A comprehensive gene-centric pleiotropic association analysis for 14 psychiatric disorders with GWAS summary statistics

BMC Medicine

Jiahao Qiao¹  Zhonghe Shao¹  Ting Wang¹  Haojie Lu¹  Shuiping Huang²  Ping Zeng² 

[1] Department of Biostatistics, School of Public Health, Xuzhou Medical University, 221004, Xuzhou, Jiangsu, China;Department of Biostatistics, School of Public Health, Xuzhou Medical University, 221004, Xuzhou, Jiangsu, China;Center for Medical Statistics and Data Analysis, Xuzhou Medical University, 221004, Xuzhou, Jiangsu, China;Key Laboratory of Human Genetics and Environmental Medicine, Xuzhou Medical University, 221004, Xuzhou, Jiangsu, China;

关键词: Psychiatric disorder;    Pleiotropy;    Genetic correlation;    Gene-based association analysis;    Genome-wide association study;    Summary statistics;    Pleiotropic analysis under composite null hypothesis;    Mendelian randomization;    Causal inference;    Instrumental variable;   

DOI  :  10.1186/s12916-021-02186-z

来源: Springer

【 摘 要 】

BackgroundRecent genome-wide association studies (GWASs) have revealed the polygenic nature of psychiatric disorders and discovered a few of single-nucleotide polymorphisms (SNPs) associated with multiple psychiatric disorders. However, the extent and pattern of pleiotropy among distinct psychiatric disorders remain not completely clear.MethodsWe analyzed 14 psychiatric disorders using summary statistics available from the largest GWASs by far. We first applied the cross-trait linkage disequilibrium score regression (LDSC) to estimate genetic correlation between disorders. Then, we performed a gene-based pleiotropy analysis by first aggregating a set of SNP-level associations into a single gene-level association signal using MAGMA. From a methodological perspective, we viewed the identification of pleiotropic associations across the entire genome as a high-dimensional problem of composite null hypothesis testing and utilized a novel method called PLACO for pleiotropy mapping. We ultimately implemented functional analysis for identified pleiotropic genes and used Mendelian randomization for detecting causal association between these disorders.ResultsWe confirmed extensive genetic correlation among psychiatric disorders, based on which these disorders can be grouped into three diverse categories. We detected a large number of pleiotropic genes including 5884 associations and 2424 unique genes and found that differentially expressed pleiotropic genes were significantly enriched in pancreas, liver, heart, and brain, and that the biological process of these genes was remarkably enriched in regulating neurodevelopment, neurogenesis, and neuron differentiation, offering substantial evidence supporting the validity of identified pleiotropic loci. We further demonstrated that among all the identified pleiotropic genes there were 342 unique ones linked with 6353 drugs with drug-gene interaction which can be classified into distinct types including inhibitor, agonist, blocker, antagonist, and modulator. We also revealed causal associations among psychiatric disorders, indicating that genetic overlap and causality commonly drove the observed co-existence of these disorders.ConclusionsOur study is among the first large-scale effort to characterize gene-level pleiotropy among a greatly expanded set of psychiatric disorders and provides important insight into shared genetic etiology underlying these disorders. The findings would inform psychiatric nosology, identify potential neurobiological mechanisms predisposing to specific clinical presentations, and pave the way to effective drug targets for clinical treatment.

【 授权许可】

CC BY