Citations 2
Design and quality control of large-scale two-sample Mendelian randomization studies
Haycock, Philip , Borges, Maria Carolina , Burrows, Kimberley , Harrison, Sean , Burgess, Stephen , Chang, Xuling , Khankari, Nikhil K. , Tsilidis, Kostas K, Gaunt, Tom R. , Hemani, Gibran , Zheng, Jie , Truong, Thérèse , Law, Matthew H. , Mariosa, Daniela , Hung, Rayjean J, Smith, George Davey , Relton, Caroline L. , Martin, Richard M. , Peters, Ulrike , Rice, Terri, Gallinger, Steve, Cornish, Alex J. , Vijayakrishnan, Jayaram , Wrensch, Margaret , Korat, Andres V Ardisson , Wheeler, Bill, Nilsson, Björn , Smith, Caren E. , Riadi, David, Claus, Elizabeth B. , Hosnijeh, Ng, Irene Oi‐Lin , Passow, Jeanette E, Han, Jiali , Schildkraut, Joellen M. , Wiemels, Joseph L. , Yang, Keming , Wu, Lang , Boutron, Marie-Christine, Iles, Mark M. , Stanulla, Martin , Gaudet, Mia M., Lenha, Mobuchon, Camp, Nicki J, Sham, Pak C. , Brennan, Paul , Jenkins, Robert B. , Diskin, Sharon J. , Berndt, Sonja I. , Tsavachidis, Spiridon, Channock, Stephen J., Galesloot, Tessel E. , Haycock, Philip , Harrison, Sean , Chang, Xuling , Zheng, Jie , Law, Matthew H , Birmann, Brenda M , Mariosa, Daniela , Zheng, Wei , Davey Smith, George , Borges, Maria C , Martin, Richard M , Iles, Mark M , Truong, Therese , Lemaître, Rozenn N. , Westra, Jason , Tsilidis, Kostas K, O’Mara, Tracy A. , Spurdle, Amanda B. , Slager, Susan L. , Birmann, Brenda M. , Hosnijeh, Fatemeh Saberi , Amos, Christopher I. , Hung, Rayjean J, Zheng, Wei , Gunter, Marc J. , Tintle, Nathan , Cheng, Iona C. , Jenkins, Mark A. , Sud, Amit , Johansson, Mattias , Norman, Aaron D. , Klein, Alison P. , Clay‐Gilmour, Alyssa , Franke, André , Heng, Chew‐Kiat , Song, Ci , Ellinghaus, Eva , Ostroumova, Evgenia , Vathaire, Florent de , Cugliari, Giovanni , Matullo, Giuseppe , Cerhan, James R. , Passow, Jeanette E. Eckel, Foo, Jia Nee , Liu, Jianjun , Barnholtz‐Sloan, Jill S. , Maris, John M. , Hemminki, Kari , Kiemeney, Lambertus A. , Ámundadóttir, Laufey T. , Stern, Marc‐Henri , Purdue, Mark P. , Bondy, Melissa L. , Guénel, Pascal , Taylor, Philip R. , Gharahkhani, Puya , Ostrom, Quinn T. , Stolzenberg‐Solomon, Rachael Z. , Dorajoo, Rajkumar , Houlston, Richard S. , Enroth, Stefan , Harrison, Tabitha A. , Gyllensten, Ulf B , Vijai, Joseph , Shi, Yufang , Yang, Wenjian , Lin, Yi , Van Den Eeden, Stephen K
International Journal of Epidemiology | February 11, 2023 | Volume: 52 | Issue: 5 | Pages: 1498-1521
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).
Lack of association between modifiable exposures and glioma risk: A Mendelian randomisation analysis
Saunders, Charlie N. , Cornish, Alex J. , Kinnersley, Ben , Claus, Elizabeth B. , Schildkraut, Joellen M. , Barnholtz‐Sloan, Jill S. , Bernstein, Jonine L. , Lai, Rose , Rajaraman, Preetha , Johansen, Christoffer , Jenkins, Robert B. , Melin, Beatrice , Wrensch, Margaret , Sanson, Marc , Bondy, Melissa L. , Houlston, Richard S. , Houlston, Richard S , Barnholtz-Sloan, Jill S , Sanson, Marc , Law, Philip , Il’yasova, Dora , Olson, Sara H. , Chanock, Stephen Jacob
Neuro-Oncology | October 23, 2019 | Volume: No volume available | Issue: No issue available | Pages: No page available
Abstract Background The etiological basis of glioma is poorly understood. We have used genetic markers in a Mendelian Randomisation (MR) framework to examine if lifestyle, cardiometabolic and inflammatory factors influence the risk of glioma. This methodology reduces bias from confounding and is not affected by reverse causation. Methods We identified genetic instruments for 37 potentially modifiable risk factors and evaluated their association with glioma risk using data from a genome-wide association study of 12,488 glioma patients and 18,169 controls. We used the estimated odds ratio of glioma associated with each of the genetically defined traits to infer evidence for a causal relationship with the following exposures: lifestyle and dietary factors (height, plasma IGF-1, blood carnitine, blood methionine, blood selenium, blood zinc, circulating adiponectin, circulating carotenoids, iron status, serum calcium, vitamin [A1, B12, B6, E and 25-hydroxyvitamin D], fatty acids levels [mono-unsaturated, omega-3 and omega-6] and circulating fetuin-A); cardiometabolic factors (birth weight, HDL cholesterol, LDL cholesterol, total cholesterol, total triglycerides, basal metabolic rate, body fat percentage, body mass index, fasting glucose, fasting proinsulin, HbA1C levels, diastolic and systolic blood pressure, waist circumference, waist-to-hip ratio) were included; inflammatory factors (C-reactive protein (CRP), plasma IL-6 sRa and serum IgE). Results After correction for the testing of multiple potential risk factors and excluding associations driven by one single nucleotide polymorphism (SNP) no significant association with glioma risk was observed (i.e. PCorrected > 0.05). Conclusions This study did not provide evidence supporting any of the 37 factors examined as having a significant influence on glioma risk.