Multi-ancestry genome-wide study in >2.5 million individuals reveals heterogeneity in mechanistic pathways of type 2 diabetes and complications

Type 2 diabetes (T2D) is a heterogeneous disease that develops through diverse pathophysiological processes. To characterise the genetic contribution to these processes across ancestry groups, we aggregate genome-wide association study (GWAS) data from 2,535,601 individuals (39.7% non-European ancestry), including 428,452 T2D cases. We identify 1,289 independent association signals at genome-wide significance (P&lt;5×10 - 8 ) that map to 611 loci, of which 145 loci are previously unreported. We define eight non-overlapping clusters of T2D signals characterised by distinct profiles of cardiometabolic trait associations. These clusters are differentially enriched for cell-type specific regions of open chromatin, including pancreatic islets, adipocytes, endothelial, and enteroendocrine cells. We build cluster-specific partitioned genetic risk scores (GRS) in an additional 137,559 individuals of diverse ancestry, including 10,159 T2D cases, and test their association with T2D-related vascular outcomes. Cluster-specific partitioned GRS are more strongly associated with coronary artery disease and end-stage diabetic nephropathy than an overall T2D GRS across ancestry groups, highlighting the importance of obesity-related processes in the development of vascular outcomes. Our findings demonstrate the value of integrating multi-ancestry GWAS with single-cell epigenomics to disentangle the aetiological heterogeneity driving the development and progression of T2D, which may offer a route to optimise global access to genetically-informed diabetes care. </p

Genetic drivers of heterogeneity in type 2 diabetes pathophysiology

Type 2 diabetes (T2D) is a heterogeneous disease that develops through diverse pathophysiological processes1,2 and molecular mechanisms that are often specific to cell type3,4. Here, to characterize the genetic contribution to these processes across ancestry groups, we aggregate genome-wide association study data from 2,535,601 individuals (39.7% not of European ancestry), including 428,452 cases of T2D. We identify 1,289 independent association signals at genome-wide significance (P &lt; 5 × 10-8) that map to 611 loci, of which 145 loci are, to our knowledge, previously unreported. We define eight non-overlapping clusters of T2D signals that are characterized by distinct profiles of cardiometabolic trait associations. These clusters are differentially enriched for cell-type-specific regions of open chromatin, including pancreatic islets, adipocytes, endothelial cells and enteroendocrine cells. We build cluster-specific partitioned polygenic scores5 in a further 279,552 individuals of diverse ancestry, including 30,288 cases of T2D, and test their association with T2D-related vascular outcomes. Cluster-specific partitioned polygenic scores are associated with coronary artery disease, peripheral artery disease and end-stage diabetic nephropathy across ancestry groups, highlighting the importance of obesity-related processes in the development of vascular outcomes. Our findings show the value of integrating multi-ancestry genome-wide association study data with single-cell epigenomics to disentangle the aetiological heterogeneity that drives the development and progression of T2D. This might offer a route to optimize global access to genetically informed diabetes care.</p

Genetic drivers of heterogeneity in type 2 diabetes pathophysiology

Type 2 diabetes (T2D) is a heterogeneous disease that develops through diverse pathophysiological processes1,2 and molecular mechanisms that are often specific to cell type3,4. Here, to characterize the genetic contribution to these processes across ancestry groups, we aggregate genome-wide association study data from 2,535,601 individuals (39.7% not of European ancestry), including 428,452 cases of T2D. We identify 1,289 independent association signals at genome-wide significance (P &lt; 5 × 10-8) that map to 611 loci, of which 145 loci are, to our knowledge, previously unreported. We define eight non-overlapping clusters of T2D signals that are characterized by distinct profiles of cardiometabolic trait associations. These clusters are differentially enriched for cell-type-specific regions of open chromatin, including pancreatic islets, adipocytes, endothelial cells and enteroendocrine cells. We build cluster-specific partitioned polygenic scores5 in a further 279,552 individuals of diverse ancestry, including 30,288 cases of T2D, and test their association with T2D-related vascular outcomes. Cluster-specific partitioned polygenic scores are associated with coronary artery disease, peripheral artery disease and end-stage diabetic nephropathy across ancestry groups, highlighting the importance of obesity-related processes in the development of vascular outcomes. Our findings show the value of integrating multi-ancestry genome-wide association study data with single-cell epigenomics to disentangle the aetiological heterogeneity that drives the development and progression of T2D. This might offer a route to optimize global access to genetically informed diabetes care.</p

Nouvelles stratégies d'analyse pour explorer le rôle des variants rares du génome codant et non-codant dans les maladies complexes

