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New methods for constructing and evaluating polygenic scores

构建和评估多基因评分的新方法

基本信息

批准号：
10440469
负责人：
JONATHAN K PRITCHARD
金额：
$ 82.79万
依托单位：
STANFORD UNIVERSITY
依托单位国家：
美国
项目类别：
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/10440469
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 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

项目摘要

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