Advertisement

Strengthening the reporting of genetic association studies (STREGA)—an extension of the strengthening the reporting of observational studies in epidemiology (STROBE) statement

Published:February 17, 2009DOI:https://doi.org/10.1016/j.jclinepi.2008.12.004

      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 (STREGA) studies 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.

      Keywords

      To read this article in full you will need to make a payment

      Purchase one-time access:

      Academic & Personal: 24 hour online accessCorporate R&D Professionals: 24 hour online access
      One-time access price info
      • For academic or personal research use, select 'Academic and Personal'
      • For corporate R&D use, select 'Corporate R&D Professionals'

      Subscribe:

      Subscribe to Journal of Clinical Epidemiology
      Already a print subscriber? Claim online access
      Already an online subscriber? Sign in
      Institutional Access: Sign in to ScienceDirect

      References

        • Khoury M.J.
        • Little J.
        • Burke W.
        Human genome epidemiology: scope and strategies.
        in: Khoury M.J. Little J. Burke W. Human genome epidemiology. Oxford University Press, New York2004: 3-16
        • Genomics, Health and Society Working Group
        Genomics, health and society. Emerging issues for public policy.
        Government of Canada Policy Research Initiative, Ottawa2004
        • Lin B.K.
        • Clyne M.
        • Walsh M.
        • Gomez O.
        • Yu W.
        • Gwinn M.
        • et al.
        Tracking the epidemiology of human genes in the literature: the HuGE published literature database.
        Am J Epidemiol. 2006; 164: 1-4
        • Yu Y.
        • Yesupriya A.
        • Clyne M.
        • Wulf A.
        • Gwinn M.
        • Khoury M.J.
        HuGE literature finder. HuGE navigator.
        (Available at) (Accessed December 15, 2008)
        • Lawrence R.W.
        • Evans D.M.
        • Cardon L.R.
        Prospects and pitfalls in whole genome association studies.
        Philos Trans R Soc Lond B Biol Sci. 2005; 360: 1589-1595
        • Thomas D.C.
        Are we ready for genome-wide association studies?.
        Cancer Epidemiol Biomarkers Prev. 2006; 15: 595-598
        • Khoury M.J.
        • Little J.
        • Gwinn M.
        • Ioannidis J.P.
        On the synthesis and interpretation of consistent but weak gene-disease associations in the era of genome-wide association studies.
        Int J Epidemiol. 2007; 36: 439-445
        • Little J.
        • Khoury M.J.
        • Bradley L.
        • Clyne M.
        • Gwinn M.
        • Lin B.
        • et al.
        The human genome project is complete. How do we develop a handle for the pump?.
        Am J Epidemiol. 2003; 157: 667-673
        • Ioannidis J.P.
        • Bernstein J.
        • Boffetta P.
        • Danesh J.
        • Dolan S.
        • Hartge P.
        • et al.
        A network of investigator networks in human genome epidemiology.
        Am J Epidemiol. 2005; 162: 302-304
        • Ioannidis J.P.
        • Gwinn M.
        • Little J.
        • Higgins J.P.
        • Bernstein J.L.
        • Boffetta P.
        • et al.
        A road map for efficient and reliable human genome epidemiology.
        Nat Genet. 2006; 38: 3-5
        • von Elm E.
        • Egger M.
        The scandal of poor epidemiological research.
        BMJ. 2004; 329: 868-869
        • Nature Genetics
        Freely associating.
        Nat Genet. 1999; 22 (editorial): 1-2
        • Cardon L.
        • Bell J.
        Association study designs for complex diseases.
        Nat Rev Genet. 2001; 2: 91-99
        • Weiss S.
        Association studies in asthma genetics.
        Am J Respir Crit Care Med. 2001; 164: 2014-2015
        • Weiss S.T.
        • Silverman E.K.
        • Palmer L.J.
        Case-control association studies in pharmacogenetics.
        Pharmacogenomics J. 2001; 1: 157-158
        • Cooper D.N.
        • Nussbaum R.L.
        • Krawczak M.
        Proposed guidelines for papers describing DNA polymorphism-disease associations.
        Hum Genet. 2002; 110: 208
        • Hegele R.
        SNP judgements and freedom of association.
        Arterioscler Thromb Vasc Biol. 2002; 22: 1058-1061
        • Little J.
        • Bradley L.
        • Bray M.S.
        • Clyne M.
        • Dorman J.
        • Ellsworth D.L.
        • et al.
        Reporting, appraising, and integrating data on genotype prevalence and gene-disease associations.
        Am J Epidemiol. 2002; 156: 300-310
        • Romero R.
