Genome-wide interaction study of early-life smoking exposure on time-to-asthma onset in childhood

Background: Asthma, a heterogeneous disease with variable age of onset, results from the interplay between genetic and environmental factors. Early-life tobacco smoke (ELTS) exposure is a major asthma risk factor. Only a few genetic loci have been reported to interact with ELTS exposure in asthma. Objective: Our aim was to identify new loci interacting with ELTS exposure on time-to-asthma onset (TAO) in childhood.Methods: We conducted genome-wide interaction analyses of ELTS exposure on time-to-asthma onset in childhood in five European-ancestry studies (totaling 8,273 subjects) using Cox proportional-hazard model. The results of all five genome-wide analyses were meta-analyzed.Results: The 13q21 locus showed genome-wide significant interaction with ELTS exposure (P=4.3x10-8 for rs7334050 within KLHL1 with consistent results across the five studies). Suggestive interactions (P<5x10-6) were found at three other loci: 20p12 (rs13037508 within MACROD2; P=4.9x10-7), 14q22 (rs7493885 near NIN; P=2.9x10-6) and 2p22 (rs232542 near CYP1B1; P=4.1x10-6). Functional annotations and the literature showed that the lead SNPs at these four loci influence DNA methylation in the blood and are located nearby CpG sites reported to be associated with exposure to tobacco smoke components, which strongly support our findings.Conclusion and Clinical Relevance: We identified novel candidate genes interacting with ELTS exposure on time-to-asthma onset in childhood. These genes have plausible biological relevance related to tobacco smoke exposure. Further epigenetic and functional studies are needed to confirm these findings and to shed light on the underlying mechanisms

Identification of a new locus at 16q12 associated with time-to-asthma onset

International audienceBackground: Asthma is a heterogeneous disease in which age-of-onset plays an important role.Objective: We sought to identify the genetic variants associated with time-to-asthma onset.Methods: We conducted a large-scale meta-analysis of nine genome-wide association studies of time-to-asthma onset (total of 5,462 asthmatics with a broad range of age-of-asthma onset and 8,424 controls of European ancestry) performed using survival analysis techniques.Results: We detected five regions associated with time-to-asthma onset at genome-wide significant level (P<5x10-8). We evidenced a new locus in 16q12 region (near cylindromatosis turban tumor syndrome gene (CYLD)) and confirmed four asthma risk regions: 2q12 (IL1RL1), 6p21 (HLA-DQA1), 9p24 (IL33) and 17q12-q21 (ZPBP2-GSDMA). Conditional analyses identified two distinct signals at 9p24 (both upstream of IL33) and at 17q12-q21 (near ZPBP2 and within GSDMA). These seven distinct loci explained together 6.0% of the variance in time-to-asthma onset. In addition, we showed that genetic variants at 9p24 and 17q12-q21 were strongly associated with an earlier onset of childhood asthma (P≤0.002) whereas 16q12 SNP was associated with a later asthma onset (P=0.04). A high burden of disease risk alleles at these loci was associated with earlier age-of-asthma onset (4 years versus 9-12 years, P=10-4).Conclusion: The new susceptibility region for time-to-asthma onset at 16q12 harbors variants that correlate with the expression of CYLD and NOD2 (nucleotide-binding oligomerization domain 2), two strong candidates for asthma. This study demonstrates that incorporating the variability of age-of-asthma onset in asthma modeling is a helpful approach in the search for disease susceptibility genes

Interactions gène-gène et gène-environnement dans les études génétiques de maladies multifactorielles : application à l’asthme et l’atopie