New genome sequencing technologies enable to explore the role played by rare variants in complex diseases. To this end, rare variant association tests (RVAT) are performed that compare distributions of rare variants within genes in cases and controls. RVAT are therefore often limited to coding regions of the genome. The purpose of this work was to propose new strategies to analyse the role of rare variants in the whole genome, coding and non-coding. The work has been subdivided into two axes. The first axis is methodological with the extension of two types of RVAT to take into account sub-phenotypes of a disease. These extensions are particularly promising for the analysis of non-coding regulatory variants that could explain phenotypic differences.The second axis focuses on strategies of analysis of rare variants that cannot be currently analysed genome-wide unless computationnally-intensive methods are used. We propose new testing units to gather rare variants that are predefined on the whole-genome. Through the applications to three different diseases, we show that our developments enable a better detection of already known signals and an identification of new ones. The strategies proposed in this work will therefore enable the detection of new associations, especially by means of the R package Ravages available on Github. The detection of such associations between genetic variability and diseases will lead to a better understanding of the biological mecanisms involved in complex diseases.Les nouvelles méthodes de séquençage du génome permettent d’explorer le rôle des variants génétiques rares dans les maladies complexes. Pour ce faire, on réalise des tests d’association avec variants rares (RVAT) qui comparent les distributions de variants rares dans les gènes chez des malades et des témoins. Ces tests se limitent donc très souvent aux régions codantes du génome. Le but de ce travail est de proposer de nouvelles stratégies pour analyser le rôle des variants rares dans tout le génome, codant et non-codant. Le travail est décliné autour de deux axes. Le premier axe est méthodologique avec l’extension de deux grands types de RVAT pour tenir compte des sous-phénotypes d’une maladie. Ces extensions sont particulièrement intéressantes pour étudier le rôle de variants régulateurs non-codants pouvant expliquer des différences phénotypiques. Le deuxième axe porte sur les stratégies d’analyse des variants rares qui ne peuvent aujourd’hui pas être analysés sur tout le génome sans utiliser des méthodes lourdes en temps de calcul. Nous proposons de nouvelles unités prédéfinies sur tout le génome pour regrouper ces variants rares. A travers des applications sur trois pathologies, nous montrons que nos développements permettent de mieux détecter des signaux déjà connus et d’en identifier de nouveaux. Les stratégies d’analyse proposées dans ce travail pourront ainsi permettre de détecter de nouvelles associations, notamment grâce au package R Ravages disponible sur Github. La détection de nouvelles associations entre variabilité génétique et maladies permettra une meilleure compréhension des mécanismes biologiques impliqués dans les maladies complexes

New strategies of analysis to explore the role of coding and non-coding rare variants in complex diseases

Les nouvelles méthodes de séquençage du génome permettent d’explorer le rôle des variants génétiques rares dans les maladies complexes. Pour ce faire, on réalise des tests d’association avec variants rares (RVAT) qui comparent les distributions de variants rares dans les gènes chez des malades et des témoins. Ces tests se limitent donc très souvent aux régions codantes du génome. Le but de ce travail est de proposer de nouvelles stratégies pour analyser le rôle des variants rares dans tout le génome, codant et non-codant. Le travail est décliné autour de deux axes. Le premier axe est méthodologique avec l’extension de deux grands types de RVAT pour tenir compte des sous-phénotypes d’une maladie. Ces extensions sont particulièrement intéressantes pour étudier le rôle de variants régulateurs non-codants pouvant expliquer des différences phénotypiques. Le deuxième axe porte sur les stratégies d’analyse des variants rares qui ne peuvent aujourd’hui pas être analysés sur tout le génome sans utiliser des méthodes lourdes en temps de calcul. Nous proposons de nouvelles unités prédéfinies sur tout le génome pour regrouper ces variants rares. A travers des applications sur trois pathologies, nous montrons que nos développements permettent de mieux détecter des signaux déjà connus et d’en identifier de nouveaux. Les stratégies d’analyse proposées dans ce travail pourront ainsi permettre de détecter de nouvelles associations, notamment grâce au package R Ravages disponible sur Github. La détection de nouvelles associations entre variabilité génétique et maladies permettra une meilleure compréhension des mécanismes biologiques impliqués dans les maladies complexes.New genome sequencing technologies enable to explore the role played by rare variants in complex diseases. To this end, rare variant association tests (RVAT) are performed that compare distributions of rare variants within genes in cases and controls. RVAT are therefore often limited to coding regions of the genome. The purpose of this work was to propose new strategies to analyse the role of rare variants in the whole genome, coding and non-coding. The work has been subdivided into two axes. The first axis is methodological with the extension of two types of RVAT to take into account sub-phenotypes of a disease. These extensions are particularly promising for the analysis of non-coding regulatory variants that could explain phenotypic differences.The second axis focuses on strategies of analysis of rare variants that cannot be currently analysed genome-wide unless computationnally-intensive methods are used. We propose new testing units to gather rare variants that are predefined on the whole-genome. Through the applications to three different diseases, we show that our developments enable a better detection of already known signals and an identification of new ones. The strategies proposed in this work will therefore enable the detection of new associations, especially by means of the R package Ravages available on Github. The detection of such associations between genetic variability and diseases will lead to a better understanding of the biological mecanisms involved in complex diseases

