Cugliari, Giovanni
Mayo Clinic
Italian institute for Genomic Medicine
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).
DNA repair and cancer in colon and rectum: Novel players in genetic susceptibility
Pardini, Barbara , Corrado, Alda, Berndt, Sonja I. , Brenner, Hermann , Caan, Bette J. , Casey, Graham , Chang‐Claude, Jenny , Gala, Manish , Gallinger, Steven , Haile, Robert W. , Harrison, Tabitha A. , Hayes, Richard B. , Hoffmeister, Michael , Huyghe, Jeroen R. , Jenkins, Mark A. , Lindor, Noralane M. , Nan, Hongmei , Slattery, Martha L. , Vodičková, Ludmila, Vodička, Pavel , Gemignani, Federica , Peters, Ulrike , Naccarati, Alessio , Hoffmeister, Michael , Slattery, Martha L , Jenkins, Mark A , Haile, Robert W , Harrison, Tabitha A , Brenner, Hermann , Hayes, Richard B , Nan, Hongmei , Vodicka, Pavel , Caan, Bette J , Paolicchi, Elisa , Cugliari, Giovanni , Bezieau, Stéphane , Bien, Stephanie A. , Campbell, Peter T. , Chan, Andrew T. , Cotterchio, Michelle , Hopper, John L. , Marchand, Loı̈c Le , Lin, Yi , Newcomb, Polly A. , Ogino, Shuji , Potter, John D. , Schoen, Robert E. , White, Emily E. , Vymetalkova, Veronika Polakova , Landi, Stefano
International Journal of Cancer | June 18, 2019 | Volume: 146 | Issue: 2 | Pages: 363-372
Interindividual differences in DNA repair systems may play a role in modulating the individual risk of developing colorectal cancer. To better ascertain the role of DNA repair gene polymorphisms on colon and rectal cancer risk individually, we evaluated 15,419 single nucleotide polymorphisms (SNPs) within 185 DNA repair genes using GWAS data from the Colon Cancer Family Registry (CCFR) and the Genetics and Epidemiology of Colorectal Cancer Consortium (GECCO), which included 8,178 colon cancer, 2,936 rectum cancer cases and 14,659 controls. Rs1800734 (in MLH1 gene) was associated with colon cancer risk (p‐value = 3.5 × 10−6) and rs2189517 (in RAD51B) with rectal cancer risk (p‐value = 5.7 × 10−6). The results had statistical significance close to the Bonferroni corrected p‐value of 5.8 × 10−6. Ninety‐four SNPs were significantly associated with colorectal cancer risk after Binomial Sequential Goodness of Fit (BSGoF) procedure and confirmed the relevance of DNA mismatch repair (MMR) and homologous recombination pathways for colon and rectum cancer, respectively. Defects in MMR genes are known to be crucial for familial form of colorectal cancer but our findings suggest that specific genetic variations in MLH1 are important also in the individual predisposition to sporadic colon cancer. Other SNPs associated with the risk of colon cancer (e.g., rs16906252 in MGMT) were found to affect mRNA expression levels in colon transverse and therefore working as possible cis‐eQTL suggesting possible mechanisms of carcinogenesis.