ArticleBMC bioinformatics2012
Detecting disease-associated genes with confounding variable adjustment and the impact on genomic meta-analysis: with application to major depressive disorder.
Article in BMC bioinformatics, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 45 papers, 6 of them syntheses that pooled it.
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Who cites it
45 citing papers in PubMed, 6 syntheses or guidelines pooled it.
- Analytic approaches to clinical validation of results from preclinical models of glioblastoma: A systematic review.PloS one · 2022Pooled it
- Systematic review and meta-analysis of human transcriptomics reveals neuroinflammation, deficient energy metabolism, and proteostasis failure across neurodegeneration.Neurobiology of disease · 2021Pooled it
- MetaDE-Based Analysis of circRNA Expression Profiles Involved in Gastric Cancer.Digestive diseases and sciences · 2020Pooled it
- FOLFOX treatment response prediction in metastatic or recurrent colorectal cancer patients via machine learning algorithms.Cancer medicine · 2020Pooled it
- Opposite Molecular Signatures of Depression in Men and Women.Biological psychiatry · 2018Pooled it
- Integrated Analyses of Gene Expression Profiles Digs out Common Markers for Rheumatic Diseases.PloS one · 2015Pooled it
- Interactive molecular causal networks of hypertension using a fast machine learning algorithm MRdualPC.BMC medical research methodology · 2024Article
- Combination of furosemide, gold, and dopamine as a potential therapy for breast cancer.Functional & integrative genomics · 2023Article
- Exploring potential genes and mechanisms linking erectile dysfunction and depression.Frontiers in endocrinology · 2023Article
- Lower Levels of GABAergic Function Markers in Corticotropin-Releasing Hormone-Expressing Neurons in the sgACC of Human Subjects With Depression.Frontiers in psychiatry · 2022Article
- Transcriptome-based polygenic score links depression-related corticolimbic gene expression changes to sex-specific brain morphology and depression risk.Neuropsychopharmacology : official publication of the American College of Neuropsychopharmacology · 2021Article
- An OMICs-based meta-analysis to support infection state stratification.Bioinformatics (Oxford, England) · 2021Article
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- Regulatory role of DEPTOR‑mediated cellular autophagy and mitochondrial reactive oxygen species in angiogenesis in multiple myeloma.International journal of molecular medicine · 2021Article
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- Diurnal rhythms in gene expression in the prefrontal cortex in schizophrenia.Nature communications · 2019Article
- Airway host-microbiome interactions in chronic obstructive pulmonary disease.Respiratory research · 2019Article
- Computational Drug Repositioning for Gastric Cancer using Reversal Gene Expression Profiles.Scientific reports · 2019Article
- Screening of Drug Repositioning Candidates for Castration Resistant Prostate Cancer.Frontiers in oncology · 2019Article
- Transcriptome alterations of prefrontal cortical parvalbumin neurons in schizophrenia.Molecular psychiatry · 2018Article
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Authors and funding
5 authors.
Funding
Abstract
backgroundDetecting candidate markers in transcriptomic studies often encounters difficulties in complex diseases, particularly when overall signals are weak and sample size is small. Covariates including demographic, clinical and technical variables are often confounded with the underlying disease effects, which further hampers accurate biomarker detection. Our motivating example came from an analysis of five microarray studies in major depressive disorder (MDD), a heterogeneous psychiatric illness with mostly uncharacterized genetic mechanisms.
resultsWe applied a random intercept model to account for confounding variables and case-control paired design. A variable selection scheme was developed to determine the effective confounders in each gene. Meta-analysis methods were used to integrate information from five studies and post hoc analyses enhanced biological interpretations. Simulations and application results showed that the adjustment for confounding variables and meta-analysis improved detection of biomarkers and associated pathways.
conclusionsThe proposed framework simultaneously considers correction for confounding variables, selection of effective confounders, random effects from paired design and integration by meta-analysis. The approach improved disease-related biomarker and pathway detection, which greatly enhanced understanding of MDD neurobiology. The statistical framework can be applied to similar experimental design encountered in other complex and heterogeneous diseases.
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