主講人：李啟寨 中國科學院數學與系統科學研究院研究員

時間：2024年12月28日9：00

地點：三號樓332室

舉辦單位：數理學院

主講人介紹：中國科學院數學與系統科學研究院研究員，系統科學研究所副所長；2001年本科畢業于中國科學技術大學，2006年博士畢業于中國科學院數學與系統科學研究院，2006-2009年在美國國立衛生健康研究院(NIH)國家癌癥研究所(NCI)從事博士后研究；2016年當選國際統計學會推選會員(ISI Elected Member), 2017年獲國家優秀青年科學基金，2020年當選美國統計學會會士(ASA Fellow)，2023年獲國家杰出青年科學基金；研究方向：生物醫學統計、遺傳統計、復雜數據的統計推斷等；在Nature Genetics, Science Advances, Angewandte Chemie-International Edition, Cancer Research, American Journal of Human Genetics, Bioinformatics,IEEE Transactions on Pattern Analysis and Machine Intelligence, Journal of the American Statistical Association, Journal of the Royal Statistical Society Series B, Biometrics等期刊發表SCI論文110余篇；現任中國數學會常務理事、中國現場統計研究會常務理事等。

內容介紹：In Genome-wide association studies (GWAS), summary statistics have become one of the most popular formats for data sharing and analyzing. This paper focuses on utilizing GWAS summary statistics as auxiliary data to enhance the estimation efficiency of Polygenic risk score (PRS) models. Existing methods heavily rely on the complete homogeneity assumption that all studies are under the same parametric model, which is unrealistic given the diverse populations studied in different GWAS. Biological evidence suggests that risk variants can have different effect sizes in different populations. To address this limitation, we introduce SS-trans, a novel framework that effectively leverages heterogeneous summary data from external studies to enhance statistical analysis in the internal study of interest. Unlike existing approaches, our framework relaxes the requirement of complete homogeneity and only necessitates partial parameter similarity across studies. Our theoretical analysis demonstrates significant improvements in estimation accuracy within the internal study, even when external studies exhibit only local similarity. The advantage of the proposed framework is also supported by extensive numerical experiments on both synthetic data and real data of Gene Environment Association Studies type 2 diabetes dataset.