        • Kuivaniemi H.
        • Tromp G.
        • Olson J.M.
        The design, execution, and interpretation of genetic association studies to decipher complex diseases.
        Am J Obstet Gynecol. 2002; 187: 1299-1312
        • Colhoun H.M.
        • McKeigue P.M.
        • Davey Smith G.
        Problems of reporting genetic associations with complex outcomes.
        Lancet. 2003; 361: 865-872
        • van Duijn C.M.
        • Porta M.
        Good prospects for genetic and molecular epidemiologic studies in the European Journal of Epidemiology.
        Eur J Epidemiol. 2003; 18: 285-286
        • Crossman D.
        • Watkins H.
        Jesting Pilate, genetic case-control association studies, and heart.
        Heart. 2004; 90: 831-832
        • Huizinga T.W.
        • Pisetsky D.S.
        • Kimberly R.P.
        Associations, populations, and the truth: recommendations for genetic association studies in Arthritis & Rheumatism.
        Arthritis Rheum. 2004; 50: 2066-2071
        • Little J.
        Reporting and review of human genome epidemiology studies.
        in: Khoury M.J. Little J. Burke W. Human genome epidemiology: a scientific foundation for using genetic information to improve health and prevent disease. Oxford University Press, New York2004: 168-192
        • Rebbeck T.R.
        • Martinez M.E.
        • Sellers T.A.
        • Shields P.G.
        • Wild C.P.
        • Potter J.D.
        Genetic variation and cancer: improving the environment for publication of association studies.
        Cancer Epidemiol Biomarkers Prev. 2004; 13: 1985-1986
        • Tan N.
        • Mulley J.
        • Berkovic S.
        Association studies in epilepsy: “the truth is out there”.
        Epilepsia. 2004; 45: 1429-1442
      1. Anonymous. Framework for a fully powered risk engine.
        Nat Genet. 2005; 37: 1153
        • Ehm M.G.
        • Nelson M.R.
        • Spurr N.K.
        Guidelines for conducting and reporting whole genome/large-scale association studies.
        Hum Mol Genet. 2005; 14: 2485-2488
        • Freimer N.B.
        • Sabatti C.
        Guidelines for association studies in human molecular genetics.
        Hum Mol Genet. 2005; 14: 2481-2483
        • Hattersley A.T.
        • McCarthy M.I.
        What makes a good genetic association study?.
        Lancet. 2005; 366: 1315-1323
        • Manly K.
        Reliability of statistical associations between genes and disease.
        Immunogenetics. 2005; 57: 549-558
        • Shen H.
        • Liu Y.
        • Liu P.
        • Recker R.
        • Deng H.
        Nonreplication in genetic studies of complex diseases—lessons learned from studies of osteoporosis and tentative remedies.
        J Bone Miner Res. 2005; 20: 365-376
        • Vitali S.
        • Randolph A.
        Assessing the quality of case-control association studies on the genetic basis of sepsis.
        Pediatr Crit Care Med. 2005; 6: S74-S77
        • Wedzicha J.A.
        • Hall I.P.
        Publishing genetic association studies in Thorax.
        Thorax. 2005; 60: 357
        • Hall I.P.
        • Blakey J.D.
        Genetic association studies in Thorax.
        Thorax. 2005; 60: 357-359
        • DeLisi L.E.
        • Faraone S.V.
        When is a “positive” association truly a “positive” in psychiatric genetics? A commentary based on issues debated at the World Congress of Psychiatric Genetics, Boston, October 12–18, 2005.
        Am J Med Genet B Neuropsychiatr Genet. 2006; 141: 319-322
        • Saito Y.A.
        • Talley N.J.
        • de Andrade M.
        • Petersen G.M.
        Case-control genetic association studies in gastrointestinal disease: review and recommendations.
        Am J Gastroenterol. 2006; 101: 1379-1389
        • Uhlig K.
        • Menon V.
        • Schmid C.H.
        Recommendations for reporting of clinical research studies.
        Am J Kidney Dis. 2007; 49: 3-7
        • NCI-NHGRI Working Group on Replication in Association Studies
        • Chanock S.J.
        • Manolio T.
        • Boehnke M.
        • Boerwinkle E.
        • Hunter D.J.
        • et al.
        Replicating genotype-phenotype associations.
        Nature. 2007; 447: 655-660
        • Begg C.B.
        Reflections on publication criteria for genetic association studies.
        Cancer Epidemiol Biomarkers Prev. 2005; 14: 1364-1365
        • Byrnes G.
        • Gurrin L.
        • Dowty J.
        • Hopper J.L.
