Literature DB >> 14614241

The ubiquitous nature of epistasis in determining susceptibility to common human diseases.

Jason H Moore1.   

Abstract

There is increasing awareness that epistasis or gene-gene interaction plays a role in susceptibility to common human diseases. In this paper, we formulate a working hypothesis that epistasis is a ubiquitous component of the genetic architecture of common human diseases and that complex interactions are more important than the independent main effects of any one susceptibility gene. This working hypothesis is based on several bodies of evidence. First, the idea that epistasis is important is not new. In fact, the recognition that deviations from Mendelian ratios are due to interactions between genes has been around for nearly 100 years. Second, the ubiquity of biomolecular interactions in gene regulation and biochemical and metabolic systems suggest that relationship between DNA sequence variations and clinical endpoints is likely to involve gene-gene interactions. Third, positive results from studies of single polymorphisms typically do not replicate across independent samples. This is true for both linkage and association studies. Fourth, gene-gene interactions are commonly found when properly investigated. We review each of these points and then review an analytical strategy called multifactor dimensionality reduction for detecting epistasis. We end with ideas of how hypotheses about biological epistasis can be generated from statistical evidence using biochemical systems models. If this working hypothesis is true, it suggests that we need a research strategy for identifying common disease susceptibility genes that embraces, rather than ignores, the complexity of the genotype to phenotype relationship. Copyright 2003 S. Karger AG, Basel

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Year:  2003        PMID: 14614241     DOI: 10.1159/000073735

Source DB:  PubMed          Journal:  Hum Hered        ISSN: 0001-5652            Impact factor:   0.444


  267 in total

1.  WDR36 and P53 gene variants and susceptibility to primary open-angle glaucoma: analysis of gene-gene interactions.

Authors:  Cristina Blanco-Marchite; Francisco Sánchez-Sánchez; María-Pilar López-Garrido; Mercedes Iñigez-de-Onzoño; Francisco López-Martínez; Enrique López-Sánchez; Lydia Alvarez; Pedro-Pablo Rodríguez-Calvo; Carmen Méndez-Hernández; Luis Fernández-Vega; Julián García-Sánchez; Miguel Coca-Prados; Julián García-Feijoo; Julio Escribano
Journal:  Invest Ophthalmol Vis Sci       Date:  2011-10-31       Impact factor: 4.799

2.  GEIRA: gene-environment and gene-gene interaction research application.

Authors:  Bo Ding; Henrik Källberg; Lars Klareskog; Leonid Padyukov; Lars Alfredsson
Journal:  Eur J Epidemiol       Date:  2011-04-26       Impact factor: 8.082

3.  Genetic variations in the transforming growth factor-beta pathway as predictors of survival in advanced non-small cell lung cancer.

Authors:  Moubin Lin; David J Stewart; Margaret R Spitz; Michelle A T Hildebrandt; Charles Lu; Jie Lin; Jian Gu; Maosheng Huang; Scott M Lippman; Xifeng Wu
Journal:  Carcinogenesis       Date:  2011-04-22       Impact factor: 4.944

4.  Interaction between CTLA4 gene and IBD5 locus in Hungarian Crohn's disease patients.

Authors:  Veronika Csöngei; Luca Járomi; Eniko Sáfrány; Csilla Sipeky; Lili Magyari; Noémi Polgár; Judit Bene; Patrícia Sarlós; Lilla Lakner; Eszter Baricza; Melinda Szabó; Gábor Rappai; Béla Melegh
Journal:  Int J Colorectal Dis       Date:  2011-04-26       Impact factor: 2.571

5.  TRM: a powerful two-stage machine learning approach for identifying SNP-SNP interactions.

Authors:  Hui-Yi Lin; Y Ann Chen; Ya-Yu Tsai; Xiaotao Qu; Tung-Sung Tseng; Jong Y Park
Journal:  Ann Hum Genet       Date:  2011-12-11       Impact factor: 1.670

6.  Genome-wide conditional search for epistatic disease-predisposing variants in human association studies.

Authors:  Gao Wang; Yaning Yang; Jurg Ott
Journal:  Hum Hered       Date:  2010-04-23       Impact factor: 0.444

7.  A screening methodology based on Random Forests to improve the detection of gene-gene interactions.

Authors:  Lizzy De Lobel; Pierre Geurts; Guy Baele; Francesc Castro-Giner; Manolis Kogevinas; Kristel Van Steen
Journal:  Eur J Hum Genet       Date:  2010-05-12       Impact factor: 4.246

8.  Buckley-James boosting for survival analysis with high-dimensional biomarker data.

Authors:  Zhu Wang; C Y Wang
Journal:  Stat Appl Genet Mol Biol       Date:  2010-06-08

9.  Detecting maternal-fetal genotype interactions associated with conotruncal heart defects: a haplotype-based analysis with penalized logistic regression.

Authors:  Ming Li; Stephen W Erickson; Charlotte A Hobbs; Jingyun Li; Xinyu Tang; Todd G Nick; Stewart L Macleod; Mario A Cleves
Journal:  Genet Epidemiol       Date:  2014-03-02       Impact factor: 2.135

10.  Epistasis between hyperglycemic QTLs revealed in a double congenic of the OLETF rat.

Authors:  Hiroyuki Kose; Yoshimi Bando; Keisuke Izumi; Takahisa Yamada; Kozo Matsumoto
Journal:  Mamm Genome       Date:  2007-08-21       Impact factor: 2.957

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