Design and quality control of large-scale two-sample Mendelian randomization studies-Reference-Cited by-全球学者库

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

Published:2023-04-12 Issue:5 Volume:52 Page:1498-1521
ISSN:0300-5771
Container-title:International Journal of Epidemiology
language:en
Short-container-title:

Author:

Haycock Philip C¹²^ORCID,Borges Maria Carolina¹²,Burrows Kimberley¹²^ORCID,Lemaitre Rozenn N³,Harrison Sean¹²^ORCID,Burgess Stephen⁴,Chang Xuling⁵⁶^ORCID,Westra Jason⁷,Khankari Nikhil K⁸,Tsilidis Kostas K⁹¹⁰,Gaunt Tom¹²,Hemani Gibran¹²,Zheng Jie¹²^ORCID,Truong Therese¹¹,O’Mara Tracy A¹²¹³,Spurdle Amanda B¹²¹³,Law Matthew H¹⁴¹⁵^ORCID,Slager Susan L¹⁶,Birmann Brenda M¹⁷^ORCID,Saberi Hosnijeh Fatemeh¹⁸,Mariosa Daniela¹⁹^ORCID,Amos Christopher I²⁰,Hung Rayjean J²¹,Zheng Wei²²^ORCID,Gunter Marc J²³,Davey Smith George¹²^ORCID,Relton Caroline¹²,Martin Richard M¹²²⁴,Tintle Nathan,Peters Ulrike,Rice Terri,Cheng Iona,Jenkins Mark,Gallinger Steve,Cornish Alex J,Sud Amit,Vijayakrishnan Jayaram,Wrensch Margaret,Johansson Mattias,Norman Aaron D,Klein Alison,Clay-Gilmour Alyssa,Franke Andre,Ardisson Korat Andres V,Wheeler Bill,Nilsson Björn,Smith Caren,Heng Chew-Kiat,Song Ci,Riadi David,Claus Elizabeth B,Ellinghaus Eva,Ostroumova Evgenia,Hosnijeh ,de Vathaire Florent,Cugliari Giovanni,Matullo Giuseppe,Ng Irene Oi-Lin,Cerhan James R,Passow Jeanette E,Foo Jia Nee,Han Jiali,Liu Jianjun,Barnholtz-Sloan Jill,Schildkraut Joellen M,Maris John,Wiemels Joseph L,Hemminki Kari,Yang Keming,Kiemeney Lambertus A,Wu Lang,Amundadottir Laufey,Stern Marc-Henri,Boutron Marie-Christine,Iles Mark Martin,Purdue Mark P,Stanulla Martin,Bondy Melissa,Gaudet Mia,Lenha Mobuchon,Camp Nicki J,Sham Pak Chung,Guénel Pascal,Brennan Paul,Taylor Philip R,Gharahkhani Puya,Ostrom Quinn,Stolzenberg-Solomon Rachael,Dorajoo Rajkumar,Houlston Richard,Jenkins Robert B,Diskin Sharon,Berndt Sonja I,Tsavachidis Spiridon,Enroth Stefan,Channock Stephen J,Harrison Tabitha,Galesloot Tessel,Gyllensten Ulf,Joseph Vijai,Shi Y,Yang Wenjian,Lin Yi,Van Den Eeden Stephen K,

Affiliation:

1. MRC Integrative Epidemiology Unit (IEU), University of Bristol , Bristol, UK

2. Population Health Sciences, Bristol Medical School, University of Bristol , Bristol, UK

3. Department of Medicine, University of Washington , Seattle, WA, USA

4. MRC Biostatistics Unit, University of Cambridge , Cambridge, UK

5. Department of Paediatrics, Yong Loo Lin School of Medicine, National University of Singapore , Singapore, Singapore

6. Khoo Teck Puat—National University Children's Medical Institute, National University Health System , Singapore, Singapore

7. Department of Mathematics, Statistics, and Computer Science, Dordt College, Sioux Center , IA, USA

8. Division of Genetic Medicine, Department of Medicine, Vanderbilt University Medical Center , Nashville, TN, USA

9. Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London , London, UK

10. Department of Hygiene and Epidemiology, University of Ioannina School of Medicine , Ioannina, Greece

11. Université Paris-Saclay, UVSQ, Inserm, Gustave Roussy, Team “Exposome, Heredity, Cancer and Health”, CESP , Villejuif, France

12. Genetics and Computational Biology Division, QIMR Berghofer Medical Research Institute , Brisbane, QLD, Australia

13. School of Medicine, Faculty of Health Sciences, University of Queensland , Brisbane, Australia

14. Statistical Genetics, QIMR Berghofer Medical Research Institute , Brisbane, QLD, Australia

15. School of Biomedical Sciences, Faculty of Health, and Institute of Health and Biomedical Innovation, Queensland University of Technology , Kelvin Grove, QLD, Australia

16. Department of Health Sciences Research, Mayo Clinic , Rochester, MN, USA

17. Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School , Boston, MA, USA

18. Institute for Risk Assessment Sciences, Utrecht University , Utrecht, The Netherlands

19. Genomic Epidemiology Branch, International Agency for Research on Cancer (IARC) , Lyon, France

20. Dan L Duncan Comprehensive Cancer Center Baylor College of Medicine , Houston , USA

21. Lunenfeld-Tanenbaum Research Institute, Sinai Health and University of Toronto , Toronto, Canada

22. Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, Vanderbilt University Medical Center , Nashville, TN, USA

23. Section of Nutrition and Metabolism, International Agency for Research on Cancer (IARC) , Lyon, France

24. NIHR Biomedical Research Centre at University Hospitals Bristol and Weston NHS Foundation Trust and the University of Bristol , Bristol, UK

Abstract

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).

Funder

Cancer Research UK

Integrative Cancer Epidemiology Programme

National Institute for Health Research

Biomedical Research Centre at University Hospitals Bristol

Weston NHS Foundation Trust

University of Bristol

National Institute for Health Research Senior Investigator

Department of Health and Social Care

MRC Integrative Epidemiology Unit

Medical Research Council

Skills Development Fellowship