GCPBayes pipeline: a tool for exploring pleiotropy at the gene level

International audienceCross-phenotype association using gene-set analysis can help to detect pleiotropic genes and inform about common mechanisms between diseases. Although there are an increasing number of statistical methods for exploring pleiotropy, there is a lack of proper pipelines to apply gene-set analysis in this context and using genome-scale data in a reasonable running time. We designed a user-friendly pipeline to perform cross-phenotype gene-set analysis between two traits using GCPBayes, a method developed by our team. All analyses could be performed automatically by calling for different scripts in a simple way (using a Shiny app, Bash or R script). A Shiny application was also developed to create different plots to visualize outputs from GCPBayes. Finally, a comprehensive and step-by-step tutorial on how to use the pipeline is provided in our group's GitHub page. We illustrated the application on publicly available GWAS (genome-wide association studies) summary statistics data to identify breast cancer and ovarian cancer susceptibility genes. We have shown that the GCPBayes pipeline could extract pleiotropic genes previously mentioned in the literature, while it also provided new pleiotropic genes and regions that are worthwhile for further investigation. We have also provided some recommendations about parameter selection for decreasing computational time of GCPBayes on genome-scale data

Multiethnic genome-wide association study of differentiated thyroid cancer in the EPITHYR consortium

International audienceIncidence of differentiated thyroid carcinoma (DTC) varies considerably between ethnic groups, with particularly high incidence rates in Pacific Islanders. DTC is one of the cancers with the highest familial risk suggesting a major role of genetic risk factors, but only few susceptibility loci were identified so far. In order to assess the contribution of known DTC susceptibility loci and to identify new ones, we conducted a multiethnic genome-wide association study (GWAS) in individuals of European ancestry and of Oceanian ancestry from Pacific Islands. Our study included 1554 cases/1973 controls of European ancestry and 301 cases/348 controls of Oceanian ancestry from seven population-based case-control studies participating to the EPITHYR consortium. All participants were genotyped using the OncoArray-500K Beadchip (Illumina). We confirmed the association with the known DTC susceptibility loci at 2q35, 8p12, 9q22.33 and 14q13.3 in the European ancestry population and suggested two novel signals at 1p31.3 and 16q23.2, which were associated with thyroid-stimulating hormone levels in previous GWAS. We additionally replicated an association with 5p15.33 reported previously in Chinese and European populations. Except at 1p31.3, all associations were in the same direction in the population of Oceanian ancestry. We also observed that the frequencies of risk alleles at 2q35, 5p15.33 and 16q23.2 were significantly higher in Oceanians than in Europeans. However, additional GWAS and epidemiological studies in Oceanian populations are needed to fully understand the highest incidence observed in these populations

Investigation of Shared Genetic Risk Factors Between Parkinson's Disease and Cancers

BackgroundEpidemiological studies that examined the association between Parkinson's disease (PD) and cancers led to inconsistent results, but they face a number of methodological difficulties.ObjectiveWe used results from genome-wide association studies (GWASs) to study the genetic correlation between PD and different cancers to identify common genetic risk factors.MethodsWe used individual data for participants of European ancestry from the Courage-PD (Comprehensive Unbiased Risk Factor Assessment for Genetics and Environment in Parkinson's Disease; PD, N = 16,519) and EPITHYR (differentiated thyroid cancer, N = 3527) consortia and summary statistics of GWASs from iPDGC (International Parkinson Disease Genomics Consortium; PD, N = 482,730), Melanoma Meta-Analysis Consortium (MMAC), Breast Cancer Association Consortium (breast cancer), the Prostate Cancer Association Group to Investigate Cancer Associated Alterations in the Genome (prostate cancer), International Lung Cancer Consortium (lung cancer), and Ovarian Cancer Association Consortium (ovarian cancer) (N comprised between 36,017 and 228,951 for cancer GWASs). We estimated the genetic correlation between PD and cancers using linkage disequilibrium score regression. We studied the association between PD and polymorphisms associated with cancers, and vice versa, using cross-phenotypes polygenic risk score (PRS) analyses.ResultsWe confirmed a previously reported positive genetic correlation of PD with melanoma (Gcorr = 0.16 [0.04; 0.28]) and reported an additional significant positive correlation of PD with prostate cancer (Gcorr = 0.11 [0.03; 0.19]). There was a significant inverse association between the PRS for ovarian cancer and PD (odds ratio [OR] = 0.89 [0.84; 0.94]). Conversely, the PRS of PD was positively associated with breast cancer (OR = 1.08 [1.06; 1.10]) and inversely associated with ovarian cancer (OR = 0.95 [0.91; 0.99]). The association between PD and ovarian cancer was mostly driven by rs183211 located in an intron of the NSF gene (17q21.31).ConclusionsWe show evidence in favor of a contribution of pleiotropic genes to the association between PD and specific cancers. © 2023 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society. This article has been contributed to by U.S. Government employees and their work is in the public domain in the USA