Literature DB >> 18490260

A novel strategy for detecting multiple loci in Genome-Wide Association Studies of complex diseases.

Jing Li1.   

Abstract

Large-scale Genome-Wide Association Studies (GWAS) for complex diseases are increasingly common, due to recent advances in genotyping technology. Gene-gene interactions play an important role in the etiology of complex diseases and have to be addressed in GWAS. In this paper, an efficient strategy based on two-stage analysis is proposed. It combines a single-locus approach with a Goodness-Of-Fit (GOF) test in stage one, and selects a promising subset of SNPs to be modelled using a full interaction model in stage two. Extensive simulations using different disease models with different levels of epistasis demonstrate that it achieves higher power than existing approaches.

Mesh:

Year:  2008        PMID: 18490260      PMCID: PMC3326663          DOI: 10.1504/IJBRA.2008.018342

Source DB:  PubMed          Journal:  Int J Bioinform Res Appl        ISSN: 1744-5485


  15 in total

1.  A complete enumeration and classification of two-locus disease models.

Authors:  W Li; J Reich
Journal:  Hum Hered       Date:  2000 Nov-Dec       Impact factor: 0.444

2.  A perspective on epistasis: limits of models displaying no main effect.

Authors:  Robert Culverhouse; Brian K Suarez; Jennifer Lin; Theodore Reich
Journal:  Am J Hum Genet       Date:  2002-01-08       Impact factor: 11.025

Review 3.  Epistasis: what it means, what it doesn't mean, and statistical methods to detect it in humans.

Authors:  Heather J Cordell
Journal:  Hum Mol Genet       Date:  2002-10-01       Impact factor: 6.150

Review 4.  Mathematical multi-locus approaches to localizing complex human trait genes.

Authors:  Josephine Hoh; Jurg Ott
Journal:  Nat Rev Genet       Date:  2003-09       Impact factor: 53.242

Review 5.  Genome-wide association studies for common diseases and complex traits.

Authors:  Joel N Hirschhorn; Mark J Daly
Journal:  Nat Rev Genet       Date:  2005-02       Impact factor: 53.242

6.  Genome-wide strategies for detecting multiple loci that influence complex diseases.

Authors:  Jonathan Marchini; Peter Donnelly; Lon R Cardon
Journal:  Nat Genet       Date:  2005-03-27       Impact factor: 38.330

7.  A penalized maximum likelihood method for estimating epistatic effects of QTL.

Authors:  Y-M Zhang; S Xu
Journal:  Heredity (Edinb)       Date:  2005-07       Impact factor: 3.821

8.  Genome-wide association study in esophageal cancer using GeneChip mapping 10K array.

Authors:  Nan Hu; Chaoyu Wang; Ying Hu; Howard H Yang; Carol Giffen; Ze-Zhong Tang; Xiao-Yu Han; Alisa M Goldstein; Michael R Emmert-Buck; Kenneth H Buetow; Philip R Taylor; Maxwell P Lee
Journal:  Cancer Res       Date:  2005-04-01       Impact factor: 12.701

9.  A genome-wide association scan of nonsynonymous SNPs identifies a susceptibility variant for Crohn disease in ATG16L1.

Authors:  Jochen Hampe; Andre Franke; Philip Rosenstiel; Andreas Till; Markus Teuber; Klaus Huse; Mario Albrecht; Gabriele Mayr; Francisco M De La Vega; Jason Briggs; Simone Günther; Natalie J Prescott; Clive M Onnie; Robert Häsler; Bence Sipos; Ulrich R Fölsch; Thomas Lengauer; Matthias Platzer; Christopher G Mathew; Michael Krawczak; Stefan Schreiber
Journal:  Nat Genet       Date:  2006-12-31       Impact factor: 38.330

10.  Two-stage two-locus models in genome-wide association.

Authors:  David M Evans; Jonathan Marchini; Andrew P Morris; Lon R Cardon
Journal:  PLoS Genet       Date:  2006-09-22       Impact factor: 5.917

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  4 in total

1.  Detecting genome-wide epistases based on the clustering of relatively frequent items.

Authors:  Minzhu Xie; Jing Li; Tao Jiang
Journal:  Bioinformatics       Date:  2011-11-03       Impact factor: 6.937

2.  Cloud computing for detecting high-order genome-wide epistatic interaction via dynamic clustering.

Authors:  Xuan Guo; Yu Meng; Ning Yu; Yi Pan
Journal:  BMC Bioinformatics       Date:  2014-04-10       Impact factor: 3.169

3.  Detecting epistatic effects in association studies at a genomic level based on an ensemble approach.

Authors:  Jing Li; Benjamin Horstman; Yixuan Chen
Journal:  Bioinformatics       Date:  2011-07-01       Impact factor: 6.937

4.  JS-MA: A Jensen-Shannon Divergence Based Method for Mapping Genome-Wide Associations on Multiple Diseases.

Authors:  Xuan Guo
Journal:  Front Genet       Date:  2020-10-30       Impact factor: 4.599

  4 in total

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