Asthma results from multiple genetic and environmental factors and from interactions between these factors. The global aim of this thesis was to propose gene-gene and gene-environment interaction strategies of analysis to identify new genes associated with the risk of asthma and atopy. To identify new genes underlying atopy, we have proposed a gene-gene interaction strategy of analysis. This strategy integrates a genome-wide association study (GWAS) to which a statistical filtering of the results is applied, and then a selection of the genes most likely to interact using a text mining method applied to the scientific literature from PubMed. The tests of interactions between genetic variants are applied to the selected gene pairs. These analyzes, conducted in three family studies (n = 3,244), identified an interaction between two genes (ADGRV1 and DNAH5) involved in ciliary mobility, an emerging mechanism in asthma. Our second goal was to identify new genes and gene-environment interactions that influence time-to-asthma onset. A meta-analysis of GWAS of the time-to-asthma onset, conducted in nine studies (n = 19,348), identified a new locus associated with the risk of asthma (16q12) and confirmed four more. Five of these nine studies included environmental factor data on early-life tobacco smoke (ELTS) exposure. We conducted a genome-environment-wide interaction analysis of ELTS exposure on time-to-asthma onset in childhood in the five studies (n = 8,273), using survival analysis methods. The results of all five studies were meta-analyzed and followed by functional annotations. We identified four genes with biologically relevant functions related to tobacco smoke exposure.L’asthme résulte de multiples facteurs génétiques et environnementaux et des interactions entre ces facteurs. L’objectif de cette thèse a été de proposer des stratégies d’analyses d’interactions gène-gène et gène-environnement pour identifier de nouveaux gènes associés à l’asthme et l’atopie. Pour identifier de nouveaux gènes dans l’atopie, nous avons proposé une stratégie d’analyse d’interaction gène-gène qui intègre une analyse pan-génomique (GWAS) à laquelle est appliquée un filtrage statistique des résultats, puis un filtrage des gènes susceptibles d’interagir à l’aide d’une méthode de fouille de textes appliquée aux données de la littérature. Les tests d’interactions entre variants génétiques sont appliqués aux paires de gènes sélectionnées. Ces analyses, menées dans trois études familiales (n=3244), ont permis d’identifier une interaction entre deux gènes (ADGRV1 et DNAH5) impliqués dans la mobilité ciliaire, mécanisme émergent dans l’asthme. Notre deuxième objectif était d’identifier de nouveaux gènes et des interactions gène-environnement influençant le délai de survenu de l’asthme. Une méta-analyse de GWAS du délai de survenue de l’asthme, menée dans neuf études (n=19348), a permis d’identifier un nouveau locus (16q12) et d’en confirmer quatre autres. Cinq de ces études comportaient des données sur l’exposition au tabagisme passif pendant la petite enfance (ELTS) (n=8273). Une méta-analyse des cinq études d’interactions gène-ELTS, sur l’ensemble du génome, influençant le délai de survenue de l’asthme, suivie par des annotations fonctionnelles, a permis d’identifier quatre gènes, ayant des fonctions biologiquement pertinentes en lien avec l'exposition au tabac

Gene-gene and gene-environment interactions in genetic studies of multifactorial diseases : application to asthma and atopy

L’asthme résulte de multiples facteurs génétiques et environnementaux et des interactions entre ces facteurs. L’objectif de cette thèse a été de proposer des stratégies d’analyses d’interactions gène-gène et gène-environnement pour identifier de nouveaux gènes associés à l’asthme et l’atopie. Pour identifier de nouveaux gènes dans l’atopie, nous avons proposé une stratégie d’analyse d’interaction gène-gène qui intègre une analyse pan-génomique (GWAS) à laquelle est appliquée un filtrage statistique des résultats, puis un filtrage des gènes susceptibles d’interagir à l’aide d’une méthode de fouille de textes appliquée aux données de la littérature. Les tests d’interactions entre variants génétiques sont appliqués aux paires de gènes sélectionnées. Ces analyses, menées dans trois études familiales (n=3244), ont permis d’identifier une interaction entre deux gènes (ADGRV1 et DNAH5) impliqués dans la mobilité ciliaire, mécanisme émergent dans l’asthme. Notre deuxième objectif était d’identifier de nouveaux gènes et des interactions gène-environnement influençant le délai de survenu de l’asthme. Une méta-analyse de GWAS du délai de survenue de l’asthme, menée dans neuf études (n=19348), a permis d’identifier un nouveau locus (16q12) et d’en confirmer quatre autres. Cinq de ces études comportaient des données sur l’exposition au tabagisme passif pendant la petite enfance (ELTS) (n=8273). Une méta-analyse des cinq études d’interactions gène-ELTS, sur l’ensemble du génome, influençant le délai de survenue de l’asthme, suivie par des annotations fonctionnelles, a permis d’identifier quatre gènes, ayant des fonctions biologiquement pertinentes en lien avec l'exposition au tabac.Asthma results from multiple genetic and environmental factors and from interactions between these factors. The global aim of this thesis was to propose gene-gene and gene-environment interaction strategies of analysis to identify new genes associated with the risk of asthma and atopy. To identify new genes underlying atopy, we have proposed a gene-gene interaction strategy of analysis. This strategy integrates a genome-wide association study (GWAS) to which a statistical filtering of the results is applied, and then a selection of the genes most likely to interact using a text mining method applied to the scientific literature from PubMed. The tests of interactions between genetic variants are applied to the selected gene pairs. These analyzes, conducted in three family studies (n = 3,244), identified an interaction between two genes (ADGRV1 and DNAH5) involved in ciliary mobility, an emerging mechanism in asthma. Our second goal was to identify new genes and gene-environment interactions that influence time-to-asthma onset. A meta-analysis of GWAS of the time-to-asthma onset, conducted in nine studies (n = 19,348), identified a new locus associated with the risk of asthma (16q12) and confirmed four more. Five of these nine studies included environmental factor data on early-life tobacco smoke (ELTS) exposure. We conducted a genome-environment-wide interaction analysis of ELTS exposure on time-to-asthma onset in childhood in the five studies (n = 8,273), using survival analysis methods. The results of all five studies were meta-analyzed and followed by functional annotations. We identified four genes with biologically relevant functions related to tobacco smoke exposure

