Rigorous organization and quality control (QC) are necessary to facilitate successful genome-wide association meta-analyses (GWAMAs) of statistics aggregated across multiple genome-wide association studies. This protocol provides guidelines for (i) organizational aspects of GWAMAs, and for (ii) QC at the study file level, the meta-level across studies and the meta-analysis output level. Real-world examples highlight issues experienced and solutions developed by the GIANT Consortium that has conducted meta-analyses including data from 125 studies comprising more than 330,000 individuals. We provide a general protocol for conducting GWAMAs and carrying out QC to minimize errors and to guarantee maximum use of the data. We also include details for the use of a powerful and flexible software package called EasyQC. Precise timings will be greatly influenced by consortium size. For consortia of comparable size to the GIANT Consortium, this protocol takes a minimum of about 10 months to complete.
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Hindorff, L.A. et al. Potential etiologic and functional implications of genome-wide association loci for human diseases and traits. Proc. Natl. Acad. Sci. USA 106, 9362–9367 (2009).
McCarthy, M.I. & Hirschhorn, J.N. Genome-wide association studies: past, present and future. Human Mol. Genet. 17, R100–R101 (2008).
Hirschhorn, J.N. & Gajdos, Z.K. Genome-wide association studies: results from the first few years and potential implications for clinical medicine. Annu. Rev. Med. 62, 11–24 (2011).
Visscher, P.M., Brown, M.A., McCarthy, M.I. & Yang, J. Five years of GWAS discovery. Am. J. Hum. Genet. 90, 7–24 (2012).
Anderson, C.A. et al. Data quality control in genetic case-control association studies. Nat. Protoc. 5, 1564–1573 (2010).
Randall, J.C. et al. Sex-stratified genome-wide association studies including 270,000 individuals show sexual dimorphism in genetic loci for anthropometric traits. PLoS Genet. 9, e1003500 (2013).
Surakka, I. et al. A genome-wide screen for interactions reveals a new locus on 4p15 modifying the effect of waist-to-hip ratio on total cholesterol. PLoS Genet. 7, e1002333 (2011).
Manning, A.K. et al. A genome-wide approach accounting for body mass index identifies genetic variants influencing fasting glycemic traits and insulin resistance. Nat. Genet. 44, 659–669 (2012).
Voight, B.F. et al. The metabochip, a custom genotyping array for genetic studies of metabolic, cardiovascular, and anthropometric traits. PLoS Genet. 8, e1002793 (2012).
Cortes, A. & Brown, M.A. Promise and pitfalls of the Immunochip. Arthritis Res. Ther. 13, 101 (2011).
Huyghe, J.R. et al. Exome array analysis identifies new loci and low-frequency variants influencing insulin processing and secretion. Nat. Genet. 45, 197–201 (2013).
Teslovich, T.M. et al. Biological, clinical and population relevance of 95 loci for blood lipids. Nature 466, 707–713 (2010).
Heid, I.M. et al. Meta-analysis identifies 13 new loci associated with waist-hip ratio and reveals sexual dimorphism in the genetic basis of fat distribution. Nat. Genet. 42, 949–960 (2010).
Lango Allen, H. et al. Hundreds of variants clustered in genomic loci and biological pathways affect human height. Nature 467, 832–838 (2010).
Speliotes, E.K. et al. Association analyses of 249,796 individuals reveal 18 new loci associated with body mass index. Nat. Genet. 42, 937–948 (2010).
Scott, R.A. et al. Large-scale association analyses identify new loci influencing glycemic traits and provide insight into the underlying biological pathways. Nat. Genet. 44, 991–1005 (2012).
Schunkert, H. et al. Large-scale association analysis identifies 13 new susceptibility loci for coronary artery disease. Nat. Genet. 43, 333–338 (2011).
Loos, R.J. et al. Common variants near MC4R are associated with fat mass, weight and risk of obesity. Nat. Genet. 40, 768–775 (2008).
Willer, C.J. et al. Six new loci associated with body mass index highlight a neuronal influence on body weight regulation. Nat. Genet. 41, 25–34 (2009).
Lindgren, C.M. et al. Genome-wide association scan meta-analysis identifies three loci influencing adiposity and fat distribution. PLoS Genet. 5, e1000508 (2009).
Berndt, S.I. et al. Genome-wide meta-analysis identifies 11 new loci for anthropometric traits and provides insights into genetic architecture. Nat. Genet. 45, 501–512 (2013).
Cochran, W.G. The combination of estimates from different experiments. Biometrics 10, 101–129 (1954).
Manning, A.K. et al. Meta-analysis of gene-environment interaction: joint estimation of SNP and SNP × environment regression coefficients. Genet. Epidemiol. 35, 11–18 (2011).
de Bakker, P.I. et al. Practical aspects of imputation-driven meta-analysis of genome-wide association studies. Hum. Mol. Genet. 17, R122–R128 (2008).
Fuchsberger, C., Taliun, D., Pramstaller, P.P., Pattaro, C. & CKDGen Consortium. GWAtoolbox: an R package for fast quality control and handling of genome-wide association studies meta-analysis data. Bioinformatics 28, 444–445 (2012).
Kottgen, A. et al. Genome-wide association analyses identify 18 new loci associated with serum urate concentrations. Nat. Genet. 45, 145–154 (2013).
Kottgen, A. et al. New loci associated with kidney function and chronic kidney disease. Nat. Genet. 42, 376–384 (2010).
Schizophrenia Psychiatric Genome-Wide Association Study Consortium. Genome-wide association study identifies five new schizophrenia loci. Nat. Genet. 43, 969–976 (2011).
