STrengthening the REporting of Genetic Association studies (STREGA) – an extension of the STROBE statement

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Abstract

Making sense of rapidly evolving evidence on genetic associations is crucial to making genuine advances in human genomics and the eventual integration of this information in the practice of medicine and public health. Assessment of the strengths and weaknesses of this evidence, and hence the ability to synthesize it, has been limited by inadequate reporting of results. The STrengthening the REporting of Genetic Association studies (STREGA) initiative builds on the STrengthening the Reporting of OBservational Studies in Epidemiology (STROBE) Statement and provides additions to 12 of the 22 items on the STROBE checklist. The additions concern population stratification, genotyping errors, modelling haplotype variation, Hardy–Weinberg equilibrium, replication, selection of participants, rationale for choice of genes and variants, treatment effects in studying quantitative traits, statistical methods, relatedness, reporting of descriptive and outcome data and the volume of data issues that are important to consider in genetic association studies. The STREGA recommendations do not prescribe or dictate how a genetic association study should be designed, but seek to enhance the transparency of its reporting, regardless of choices made during design, conduct or analysis.

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Most cited references 129

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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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Author and article information

Journal

Journal ID (nlm-ta): Eur J Clin Invest

Journal ID (publisher-id): eci

Title: European Journal of Clinical Investigation

Publisher: Blackwell Publishing Ltd

ISSN (Print): 0014-2972

ISSN (Electronic): 1365-2362

Publication date (Print): April 2009

Volume: 39

Issue: 4

Pages: 247-266

Affiliations

[* ]simpleCanada Research Chair in Human Genome Epidemiology Ottawa, ON, Canada

[† ]simpleUniversity of Ottawa Ottawa, ON, Canada

[‡ ]simpleMRC Biostatistics Unit Cambridge, UK

[§ ]simpleUniversity of Ioannina, Ioannina, Greece; and Tufts University School of Medicine Boston, MA, USA

[¶ ]simpleUniversity of Toronto Toronto, ON, Canada

[** ]simpleUniversity of Bern, Bern, Switzerland; and University Medical Centre Freiburg, Germany

[†† ]simpleNational Office of Public Health Genomics Atlanta, GA, USA

[‡‡ ]simplePublic Library of Science San Francisco, CA, USA

[§§ ]simpleUniversity of Bristol Bristol, UK

[¶¶ ]simpleUniversity of Ottawa Ottawa, ON, Canada

[*** ]simpleUniversity of Texas, MD Anderson Cancer Center Houston, TX, USA

[††† ]simpleAmerican Journal of Human Genetics Boston, MA, USA

[‡‡‡ ]simpleUniversity of Western Ontario; and Robarts Research Institute London, Ottawa, ON, Canada

[§§§ ]simplePaediatric and Perinatal Epidemiology Bristol, UK

[¶¶¶ ]simpleEuropean Journal of Epidemiology Rotterdam, The Netherlands

[**** ]simpleCancer Care Ontario; and Prosserman Centre for Health Research at the Samuel Lunenfeld Research Institute Toronto, ON, Canada

[†††† ]simpleCanada Research Chair in Genetics of Complex Diseases Toronto, ON, Canada

[‡‡‡‡ ]simpleUniversity of Ottawa Heart Institute Ottawa, ON, Canada

[§§§§ ]simpleMcGill University and Genome Quebec Innovation Center Montreal, QC, Canada

[¶¶¶¶ ]simpleDana-Farber Cancer Institute Boston, MA, USA

[***** ]simpleLancet New York, New York, USA

[††††† ]simpleGenetics in Medicine Minneapolis, MN, USA

[‡‡‡‡‡ ]simpleMcGill University, Montreal QC, Canada

[§§§§§ ]simpleUniversity of Ottawa Heart Institute Ottawa, ON, Canada

Author notes

Correspondence to: Julian Little, Canada Research Chair in Human Genome Epidemiology, Department of Epidemiology and Community Medicine, University of Ottawa, 451 Smyth Road, room 3105, Ottawa, ON, Canada K1H 8M5. Tel.: 613-562-5800 ext. 8159; fax: 613-562-5465; e-mail: jlittle@ 123456uottawa.ca

Article

DOI: 10.1111/j.1365-2362.2009.02125.x

PMC ID: 2730482

PubMed ID: 19297801

SO-VID: 4353121f-ebe7-4992-86cf-8b9b29befd27

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Re-use of this article is permitted in accordance with the Creative Commons Deed, Attribution 2.5, which does not permit commercial exploitation.

History

Date received : 09 January 2009

Date accepted : 06 February 2009

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Most cited references 129

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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