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: 10263365
• 负责人: JONATHAN K PRITCHARD
• 金额: $ 82.49万
• 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/10263365
• 关键词: African American; 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; Estimation Techniques; European; 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.

摘要 在过去的十年里，在确定复杂疾病的遗传基础方面取得了重大进展 并为风险增加的个体开发多基因预测因子。多基因预测模型 现在正接近几种重要疾病的临床相关点。但由于大部分 多基因风险是由于极大量的小效应变异，很难最大限度地构建 即使使用非常大的GWAS样本，也能有效预测模型。目前，最大的样本是 为欧洲血统的人提供。在这些样本中开发的预测模型通常不支持 也可以很好地应用到其他群体中，尽管人们还没有完全理解这种有限的可移植性的确切原因。在 在这个项目中，我们将（1）衡量不同因素的具体重要性，这些因素有助于有限的 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 预测器使用跨群体的联合推断，并使用功能信息作为先验;以及（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 临床环境中的前瞻性预测数据。总之，我们的项目将提供一个有效的框架， 多基因预测的统计方法内和跨种群。