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.

项目总结/摘要 晚发性阿尔茨海默病（LOAD）影响大部分人群，并且具有高度遗传性， 虽然由于难以获得良好的表型数据，全基因组关联研究（GWAS）的 LOAD在识别相关基因方面取得了有限的成功。额外的统计功效可能会产生 许多发现与LOAD的生物学有关，因为它具有其他复杂的表型。 这项研究计划提出了替代数据源和新方法，以提高统计能力， 遗传学的研究。首先，由于LOAD是在生命的晚期被诊断出来的，因此大型的横断面研究不能 很容易将个体分类为病例或对照。这种限制可以稍微减弱使用血统 信息，就像在现有方法GWAX中所做的那样。特里博士会将GWAX扩展到病例状态， 年龄和父母双方的其他特征。这些结果将采用现有的病例对照进行荟萃分析， 使用GWAS的多性状分析（MTAG）的结果，导致功率大幅增加， 由于病例分类错误而产生的偏倚风险。第二，负荷和教育程度（EA）具有遗传性 相关性为-0.3，表明它们可能与共同和独特的生物学途径相关。 博士Turley将通过对SNP进行分类和分析来更好地理解LOAD，这些SNP要么是共同的，要么是共同的。 使用Bayes-MTAG与LOAD唯一关联，这是他正在开发的MTAG的扩展。第三，缺乏 的非欧洲GWAS队列导致多基因评分在这些人群中表现不佳。博士 Turley将开发多种族荟萃分析（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， 非欧洲人群的负荷预测。在每一个目标中开发的方法将增加 统计能力，识别新的基因座，阐明生物学途径，提高多基因预测。 在他的导师Benjamin Neale博士、他的共同导师林希宏博士和其他团队的指导下， 作为顾问，Turley博士将进行严格的培训计划，以实现本提案的目标， 成为一名独立的研究员。培训的领域包括：（一）流行病学和遗传学， 老龄化，（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 发展在这些领域的发展将通过课程完成，出席 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 以马克思最重要的是，该计划包括一个详细的时间轴，但特里博士和他的指导团队 可以监测和评估进展。总的来说，候选人的培训环境很好，导师们 和顾问是世界一流的，拟议的研究解决了一个关键和及时的未满足的需求，以及额外的 在此期间开发的技能奖无疑将为候选人建立一个强大的基础 在阿尔茨海默病和统计遗传学方面的独立领导地位。