Literature DB >> 19733727

Epistasis and its implications for personal genetics.

Jason H Moore1, Scott M Williams.   

Abstract

The widespread availability of high-throughput genotyping technology has opened the door to the era of personal genetics, which brings to consumers the promise of using genetic variations to predict individual susceptibility to common diseases. Despite easy access to commercial personal genetics services, our knowledge of the genetic architecture of common diseases is still very limited and has not yet fulfilled the promise of accurately predicting most people at risk. This is partly because of the complexity of the mapping relationship between genotype and phenotype that is a consequence of epistasis (gene-gene interaction) and other phenomena such as gene-environment interaction and locus heterogeneity. Unfortunately, these aspects of genetic architecture have not been addressed in most of the genetic association studies that provide the knowledge base for interpreting large-scale genetic association results. We provide here an introductory review of how epistasis can affect human health and disease and how it can be detected in population-based studies. We provide some thoughts on the implications of epistasis for personal genetics and some recommendations for improving personal genetics in light of this complexity.

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Mesh:

Year:  2009        PMID: 19733727      PMCID: PMC2771593          DOI: 10.1016/j.ajhg.2009.08.006

Source DB:  PubMed          Journal:  Am J Hum Genet        ISSN: 0002-9297            Impact factor:   11.025


  95 in total

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Review 2.  Detecting epistatic interactions contributing to quantitative traits.

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3.  STUDENTJAMA. The challenges of whole-genome approaches to common diseases.

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Authors:  Jason H Moore
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Review 5.  Genome-wide association studies for common diseases and complex traits.

Authors:  Joel N Hirschhorn; Mark J Daly
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6.  A balanced accuracy function for epistasis modeling in imbalanced datasets using multifactor dimensionality reduction.

Authors:  Digna R Velez; Bill C White; Alison A Motsinger; William S Bush; Marylyn D Ritchie; Scott M Williams; Jason H Moore
Journal:  Genet Epidemiol       Date:  2007-05       Impact factor: 2.135

7.  Annotation: the analysis of variance and the analysis of causes.

Authors:  R C Lewontin
Journal:  Am J Hum Genet       Date:  1974-05       Impact factor: 11.025

8.  A computationally efficient hypothesis testing method for epistasis analysis using multifactor dimensionality reduction.

Authors:  Kristine A Pattin; Bill C White; Nate Barney; Jiang Gui; Heather H Nelson; Karl T Kelsey; Angeline S Andrew; Margaret R Karagas; Jason H Moore
Journal:  Genet Epidemiol       Date:  2009-01       Impact factor: 2.135

9.  Genome-wide association studies in cancer.

Authors:  Douglas F Easton; Rosalind A Eeles
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10.  Screening large-scale association study data: exploiting interactions using random forests.

Authors:  Kathryn L Lunetta; L Brooke Hayward; Jonathan Segal; Paul Van Eerdewegh
Journal:  BMC Genet       Date:  2004-12-10       Impact factor: 2.797

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  161 in total

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

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Journal:  Invest Ophthalmol Vis Sci       Date:  2011-10-31       Impact factor: 4.799

4.  The dawn of pediatric personalized therapeutics.

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Review 5.  Assessing gene-gene interactions in pharmacogenomics.

Authors:  Hsien-Yuan Lane; Guochuan E Tsai; Eugene Lin
Journal:  Mol Diagn Ther       Date:  2012-02-01       Impact factor: 4.074

6.  Inclusion of gene-gene and gene-environment interactions unlikely to dramatically improve risk prediction for complex diseases.

Authors:  Hugues Aschard; Jinbo Chen; Marilyn C Cornelis; Lori B Chibnik; Elizabeth W Karlson; Peter Kraft
Journal:  Am J Hum Genet       Date:  2012-05-24       Impact factor: 11.025

7.  Identification of epistatic effects using a protein-protein interaction database.

Authors:  Yan V Sun; Sharon L R Kardia
Journal:  Hum Mol Genet       Date:  2010-08-24       Impact factor: 6.150

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

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Journal:  Hum Hered       Date:  2010-04-23       Impact factor: 0.444

9.  A genome-wide gene-gene interaction analysis identifies an epistatic gene pair for lung cancer susceptibility in Han Chinese.

Authors:  Minjie Chu; Ruyang Zhang; Yang Zhao; Chen Wu; Huan Guo; Baosen Zhou; Jiachun Lu; Yongyong Shi; Juncheng Dai; Guangfu Jin; Hongxia Ma; Jing Dong; Yongyue Wei; Cheng Wang; Jianhang Gong; Chongqi Sun; Meng Zhu; Yongyong Qiu; Tangchun Wu; Zhibin Hu; Dongxin Lin; Hongbing Shen; Feng Chen
Journal:  Carcinogenesis       Date:  2013-12-09       Impact factor: 4.944

10.  Testing gene-gene interactions in genome wide association studies.

Authors:  Jie Kate Hu; Xianlong Wang; Pei Wang
Journal:  Genet Epidemiol       Date:  2014-01-15       Impact factor: 2.135

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