medRxiv preprint doi: https://doi.org/10.1101/2020.10.20.20216358; this version posted May 19, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY 4.0 International license . Exploiting collider bias to apply two-sample summary data Mendelian randomization methods to one-sample individual level data Ciarrah Barry1;2, Junxi Liu1;2, Rebecca Richmond1;2 Martin K Rutter3;4, Deborah A Lawlor1;2, Frank Dudbridge5 & Jack Bowden6;1;∗ 1MRC Integrative Epidemiology Unit at the University of Bristol, U.K. 2Population Health Sciences, University of Bristol, U.K. 3 Division of Diabetes, Endocrinology and Gastroenterology, School of Medical Sciences, Faculty of Biology, Medicine and Health, University of Manchester, UK 4 Diabetes, Endocrinology and Metabolism Centre, Manchester University NHS Foundation Trust, Manchester 5Department of Health Sciences, University of Leicester, Leicester, U.K. 6Exeter Diabetes Group (ExCEED), College of Medicine and Health, University of Exeter, Exeter, U.K. ∗Address for correspondence: Jack Bowden, University of Exeter College of Medicine and Health RILD Building, RD&E Hospital, Barrack Road, Exeter, Devon, EX2 5DW
[email protected] Abstract Over the last decade the availability of SNP-trait associations from genome- wide association studies data has led to an array of methods for performing Mendelian randomization studies using only summary statistics. A common feature of these methods, besides their intuitive simplicity, is the ability to com- bine data from several sources, incorporate multiple variants and account for biases due to weak instruments and pleiotropy.