Methods for Genetic Association Analysis of Longitudinal and Multiple Phenotypes

纵向和多重表型的遗传关联分析方法

• 批准号: 8898910
• 负责人: RANY SALEM
• 金额: $ 13.2万
• 依托单位: BROAD INSTITUTE, INC.
• 依托单位国家: 美国
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-09-01 至 2016-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8898910
• 关键词: Accounting; Age; Architecture; Area; Behavioral; Biological; Biology; Blood Pressure; Body fat; Candidate Disease Gene; Cardiovascular Diseases; Cholesterol; Clinical; Complex; DNA Sequence; Data; Data Set; Dependence; Development; Diabetes Mellitus; Diagnosis; Disease; Environmental Risk Factor; Epidemiologic Methods; Face; Factor Analysis; Foundations; Future; Genes; Genetic; Genetic Polymorphism; Genetic Research; Genetic study; Genotype; Goals; Grant; Hand; Height; Heterogeneity; Human Biology; Human Genetics; Hypertension; Individual; Justice; Lead; Life Cycle Stages; Light; Lipids; Measurement; Measures; Medical; Mentors; Metabolic Diseases; Metabolic syndrome; Methodology; Methods; Modeling; Obesity; Outcome; Pathogenesis; Phenotype; Polygenic Traits; Procedures; Public Health; Quality Control; Renal function; Research; Research Design; Research Personnel; Resources; Sample Size; Sampling; Scientist; Staging; Statistical Methods; Structure; Technology; Testing; Time; Training; Triglycerides; Work; base; career; case control; cohort; cost effective; database of Genotypes and Phenotypes; experience; follow-up; genetic association; genetic variant; genome wide association study; genome-wide; human disease; improved; insight; longitudinal analysis; method development; novel; pleiotropism; simulation; skills; statistics; trait

DESCRIPTION (provided by applicant): Dr. Salem's ultimate career goal is to be a successful and independent human genetics researcher, applying statistical and epidemiological methods to understand the genetic architecture of complex traits and disease. During the time period of his K99 grant, Dr. Salem will acquire the requisite skills in advanced statistical methodology and phenotype harmonization to achieve this goal through a combination of formal course work, attendance of seminars, mentoring and hands on research experience. Biomedical researchers face many challenges when dissecting the genetic basis of complex traits and diseases of medical and public health importance such as diabetes, hypertension, and cardiovascular disease. These traits and diseases are notoriously difficult to study as they are influenced by the interplay of multiple genetic, environmental, and behavioral factors. GWAS have identified thousands of common polymorphisms contributing to many complex traits and disease. These studies have tended to focus on a single phenotype at a single time point. The complexity of polygenic traits may not admit to such simple characterizations. Use of more informative phenotypes, study designs and analyses, has the potential to shed light on new biology. One way to improve the yield of association studies both in terms of novel loci and biological insights is for researchers to consider more elaborate analyses. Hypotheses that leverage more sophisticated approaches may yield new discoveries. For example, the traditional use of a phenotypic measurement at a single point at a specific time in current case-control candidate gene and GWA studies does not do justice to the likely age or time dependence and/or general developmental pathogenesis of most biomedical traits and diseases, nor does it illuminate the potential shared genetics between phenotypes. This project aims to develop a resource of ~145,000 subjects from dbGaP and evaluate the contribution of genes on temporal changes and the interplay between traits via statistical methods development and application. This project will be guided by an important public health and clinical problem, metabolic disease. Through his prior research experience and training, Dr. Salem has acquired a strong foundation in statistics and genetics. He now seeks further training in the advanced statistical methodologies and phenotype characterization required to fully understand complex traits. He will be mentored by Dr. Joel Hirschhorn, a leading investigator in the genetics of obesity and height, and will be co-mentored by leaders in the area of statistics and phenotype harmonization. The research proposed in this K99 application has broad implications for understanding complex traits and disease, particularly metabolic syndrome. It also has the potential to significantly understanding of and impact the diagnosis and treat of metabolic syndrome. Dr. Salem is confident that completion of the work and training plan will prepare him for a successful career as an independent investigator in human genetics.

描述（由申请人提供）：塞勒姆博士的最终职业目标是成为一名成功且独立的人类遗传学研究人员，采用统计和流行病学方法来了解复杂性状和疾病的遗传结构。在他的K99赠款期间，塞勒姆博士将通过正式的课程工作，研讨会的出席，指导和研究研究经验的结合来获得先进统计方法和表型协调的必要技能，以实现这一目标。生物医学研究人员在剖析复杂性状和医学和公共健康重要性的遗传基础时面临许多挑战，例如糖尿病，高血压和心血管疾病。众所周知，这些特征和疾病很难研究 多种遗传，环境和行为因素的相互作用。 GWAS已经确定了成千上万的共同多态性，导致许多复杂的特征和疾病。这些研究倾向于在一个时间点关注单个表型。多基因性状的复杂性可能不承认如此简单的特征。使用更有信息的表型，研究设计和分析有可能阐明新生物学。从新的基因座和生物学见解方面提高关联研究产量的一种方法 是让研究人员考虑更多详细的分析。利用更复杂的方法的假设可能会产生新的发现。例如，在当前病例对照候选基因和GWA研究中，在特定时间在单个点进行表型测量的传统使用并不能使大多数生物医学特征和疾病的可能的年龄或时间依赖性和/或一般发育发病机理，也不会阐明现象型之间潜在的共享遗传学之间的潜在共享遗传学。该项目旨在从DBGAP开发约145,000名受试者的资源，并通过统计方法的开发和应用来评估基因对时间变化以及性状之间的相互作用的贡献。该项目将以重要的公共卫生和临床问题（代谢疾病）为指导。通过他先前的研究经验和培训，塞勒姆博士在统计和遗传学方面取得了坚实的基础。现在，他寻求进一步的培训，以完全理解复杂性状所需的先进统计方法和表型表征。他将由肥胖和身高遗传学领先的研究者乔尔·赫斯霍恩（Joel Hirschhorn）博士指导，并将由统计和表型统一领域的领导人联合授予。该K99应用中提出的研究对了解复杂性状和疾病，尤其是代谢综合征具有广泛的影响。它还具有显着理解和影响代谢综合征的诊断和治疗的潜力。塞勒姆博士有信心完成工作和培训计划将使他成为成功的人类遗传研究者的成功事业。