Genetic Clusters to determine Asthma risk and Treatment decisions – GenCAST

Asthma is a frequent disabling chronic respiratory disease. In France, asthma prevalence in children is 11%. Global asthma related costs are high ($81.9 billion in USA). It is a complex multifactorial disorder resulting from the interplay between genetic and environmental factors. Asthma is a heterogeneous disease with a large spectrum of clinical expression reflecting the multiple underlying physiopathological mechanisms or endotypes. Recent studies leveraged phenotypic and biological data to identify subtypes of individuals with asthma but have not included genetic data as a driving component of clustering process.
Our main objective is to characterize asthma endotypes using genetic information from a broad range of phenotypes involved in asthma pathophysiological mechanisms and to further explore the relevance of the inferred endotypes conditional on environmental factors for clinical purposes. The central hypothesis that heterogeneity in asthma clinical presentations can be partly explained by heterogeneity in the contribution of genetic pathways and their specific dependence to non-genetic factors is supported by several observations.
The project will develop with four tasks:
Task 1 is aimed to build a comprehensive and curated catalogue of genome-wide association study (GWAS) summary statistics on asthma phenotypes and on asthma-related physiological and biological phenotypes (cytokines, Immunoglobulin E, lung function, BMI…),
Task 2 is aimed to assess competitive strategies for clustering method specifically adapted to GWAS summary statistics and to develop an innovative solution based on this investigation,
Task 3 is aimed to identify the genetic clusters underlying asthma by applying the clustering approach developed in task 2 on data from task 1, and to characterize their associations with candidate biomarkers and pathways,
Task 4 is aimed to assess the relevance of asthma genetic clusters to characterize asthma endotypes and to explore both their utility for prediction purposes and their dependences to environmental and clinical factors using independent individual-level data from two cohorts: the UK Biobank cohort, one of the largest human genetic population cohort (http://www.ukbiobank.ac.uk/), and the French Epidemiological study on the Genetics and Environment of Asthma (EGEA), an internationally renowned asthma cohort (https://egeanet.vjf.inserm.fr).
The project builds on collaboration between two laboratories with highly complementary expertise and world leader in statistical genetics applied to large-scale GWAS of asthma- and allergy–associated phenotypes (Partner 1) and statistical genetics development methods (Partner 2) which maximizes the success of the study.
This fully data-driven approach to infer genetic pathways will provide ground for classifying asthma cases into more homogeneous subtypes, and therefore, opportunities for implementing more targeted prevention and intervention strategies, and will have major implications in deepening knowledge of the pathogenesis of asthma.