        Publication policy or publication bias?.
        Cancer Epidemiol Biomarkers Prev. 2005; 14: 1363
        • Pharoah P.D.
        • Dunning A.M.
        • Ponder B.A.
        • Easton D.F.
        The reliable identification of disease-gene associations.
        Cancer Epidemiol Biomarkers Prev. 2005; 14: 1362
        • Wacholder S.
        Publication environment and broad investigation of the genome.
        Cancer Epidemiol Biomarkers Prev. 2005; 14: 1361
        • Whittemore A.S.
        Genetic association studies: time for a new paradigm?.
        Cancer Epidemiol Biomarkers Prev. 2005; 14: 1359-1360
        • Bogardus Jr., S.T.
        • Concato J.
        • Feinstein A.R.
        Clinical epidemiological quality in molecular genetic research. The need for methodological standards.
        JAMA. 1999; 281: 1919-1926
        • Peters D.L.
        • Barber R.C.
        • Flood E.M.
        • Garner H.R.
        • O'Keefe G.E.
        Methodologic quality and genotyping reproducibility in studies of tumor necrosis factor -308 G–>A single nucleotide polymorphism and bacterial sepsis: implications for studies of complex traits.
        Crit Care Med. 2003; 31: 1691-1696
        • Clark M.F.
        • Baudouin S.V.
        A systematic review of the quality of genetic association studies in human sepsis.
        Intensive Care Med. 2006; 32: 1706-1712
        • Lee W.
        • Bindman J.
        • Ford T.
        • Glozier N.
        • Moran P.
        • Stewart R.
        • et al.
        Bias in psychiatric case-control studies: literature survey.
        Br J Psychiatry. 2007; 190: 204-209
        • Yesupriya A.
        • Evangelou E.
        • Kavvoura F.K.
        • Patsopoulos N.A.
        • Clyne M.
        • Walsh M.
        • et al.
        Reporting of human genome epidemiology (HuGE) association studies: an empirical assessment.
        BMC Med Res Methodol. 2008; 8: 31
        • Reid M.C.
        • Lachs M.S.
        • Feinstein A.R.
        Use of methodological standards in diagnostic test research. Getting better but still not good.
        JAMA. 1995; 274: 645-651
        • Brazma A.
        • Hingamp P.
        • Quackenbush J.
        • Sherlock G.
        • Spellman P.
        • Stoeckert C.
        • et al.
        Minimum information about a microarray experiment (MIAME)—toward standards for microarray data.
        Nat Genet. 2001; 29: 356-371
        • Pocock S.J.
        • Collier T.J.
        • Dandreo K.J.
        • de Stavola B.L.
        • Goldman M.B.
        • Kalish L.A.
        • et al.
        Issues in the reporting of epidemiological studies: a survey of recent practice.
        BMJ. 2004; 329: 883
        • Altman D.
        • Moher D.
        Developing guidelines for reporting healthcare research: scientific rationale and procedures.
        Med Clin (Barc). 2005; 125: 8-13
        • Gluud L.L.
        Bias in clinical intervention research.
        Am J Epidemiol. 2006; 163: 493-501
        • von Elm E.
        • Altman D.G.
        • Egger M.
        • Pocock S.J.
        • Gotzsche P.C.
        • Vandenbroucke J.P.
        • et al.
        The strengthening the reporting of observational studies in epidemiology (STROBE) statement: guidelines for reporting observational studies.
        PLoS Med. 2007; 4: e296
        • Vandenbroucke J.P.
        • von Elm E.
        • Altman D.G.
        • Gotzsche P.C.
        • Mulrow C.D.
        • Pocock S.J.
        • et al.
        Strengthening the reporting of observational studies in epidemiology (STROBE): explanation and elaboration.
        Ann Intern Med. 2007; 147: W163-W194
      2. Little J, Higgins JPT (editors). The HuGENet™ HuGE Review Handbook, version 1.0. 2006; Available at http://www.hugenet.ca. Accessed February 28, 2006.

        • Higgins J.P.
        • Little J.
        • Ioannidis J.P.
        • Bray M.S.
        • Manolio T.A.
        • Smeeth L.
        • et al.
        Turning the pump handle: evolving methods for integrating the evidence on gene-disease association.
        Am J Epidemiol. 2007; 166: 863-866
        • Altman D.G.
        • Schulz K.F.
        • Moher D.
        • Egger M.
        • Davidoff F.
        • Elbourne D.
        • et al.
        The revised CONSORT statement for reporting randomized trials: explanation and elaboration.
        Ann Intern Med. 2001; 134: 663-694
        • Moher D.
