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.

项目概要/摘要 晚发性阿尔茨海默病 (LOAD) 影响很大一部分人群，并且具有高度遗传性， 尽管由于获得良好表型数据的困难，全基因组关联研究（GWAS） LOAD 在识别相关基因方面取得的成功有限。额外的统计能力可能会产生 许多发现与 LOAD 的生物学相关，就像其他复杂表型的发现一样。 该研究计划提出了替代数据源和新方法，以提高统计能力 LOAD 的遗传学研究。首先，由于 LOAD 是在晚年才被诊断出来的，因此大型横断面研究无法 轻松地将个体分类为病例或对照。使用谱系可以在一定程度上减弱这种限制 信息，如现有方法 GWAX 中所做的那样。 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。这些结果将与可用的病例对照进行荟萃分析 基于使用 GWAS 多特征分析 (MTAG) 的结果，可大幅提高功效并减少 由于病例分类错误而导致偏倚的风险。其次，LOAD和教育程度（EA）具有遗传性 相关性为-0.3，表明它们可能与常见和独特的生物途径相关。 Turley 博士将通过对共同或共同存在的 SNP 进行分类和分析，寻求更好地理解 LOAD。 与使用 Bayes-MTAG（他正在开发的 MTAG 扩展）的 LOAD 相关联。三、缺乏 非欧洲 GWAS 队列的多基因评分在这些人群中表现不佳。博士。 Turley 将开发多祖先荟萃分析 (MAMA)，这是 MTAG 的跨种族荟萃分析扩展， 解释了不同祖先之间连锁不平衡和遗传结构的差异，以改善 非欧洲人群的 LOAD 预测。针对每个目标开发的方法将会增加 统计能力，识别新基因座，阐明生物学途径，并改进多基因预测。 在他的导师 Benjamin Neale 博士、他的共同导师 Xihong Lin 博士以及其他团队的指导下 作为顾问，特利博士将实行严格的培训计划，以实现本提案的目标并 发展成为一名独立的研究者。该培训的领域包括 (i) 流行病学和遗传学 老龄化，(ii) 统计和群体遗传学，(iii) 大规模数据分析和工具，以及 (iv) 专业 发展。这些领域的发展将通过课程作业、参加 会议和研讨会、领导团队和指导他人的经验以及他的定期反馈 委员会。最重要的是，该计划包括详细的时间表，但特利博士和他的指导团队 可以监控和评估进展。总体来说，候选人的培训环境非常好，导师 和顾问都是世界一流的，拟议的研究解决了关键且及时的未满足需求，以及额外的 在此奖项期间培养的技能无疑将为候选人建立坚实的基础 在阿尔茨海默病和统计遗传学领域具有独立领导地位。