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

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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     Kamatani N, Sekine A, Kitamoto T, Iida A, Saito S, et al. Large-scale single-nucleotide polymorphism (SNP) and haplotype analyses, using dense SNP maps, of 199 drug-related genes in 752 subjects: The analysis of the association between uncommon SNPs within haplotype blocks and the haplotypes constructed with haplotype-tagging SNPs. Am J Hum Genet. 2004;75:190-203.

     Not in our library10.1086/422853

114. 114.

     Zhang W, Collins A, Morton NE. Does haplotype diversity predict power for association mapping of disease susceptibility? Hum Genet. 2004;115:157-64.

     Not in our library10.1007/s00439-004-1122-x

115. 115.

     Carlson CS, Eberle MA, Rieder MJ, Yi Q, Kruglyak L, et al. Selecting a maximally informative set of single-nucleotide polymorphisms for association analysis using linkage disequilibrium. Am J Hum Genet. 2004;74:106-20.

     Not in our library10.1086/381000

116. 116.

     van Hylckama Vlieg A, Sandkuijl LA, Rosendaal FR, Bertina RM, Vos HL. Candidate gene approach in association studies: would the factor V leiden mutation have been found by this approach? Eur J Hum Genet. 2004;12:478-82.

     Not in our library10.1038/sj.ejhg.5201183

117. 117.

     Greenspan G, Geiger D. Model-based inference of haplotype block variation. J Comput Biol. 2004;11:493-504.

     Not in our library10.1089/1066527041410300

118. 118.

     Kimmel G, Shamir R. GERBIL: genotype resolution and block identification using likelihood. Proc Natl Acad Sci USA. 2005;102:158-62.

     Not in our library10.1073/pnas.0404730102

119. 119.

     Cardon LR, Abecasis GR. Using haplotype blocks to map human complex triat loci. Trends Genet. 2003;19:135-40.

     Not in our library10.1016/s0168-9525(03)00022-2

120. 120.

     Ke X, Hunt S, Tapper W, Lawrence R, Stavrides G, et al. The impact of SNP density on fine-scale patterns of linkage disequilibrium. Hum Mol Genet. 2004;13:577-88.

     Not in our library10.1093/hmg/ddh060

121. 121.
     Imputation-Based Analysis of Association Studies: Candidate Regions and Quantitative Traits · 2007

     No notice
122. 122.

     Marchini J, Howie B, Myers S, McVean G, Donnelly P. A new multipoint method for genome-wide association studies by imputation of genotypes. Nat Genet. 2007;39:906-13.

     Not in our library10.1038/ng2088

123. 123.

     Hardy GH. Mendelian proportions in a mixed population. Science. 1908;28:49-50.

     Not in our library10.1126/science.28.706.49

124. 124.

     Weinberg W. Über den nachweis der vererbung beim menschen. Jahrhefte Des Vereines Für Vaterländische Naturkunde in Württemberg. 1908;64:368-82.

     Not in our library

125. 125.

     Minelli C, Thompson JR, Abrams KR, Thakkinstian A, Attia J. How should we use information about HWE in the meta-analyses of genetic association studies? Int J Epidemiol. 2008;37:136-46.

     Not in our library10.1093/ije/dym234

126. 126.

     Xu J, Turner A, Little J, Bleecker ER, Meyers DA. Positive results in association studies are associated with departure from Hardy-Weinberg equilibrium: hint for genotyping error? Hum Genet. 2002;111:573-4.

     Not in our library10.1007/s00439-002-0819-y

127. 127.

     Hosking L, Lumsden S, Lewis K, Yeo A, McCarthy L, et al. Detection of genotyping errors by Hardy–Weinberg equilibrium testing. Eur J Hum Genet. 2004;12:395-9.

     Not in our library10.1038/sj.ejhg.5201164

128. 128.

     Salanti G, Amountza G, Ntzani EE, Ioannidis JP. Hardy–Weinberg equilibrium in genetic association studies: an empirical evaluation of reporting, deviations, and power. Eur J Hum Genet. 2005;13:840-8.

     Not in our library10.1038/sj.ejhg.5201410

129. 129.

     Pearson TA, Manolio TA. How to interpret a genome-wide association study. JAMA. 2008;299:1335-44.

     Not in our library10.1001/jama.299.11.1335

130. 130.

     McCarthy MI, Abecasis GR, Cardon LR, Goldstein DB, Little J, et al. Genome-wide association studies for complex traits: consensus, uncertainty and challenges. Nat Rev Genet. 2008;9:356-69.

     Not in our library10.1038/nrg2344

131. 131.

     Zou GY, Donner A. The merits of testing Hardy–Weinberg equilibrium in the analysis of unmatched case-control data: A cautionary note. Ann Hum Genet. 2006;70:923-33.

     Not in our library10.1111/j.1469-1809.2006.00267.x

132. 132.

     Shoemaker J, Painter I, Weir BS. A Bayesian characterization of Hardy–Weinberg disequilibrium. Genetics. 1998;149:2079-88.

     Not in our library10.1093/genetics/149.4.2079

133. 133.

     Ayres KL, Balding DJ. Measuring departures from Hardy–Weinberg: a Markov chain Monte Carlo method for estimating the inbreeding coefficient. Heredity. 1998;80:769-77.