Knoppers, B.M., Dove, E.S., Litton, J.E. & Nietfeld, J.J. Questioning the limits of genomic privacy. Am. J. Hum. Genet. 91, 577–578: author reply 579 (2012).
Gymrek, M., McGuire, A.L., Golan, D., Halperin, E. & Erlich, Y. Identifying personal genomes by surname inference. Science 339, 321–324 (2013).
Visscher, P.M. & Hill, W.G. The limits of individual identification from sample allele frequencies: theory and statistical analysis. PLoS Genet. 5, e1000628 (2009).
International HapMap Consortium. et al. Integrating common and rare genetic variation in diverse human populations. Nature 467, 52–58 (2010).
Genomes Project Consortium. et al. A map of human genome variation from population-scale sequencing. Nature 467, 1061–1073 (2010).
Devlin, B. & Roeder, K. Genomic control for association studies. Biometrics 55, 997–1004 (1999).
Yang, J. et al. Genomic inflation factors under polygenic inheritance. Eur. J. Hum. Genet. 19, 807–812 (2011).
Willer, C.J., Li, Y. & Abecasis, G.R. METAL: fast and efficient meta-analysis of genomewide association scans. Bioinformatics 26, 2190–2191 (2010).
Higgins, J.P., Thompson, S.G., Deeks, J.J. & Altman, D.G. Measuring inconsistency in meta-analyses. BMJ 327, 557–560 (2003).
DerSimonian, R. & Laird, N. Meta-analysis in clinical trials. Control. Clin. Trials 7, 177–188 (1986).
Whitlock, M.C. Combining probability from independent tests: the weighted Z-method is superior to Fisher's approach. J. Evol. Biol. 18, 1368–1373 (2005).
R Core Team. R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing (2013).
This work was supported by grants from the German Federal Ministry of Education and Research (BMBF) (01ER1206 for I.M.H.); the Leenaards Foundation and the Swiss National Science Foundation (31003A-143914 for Z.K.); the US National Institutes of Health (DK078150, T32 HL007427 for D.C.C.-C.; R01DK075787 for T.E.); the UK Medical Research Council (MRC; U106179471, U106179472 for F.R.D.); the European Research Council (SZ-245 50371-GLUCOSEGENES-FP7-IDEAS-ERC for A.R.W.); the Targeted Financing from the Estonian Ministry of Science and Education (SF0180142s08 for T.E.); the Development Fund of the University of Tartu (SP1GVARENG for T.E.); the European Regional Development Fund to the Centre of Excellence in Genomics (EXCEGEN, 3.2.0304.11-0312 for T.E.); and FP7 (313010 for T.E.). We are also thankful for the GIANT Consortium and the many participating research groups that have allowed us to develop this protocol.
The authors declare no competing financial interests.
A full list of members is available in the Supplementary Note.
Integrated supplementary information
The DATA_UPLOAD directory is used for the collection of raw study-specific results, i.e. used by the collaborators to upload their results. Once all or at least files from >80% of studies have been collected, the DATA_UPLOAD folder should be frozen. The folder should be protected from further changes, be renamed to DATA_UPLOAD_FREEZE and a new DATA_UPLOAD folder should be created to collect any additional results. The CLEANED_FILES directory should be used for collection of cleaned files that passed the file-level QC routines. The META_ANALYSIS directory should be used to upload meta-analysis results and contains sub-folders, one for each meta-analyst (folders ANALYST_1 and ANALYST_2) and one to collect and freeze the final meta-analysis results (FINAL_RESULT).
On the example of the phenotype hip circumference with and without adjustment for BMI (HIP, HIPadjBMI) in the GIANT Metabochip studies (81,000 subjects), it can be seen that (a) the trait transformation issue only affected the trait adjusted for BMI (SE-N plots; magenta: uncleaned studies affected by the issue; green: cleaned studies) ,(b) the uncleaned data had deteriorated power for the BMI-adjusted trait (QQ plot of association P-values from the Meta-analysis for all SNPs; red: meta-analysis on uncleaned data; green: meta-analysis on cleaned data) and (c) the uncleaned data yielded estimates biased towards the null for the BMI-adjusted trait (estimates from the Meta-analysis on uncleaned data on Y-axis and from cleaned data on X-axis).
Example EasyQC panel of plots to check whether reported P-Values (X-axis, on -log10 scale) match P-Values calculated from the Z-statistic using the reported beta estimates and standard errors (Y-axis, on –log10 scale) with one plot per file. Clearly, several files show deviations, which were due to deviating software specifications used by these studies, which were resolved with study analysts.
Example panel of plots to check issues with allele frequencies. Each plot contrasts the allele frequency of the input file (y-axis) with the allele frequency of the reference (x-axis). In this case the meta-analyzed GIANT height results have been used as reference to compare it to study-specific GWA results for height. Several issues can immediately be detected, which should be solved with the study analysts.
Ftp-site directory structure. (PDF 219 kb)
Effect of the trait transformation issue. (PDF 384 kb)
EasyQC panel of P-Z plots. (PDF 772 kb)
EasyQC panel of EAF-plots. (PDF 670 kb)
Description of EasyQC report variables (File-level QC). (PDF 218 kb)
Description of EasyQC report variables (Meta-level QC). (PDF 213 kb)
Description of EasyQC report variables (Meta-analysis QC). (PDF 439 kb)
Creation of the SNP identifier reference panel. (PDF 402 kb)
Exemplary GWA analysis plan. (PDF 574 kb)
Membership list of the GIANT Consortium. (PDF 711 kb)
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Winkler, T., Day, F., Croteau-Chonka, D. et al. Quality control and conduct of genome-wide association meta-analyses. Nat Protoc 9, 1192–1212 (2014). https://doi.org/10.1038/nprot.2014.071
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