STrengthening the REporting of Genetic Association Studies (STREGA)— An Extension of the STROBE Statement

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Abstract

Julian Little and colleagues present the STREGA recommendations, which are aimed at improving the reporting of genetic association studies.

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Most cited references 118

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Genome-wide association studies for complex traits: consensus, uncertainty and challenges.

Mark McCarthy, Gonçalo R Abecasis, Lon Cardon … (2008)

The past year has witnessed substantial advances in understanding the genetic basis of many common phenotypes of biomedical importance. These advances have been the result of systematic, well-powered, genome-wide surveys exploring the relationships between common sequence variation and disease predisposition. This approach has revealed over 50 disease-susceptibility loci and has provided insights into the allelic architecture of multifactorial traits. At the same time, much has been learned about the successful prosecution of association studies on such a scale. This Review highlights the knowledge gained, defines areas of emerging consensus, and describes the challenges that remain as researchers seek to obtain more complete descriptions of the susceptibility architecture of biomedical traits of interest and to translate the information gathered into improvements in clinical management.

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A fast and flexible statistical model for large-scale population genotype data: applications to inferring missing genotypes and haplotypic phase.

Paul Scheet, Matthew Stephens (2006)

We present a statistical model for patterns of genetic variation in samples of unrelated individuals from natural populations. This model is based on the idea that, over short regions, haplotypes in a population tend to cluster into groups of similar haplotypes. To capture the fact that, because of recombination, this clustering tends to be local in nature, our model allows cluster memberships to change continuously along the chromosome according to a hidden Markov model. This approach is flexible, allowing for both "block-like" patterns of linkage disequilibrium (LD) and gradual decline in LD with distance. The resulting model is also fast and, as a result, is practicable for large data sets (e.g., thousands of individuals typed at hundreds of thousands of markers). We illustrate the utility of the model by applying it to dense single-nucleotide-polymorphism genotype data for the tasks of imputing missing genotypes and estimating haplotypic phase. For imputing missing genotypes, methods based on this model are as accurate or more accurate than existing methods. For haplotype estimation, the point estimates are slightly less accurate than those from the best existing methods (e.g., for unrelated Centre d'Etude du Polymorphisme Humain individuals from the HapMap project, switch error was 0.055 for our method vs. 0.051 for PHASE) but require a small fraction of the computational cost. In addition, we demonstrate that the model accurately reflects uncertainty in its estimates, in that probabilities computed using the model are approximately well calibrated. The methods described in this article are implemented in a software package, fastPHASE, which is available from the Stephens Lab Web site.

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A genome-wide association study identifies alleles in FGFR2 associated with risk of sporadic postmenopausal breast cancer.

David Hunter, Peter Kraft, Kevin B. Jacobs … (2007)

We conducted a genome-wide association study (GWAS) of breast cancer by genotyping 528,173 SNPs in 1,145 postmenopausal women of European ancestry with invasive breast cancer and 1,142 controls. We identified four SNPs in intron 2 of FGFR2 (which encodes a receptor tyrosine kinase and is amplified or overexpressed in some breast cancers) that were highly associated with breast cancer and confirmed this association in 1,776 affected individuals and 2,072 controls from three additional studies. Across the four studies, the association with all four SNPs was highly statistically significant (P(trend) for the most strongly associated SNP (rs1219648) = 1.1 x 10(-10); population attributable risk = 16%). Four SNPs at other loci most strongly associated with breast cancer in the initial GWAS were not associated in the replication studies. Our summary results from the GWAS are available online in a form that should speed the identification of additional risk loci.

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Journal

Journal ID (nlm-ta): PLoS Med

Journal ID (publisher-id): pmed

Journal ID (publisher-id): plme

Journal ID (pmc): plosmed

Title: PLoS Medicine

Publisher: Public Library of Science (San Francisco, USA )

ISSN (Print): 1549-1277

ISSN (Electronic): 1549-1676

Publication date (Print): February 2009

Publication date (Electronic): 3 February 2009

Volume: 6

Issue: 2

Electronic Location Identifier: e1000022

Affiliations

[1 ] Canada Research Chair in Human Genome Epidemiology

[2 ] Department of Epidemiology and Community Medicine, University of Ottawa, Ottawa, Ontario, Canada

[3 ] MRC Biostatistics Unit, Cambridge, United Kingdom

[4 ] Department of Hygiene and Epidemiology, School of Medicine, University of Ioannina, Ioannina, Greece; and Center for Genetic Epidemiology and Modeling, Tufts University School of Medicine, Boston, Massachusetts, United States of America

