当前位置: X-MOL 学术J. Am. Stat. Assoc. › 论文详情
Our official English website, www.x-mol.net, welcomes your feedback! (Note: you will need to create a separate account there.)
A penalized regression framework for building polygenic risk models based on summary statistics from genome-wide association studies and incorporating external information
Journal of the American Statistical Association ( IF 3.0 ) Pub Date : 2020-10-12 , DOI: 10.1080/01621459.2020.1764849
Ting-Huei Chen 1 , Nilanjan Chatterjee 2 , Maria Teresa Landi 3 , Jianxin Shi 4
Affiliation  

Large-scale genome-wide association (GWAS) studies provide opportunities for developing genetic risk prediction models that have the potential to improve disease prevention, intervention or treatme...

中文翻译:

基于全基因组关联研究的汇总统计数据并结合外部信息构建多基因风险模型的惩罚回归框架

大规模全基因组关联 (GWAS) 研究为开发遗传风险预测模型提供了机会,这些模型有可能改善疾病预防、干预或治疗……
更新日期:2020-10-12
down
wechat
bug