SynthesisJournal of affective disorders2019
Multivariate analysis of genome-wide data to identify potential pleiotropic genes for five major psychiatric disorders using MetaCCA.
Synthesis in Journal of affective disorders, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed, 20 citations in OpenAlex.
- Aberrant extracellular dopamine clearance in the prefrontal cortex exhibits ADHD-like behavior in NCX3 heterozygous mice.The FEBS journal · 2025Article
- Genetic architecture of ADHD and overlap with other psychiatric disorders and cognition-related phenotypes.Neuroscience and biobehavioral reviews · 2023Review
- Simultaneous modeling of multivariate heterogeneous responses and heteroskedasticity via a two-stage composite likelihood.Biometrical journal. Biometrische Zeitschrift · 2023Article
- Gene-based mediation analysis in epigenetic studies.Briefings in bioinformatics · 2021Article
- Epigenome-wide analysis uncovers a blood-based DNA methylation biomarker of lifetime cannabis use.American journal of medical genetics. Part B, Neuropsychiatric genetics : the official publication of the International Society of Psychiatric Genetics · 2021Article
- Identification of pleiotropic genes between risk factors of stroke by multivariate metaCCA analysis.Molecular genetics and genomics : MGG · 2020Article
- Effect of non-normality and low count variants on cross-phenotype association tests in GWAS.European journal of human genetics : EJHG · 2020Article
- Identifying Shared Risk Genes for Asthma, Hay Fever, and Eczema by Multi-Trait and Multiomic Association Analyses.Frontiers in genetics · 2020Article
- Identification of 67 Pleiotropic Genes Associated With Seven Autoimmune/Autoinflammatory Diseases Using Multivariate Statistical Analysis.Frontiers in immunology · 2020Article
Corrections and comments
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Authors and funding
12 authors at 3 institutions in 2 countries.
Funding
Abstract
backgroundGenome-wide association studies have been extensively applied in identifying SNP associated with major psychiatric disorders. However, the SNPs identified by the prevailing univariate approach only explain a small percentage of the genetic variance of traits, and the extensive data have shown the major psychiatric disorders have common biological mechanisms and the overlapping pathophysiological pathways.
methodsWe applied the genetic pleiotropy-informed metaCCA method on summary statistics data from the Psychiatric Genomics Consortium Cross-Disorder Group to examine the overlapping genetic relations between the five major psychiatric disorders. Furthermore, to refine all genes, we performed gene-based association analyses for the five disorders respectively using VEGAS2. Gene enrichment analysis was applied to explore the potential functional significance of the identified genes.
resultsAfter metaCCA analysis, 1147 SNPs reached the Bonferroni corrected threshold (p < 1.06 × 10 LIMITATIONS: Alternative approaches and experimental studies may be applied to check whether novel genes could still be identified/substantiated with these methods.
conclusionsThe metaCCA method identified novel variants associated with psychiatric disorders by effectively incorporating information from different GWAS datasets. Our analyses may provide insights for some common therapeutic approaches of these five major psychiatric disorders based on the pleiotropic genes and common mechanisms identified.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.