ArticleMolecular psychiatry2012
Statistical epistasis and progressive brain change in schizophrenia: an approach for examining the relationships between multiple genes.
Article in Molecular psychiatry, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 1 of them a synthesis that pooled it.
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Who cites it
14 citing papers in PubMed, 1 synthesis or guideline pooled it, 31 citations in OpenAlex.
- Progressive brain changes in schizophrenia related to antipsychotic treatment? A meta-analysis of longitudinal MRI studies.Neuroscience and biobehavioral reviews · 2013Pooled it
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- Decoding the genomic symphony: unravelling brain disorders through data integration and machine learning.Molecular psychiatry · 2025Review
- Prenatal dexamethasone exposure induces anxiety- and depressive-like behavior of male offspring rats through intrauterine programming of the activation of NRG1-ErbB4 signaling in hippocampal PV interneurons.Cell biology and toxicology · 2023Article
- Detecting genetic epistasis by differential departure from independence.Molecular genetics and genomics : MGG · 2022Article
- Multivariate Pattern Analysis of Genotype-Phenotype Relationships in Schizophrenia.Schizophrenia bulletin · 2018Article
- Imaging Genetics and Genomics in Psychiatry: A Critical Review of Progress and Potential.Biological psychiatry · 2017Review
- Association studies of genomic variants with treatment response to risperidone, clozapine, quetiapine and chlorpromazine in the Chinese Han population.The pharmacogenomics journal · 2016Article
- Genetic Moderation of Stress Effects on Corticolimbic Circuitry.Neuropsychopharmacology : official publication of the American College of Neuropsychopharmacology · 2016Review
- The ENIGMA Consortium: large-scale collaborative analyses of neuroimaging and genetic data.Brain imaging and behavior · 2014Review
- DISC1 genetics, biology and psychiatric illness.Frontiers in biology · 2013Article
- The DAO gene is associated with schizophrenia and interacts with other genes in the Taiwan Han Chinese population.PloS one · 2013Article
- An odor-specific threshold deficit implicates abnormal cAMP signaling in youths at clinical risk for psychosis.Schizophrenia research · 2012Article
Corrections and comments
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Authors and funding
8 authors at 2 institutions in 1 country.
Funding
Abstract
Although schizophrenia is generally considered to occur as a consequence of multiple genes that interact with one another, very few methods have been developed to model epistasis. Phenotype definition has also been a major challenge for research on the genetics of schizophrenia. In this report, we use novel statistical techniques to address the high dimensionality of genomic data, and we apply a refinement in phenotype definition by basing it on the occurrence of brain changes during the early course of the illness, as measured by repeated magnetic resonance scans (i.e., an 'intermediate phenotype.') The method combines a machine-learning algorithm, the ensemble method using stochastic gradient boosting, with traditional general linear model statistics. We began with 14 genes that are relevant to schizophrenia, based on association studies or their role in neurodevelopment, and then used statistical techniques to reduce them to five genes and 17 single nucleotide polymorphisms (SNPs) that had a significant statistical interaction: five for PDE4B, four for RELN, four for ERBB4, three for DISC1 and one for NRG1. Five of the SNPs involved in these interactions replicate previous research in that, these five SNPs have previously been identified as schizophrenia vulnerability markers or implicate cognitive processes relevant to schizophrenia. This ability to replicate previous work suggests that our method has potential for detecting a meaningful epistatic relationship among the genes that influence brain abnormalities in schizophrenia.
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Registered trials
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.