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 迟发性阿尔茨海默病(LOAD)影响着人类人口的很大一部分，具有高度的遗传性， 虽然由于很难获得良好的表型数据，但全基因组关联研究(GWAS) LOAD在识别相关基因方面取得的成功有限。额外的统计力量很可能会产生 许多发现与负载的生物学有关，就像它对其他复杂表型的发现一样。 这项研究计划提出了替代数据来源和新的方法，以增加统计能力 负荷量的遗传学研究。首先，因为负荷是在生命后期被诊断出来的，所以大型的横断面研究不能 很容易将个人归类为病例或对照。使用谱系可以在一定程度上减弱这一限制 信息，如现有方法GWAX中所做的那样。特利博士将把GWAX扩展到病例状态， 父母双方的年龄等特征。这些结果将通过可用的病例对照进行荟萃分析。 基于多性状分析(MTAG)的结果，导致功率大幅增加和减少 由于错误分类案例而产生偏见的风险。其次，负担和教育程度(EA)有遗传关系 相关系数为-0.3，表明它们可能与共同的和独特的生物学途径有关。 特利博士将通过分类和分析联合或联合的SNP来寻求更好地了解负载 与使用贝叶斯-MTAG的LOAD唯一关联，贝叶斯-MTAG是他正在开发的MTAG的扩展。第三，缺乏 在非欧洲GWA群中，多基因得分在这些人群中表现不佳。Dr。 Turley将开发多祖先元分析(MAMA)，这是MTAG的跨种族元分析扩展， 解释了不同祖先之间的连锁不平衡和遗传结构的差异，以改善 非欧洲人群的负荷预测。在这些目标中开发的方法将会增加 统计能力，识别新的基因座，阐明生物途径，并改进多基因预测。 在他的导师本杰明·尼尔博士、他的另一位导师林锡鸿博士和其他团队的指导下， 顾问，特利博士将进行严格的培训计划，以实现这项提案的目标，并 发展成为一名独立的研究员。这项培训的领域包括：(一)流行病学和遗传学 老龄化，(2)统计和人口遗传学，(3)大规模数据分析和工具，以及(4)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，以及来自他的定期反馈 委员会审议阶段。最重要的是，该计划包括一个详细的时间表，但特利博士和他的指导团队 可以监控和评估进度。总的来说，候选人的培训环境很好，导师们 和顾问是世界级的，拟议的研究解决了一个关键和及时的未得到满足的需求，以及额外的 在这个奖项中培养的技能无疑将为候选人奠定坚实的基础 在阿尔茨海默氏症和统计遗传学方面的独立领导。