        • Schultz K.F.
        • Altman D.
        The CONSORT statement: revised recommendations for improving the quality of reports of parallel-group randomized trials.
        JAMA. 2001; 285: 1987-1991
        • Pompanon F.
        • Bonin A.
        • Bellemain E.
        • Taberlet P.
        Genotyping errors: causes, consequences and solutions.
        Nat Rev Genet. 2005; 6: 847-859
        • Akey J.M.
        • Zhang K.
        • Xiong M.
        • Doris P.
        • Jin L.
        The effect that genotyping errors have on the robustness of common linkage-disequilibrium measures.
        Am J Hum Genet. 2001; 68: 1447-1456
        • Dequeker E.
        • Ramsden S.
        • Grody W.W.
        • Stenzel T.T.
        • Barton D.E.
        Quality control in molecular genetic testing.
        Nat Rev Genet. 2001; 2: 717-723
        • Mitchell A.A.
        • Cutler D.J.
        • Chakravarti A.
        Undetected genotyping errors cause apparent overtransmission of common alleles in the transmission/disequilibrium test.
        Am J Hum Genet. 2003; 72: 598-610
        • Rothman N.
        • Stewart W.F.
        • Caporaso N.E.
        • Hayes R.B.
        Misclassification of genetic susceptibility biomarkers: implications for case-control studies and cross-population comparisons.
        Cancer Epidemiol Biomarkers Prev. 1993; 2: 299-303
        • Garcia-Closas M.
        • Wacholder S.
        • Caporaso N.
        • Rothman N.
        Inference issues in cohort and case-control studies of genetic effects and gene-environment interactions.
        in: Khoury M.J. Little J. Burke W. Human genome epidemiology: a scientific foundation for using genetic information to improve health and prevent disease. Oxford University Press, New York2004: 127-144
        • Wong M.Y.
        • Day N.E.
        • Luan J.A.
        • Wareham N.J.
        Estimation of magnitude in gene-environment interactions in the presence of measurement error.
        Stat Med. 2004; 23: 987-998
        • Clayton D.G.
        • Walker N.M.
        • Smyth D.J.
        • Pask R.
        • Cooper J.D.
        • Maier L.M.
        • et al.
        Population structure, differential bias and genomic control in a large-scale, case-control association study.
        Nat Genet. 2005; 37: 1243-1246
        • Knowler W.C.
        • Williams R.C.
        • Pettitt D.J.
        • Steinberg A.G.
        Gm3;5,13,14 and type 2 diabetes mellitus: an association in American Indians with genetic admixture.
        Am J Human Genet. 1988; 43: 520-526
        • Gelernter J.
        • Goldman D.
        • Risch N.
        The A1 allele at the D2 dopamine receptor gene and alcoholism: a reappraisal.
        JAMA. 1993; 269: 1673-1677
        • Kittles R.A.
        • Chen W.
        • Panguluri R.K.
        • Ahaghotu C.
        • Jackson A.
        • Adebamowo C.A.
        • et al.
        CYP3A4-V and prostate cancer in African Americans: causal or confounding association because of population stratification?.
        Hum Genet. 2002; 110: 553-560
        • Thomas D.C.
        • Witte J.S.
        Point: population stratification: a problem for case control studies of candidate-gene associations?.
        Cancer Epidemiol Biomarkers Prev. 2002; 11: 505-512
        • Wacholder S.
        • Chatterjee N.
        • Hartge P.
        Joint effects of genes and environment distorted by selection biases: implications for hospital-based case-control studies.
        Cancer Epidemiol Biomarkers Prev. 2002; 11: 885-889
        • Cardon L.R.
        • Palmer L.J.
        Population stratification and spurious allelic association.
        Lancet. 2003; 361: 598-604
        • Wacholder S.
        • Rothman N.
        • Caporaso N.
        Population stratification in epidemiologic studies of common genetic variants and cancer: quantification of bias.
        J Natl Cancer Inst. 2000; 92: 1151-1158
        • Ardlie K.G.
        • Lunetta K.L.
        • Seielstad M.
        Testing for population subdivision and association in four case-control studies.
        Am J Human Genet. 2002; 71: 304-311
        • Edland S.D.
        • Slager S.
        • Farrer M.
        Genetic association studies in Alzheimer's disease research: challenges and opportunities.
        Stat Med. 2004; 23: 169-178
        • Millikan R.C.
        Re: population stratification in epidemiologic studies of common genetic variants and cancer: quantification of bias.
        J Natl Cancer Inst. 2001; 93: 156-157
        • Wang Y.
        • Localio R.
        • Rebbeck T.R.
