Identifying pleiotropic genes for major psychiatric disorders with GWAS summary statistics using multivariate adaptive association tests

Genome wide association studies (GWAS) have discovered a few of single nucleotide polymorphisms (SNPs) related to major psychiatric disorders. However, it is not completely clear which genes play a pleiotropic role in multiple disorders. The study aimed to identify the pleiotropic genes across five...

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Veröffentlicht in:Journal of psychiatric research 2022-11, Vol.155, p.471-482
Hauptverfasser: Wang, Yuping, Yang, Yongli, Jia, Xiaocan, Zhao, Chenyu, Yang, Chaojun, Fan, Jingwen, Wu, Meina, Yu, Mengdie, Dong, Ani, Wang, Nana, Lian, Jiao, Shi, Xuezhong
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container_issue
container_start_page 471
container_title Journal of psychiatric research
container_volume 155
creator Wang, Yuping
Yang, Yongli
Jia, Xiaocan
Zhao, Chenyu
Yang, Chaojun
Fan, Jingwen
Wu, Meina
Yu, Mengdie
Dong, Ani
Wang, Nana
Lian, Jiao
Shi, Xuezhong
description Genome wide association studies (GWAS) have discovered a few of single nucleotide polymorphisms (SNPs) related to major psychiatric disorders. However, it is not completely clear which genes play a pleiotropic role in multiple disorders. The study aimed to identify the pleiotropic genes across five psychiatric disorders using multivariate adaptive association tests. Summary statistics of five psychiatric disorders were downloaded from Psychiatric Genomics Consortium. We applied linkage disequilibrium score regression (LDSC) to estimate genetic correlation and conducted tissue and cell type specificity analyses based on Multi-marker Analysis of GenoMic Annotation (MAGMA). Then, we identified the pleiotropic genes using MTaSPUsSet and aSPUs tests. We ultimately performed the functional analysis for pleiotropic genes. We confirmed the significant genetic correlation and brain tissue and neuron specificity among five disorders. 100 pleiotropic genes were detected to be significantly associated with five psychiatric disorders, of which 55 were novel genes. These genes were functionally enriched in neuron differentiation and synaptic transmission. The effect direction of pleiotropic genes couldn't be distinguished due to without individual-level data. We identified pleiotropic genes using multivariate adaptive association tests and explored their biological function. The findings may provide novel insight into the development and implementation of prevention and treatment as well as targeted drug discovery in practice. •The MTaSPUsSet, a novel adaptive association test, considers association patterns.•More pleiotropic genes were found by aggregating multiple related phenotypes.•It is a cost-effective study based on GWAS summary statistics.
doi_str_mv 10.1016/j.jpsychires.2022.09.038
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However, it is not completely clear which genes play a pleiotropic role in multiple disorders. The study aimed to identify the pleiotropic genes across five psychiatric disorders using multivariate adaptive association tests. Summary statistics of five psychiatric disorders were downloaded from Psychiatric Genomics Consortium. We applied linkage disequilibrium score regression (LDSC) to estimate genetic correlation and conducted tissue and cell type specificity analyses based on Multi-marker Analysis of GenoMic Annotation (MAGMA). Then, we identified the pleiotropic genes using MTaSPUsSet and aSPUs tests. We ultimately performed the functional analysis for pleiotropic genes. We confirmed the significant genetic correlation and brain tissue and neuron specificity among five disorders. 100 pleiotropic genes were detected to be significantly associated with five psychiatric disorders, of which 55 were novel genes. 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subjects Genetic Pleiotropy
Genetic Predisposition to Disease - genetics
Genome-Wide Association Study
GWAS
Humans
Linkage Disequilibrium
Mental Disorders - genetics
Multiple adaptive association tests
Pleiotropic
Polymorphism, Single Nucleotide - genetics
Psychiatric disorders
Summary statistics
title Identifying pleiotropic genes for major psychiatric disorders with GWAS summary statistics using multivariate adaptive association tests
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