An overview of detecting gene-trait associations by integrating GWAS summary statistics and eQTLs  

作　　者：Yang Zhang Mengyao Wang Zhenguo Li Xuan Yang Keqin Li Ao Xie Fang Dong Shihan Wang Jianbing Yan Jianxiao Liu 

机构地区：[1]National Key Laboratory of Crop Genetic Improvement,Huazhong Agricultural University,Wuhan 430070,China [2]Key Laboratory of Smart Farming for Agricultural Animals,Huazhong Agricultural University,Wuhan 430070,China [3]Hubei Key Laboratory of Agricultural Bioinformatics,Huazhong Agricultural University,Wuhan 430070,China [4]College of Informatics,Huazhong Agricultural University,Wuhan 430070,China [5]College of Life Sciences,Nankai University,Tianjin 300071,China

出　　处：《Science China(Life Sciences)》2024年第6期1133-1154,共22页中国科学（生命科学英文版）

基　　金：supported by the National Key Research and Development Program of China(2022YFD1201504);the Fundamental Research Funds for the Central Universities(2662022YLYJ010,2021ZKPY018,2662021JC008,SZYJY2021003);the Major Science and Technology Project of Hubei Province(2021AFB002);the Major Project of Hubei Hongshan Laboratory(2022HSZD031);the Yingzi Tech&Huazhong Agricultural University Intelligent Research Institute of Food Health(IRIFH202209)。

摘　　要：Detecting genes that affect specific traits(such as human diseases and crop yields)is important for treating complex diseases and improving crop quality.A genome-wide association study(GWAS)provides new insights and directions for understanding complex traits by identifying important single nucleotide polymorphisms.Many GWAS summary statistics data related to various complex traits have been gathered recently.Studies have shown that GWAS risk loci and expression quantitative trait loci(e QTLs)often have a lot of overlaps,which makes gene expression gradually become an important intermediary to reveal the regulatory role of GWAS.In this review,we review three types of gene-trait association detection methods of integrating GWAS summary statistics and e QTLs data,namely colocalization methods,transcriptome-wide association study-oriented approaches,and Mendelian randomization-related methods.At the theoretical level,we discussed the differences,relationships,advantages,and disadvantages of various algorithms in the three kinds of gene-trait association detection methods.To further discuss the performance of various methods,we summarize the significant gene sets that influence highdensity lipoprotein,low-density lipoprotein,total cholesterol,and triglyceride reported in 16 studies.We discuss the performance of various algorithms using the datasets of the four lipid traits.The advantages and limitations of various algorithms are analyzed based on experimental results,and we suggest directions for follow-up studies on detecting gene-trait associations.

关 键 词：gene-trait association GWAS EQTL COLOCALIZATION transcriptome-wide association study(TWAS) Mendelian randomization(MR) 

分 类 号：Q811.4[生物学—生物工程]