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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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
Supplementary Figure 1 Ftp-site directory structure.
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).
Supplementary Figure 2 Effect of the trait transformation issue.
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).
Supplementary Figure 3 EasyQC panel of P-Z plots.
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.
Supplementary Figure 4 EasyQC panel of EAF-plots.
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.
Supplementary Figure 1
Ftp-site directory structure. (PDF 219 kb)
Supplementary Figure 2
Effect of the trait transformation issue. (PDF 384 kb)
Supplementary Figure 3
EasyQC panel of P-Z plots. (PDF 772 kb)
Supplementary Figure 4
EasyQC panel of EAF-plots. (PDF 670 kb)
Supplementary Table 1
Description of EasyQC report variables (File-level QC). (PDF 218 kb)
Supplementary Table 2
Description of EasyQC report variables (Meta-level QC). (PDF 213 kb)
Supplementary Table 3
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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