Integrated Analysis of Summary Statistics to Identify Pleiotropic Genes and Pathways for the Comorbidity of Schizophrenia and Cardiometabolic Disease
Bibliographic Data
| ID | 15520304 |
|---|---|
| Authors | Hao Liu (0000-0002-4971-7315, Huazhong University of Science and Technology), Yang Sun (0009-0001-1587-6633, Huazhong University of Science and Technology), Xinxin Zhang (0000-0002-2975-6800, Tongji Hospital), Shiyang Li (0009-0003-3790-1208, Tongji Hospital), Dong Hu (0000-0003-2417-119X, Tongji Hospital), Lei Xiao (0000-0002-4473-866X, Tongji Hospital), Yanghui Chen (0000-0003-0175-7868, Tongji Hospital), Lin He (0000-0001-9295-2217, Shanghai Mental Health Center, corresponding author), Dao Wen Wang (0000-0002-9774-3980, Tongji Hospital, corresponding author) |
| Year | 2020 |
| Volume | 11 |
| Pages | 256-256 |
| Publication date | 2020-04-17 |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Frontiers in Psychiatry (JOURNAL) |
| Journal identifiers | ISSN: 1664-0640 • E-ISSN: 1664-0640 |
| Publisher | Frontiers Media (PUBLISHER • CH) |
| DOI | 10.3389/fpsyt.2020.00256 |
| PMID | 32425817 |
| OpenAlex | W3016550068 |
| Language | EN |
| References cited | 87 |
Genome-wide association studies (GWAS) have identified abundant risk loci associated with schizophrenia (SCZ), cardiometabolic disease (CMD) including body mass index, coronary artery diseases, type 2 diabetes, low- and high-density lipoprotein, total cholesterol, and triglycerides. Although recent studies have suggested that genetic risk shared between these disorders, the pleiotropic genes and biological pathways shared between them are still vague. Here we integrated comprehensive multi-dimensional data from GWAS, expression quantitative trait loci (eQTL), and gene set database to systematically identify potential pleiotropic genes and biological pathways shared between SCZ and CMD. By integrating the results from different approaches including FUMA, Sherlock, SMR, UTMOST, FOCUS, and DEPICT, we revealed 21 pleiotropic genes that are likely to be shared between SCZ and CMD. These genes include VRK2 , SLC39A8 , NT5C2 , AMBRA1 , ARL6IP4 , OGFOD2 , PITPNM2 , CDK2AP1 , C12orf65 , ABCB9 , SETD8 , MPHOSPH9, FES , FURIN , INO80E , YPEL3 , MAPK3 , SREBF1 , TOM1L2 , GATAD2A , and TM6SF2 . In addition, we also performed the gene-set enrichment analysis using the software of GSA-SNP2 and MAGMA with GWAS summary statistics and identified three biological pathways (MAPK-TRK signaling, growth hormone signaling, and regulation of insulin secretion signaling) shared between them. Our study provides insights into the pleiotropic genes and biological pathways underlying mechanisms for the comorbidity of SCZ and CMD. However, further genetic and functional studies are required to validate the role of these potential pleiotropic genes and pathways in the etiology of the comorbidity of SCZ and CMD, which should provide potential targets for future diagnostics and therapeutics
Bioinformatics · Biological pathway · Biology · Candidate gene · Comorbidity · Computational biology · Disease · Gene · Gene expression · Genetic association · Genetic variants · Genome-wide association study · Genotype · KEGG · Mendelian randomization · Single-nucleotide polymorphism · Transcriptome · Bioinformatics and Genomic Networks · Genetic Associations and Epidemiology · Genetic Mapping and Diversity in Plants and Animals · Medicine · Genetics · Internal Medicine
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| Highly cited | No |
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