Literature DB >> 20576100

"Does replication groups scoring reduce false positive rate in SNP interaction discovery? Response".

Javier Gayán1, Antonio González-Pérez, Agustín Ruiz.   

Abstract

BACKGROUND: The genomewide evaluation of genetic epistasis is a computationally demanding task, and a current challenge in Genetics. HFCC (Hypothesis-Free Clinical Cloning) is one of the methods that have been suggested for genomewide epistasis analysis. In order to perform an exhaustive search of epistasis, HFCC has implemented several tools and data filters, such as the use of multiple replication groups, and direction of effect and control filters. A recent article has claimed that the use of multiple replication groups (as implemented in HFCC) does not reduce the false positive rate, and we hereby try to clarify these issues. RESULTS/DISCUSSION: HFCC uses, as an analysis strategy, the possibility of replicating findings in multiple replication groups, in order to select a liberal subset of preliminary results that are above a statistical criterion and consistent in direction of effect. We show that the use of replication groups and the direction filter reduces the false positive rate of a study, although at the expense of lowering the overall power of the study. A post-hoc analysis of these selected signals in the combined sample could then be performed to select the most promising results.
CONCLUSION: Replication of results in independent samples is generally used in scientific studies to establish credibility in a finding. Nonetheless, the combined analysis of several datasets is known to be a preferable and more powerful strategy for the selection of top signals. HFCC is a flexible and complete analysis tool, and one of its analysis options combines these two strategies: a preliminary multiple replication group analysis to eliminate inconsistent false positive results, and a post-hoc combined-group analysis to select the top signals.

Entities:  

Mesh:

Year:  2010        PMID: 20576100      PMCID: PMC2996931          DOI: 10.1186/1471-2164-11-403

Source DB:  PubMed          Journal:  BMC Genomics        ISSN: 1471-2164            Impact factor:   3.969


  9 in total

1.  A global view of epistasis.

Authors:  Jason H Moore
Journal:  Nat Genet       Date:  2005-01       Impact factor: 38.330

2.  Joint analysis is more efficient than replication-based analysis for two-stage genome-wide association studies.

Authors:  Andrew D Skol; Laura J Scott; Gonçalo R Abecasis; Michael Boehnke
Journal:  Nat Genet       Date:  2006-01-15       Impact factor: 38.330

3.  Replicating genotype-phenotype associations.

Authors:  Stephen J Chanock; Teri Manolio; Michael Boehnke; Eric Boerwinkle; David J Hunter; Gilles Thomas; Joel N Hirschhorn; Goncalo Abecasis; David Altshuler; Joan E Bailey-Wilson; Lisa D Brooks; Lon R Cardon; Mark Daly; Peter Donnelly; Joseph F Fraumeni; Nelson B Freimer; Daniela S Gerhard; Chris Gunter; Alan E Guttmacher; Mark S Guyer; Emily L Harris; Josephine Hoh; Robert Hoover; C Augustine Kong; Kathleen R Merikangas; Cynthia C Morton; Lyle J Palmer; Elizabeth G Phimister; John P Rice; Jerry Roberts; Charles Rotimi; Margaret A Tucker; Kyle J Vogan; Sholom Wacholder; Ellen M Wijsman; Deborah M Winn; Francis S Collins
Journal:  Nature       Date:  2007-06-07       Impact factor: 49.962

Review 4.  Meta-analysis of genetic association studies: methodologies, between-study heterogeneity and winner's curse.

Authors:  Hirofumi Nakaoka; Ituro Inoue
Journal:  J Hum Genet       Date:  2009-10-23       Impact factor: 3.172

5.  Who's afraid of epistasis?

Authors:  W N Frankel; N J Schork
Journal:  Nat Genet       Date:  1996-12       Impact factor: 38.330

Review 6.  Detecting gene-gene interactions that underlie human diseases.

Authors:  Heather J Cordell
Journal:  Nat Rev Genet       Date:  2009-06       Impact factor: 53.242

7.  Does replication groups scoring reduce false positive rate in SNP interaction discovery?

Authors:  Marko Toplak; Tomaz Curk; Janez Demsar; Blaz Zupan
Journal:  BMC Genomics       Date:  2010-01-22       Impact factor: 3.969

8.  Common polygenic variation contributes to risk of schizophrenia and bipolar disorder.

Authors:  Shaun M Purcell; Naomi R Wray; Jennifer L Stone; Peter M Visscher; Michael C O'Donovan; Patrick F Sullivan; Pamela Sklar
Journal:  Nature       Date:  2009-07-01       Impact factor: 49.962

9.  A method for detecting epistasis in genome-wide studies using case-control multi-locus association analysis.

Authors:  Javier Gayán; Antonio González-Pérez; Fernando Bermudo; María Eugenia Sáez; Jose Luis Royo; Antonio Quintas; Jose Jorge Galan; Francisco Jesús Morón; Reposo Ramirez-Lorca; Luis Miguel Real; Agustín Ruiz
Journal:  BMC Genomics       Date:  2008-07-31       Impact factor: 3.969

  9 in total

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