        Evaluating bias due to population stratification in case-control association studies of admixed populations.
        Genet Epidemiol. 2004; 27: 14-20
        • Ioannidis J.P.
        • Ntzani E.E.
        • Trikalinos T.A.
        “Racial” differences in genetic effects for complex diseases.
        Nat Genet. 2004; 36: 1312-1318
        • Marchini J.
        • Cardon L.R.
        • Phillips M.S.
        • Donnelly P.
        The effects of human population structure on large genetic association studies.
        Nat Genet. 2004; 36: 512-517
        • Freedman M.L.
        • Reich D.
        • Penney K.L.
        • McDonald G.J.
        • Mignault A.A.
        • Patterson N.
        • et al.
        Assessing the impact of population stratification on genetic association studies.
        Nat Genet. 2004; 36: 388-393
        • Khlat M.
        • Cazes M.H.
        • Genin E.
        • Guiguet M.
        Robustness of case-control studies of genetic factors to population stratification: magnitude of bias and type I error.
        Cancer Epidemiol Biomarkers Prev. 2004; 13: 1660-1664
        • Balding D.J.
        A tutorial on statistical methods for population association studies.
        Nat Rev Genet. 2006; 7: 781-791
        • Wellcome Trust Case Control Consortium
        Genome-wide association study of 14,000 cases of seven common diseases and 3,000 shared controls.
        Nature. 2007; 447: 661-678
        • Ioannidis J.P.
        Non-replication and inconsistency in the genome-wide association setting.
        Hum Hered. 2007; 64: 203-213
        • Parkes M.
        • Barrett J.C.
        • Prescott N.J.
        • Tremelling M.
        • Anderson C.A.
        • Fisher S.A.
        • et al.
        Sequence variants in the autophagy gene IRGM and multiple other replicating loci contribute to Crohn's disease susceptibility.
        Nat Genet. 2007; 39: 830-832
        • Todd J.A.
        • Walker N.M.
        • Cooper J.D.
        • Smyth D.J.
        • Downes K.
        • Plagnol V.
        • et al.
        Robust associations of four new chromosome regions from genome-wide analyses of type 1 diabetes.
        Nat Genet. 2007; 39: 857-864
        • Zeggini E.
        • Weedon M.N.
        • Lindgren C.M.
        • Frayling T.M.
        • Elliott K.S.
        • Lango H.
        • et al.
        Replication of genome-wide association signals in UK samples reveals risk loci for type 2 diabetes.
        Science. 2007; 316: 1336-1341
        • Diabetes Genetics Initiative of Broad Institute of Harvard and MIT, Lund University, and Novartis Institutes of BioMedical Research
        • Saxena R.
        • Voight B.F.
        • Lyssenko V.
        • Burtt N.P.
        • de Bakker P.I.
        • et al.
        Genome-wide association analysis identifies loci for type 2 diabetes and triglyceride levels.
        Science. 2007; 316: 1331-1336
        • Scott L.J.
        • Mohlke K.L.
        • Bonnycastle L.L.
        • Willer C.J.
        • Li Y.
        • Duren W.L.
        • et al.
        A genome-wide association study of type 2 diabetes in Finns detects multiple susceptibility variants.
        Science. 2007; 316: 1341-1345
        • Helgadottir A.
        • Thorleifsson G.
        • Manolescu A.
        • Gretarsdottir S.
        • Blondal T.
        • Jonasdottir A.
        • et al.
        A common variant on chromosome 9p21 affects the risk of myocardial infarction.
        Science. 2007; 316: 1491-1493
        • McPherson R.
        • Pertsemlidis A.
        • Kavaslar N.
        • Stewart A.
        • Roberts R.
        • Cox D.R.
        • et al.
        A common allele on chromosome 9 associated with coronary heart disease.
        Science. 2007; 316: 1488-1491
        • Easton D.F.
        • Pooley K.A.
        • Dunning A.M.
        • Pharoah P.D.
        • Thompson D.
        • Ballinger D.G.
        • et al.
        Genome-wide association study identifies novel breast cancer susceptibility loci.
        Nature. 2007; 447: 1087-1093
        • Hunter D.J.
        • Kraft P.
        • Jacobs K.B.
        • Cox D.G.
        • Yeager M.
        • Hankinson S.E.
        • et al.
        A genome-wide association study identifies alleles in FGFR2 associated with risk of sporadic postmenopausal breast cancer.
        Nat Genet. 2007; 39: 870-874
        • Stacey S.N.
        • Manolescu A.
        • Sulem P.
        • Rafnar T.
        • Gudmundsson J.