Nouvelles stratégies d'analyse pour explorer le rôle des variants rares du génome codant et non-codant dans les maladies complexes

New genome sequencing technologies enable to explore the role played by rare variants in complex diseases. To this end, rare variant association tests (RVAT) are performed that compare distributions of rare variants within genes in cases and controls. RVAT are therefore often limited to coding regions of the genome. The purpose of this work was to propose new strategies to analyse the role of rare variants in the whole genome, coding and non-coding. The work has been subdivided into two axes. The first axis is methodological with the extension of two types of RVAT to take into account sub-phenotypes of a disease. These extensions are particularly promising for the analysis of non-coding regulatory variants that could explain phenotypic differences.The second axis focuses on strategies of analysis of rare variants that cannot be currently analysed genome-wide unless computationnally-intensive methods are used. We propose new testing units to gather rare variants that are predefined on the whole-genome. Through the applications to three different diseases, we show that our developments enable a better detection of already known signals and an identification of new ones. The strategies proposed in this work will therefore enable the detection of new associations, especially by means of the R package Ravages available on Github. The detection of such associations between genetic variability and diseases will lead to a better understanding of the biological mecanisms involved in complex diseases.Les nouvelles méthodes de séquençage du génome permettent d’explorer le rôle des variants génétiques rares dans les maladies complexes. Pour ce faire, on réalise des tests d’association avec variants rares (RVAT) qui comparent les distributions de variants rares dans les gènes chez des malades et des témoins. Ces tests se limitent donc très souvent aux régions codantes du génome. Le but de ce travail est de proposer de nouvelles stratégies pour analyser le rôle des variants rares dans tout le génome, codant et non-codant. Le travail est décliné autour de deux axes. Le premier axe est méthodologique avec l’extension de deux grands types de RVAT pour tenir compte des sous-phénotypes d’une maladie. Ces extensions sont particulièrement intéressantes pour étudier le rôle de variants régulateurs non-codants pouvant expliquer des différences phénotypiques. Le deuxième axe porte sur les stratégies d’analyse des variants rares qui ne peuvent aujourd’hui pas être analysés sur tout le génome sans utiliser des méthodes lourdes en temps de calcul. Nous proposons de nouvelles unités prédéfinies sur tout le génome pour regrouper ces variants rares. A travers des applications sur trois pathologies, nous montrons que nos développements permettent de mieux détecter des signaux déjà connus et d’en identifier de nouveaux. Les stratégies d’analyse proposées dans ce travail pourront ainsi permettre de détecter de nouvelles associations, notamment grâce au package R Ravages disponible sur Github. La détection de nouvelles associations entre variabilité génétique et maladies permettra une meilleure compréhension des mécanismes biologiques impliqués dans les maladies complexes

Rare variant association testing in the non-coding genome

International audienceThe development of next-generation sequencing technologies has opened-up some new possibilities to explore the contribution of genetic variants to human diseases and in particular that of rare variants. Statistical methods have been developed to test for association with rare variants that require the definition of testing units and, in these testing units, the selection of qualifying variants to include in the test. In the coding regions of the genome, testing units are usually the different genes and qualifying variants are selected based on their functional effects on the encoded proteins. Extending these tests to the non-coding regions of the genome is challenging. Testing units are difficult to define as the non-coding genome organisation is still rather unknown. Qualifying variants are difficult to select as the functional impact of non-coding variants on gene expression is hard to predict. These difficulties could explain why very few investigators so far have analysed the non-coding parts of their whole genome sequencing data. These non-coding parts yet represent the vast majority of the genome and some studies suggest that they could play a major role in disease susceptibility. In this review, we discuss recent experimental and statistical developments to gain knowledge on the non-coding genome and how this knowledge could be used to include rare non-coding variants in association tests. We describe the few studies that have considered variants from the non-coding genome in association tests and how they managed to define testing units and select qualifying variants

Ravages: An R package for the simulation and analysis of rare variants in multicategory phenotypes

Abstract Current software packages for the analysis and the simulations of rare variants are only available for binary and continuous traits. Ravages provides solutions in a single R package to perform rare variant association tests for multicategory, binary and continuous phenotypes, to simulate datasets under different scenarios and to compute statistical power. Association tests can be run in the whole genome thanks to C++ implementation of most of the functions, using either RAVA‐FIRST, a recently developed strategy to filter and analyse genome‐wide rare variants, or user‐defined candidate regions. Ravages also includes a simulation module that generates genetic data for cases who can be stratified into several subgroups and for controls. Through comparisons with existing programmes, we show that Ravages complements existing tools and will be useful to study the genetic architecture of complex diseases. Ravages is available on the CRAN at https://cran.r-project.org/web/packages/Ravages/ and maintained on Github at https://github.com/genostats/Ravages