Design and quality control of large-scale two-sample Mendelian randomization studies

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Haycock, Philip C. | Borges, Maria Carolina | Burrows, Kimberley | Lemaitre, Rozenn N. | Harrison, Sean | Burgess, Stephen | Chang, Xuling | Westra, Jason M. | Khankari, Nikhil K. | Tsilidis, Konstantinos K. | Gaunt, Tom R. | Hemani, Gibran | Zheng, Jie | Truong, Thérèse | O'Mara, Tracy A. | Spurdle, Amanda B. | Law, Matthew H. | Slager, Susan L. | Birmann, Brenda M. | Saberi Hosnijeh, Fatemeh | Mariosa, Daniela | Amos, Christopher Ian | Hung, Rayjean J. | Zheng, Wei | Gunter, Marc J.R. | Davey Smith, George | Relton, Caroline L. | Martin, Richard Michael

Edité par CCSD ; Oxford University Press (OUP) -

International audience. Background: Mendelian randomization (MR) studies are susceptible to metadata errors (e.g. incorrect specification of the effect allele column) and other analytical issues that can introduce substantial bias into analyses. We developed a quality control (QC) pipeline for the Fatty Acids in Cancer Mendelian Randomization Collaboration (FAMRC) that can be used to identify and correct for such errors. Methods: We collated summary association statistics from fatty acid and cancer genome-wide association studies (GWAS) and subjected the collated data to a comprehensive QC pipeline. We identified metadata errors through comparison of study-specific statistics to external reference data sets (the National Human Genome Research Institute-European Bioinformatics Institute GWAS catalogue and 1000 genome super populations) and other analytical issues through comparison of reported to expected genetic effect sizes. Comparisons were based on three sets of genetic variants: (i) GWAS hits for fatty acids, (ii) GWAS hits for cancer and (iii) a 1000 genomes reference set. Results: We collated summary data from 6 fatty acid and 54 cancer GWAS. Metadata errors and analytical issues with the potential to introduce substantial bias were identified in seven studies (11.6%). After resolving metadata errors and analytical issues, we created a data set of 219 842 genetic associations with 90 cancer types, generated in analyses of 566 665 cancer cases and 1 622 374 controls. Conclusions: In this large MR collaboration, 11.6% of included studies were affected by a substantial metadata error or analytical issue. By increasing the integrity of collated summary data prior to their analysis, our protocol can be used to increase the reliability of downstream MR analyses. Our pipeline is available to other researchers via the CheckSumStats package (https://github.com/MRCIEU/CheckSumStats).

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