New methods for constructing and evaluating polygenic scores

构建和评估多基因评分的新方法

• 批准号: 10674844
• 负责人: JONATHAN K PRITCHARD
• 金额: $ 83.15万
• 依托单位: STANFORD UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-09-14 至 2024-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10674844
• 关键词: African American population; Automobile Driving; Biological Models; Blood; Body mass index; Cardiovascular Diseases; Cardiovascular system; Case Study; Cells; Clinical; Clinical Data; Complex; Computing Methodologies; Coronary Arteriosclerosis; Data; Data Analyses; Data Set; Development; Disease; Disease Outcome; European; European ancestry; Family; Gene Frequency; Genetic; Genetic Risk; Genotype; Heritability; Individual; Joints; Lipids; Measurement; Measures; Medical Records; Methods; Modeling; Myocardial Infarction; Output; Performance; Phenotype; Population; Population Genetics; Recording of previous events; Risk; Risk Factors; Sample Size; Sampling; Signal Transduction; Statistical Methods; System; Target Populations; Techniques; Testing; Underserved Population; Variant; Work; base; clinical practice; clinically relevant; disorder risk; experimental study; flexibility; follow-up; functional genomics; gene environment interaction; genetic information; genome wide association study; genomic data; improved; insight; instrument; metabolic abnormality assessment; non-genetic; portability; predictive modeling; prospective; simulation; tool; trait

ABSTRACT In the last decade there has been major progress toward identifying the genetic bases of complex diseases and developing polygenic predictors for individuals who are at increased risk. Polygenic prediction models are now approaching the point of clinical relevance for several important diseases. However, since most of the polygenic risk is due to extremely large numbers of small-effect variants it is difficult to construct maximally efficient prediction models even using very large GWAS samples. At present, the largest samples are currently available for European ancestry individuals. Prediction models developed in these samples usually do not port well into other groups, although the precise reasons for the limited portability are not yet fully understood. In this project we will (1) measure the specific importance of different factors that contribute to the limited portability across groups; (2) implement and evaluate new statistical methods for computing polygenic predictors using joint inference across populations, and using functional information as priors; and (3) implement and evaluate new statistical methods for combining genetic information with other types of clinical data for prospective prediction in clinical settings. In summary our project will provide a framework of efficient statistical methods for polygenic prediction within and across populations.

抽象的 在过去的十年中，识别复杂疾病的遗传碱基取得了重大进展 并为处于风险增加的个体开发多基因预测因子。多基因预测模型是 现在接近几种重要疾病的临床相关性。但是，由于大多数 多基因风险是由于大量的小效应变体引起的，很难最大程度地构造 有效的预测模型甚至使用非常大的GWAS样品。目前，目前最大的样本是 适用于欧洲血统个人。这些样品中开发的预测模型通常不端口 尽管尚未完全理解有限的可移植性的确切原因，但尚未完全理解其他群体。在 该项目我们将（1）衡量不同因素的具体重要性，这些因素有限 跨组的可移植性； （2）实施并评估用于计算多基因的新统计方法 使用跨种群的关节推断，并将功能信息作为先验的预测因素； （3） 实施和评估新的统计方法将遗传信息与其他类型的临床结合起来 在临床环境中预测预测的数据。总之，我们的项目将提供一个高效的框架 人群内和跨种群中多基因预测的统计方法。

项目成果

Stable population structure in Europe since the Iron Age, despite high mobility.

• DOI: 10.7554/elife.79714
• 发表时间: 2024-01-30
• 期刊: eLife
• 影响因子: 7.7
• 作者: Antonio ML;Weiß CL;Gao Z;Sawyer S;Oberreiter V;Moots HM;Spence JP;Cheronet O;Zagorc B;Praxmarer E;Özdoğan KT;Demetz L;Gelabert P;Fernandes D;Lucci M;Alihodžić T;Amrani S;Avetisyan P;Baillif-Ducros C;Bedić Ž;Bertrand A;Bilić M;Bondioli L;Borówka P;Botte E;Burmaz J;Bužanić D;Candilio F;Cvetko M;De Angelis D;Drnić I;Elschek K;Fantar M;Gaspari A;Gasperetti G;Genchi F;Golubović S;Hukeľová Z;Jankauskas R;Vučković KJ;Jeremić G;Kaić I;Kazek K;Khachatryan H;Khudaverdyan A;Kirchengast S;Korać M;Kozlowski V;Krošláková M;Kušan Špalj D;La Pastina F;Laguardia M;Legrand S;Leleković T;Leskovar T;Lorkiewicz W;Los D;Silva AM;Masaryk R;Matijević V;Cherifi YMS;Meyer N;Mikić I;Miladinović-Radmilović N;Milošević Zakić B;Nacouzi L;Natuniewicz-Sekuła M;Nava A;Neugebauer-Maresch C;Nováček J;Osterholtz A;Paige J;Paraman L;Pieri D;Pieta K;Pop-Lazić S;Ruttkay M;Sanader M;Sołtysiak A;Sperduti A;Stankovic Pesterac T;Teschler-Nicola M;Teul I;Tončinić D;Trapp J;Vulović D;Waliszewski T;Walter D;Živanović M;Filah MEM;Čaušević-Bully M;Šlaus M;Borić D;Novak M;Coppa A;Pinhasi R;Pritchard JK
• 通讯作者: Pritchard JK