Genome-wide analysis of late-onset Alzheimer's disease using intergenerational, multi-trait, and cross-ancestry data

使用代际、多特征和跨血统数据对迟发性阿尔茨海默病进行全基因组分析

• 批准号: 10331595
• 负责人: Patrick Ansel Turley
• 金额: $ 24.9万
• 依托单位: UNIVERSITY OF SOUTHERN CALIFORNIA
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-07-01 至 2024-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10331595
• 关键词: Accounting; Address; Affect; Age; Aging; Alleles; Alzheimer' s Disease; Alzheimer' s disease risk; American; Attenuated; Automobile Driving; Award; Biological; Biology; Case-Control Studies; Cause of Death; Characteristics; Code; Cohort Studies; Complex; Computer software; Cross-Sectional Studies; Data; Data Analyses; Data Sources; Dementia; Development; Diagnosis; Disease; Documentation; Early Diagnosis; Early treatment; Educational workshop; Environment; Epidemiology; European; Feedback; Foundations; Genes; Genetic; Genetic Annotation; Genetic Diseases; Genetic study; Heritability; Human; Individual; Late Onset Alzheimer Disease; Leadership; Life; Light; Linkage Disequilibrium; Mentors; Meta-Analysis; Methods; Monitor; Parents; Pathway interactions; Pattern; Phase; Phenotype; Population; Population Genetics; Proxy; Publishing; Research; Research Personnel; Rest; Risk; Sample Size; Sampling; Source; Structure; Testing; Time; TimeLine; Training; Training Programs; Variant; Work; base; biobank; case control; data tools; disorder risk; experience; genetic architecture; genetic pedigree; genetic variant; genome wide association study; genome-wide; genome-wide analysis; improved; intergenerational; large scale data; novel; recruit; skills; statistics; success; symposium; trait

PROJECT SUMMARY/ABSTRACT Late-onset Alzheimer’s disease (LOAD) affects a large portion of the human population and is highly heritable, though due to the difficulty of acquiring well-phentoyped data, genome-wide association studies (GWASs) of LOAD have had limited success in identifying associated genes. Additional statistical power would likely produce many discoveries related to the biology of LOAD, as it has for other complex phenotypes. This research plan proposes alternate data sources and new methods to increase the statistical power in genetic studies of LOAD. First, because LOAD is diagnosed late in life, large, cross-sectional studies cannot easily classify individuals as cases or controls. This limitation can be somewhat attenuated using pedigree information, as is done in the existing method, GWAX. Dr. Turley will extend GWAX to account for case-status, age, and other characteristics of both parents. These results will be meta-analyzed with available case-control- based results using Multi-Trait Analysis of GWAS (MTAG), leading to substantial gains in power and reduced risk of bias due to misclassification of cases. Second, LOAD and educational attainment (EA) have a genetic correlation of -0.3, suggesting that they may be associated with both common and unique biological pathways. Dr. Turley will seek to better understand LOAD by classifying and analyzing SNPs that are either jointly or uniquely associated with LOAD using Bayes-MTAG, an extension of MTAG that he is developing. Third, a lack of non-European GWAS cohorts have resulted in polygenic scores that perform poorly in those populations. Dr. Turley will develop Multi-Ancestry Meta-Analysis (MAMA), a trans-ethnic meta-analysis extension of MTAG that accounts for differences in linkage disequilibrium and genetic architecture across ancestries, to improve prediction of LOAD in non-European populations. The methods developed in each of these aims will increase statistical power, identifying novel loci, elucidating biological pathways, and improving polygenic prediction. Under the guidance his mentor, Dr. Benjamin Neale, his co-mentor, Dr. Xihong Lin, and a team of other advisers, Dr. Turley will pursue a rigorous program of training to accomplish the aims of this proposal and to develop into an independent researcher. The domains of this training include (i) epidemiology and genetics of aging, (ii) statistical and population genetics, (iii) large-scale data analysis and tools, and (iv) professional development. Development in these domains will be accomplished through coursework, attendance at conferences and workshops, experience leading teams and mentoring others, and regular feedback from his committee. Most importantly, the plan includes a detailed timeline, but which Dr. Turley and his mentoring team can monitor and evaluate progress. Overall, the training environment for the candidate is excellent, the mentors and advisors are world-class, the proposed studies address a crucial and timely unmet need, and the additional skills developed during this award will undoubtedly provide a strong foundation for the candidate to establish independent leadership in Alzheimer’s disease and statistical genetics.

项目摘要/摘要 晚期的阿尔茨海默氏病（负载）影响了大部分人口，并且是高度可遗传的， 尽管由于难以获取良好的数据，但全基因组关联研究（GWASS） 负载在识别相关基因方面的成功有限。额外的统计能力可能会产生 与其他复杂表型一样，许多与负载生物学有关的发现。 本研究计划提出了替代数据源和新方法，以增加统计能力 负载的遗传研究。首先，由于负载被诊断出生命后期，因此大型的横断面研究不能 轻松将个人分类为案例或对照。这种限制可以通过血统可以在某种程度上减弱 信息，如现有方法Gwax所做的那样。 Turley博士将扩展Gwax，以考虑病例状态， 父母双方的年龄和其他特征。这些结果将通过可用的病例控制 - 使用GWAS（MTAG）的多特征分析的基于结果，导致了大幅提升并减少 由于案件错误分类而导致偏见的风险。其次，负载和教育程度（EA）具有遗传 -0.3的相关性，表明它们可能与常见和独特的生物学途径相关。 Turley博士将寻求通过分类和分析共同或共同分析的SNP来更好地理解负载 使用贝叶斯-MTAG（他正在开发的MTAG的扩展）与负载独特相关。第三，缺乏 在非欧洲GWAS队列中，在这些人群中的表现不佳。博士 Turley将开发多功能荟萃分析（MAMA），这是MTAG的跨种族荟萃分析扩展 在跨祖先的连锁差异和遗传结构上的差异，以改善 预测非欧洲人口的负载。这些目标中每一个中开发的方法都会增加 静态力量，识别新的局部，阐明生物学途径并改善多基因预测。 在他的精神指导下 顾问，Turley博士将进行严格的培训计划，以实现该提案的目的和 发展成为独立的研究人员。该培训的领域包括（i）流行病学和遗传学 衰老，（ii）统计和人口遗传学，（iii）大规模数据分析和工具，以及（iv）专业 发展。这些领域的开发将通过课程工作，出勤来完成 会议和讲习班，经验领导团队和其他他人，以及他的定期反馈 委员会。最重要的是，该计划包括一个详细的时间表，但Turley博士和他的指导团队 可以监视和评估进度。总体而言，候选人的培训环境非常好，此事 拟议的研究是世界一流的，顾问是一个至关重要且及时未满足的需求，还有其他 在此奖项期间开发的技能无疑将为候选人建立一个坚实的基础 阿尔茨海默氏病和统计遗传学的独立领导。