        • Gudjonsson S.A.
        • et al.
        Common variants on chromosomes 2q35 and 16q12 confer susceptibility to estrogen receptor-positive breast cancer.
        Nat Genet. 2007; 39: 865-869
        • Gudmundsson J.
        • Sulem P.
        • Steinthorsdottir V.
        • Bergthorsson J.T.
        • Thorleifsson G.
        • Manolescu A.
        • et al.
        Two variants on chromosome 17 confer prostate cancer risk, and the one in TCF2 protects against type 2 diabetes.
        Nat Genet. 2007; 39: 977-983
        • Haiman C.A.
        • Patterson N.
        • Freedman M.L.
        • Myers S.R.
        • Pike M.C.
        • Waliszewska A.
        • et al.
        Multiple regions within 8q24 independently affect risk for prostate cancer.
        Nat.Genet. 2007; 39: 638-644
        • Yeager M.
        • Orr N.
        • Hayes R.B.
        • Jacobs K.B.
        • Kraft P.
        • Wacholder S.
        • et al.
        Genome-wide association study of prostate cancer identifies a second risk locus at 8q24.
        Nat Genet. 2007; 39: 645-649
        • Zanke B.W.
        • Greenwood C.M.
        • Rangrej J.
        • Kustra R.
        • Tenesa A.
        • Farrington S.M.
        • et al.
        Genome-wide association scan identifies a colorectal cancer susceptibility locus on chromosome 8q24.
        Nat Genet. 2007; 39: 989-994
        • Tomlinson I.
        • Webb E.
        • Carvajal-Carmona L.
        • Broderick P.
        • Kemp Z.
        • Spain S.
        • et al.
        A genome-wide association scan of tag SNPs identifies a susceptibility variant for colorectal cancer at 8q24.21.
        Nat Genet. 2007; 39: 984-988
        • Haiman C.A.
        • Le Marchand L.
        • Yamamoto J.
        • Stram D.O.
        • Sheng X.
        • Kolonel L.N.
        • et al.
        A common genetic risk factor for colorectal and prostate cancer.
        Nat Genet. 2007; 39: 954-956
        • Rioux J.D.
        • Xavier R.J.
        • Taylor K.D.
        • Silverberg M.S.
        • Goyette P.
        • Huett A.
        • et al.
        Genome-wide association study identifies new susceptibility loci for Crohn disease and implicates autophagy in disease pathogenesis.
        Nat Genet. 2007; 39: 596-604
        • Libioulle C.
        • Louis E.
        • Hansoul S.
        • Sandor C.
        • Farnir F.
        • Franchimont D.
        • et al.
        Novel Crohn disease locus identified by genome-wide association maps to a gene desert on 5p13.1 and modulates expression of PTGER4.
        PLoS Genet. 2007; 3: e58
        • Duerr R.H.
        • Taylor K.D.
        • Brant S.R.
        • Rioux J.D.
        • Silverberg M.S.
        • Daly M.J.
        • et al.
        A genome-wide association study identifies IL23R as an inflammatory bowel disease gene.
        Science. 2006; 314: 1461-1463
        • Zhao L.P.
        • Li S.S.
        • Khalid N.
        A method for the assessment of disease associations with single-nucleotide polymorphism haplotypes and environmental variables in case-control studies.
        Am J Hum Genet. 2003; 72: 1231-1250
        • International HapMap Consortium
        • Frazer K.A.
        • Ballinger D.G.
        • Cox D.R.
        • Hinds D.A.
        • Stuve L.L.
        • et al.
        A second generation human haplotype map of over 3.1 million SNPs.
        Nature. 2007; 449: 851-861
        • Stephens M.
        • Smith N.J.
        • Donnelly P.
        A new statistical method for haplotype reconstruction from population data.
        Am J Hum Genet. 2001; 68: 978-989
        • Qin Z.S.
        • Niu T.
        • Liu J.S.
        Partition-ligation-expectation-maximization algorithm for haplotype inference with single-nucleotide polymorphisms.
        Am J Hum Genet. 2002; 71: 1242-1247
        • Scheet P.
        • Stephens M.
        A fast and flexible statistical model for large-scale population genotype data: applications to inferring missing genotypes and haplotypic phase.
        Am J Hum Genet. 2006; 78: 629-644
        • Browning S.R.
        Missing data imputation and haplotype phase inference for genome-wide association studies.
        Hum Genet. 2008; 124: 439-450
        • Huang Q.
        • Fu Y.X.
        • Boerwinkle E.
        Comparison of strategies for selecting single nucleotide polymorphisms for case/control association studies.