Leveraging pleiotropic association using sparse group variable selection in genomics data

International audienceBackground: Genome-wide association studies (GWAS) have identified genetic variants associated with multiple complex diseases. We can leverage this phenomenon, known as pleiotropy, to integrate multiple data sources in a joint analysis. Often integrating additional information such as gene pathway knowledge can improve statistical efficiency and biological interpretation. In this article, we propose statistical methods which incorporate both gene pathway and pleiotropy knowledge to increase statistical power and identify important risk variants affecting multiple traits. Methods: We propose novel feature selection methods for the group variable selection in multi-task regression problem. We develop penalised likelihood methods exploiting different penalties to induce structured sparsity at a gene (or pathway) and SNP level across all studies. We implement an alternating direction method of multipliers (ADMM) algorithm for our penalised regression methods. The performance of our approaches are compared to a subset based meta analysis approach on simulated data sets. A bootstrap sampling strategy is provided to explore the stability of the penalised methods. Results: Our methods are applied to identify potential pleiotropy in an application considering the joint analysis of thyroid and breast cancers. The methods were able to detect eleven potential pleiotropic SNPs and six pathways. A simulation study found that our method was able to detect more true signals than a popular competing method while retaining a similar false discovery rate. Conclusion: We developed feature selection methods for jointly analysing multiple logistic regression tasks where prior grouping knowledge is available. Our method performed well on both simulation studies and when applied to a real data analysis of multiple cancers

GCPBayes: An R package for studying Cross-Phenotype Genetic Associations with Group-level Bayesian Meta-Analysis

International audienceSeveral R packages have been developed to study cross-phenotypes associations (or pleiotropy) at the SNP-level, based on summary statistics data from genome-wide association studies (GWAS). However, none of them allow for consideration of the underlying group structure of the data. We developed an R package, entitled GCPBayes (Group level Bayesian Meta-Analysis for Studying Cross-Phenotype Genetic Associations), introduced by Baghfalaki et al. (2021), that implements continuous and Dirac spike priors for group selection, and also a Bayesian sparse group selection approach with hierarchical spike and slab priors, to select important variables at the group level and within the groups. The methods use summary statistics data from association studies or individual level data as inputs, and perform Bayesian meta-analysis approaches across multiple phenotypes to detect pleiotropy at both group-level (e.g., at the gene or pathway level) and within group (e.g., at the SNP level)

Bayesian meta-analysis models for cross cancer genomic investigation of pleiotropic effects using group structure

An increasing number of genome-wide association studies (GWAS) summary statistics is made available to the scientific community. Exploiting these results from multiple phenotypes would permit identification of novel pleiotropic associations. In addition, incorporating prior biological information in GWAS such as group structure information (gene or pathway) has shown some success in classical GWAS approaches. However, this has not been widely explored in the context of pleiotropy. We propose a Bayesian meta-analysis approach (termed GCPBayes) that uses summary-level GWAS data across multiple phenotypes to detect pleiotropy at both group-level (gene or pathway) and within group (eg, at the SNP level). We consider both continuous and Dirac spike and slab priors for group selection. We also use a Bayesian sparse group selection approach with hierarchical spike and slab priors that enables us to select important variables both at the group level and within group. GCPBayes uses a Bayesian statistical framework based on Markov chain Monte Carlo (MCMC) Gibbs sampling. It can be applied to multiple types of phenotypes for studies with overlapping or nonoverlapping subjects, and takes into account heterogeneity in the effect size and allows for the opposite direction of the genetic effects across traits. Simulations show that the proposed methods outperform benchmark approaches such as ASSET and CPBayes in the ability to retrieve pleiotropic associations at both SNP and gene-levels. To illustrate the GCPBayes method, we investigate the shared genetic effects between thyroid cancer and breast cancer in candidate pathways.</p

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

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

Background: Epidemiological studies that examined the association between Parkinson's disease (PD) and cancers led to inconsistent results, but they face a number of methodological difficulties. Objective: We used results from genome-wide association studies (GWASs) to study the genetic correlation between PD and different cancers to identify common genetic risk factors. Methods: We 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. Results: We 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). Conclusions: We show evidence in favor of a contribution of pleiotropic genes to the association between PD and specific cancers