Strengthening the reporting of genetic association studies (STREGA): an extension of the STROBE statement

There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

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, modeling 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.

Related collections

Most cited references 120

Record: found
Abstract: found
Article: not found

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.

0 comments Cited 810 times – based on 0 reviews      Review now

Bookmark

Record: found
Abstract: found
Article: not found

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.

0 comments Cited 753 times – based on 0 reviews      Review now

Bookmark

Record: found
Abstract: found
Article: not found

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.

0 comments Cited 434 times – based on 0 reviews      Review now

Bookmark

All references

Author and article information

Contributors

: jlittle@uottawa.ca

Journal

Journal ID (nlm-ta): Eur J Epidemiol

Title: European Journal of Epidemiology

Publisher: Springer Netherlands (Dordrecht )

ISSN (Print): 0393-2990

ISSN (Electronic): 1573-7284

Publication date (Electronic): 3 February 2009

Publication date (Print): January 2009

Volume: 24

Issue: 1

Pages: 37-55

Affiliations

[1 ]Canada Research Chair in Human Genome Epidemiology, Toronto, ON Canada

[2 ]Department of Epidemiology and Community Medicine, University of Ottawa, 451 Smyth Rd., Ottawa, ON K1H 8M5 Canada

[3 ]MRC Biostatistics Unit, Cambridge, UK

[4 ]Department of Hygiene and Epidemiology, School of Medicine, University of Ioannina, Ioannina, 45110 Greece

[5 ]Center for Genetic Epidemiology and Modeling, Tufts University School of Medicine, Boston, MA 02111 USA

[6 ]CIHR New Investigator and Canada Research Chair in Genetic Epidemiology, University of Toronto, Dalla Lana School of Public Health, 155 College Street, Toronto, ON M5T 3M7 Canada

[7 ]Institute of Social and Preventive Medicine, University of Bern, Finkenhubelweg 11, 3012 Bern, Switzerland

[8 ]German Cochrane Centre, Department of Medical Biometry and Medical Informatics, University Medical Centre, Freiburg, Germany

[9 ]National Office of Public Health Genomics, Centers for Disease Control & Prevention, Atlanta, GA USA

[10 ]Public Library of Science, San Francisco, CA USA

[11 ]MRC Centre for Causal Analyses in Translational Epidemiology, Department of Social Medicine, University of Bristol, Bristol, UK

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

[13 ]Department of Epidemiology, University of Texas, MD Anderson Cancer Center, 1155 Pressler Blvd. Unit 1340, Houston, TX 77030 USA

[14 ]77 Avenue Louis Pasteur, NRB160C, Boston, MA 02115 USA

[15 ]Department of Epidemiology and Biostatistics, University of Western Ontario, London, ON Canada

[16 ]Robarts Clinical Trials, Robarts Research Institute, London, ON Canada

[17 ]Bristol, UK

[18 ]Department of Epidemiology, Erasmus Medical Center, Rotterdam, The Netherlands

[19 ]Cancer Care Ontario, Toronto, ON Canada

[20 ]Prosserman Centre for Health Research at the Samuel Lunenfeld Research Institute, Toronto, ON Canada

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

[22 ]Director, Cardiovascular Research Methods Centre, University of Ottawa Heart Institute, Ottawa, ON Canada

[23 ]Genome Quebec & P3G Observatory, McGill University and Genome Quebec Innovation Center, 740 av. Docteur Penfield, Montréal, QC H3A 1A4 Canada

[24 ]Dana-Farber Cancer Institute, Boston, MA USA

[25 ]New York, NY USA

[26 ]Minneapolis, MN USA

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

[28 ]University of Ottawa Heart Institute, 40 Ruskin Street, Rm. H3100, Ottawa, ON K1Y 4W7 Canada

Article

Publisher ID: 9302

DOI: 10.1007/s10654-008-9302-y

PMC ID: 2764094

PubMed ID: 19189221

SO-VID: 99611ad8-38c3-4d9a-bd57-16caf2be1638

History

Date received : 8 March 2008

Date accepted : 4 November 2008

Custom metadata

ScienceOpen disciplines: Public health

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

Data availability:

ScienceOpen disciplines: Public health

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

Strengthening the reporting of genetic association studies (STREGA): an extension of the STROBE statement

Read this article at

Abstract

Related collections

Global Health Next Generation Network

Most cited references 120

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.

Author and article information

Contributors

Journal

Affiliations

Article

History

Categories

Custom metadata

Comments

Comment on this article

Similar content 489

Cited by 47