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

2023; Oxford University Press; Volume: 52; Issue: 5 Linguagem: Inglês

10.1093/ije/dyad018

1464-3685

Philip Haycock, Maria Carolina Borges, Kimberley Burrows, Rozenn N. Lemaître, Sean Harrison, Stephen Burgess, Xuling Chang, Jason Westra, Nikhil K. Khankari, Konstantinos K. Tsilidis, Tom R. Gaunt, Gibran Hemani, Jie Zheng, Thérèse Truong, Tracy A. O’Mara, Amanda B. Spurdle, Matthew H. Law, Susan L. Slager, Brenda M. Birmann, Fatemeh Saberi Hosnijeh, Daniela Mariosa, Christopher I. Amos, Rayjean J. Hung, Wei Zheng, Marc J. Gunter, George Davey Smith, Caroline L. Relton, Richard M. Martin, Nathan Tintle, Ulrike Peters, Terri Rice, Iona Cheng, Mark A. Jenkins, Steve Gallinger, Alex J. Cornish, Amit Sud, Jayaram Vijayakrishnan, Margaret Wrensch, Mattias Johansson, Aaron D. Norman, Alison P. Klein, Alyssa Clay‐Gilmour, André Franke, Andres V Ardisson Korat, Bill Wheeler, Björn Nilsson, Caren E. Smith, Chew‐Kiat Heng, Ci Song, David Riadi, Elizabeth B. Claus, Eva Ellinghaus, Evgenia Ostroumova, Hosnijeh, Florent de Vathaire, Giovanni Cugliari, Giuseppe Matullo, Irene Oi‐Lin Ng, James R. Cerhan, Jeanette E Passow, Jia Nee Foo, Jiali Han, Jianjun Liu, Jill S. Barnholtz‐Sloan, Joellen M. Schildkraut, John M. Maris, Joseph L. Wiemels, Kari Hemminki, Keming Yang, Lambertus A. Kiemeney, Lang Wu, Laufey T. Ámundadóttir, Marc‐Henri Stern, Marie-Christine Boutron, Mark M. Iles, Mark P. Purdue, Martin Stanulla, Melissa L. Bondy, Mia M. Gaudet, Mobuchon Lenha, Nicki J Camp, Pak C. Sham, Pascal Guénel, Paul Brennan, Philip R. Taylor, Puya Gharahkhani, Quinn T. Ostrom, Rachael Z. Stolzenberg‐Solomon, Rajkumar Dorajoo, Richard S. Houlston, Robert B. Jenkins, Sharon J. Diskin, Sonja I. Berndt, Spiridon Tsavachidis, Stefan Enroth, Stephen J. Channock, Tabitha A. Harrison, Tessel E. Galesloot, Ulf Gyllensten, Joseph Vijai, Yufang Shi, Wenjian Yang, Yi Lin, Stephen K. Van Den Eeden,

Cancer-related molecular mechanisms research

Abstract 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).