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，在确定复杂疾病的遗传基础方面取得了重大进展 并为高风险个体开发多基因预测因子。多基因预测模型是 现在正接近几种重要疾病的临床相关性。然而，由于大多数 多基因风险是由于极大量的小效应变异造成的，很难最大限度地构建 即使使用非常大的 GWAS 样本，也能建立有效的预测模型。目前最大的样本是 适用于欧洲血统的个人。在这些样本中开发的预测模型通常不会移植 尽管可移植性有限的确切原因尚未完全了解，但也可以很好地融入其他群体。在 在这个项目中，我们将（1）衡量导致有限的不同因素的具体重要性 跨群体的可移植性； (2) 实施和评估计算多基因的新统计方法 使用跨人群的联合推理并使用功能信息作为先验的预测器；和（3） 实施和评估将遗传信息与其他类型的临床相结合的新统计方法 临床环境中前瞻性预测的数据。总之，我们的项目将提供一个高效的框架 群体内和群体间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