        Hum Genet. 2003; 113: 253-257
        • Kamatani N.
        • Sekine A.
        • Kitamoto T.
        • Iida A.
        • Saito S.
        • Kogame A.
        • 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
        • Zhang W.
        • Collins A.
        • Morton N.E.
        Does haplotype diversity predict power for association mapping of disease susceptibility?.
        Hum Genet. 2004; 115: 157-164
        • Carlson C.S.
        • Eberle M.A.
        • Rieder M.J.
        • Yi Q.
        • Kruglyak L.
        • Nickerson D.A.
        Selecting a maximally informative set of single-nucleotide polymorphisms for association analysis using linkage disequilibrium.
        Am J Hum Genet. 2004; 74: 106-120
        • van Hylckama Vlieg A.
        • Sandkuijl L.A.
        • Rosendaal F.R.
        • Bertina R.M.
        • Vos H.L.
        Candidate gene approach in association studies: would the factor V Leiden mutation have been found by this approach?.
        Eur J Hum Genet. 2004; 12: 478-482
        • Greenspan G.
        • Geiger D.
        Model-based inference of haplotype block variation.
        J Comput Biol. 2004; 11: 493-504
        • Kimmel G.
        • Shamir R.
        GERBIL: genotype resolution and block identification using likelihood.
        Proc Natl Acad Sci USA. 2005; 102: 158-162
        • Cardon L.R.
        • Abecasis G.R.
        Using haplotype blocks to map human complex triat loci.
        Trends Genet. 2003; 19: 135-140
        • Ke X.
        • Hunt S.
        • Tapper W.
        • Lawrence R.
        • Stavrides G.
        • Ghori J.
        • et al.
        The impact of SNP density on fine-scale patterns of linkage disequilibrium.
        Hum Mol Genet. 2004; 13: 577-588
        • Servin B.
        • Stephens M.
        Imputation-based analysis of association studies: candidate regions and quantitative traits.
        PLoS Genet. 2007; 3: e114
        • 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: 906-913
        • Hardy G.H.
        Mendelian proportions in a mixed population.
        Science. 1908; 28: 49-50
        • Weinberg W.
        Über den Nachweis der Vererbung beim Menschen.
        Jahresh Ver Vaterl Naturkd Wurttemb. 1908; 64: 368-382
        • Minelli C.
        • Thompson J.R.
        • Abrams K.R.
        • Thakkinstian A.
        • Attia J.
        How should we use information about HWE in the meta-analyses of genetic association studies?.
        Int J Epidemiol. 2008; 37: 136-146
        • Xu J.
        • Turner A.
        • Little J.
        • Bleecker E.R.
        • Meyers D.A.
        Positive results in association studies are associated with departure from Hardy-Weinberg equilibrium: hint for genotyping error?.
        Hum Genet. 2002; 111: 573-574
        • Hosking L.
        • Lumsden S.
        • Lewis K.
        • Yeo A.
        • McCarthy L.
        • Bansal A.
        • et al.
        Detection of genotyping errors by Hardy-Weinberg equilibrium testing.
        Eur J Hum Genet. 2004; 12: 395-399
        • Salanti G.
        • Amountza G.
        • Ntzani E.E.
        • Ioannidis J.P.
        Hardy-Weinberg equilibrium in genetic association studies: an empirical evaluation of reporting, deviations, and power.
        Eur J Hum Genet. 2005; 13: 840-848
        • Pearson T.A.
        • Manolio T.A.
        How to interpret a genome-wide association study.
        JAMA. 2008; 299: 1335-1344
        • McCarthy M.I.
        • Abecasis G.R.
        • Cardon L.R.
        • Goldstein D.B.
        • Little J.
        • Ioannidis J.P.
        • et al.
        Genome-wide association studies for complex traits: consensus, uncertainty and challenges.
        Nat Rev Genet. 2008; 9: 356-369
        • Zou G.Y.
        • 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: 923-933
        • Shoemaker J.
        • Painter I.
        • Weir B.S.
        A Bayesian characterization of Hardy-Weinberg disequilibrium.
        Genetics. 1998; 149: 2079-2088
        • Ayres K.L.
        • Balding D.J.
        Measuring departures from Hardy-Weinberg: a Markov chain Monte Carlo method for estimating the inbreeding coefficient.
        Heredity. 1998; 80: 769-777
        • Trikalinos T.A.
        • Salanti G.
        • Khoury M.J.
        • Ioannidis J.P.
        Impact of violations and deviations in Hardy-Weinberg equilibrium on postulated gene-disease associations.
        Am J Epidemiol. 2006; 163: 300-309
        • Davidoff F.
