Generalizing polygenic risk prediction methods across populations for insights into psychiatric disease

在人群中推广多基因风险预测方法以深入了解精神疾病

• 批准号: 10252959
• 负责人: Alicia Martin
• 金额: $ 24.15万
• 依托单位: MASSACHUSETTS GENERAL HOSPITAL
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-09-01 至 2023-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10252959
• 关键词: Address; Admixture; Africa; African; African American; Asians; Award; Benchmarking; Biological; Biological Markers; Bypass; Collaborations; Complex; Data; Data Analyses; Data Set; Diagnosis; Disease; Educational workshop; Ensure; Environment; Ethics; Etiology; European; Evolution; Foundations; Gene Frequency; Genetic; Genetic Risk; Genetic Structures; Genetic study; Genome; Genomics; Goals; Heritability; Hispanics; Human Genetics; Hypertrophic Cardiomyopathy; Institutes; Intervention; Jordan; Latino; Leadership; Linkage Disequilibrium; Measurable; Measures; Medical; Mental disorders; Mentors; Mentorship; Methods; Mind; Minority Groups; Modeling; Mosaicism; Patients; Population; Population Genetics; Population Heterogeneity; Privacy; Psychiatry; Research; Research Design; Research Personnel; Research Training; Risk; Schizophrenia; Scoring Method; Statistical Methods; Structure; Sum; Testing; Time; Training; Variant; Work; brain tissue; case control; clinical biomarkers; cohort; data tools; design; disorder risk; experience; flexibility; gene discovery; genetic association; genetic variant; genome wide association study; genome-wide; genomic locus; health disparity; heuristics; improved; insight; large scale data; lens; member; method development; multi-ethnic; new therapeutic target; novel; polygenic risk score; precision medicine; psychiatric genomics; psychogenetics; risk prediction; simulation; skills; statistics; symposium; therapeutic target; tool; trait; translational genetics; years lived with disability

PROJECT SUMMARY/ABSTRACT Globally, mental illness is responsible for the most years lived with disability, and is especially challenging to address due to the lack of measurable biomarkers from inaccessible brain tissue. Genetics offers an objective measure of natural biological variability among diverse populations, providing a cornerstone of precision medicine with especially great promise for psychiatry, both as a gene discovery tool for therapeutic targets and as a substitute biomarker. While the Psychiatric Genomics Consortium (PGC) has amassed and jointly analyzed large-scale case-control datasets, these and other genome-wide association studies (GWAS) are Eurocentric, and the generalizability of these studies to diverse populations is low with standard approaches. The candidate hypothesizes that differences in allele frequency and the correlation structure of genetic variants along the genome (i.e. linkage disequilibrium or LD) are the primary culprits of poor generalizability, and proposes to test and improve the accuracy of genetic risk prediction across diverse populations. The proposed study will: 1) quantify genetic risk prediction accuracy of schizophrenia and related disorders across diverse populations (N≈100k cases, N≈213k controls); 2) build novel statistical methods that model LD differences across populations to improve genetic risk prediction when GWAS results are available in one or more populations, and risk prediction is desired in a mismatched population; and 3) build a method tailored to recently admixed populations that jointly models the mosaic of ancestry structure and LD to improve genetic risk prediction accuracy. The proposed studies and training plan were carefully designed to confer expertise in three domains: 1) the genetics of psychiatric disorders, 2) statistical methods development, and 3) large-scale data analysis and tools. These skills are fundamental to the candidate’s goal of becoming a leading investigator using human genetics as a lens into the evolution of complex traits, particularly psychiatric disorders. In addition to research training, the candidate will take coursework, participate in regular seminars, attend workshops and conferences, and gain mentorship experience locally and in Africa. All research will be conducted in the Analytic and Translational Genetics Unit at MGH and the Broad Institute with mentorship from Dr. Mark J. Daly, an established and prolific leader in human genetics. Additional mentorship from leading experts, Drs. Ben Neale, Karestan Koenen, Eimear Kenny, Jordan Smoller, and Sekar Kathiresan, ensures exceptional guidance. Overall, the training environment is outstanding, 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 population, statistical, and psychiatric genomics.

项目总结/摘要 在全球范围内，精神疾病是造成残疾的最主要原因， 由于缺乏可测量的生物标志物从难以接近的脑组织。遗传学提供了一个目标 衡量不同种群之间的自然生物变异性，为精确性提供了基石 医学对精神病学特别有希望，既作为治疗靶点的基因发现工具， 作为替代生物标志物。虽然精神病基因组学联盟（PGC）已经积累并联合分析了 大规模病例对照数据集，这些和其他全基因组关联研究（GWAS）是以欧洲为中心的， 并且这些研究对于不同人群的普遍性对于标准方法是低的。 候选人假设等位基因频率和遗传相关性结构的差异 基因组上的沿着变异（即连锁不平衡或LD）是概括性差的主要原因， 建议测试和提高不同人群遗传风险预测的准确性。拟议 研究将：1）量化精神分裂症和相关疾病的遗传风险预测准确性， 人群（N × 100 k病例，N × 213 k对照）; 2）建立新的统计方法来模拟LD差异 当GWAS结果在一个或多个人群中可用时， 人群，并且在不匹配的人群中期望风险预测;以及3）建立针对最近 混合群体，共同模拟祖先结构和LD的镶嵌，以提高遗传风险 预测精度 拟议的研究和培训计划经过精心设计，旨在传授三个领域的专门知识： 精神疾病的遗传学，2）统计方法的发展，以及3）大规模数据分析和工具。 这些技能是基本的候选人的目标，成为一个领先的研究人员使用人类遗传学 作为一个透镜来观察复杂特征的进化，特别是精神疾病。除了研究培训外， 候选人将参加课程，参加定期研讨会，参加研讨会和会议，并获得 在当地和非洲的辅导经验。所有研究将在分析和翻译 MGH和Broad研究所的遗传学单位，由Mark J. Daly博士指导， 人类遗传学的领导者。来自领先专家Ben Neale博士、Karestan Koenen博士、Eiffel博士的额外指导 Kenny、Jordan Smoller和Sekar Kathiresan确保了卓越的指导。总体而言，培训环境 是杰出的，导师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，以及在此奖项期间开发的额外技能无疑将提供坚实的基础 候选人在人口，统计和精神病基因组学方面建立独立的领导地位。