methods/tools will leverage only association summary statistics to innovatively integrate omics with disease data, thereby helping improve understanding of regulatory mechanisms underlying human health. We seek to address some of the open problems of human trait genetics, including methodological challenges in identifying non-additive genetic effects (e.g. gene-gene interaction, gene-environment interaction, parent-of-origin effect), effects of rare variants, and in prioritizing causal variants through integrative omics. We will bring obscure mathematical functions from statistical literature to real public health applications while illustrating them on existing databases. This research program will support diversity in three distinct ways: methodological advancement of family-based designs that overcome challenges related to racial/ethnic diversity in participants; efficient methods/tools that allow genomic researchers to conduct genetic epidemiology studies using publicly available summary data even in resource-poor environments; and help train diverse graduate students recruited annually by the Johns Hopkins School of Public Health. In the last 5 years, I have built a research profile in family-based genetic studies alongside population- based ones, have developed cutting-edge methods based on summary-level data, have enabled data-driven policy-making via reproducible data science methods/tools, and have acquired mentoring skills. My multi- disciplinary training and my prior experience puts me in a unique position to successfully complete this program.

抽象的 为了更好地理解复杂人类特征的遗传基础，两种根本不同且互补的特征 全基因组关联研究（GWAS）中采用的设计是基于人群和基于家庭的设计。 随着生物样本库和大型生物医学数据库的出现，近年来，生物样本量呈爆炸式增长。 成人特征/疾病的遗传学研究，以及因此，人口方法学的快速进步 这些生物库所依赖的基于设计。相比之下，基于家庭的设计方法却很少受到关注。 尽管它们在低流行/罕见疾病的遗传基础研究中发挥着重要作用，但没有引起人们的注意 疾病和儿童健康结果。基于系列设计的分析方法可以防止 人口分层和混合（从而允许参与者之间的种族/民族多样性），并且可以 比类似样本量的基于人群的研究更有力。大规模的另一个后果 生物样本库是聚合水平基因型-性状关联结果（或 GWAS 总结）的公开可用性 统计）用于广泛的复杂人类特征，包括介于两者之间的分子特征 基因型和疾病相关特征。可以利用 GWAS 摘要统计来理解生物学的方法 潜在疾病的需求量很大，因为它们几乎同样有效并且避免了后勤/伦理问题 与共享个人层面的数据有关。在本申请中，我提出了一个开发研究计划 用于遗传流行病学研究的新颖统计方法和开放获取工具，特别是 专注于基于家庭的设计。其中一些方法/工具将仅利用关联摘要 统计数据以创新方式将组学与疾病数据相结合，从而帮助提高对疾病的理解 人类健康的调控机制。我们寻求解决人类的一些开放性问题 性状遗传学，包括识别非加性遗传效应（例如基因-基因）的方法学挑战 相互作用、基因-环境相互作用、亲本效应）、罕见变异的影响以及优先顺序 通过综合组学研究因果变异。我们将从统计文献中引入晦涩的数学函数 真实的公共卫生应用程序，同时在现有数据库上进行说明。该研究计划将 以三种不同的方式支持多样性： 基于家庭的设计的方法论进步，克服了 与参与者的种族/民族多样性相关的挑战；使基因组研究人员能够使用的有效方法/工具 即使在资源匮乏的情况下，也可以使用公开的汇总数据进行遗传流行病学研究 环境；并帮助培训约翰霍普金斯大学公立学院每年招收的多元化研究生 健康。在过去的 5 年里，我在基于家庭的遗传学研究以及人口研究方面建立了研究概况 开发了基于摘要级数据的尖端方法，实现了数据驱动 通过可重复的数据科学方法/工具制定政策，并获得指导技能。我的多 纪律培训和我之前的经验使我处于一个独特的位置来成功完成这个项目。