Design and quality control of large-scale two-sample Mendelian randomization studies
Authors: Haycock Philip C , Borges Maria Carolina , Burrows Kimberley , Lemaitre Rozenn N , Harrison Sean , Burgess Stephen , Chang Xuling , Westra Jason , Khankari Nikhil K , Tsilidis Kostas K , Gaunt Tom , Hemani Gibran , Zheng Jie , Truong Therese , O’Mara Tracy A , Spurdle Amanda B , Law Matthew H , Slager Susan L , Birmann Brenda M , Saberi Hosnijeh Fatemeh , Mariosa Daniela , Amos Christopher I , Hung Rayjean J , Zheng Wei , Gunter Marc J , Davey Smith George , Relton Caroline , Martin Richard M ,   Fatty Acids in Cancer Mendelian Randomization Collaboration, 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 
Published: October 2023

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).