主講人：李啟寨 中國(guó)科學(xué)院數(shù)學(xué)與系統(tǒng)科學(xué)研究院研究員
時(shí)間：2024年12月28日9：00
地點(diǎn)：三號(hào)樓332室
舉辦單位：數(shù)理學(xué)院
主講人介紹：中國(guó)科學(xué)院數(shù)學(xué)與系統(tǒng)科學(xué)研究院研究員，系統(tǒng)科學(xué)研究所副所長(zhǎng)；2001年本科畢業(yè)于中國(guó)科學(xué)技術(shù)大學(xué)，2006年博士畢業(yè)于中國(guó)科學(xué)院數(shù)學(xué)與系統(tǒng)科學(xué)研究院，2006-2009年在美國(guó)國(guó)立衛(wèi)生健康研究院(NIH)國(guó)家癌癥研究所(NCI)從事博士后研究；2016年當(dāng)選國(guó)際統(tǒng)計(jì)學(xué)會(huì)推選會(huì)員(ISI Elected Member), 2017年獲國(guó)家優(yōu)秀青年科學(xué)基金，2020年當(dāng)選美國(guó)統(tǒng)計(jì)學(xué)會(huì)會(huì)士(ASA Fellow)，2023年獲國(guó)家杰出青年科學(xué)基金；研究方向：生物醫(yī)學(xué)統(tǒng)計(jì)、遺傳統(tǒng)計(jì)、復(fù)雜數(shù)據(jù)的統(tǒng)計(jì)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(fā)表SCI論文110余篇；現(xiàn)任中國(guó)數(shù)學(xué)會(huì)常務(wù)理事、中國(guó)現(xiàn)場(chǎng)統(tǒng)計(jì)研究會(huì)常務(wù)理事等。
內(nèi)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.