     Not in our library10.1046/j.1365-2540.1998.00360.x

134. 134.

     Trikalinos TA, Salanti G, Khoury MJ, Ioannidis JP. Impact of violations and deviations in Hardy–Weinberg equilibrium on postulated gene-disease associations. Am J Epidemiol. 2006;163:300-9.

     Not in our library10.1093/aje/kwj046

135. 135.

     Davidoff F, Batalden P, Stevens D, Ogrinc G, Mooney S, et al. Publication guidelines for improvement studies in health care: evolution of the SQUIRE project. Ann Intern Med. 2008;149:670-6.

     Not in our library10.7326/0003-4819-149-9-200811040-00009

136. 136.

     Steinberg K, Gallagher M, Khoury MJ, Little J, Burke W. Assessing genotypes in human genome epidemiology studies. Human Genome epidemiology: A Scientific Foundation for Using Genetic Information to Improve Health and Prevent Disease. 2004;79-91.

     Not in our library

137. 137.

     Plagnol V, Cooper JD, Todd JA, Clayton DG. A method to address differential bias in genotyping in large-scale association studies. PLoS Genet. 2007;3.

     Not in our library10.1371/journal.pgen.0030074

138. 138.

     Winker MA. Race and ethnicity in medical research: requirements meet reality. J Law Med Ethics. 2006;34:520-5.

     Not in our library10.1111/j.1748-720x.2006.00065.x

139. 139.
     Genome-Wide Association Scan Shows Genetic Variants in the FTO Gene Are Associated with Obesity-Related Traits · 2007

     No notice
140. 140.

     Chan AW, Hrobjartsson A, Haahr MT, Gotzsche PC, Altman DG. Empirical evidence for selective reporting of outcomes in randomized trials: comparison of protocols to published articles. JAMA. 2004;291:2457-65.

     Not in our library10.1001/jama.291.20.2457

141. 141.

     Chan AW, Krleza-Jeric K, Schmid I, Altman DG. Outcome reporting bias in randomized trials funded by the Canadian institutes of health research. CMAJ. 2004;171:735-40.

     Not in our library10.1503/cmaj.1041086

142. 142.

     Chan AW, Altman DG. Identifying outcome reporting bias in randomised trials on PubMed: review of publications and survey of authors. BMJ. 2005;330:753.

     Not in our library10.1136/bmj.38356.424606.8f

143. 143.

     Contopoulos-Ioannidis DG, Alexiou GA, Gouvias TC, Ioannidis JP. An empirical evaluation of multifarious outcomes in pharmacogenetics: beta-2 adrenoceptor gene polymorphisms in asthma treatment. Pharmacogenet Genomics. 2006;16:705-11.

     Not in our library10.1097/01.fpc.0000236332.11304.8f

144. 144.

     Wain HM, Bruford EA, Lovering RC, Lush MJ, Wright MW, et al. Guidelines for human gene nomenclature. Genomics. 2002;79:464-70.

     Not in our library10.1006/geno.2002.6748

145. 145.

     Wain HM, Lush M, Ducluzeau F, Povey S. Genew: the human gene nomenclature database. Nucleic Acids Res. 2002;30:169-71.

     Not in our library10.1093/nar/30.1.169

146. 146.

     Sherry ST, Ward MH, Kholodov M, Baker J, Phan L, et al. dbSNP: the NCBI database of genetic variation. Nucleic Acids Res. 2001;29:308-11.

     Not in our library10.1093/nar/29.1.308

147. 147.

     Antonarakis SE. Recommendations for a nomenclature system for human gene mutations. Nomenclature working group. Hum Mutat. 1998;11:1-3.

     Not in our library10.1002/(sici)1098-1004(1998)11:1<1::aid-humu1>3.0.co;2-o

148. 148.

     den Dunnen JT, Antonarakis SE. Mutation nomenclature extensions and suggestions to describe complex mutations: a discussion. Hum Mutat. 2000;15:7-12.

     Not in our library10.1002/(sici)1098-1004(200001)15:1<7::aid-humu4>3.0.co;2-n

149. 149.

     Tobin MD, Sheehan NA, Scurrah KJ, Burton PR. Adjusting for treatment effects in studies of quantitative traits: antihypertensive therapy and systolic blood pressure. Stat Med. 2005;24:2911-35.

     Not in our library10.1002/sim.2165

150. 150.

     Lynch M, Ritland K. Estimation of pairwise relatedness with molecular markers. Genetics. 1999;152:1753-66.

     Not in our library10.1093/genetics/152.4.1753

151. 151.

     Slager SL, Schaid DJ. Evaluation of candidate genes in case-control studies: a statistical method to account for related subjects. Am J Hum Genet. 2001;68:1457-62.

     Not in our library10.1086/320608

152. 152.
     Confounding from Cryptic Relatedness in Case-Control Association Studies · 2005

     No notice
153. 153.
     Resolving Individuals Contributing Trace Amounts of DNA to Highly Complex Mixtures Using High-Density SNP Genotyping Microarrays · 2008

     No notice
154. 154.

     Zerhouni EA, Nabel EG. Protecting aggregate genomic data. Science. 2008;322:44.

     Not in our library10.1126/science.322.5898.44b

Reads

A person counts once a day per paper. We keep no cookie and no address: a reader is a hash that changes every day. Any month under five reads shows no number.