Literature DB >> 28268034

Epistasis in Neuropsychiatric Disorders.

Caleb Webber1.   

Abstract

The contribution of epistasis to human disease remains unclear. However, several studies have now identified epistatic interactions between common variants that increase the risk of a neuropsychiatric disorder, while there is growing evidence that genetic interactions contribute to the pathogenicity of rare, multigenic copy-number variants (CNVs) that have been observed in patients. This review discusses the current evidence for epistatic events and genetic interactions in neuropsychiatric disorders, how paradigm shifts in the phenotypic classification of patients would empower the search for epistatic effects, and how network and cellular models might be employed to further elucidate relevant epistatic interactions.
Copyright © 2017 Elsevier Ltd. All rights reserved.

Entities:  

Keywords:  autism; bipolar disorder; epistasis; interactions; neuropsychiatric disorders; schizophrenia

Mesh:

Year:  2017        PMID: 28268034     DOI: 10.1016/j.tig.2017.01.009

Source DB:  PubMed          Journal:  Trends Genet        ISSN: 0168-9525            Impact factor:   11.639


  4 in total

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Authors:  Chen Zhang; Xiao Xiao; Tao Li; Ming Li
Journal:  Mol Psychiatry       Date:  2020-05-18       Impact factor: 15.992

2.  A functional neuroimaging association study on the interplay between two schizophrenia genome-wide associated genes (CACNA1C and ZNF804A).

Authors:  Maria Guardiola-Ripoll; Carmen Almodóvar-Payá; Alba Lubeiro; Alejandro Sotero; Raymond Salvador; Paola Fuentes-Claramonte; Pilar Salgado-Pineda; Sergi Papiol; Jordi Ortiz-Gil; Jesús J Gomar; Amalia Guerrero-Pedraza; Salvador Sarró; Teresa Maristany; Vicente Molina; Edith Pomarol-Clotet; Mar Fatjó-Vilas
Journal:  Eur Arch Psychiatry Clin Neurosci       Date:  2022-07-07       Impact factor: 5.760

3.  The road less traveled: from genotype to phenotype in flies and humans.

Authors:  Robert R H Anholt; Trudy F C Mackay
Journal:  Mamm Genome       Date:  2017-10-20       Impact factor: 2.957

4.  Using tree-based methods for detection of gene-gene interactions in the presence of a polygenic signal: simulation study with application to educational attainment in the Generation Scotland Cohort Study.

Authors:  Joeri J Meijsen; Alexandros Rammos; Archie Campbell; Caroline Hayward; David J Porteous; Ian J Deary; Riccardo E Marioni; Kristin K Nicodemus
Journal:  Bioinformatics       Date:  2019-01-15       Impact factor: 6.937

  4 in total

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