        • Batalden P.
        • Stevens D.
        • Ogrinc G.
        • Mooney S.
        SQUIRE Development Group. Publication guidelines for improvement studies in health care: evolution of the SQUIRE Project.
        Ann Intern Med. 2008; 149: 670-676
        • Steinberg K.
        • Gallagher M.
        Assessing genotypes in human genome epidemiology studies.
        in: Khoury M.J. Little J. Burke W. Human genome epidemiology: a scientific foundation for using genetic information to improve health and prevent disease. Oxford University Press, New York2004: 79-91
        • Plagnol V.
        • Cooper J.D.
        • Todd J.A.
        • Clayton D.G.
        A method to address differential bias in genotyping in large-scale association studies.
        PLoS Genet. 2007; 3: e74
        • Winker M.A.
        Race and ethnicity in medical research: requirements meet reality.
        J Law Med Ethics. 2006; 34 (480): 520-525
        • Scuteri A.
        • Sanna S.
        • Chen W.M.
        • Uda M.
        • Albai G.
        • Strait J.
        • et al.
        Genome-wide association scan shows genetic variants in the FTO gene are associated with obesity-related traits.
        PLoS Genet. 2007; 3: e115
        • Chan A.W.
        • Hrobjartsson A.
        • Haahr M.T.
        • Gotzsche P.C.
        • Altman D.G.
        Empirical evidence for selective reporting of outcomes in randomized trials: comparison of protocols to published articles.
        JAMA. 2004; 291: 2457-2465
        • Chan A.W.
        • Krleza-Jeric K.
        • Schmid I.
        • Altman D.G.
        Outcome reporting bias in randomized trials funded by the Canadian Institutes of Health Research.
        CMAJ. 2004; 171: 735-740
        • Chan A.W.
        • Altman D.G.
        Identifying outcome reporting bias in randomised trials on PubMed: review of publications and survey of authors.
        BMJ. 2005; 330: 753
        • Contopoulos-Ioannidis D.G.
        • Alexiou G.A.
        • Gouvias T.C.
        • Ioannidis J.P.
        An empirical evaluation of multifarious outcomes in pharmacogenetics: beta-2 adrenoceptor gene polymorphisms in asthma treatment.
        Pharmacogenet Genomics. 2006; 16: 705-711
        • Wain H.M.
        • Bruford E.A.
        • Lovering R.C.
        • Lush M.J.
        • Wright M.W.
        • Povey S.
        Guidelines for human gene nomenclature.
        Genomics. 2002; 79: 464-470
        • Wain H.M.
        • Lush M.
        • Ducluzeau F.
        • Povey S.
        Genew: the human gene nomenclature database.
        Nucleic Acids Res. 2002; 30: 169-171
        • Sherry S.T.
        • Ward M.H.
        • Kholodov M.
        • Baker J.
        • Phan L.
        • Smigielski E.M.
        • et al.
        dbSNP: the NCBI database of genetic variation.
        Nucleic Acids Res. 2001; 29: 308-311
        • Antonarakis S.E.
        Recommendations for a nomenclature system for human gene mutations. Nomenclature Working Group.
        Hum Mutat. 1998; 11: 1-3
        • den Dunnen J.T.
        • Antonarakis S.E.
        Mutation nomenclature extensions and suggestions to describe complex mutations: a discussion.
        Hum Mutat. 2000; 15: 7-12
        • Tobin M.D.
        • Sheehan N.A.
        • Scurrah K.J.
        • Burton P.R.
        Adjusting for treatment effects in studies of quantitative traits: antihypertensive therapy and systolic blood pressure.
        Stat Med. 2005; 24: 2911-2935
        • Lynch M.
        • Ritland K.
        Estimation of pairwise relatedness with molecular markers.
        Genetics. 1999; 152: 1753-1766
        • Slager S.L.
        • Schaid D.J.
        Evaluation of candidate genes in case-control studies: a statistical method to account for related subjects.
        Am J Hum Genet. 2001; 68: 1457-1462
        • Voight B.F.
        • Pritchard J.K.
        Confounding from cryptic relatedness in case-control association studies.
        PLoS Genet. 2005; 1: e32
        • Homer N.
        • Szelinger S.
        • Redman M.
        • Duggan D.
        • Tembe W.
        • Muehling J.
        • et al.
        Resolving individuals contributing trace amounts of DNA to highly complex mixtures using high-density SNP genotyping microarrays.
        PLoS Genet. 2008; 4: e1000167
        • Zerhouni E.A.
        • Nabel E.G.
        Protecting aggregate genomic data.
        Science. 2008; 322: 44