[5 ] CIHR New Investigator and Canada Research Chair in Genetic Epidemiology, University of Toronto, Dalla Lana School of Public Health, Toronto, Ontario, Canada

[6 ] Institute of Social and Preventive Medicine, University of Bern, Bern, Switzerland; and German Cochrane Centre, Department of Medical Biometry and Medical Informatics, University Medical Centre, Freiburg, Germany

[7 ] National Office of Public Health Genomics, Centers for Disease Control and Prevention, Atlanta, Georgia, United States of America

[8 ] former Senior Editor, Public Library of Science, San Francisco, California, United States of America

[9 ] MRC Centre for Causal Analyses in Translational Epidemiology, Department of Social Medicine, University of Bristol, Bristol, United Kingdom

[10 ] Canada Research Chair in Health Knowledge Transfer and Uptake, Clinical Epidemiology Program, Ottawa Health Research Institute, Department of Medicine, University of Ottawa, Ottawa, Ontario, Canada

[11 ] University of Texas, MD Anderson Cancer Center, Department of Epidemiology, Houston, Texas, United States of America

[12 ] Deputy Editor, American Journal of Human Genetics, Boston, Massachusetts, United States of America

[13 ] Department of Epidemiology and Biostatistics, University of Western Ontario, London, Ontario, Canada; and Robarts Clinical Trials, Robarts Research Institute, London, Ontario, Canada

[14 ] Editor, Paediatric and Perinatal Epidemiology, Bristol, United Kingdom

[15 ] Editor, European Journal of Epidemiology, Rotterdam, The Netherlands

[16 ] Cancer Care Ontario, Toronto, Ontario, Canada; and Prosserman Centre for Health Research at the Samuel Lunenfeld Research Institute, Toronto, Ontario, Canada

[17 ] Canada Research Chair in Genetics of Complex Diseases, Hospital for Sick Children (SickKids), Toronto, Ontario, Canada

[18 ] Director, Cardiovascular Research Methods Centre, University of Ottawa Heart Institute, Ottawa, Ontario, Canada

[19 ] Genome Quebec & P3G Observatory, McGill University and Genome Quebec Innovation Center, Montreal, Quebec, Canada

[20 ] Dana-Farber Cancer Institute, Boston, Massachusetts, United States of America

[21 ] Senior Editor, Lancet, New York, New York, United States of America

[22 ] former Editor, Genetics in Medicine, Minneapolis, Minnesota, United States of America

[23 ] Canada Research Chair-James McGill Professor, Department of Epidemiology, Biostatistics and Occupational Health, Faculty of Medicine, McGill University, Montreal, Quebec, Canada

[24 ] University of Ottawa Heart Institute, Ottawa, Ontario, Canada

Author notes

* To whom correspondence should be addressed. E-mail: jlittle@ 123456uottawa.ca

Article

Publisher ID: 08-PLME-GG-0997R2

DOI: 10.1371/journal.pmed.1000022

PMC ID: 2634792

PubMed ID: 19192942

SO-VID: 786d8213-5022-41c5-9b82-a247ce2e360d

Copyright © This is an open-access article distributed under the terms of the Creative Commons Public Domain declaration, which stipulates that, once placed in the public domain, this work may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose.

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Pages: 13

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citation Little J, Higgins JPT, Ioannidis JPA, Moher D, Gagnon F, et al. (2009) STrengthening the REporting of Genetic Association Studies (STREGA)—An extension of the STROBE Statement. PLoS Med 6(2): e1000022. doi: 10.1371/journal.pmed.1000022

ScienceOpen disciplines: Medicine

Keywords: reporting recommendations,epidemiology,systematic review,genetics,gene-environment interaction,genome-wide association,gene-disease associations,meta analysis

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ScienceOpen disciplines: Medicine

Keywords: reporting recommendations, epidemiology, systematic review, genetics, gene-environment interaction, genome-wide association, gene-disease associations, meta analysis

STrengthening the REporting of Genetic Association Studies (STREGA)— An Extension of the STROBE Statement

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Most cited references 118

Genome-wide association studies for complex traits: consensus, uncertainty and challenges.

A fast and flexible statistical model for large-scale population genotype data: applications to inferring missing genotypes and haplotypic phase.

A genome-wide association study identifies alleles in FGFR2 associated with risk of sporadic postmenopausal breast cancer.

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