Master/Thèse 1 en Epidémiologie Génétique

Title: Dissection of air pollution effects on biological markers involved in allergic response in asthma

Genetic and environmental factors influencing biological markers involved in allergic response in asthma

Description of the subject: The identification of the genetic determinants of multifactorial diseases such as asthma, allergic diseases and cancers rests on the integration of methodological developments in statistical genetics together with the implementation and analysis of large scale genetic epidemiology studies of human diseases.

Asthma is a common respiratory disease characterized by variable airflow obstruction, inflammation of the airways and bronchial hyperresponsiveness. The prevalence of asthma and other allergic diseases (hay fever, eczema) has been rising steadily over the past decades in westernized societies (prevalence of 10% in France) making asthma a major public health issue. The increased in asthma and allergy prevalence is likely due to changes in exposure to environmental factors and their effects on the immune response and / or gene expression. Air pollution and Tobacco smoke are known risk factors of allergic disease exacerbations, such as asthma, and possibly of allergic disease onset through genetic variations and epigenetic changes. Asthma is a paradigm of multifactorial disease resulting from many genetic and environmental factors and the interplays between these factors. Asthma is a complex disease associated with intermediate phenotypes involved in immune response to inflammation and lung function. Although the genetic component of asthma and asthma-related phenotypes has long been established, the extent to which the genetic factors involved are common (pleiotropy) or specific to these phenotypes is unclear.

The overall goal of our asthma project is to identify the genetic determinants influencing asthma and asthma-related phenotypes (binary, quantitative and polychotomous traits) and to understand their mode of action while accounting for complex mechanisms (including gene-gene and gene-environment interactions, pleiotropy and genetic heterogeneity).

This project is based on the French Epidemiological study on the Genetics and Environment of Asthma (EGEA, http://egeanet.vjf.inserm.fr).1 This study includes 388 families ascertained through paediatric and adult asthmatics and 415 population-based controls (total of 2,120 subjects with detailed clinical, phenotypic, biological and environmental data), plus two follow-up of EGEA subjects 10 (EGEA2) and 20 years (EGEA3) after the first survey that have improved the clinical (asthma evolution) and environmental (windows of exposure) information. Environmental factors: Detailed information on smoking habits was collected at each survey and outdoor air pollution exposure was estimated with annual average concentrations of nitrogen oxides and particulate matter (NOx, NO2, PM10, PM2.5 & Ozone). Genetic data: Genome‐wide SNP data (Illumina 610K chip) and imputed data from Hapmap2 (2.5 million SNPs) and 1000Genome project (~14 million SNPs) are available in 1,904 individuals of EGEA study.2 Genome-wide DNA methylation data at two time-point measures (EGEA1 and EGEA2) will be available for a subset of individuals.

This specific project aims to characterize the genetic and environmental components of the levels of biological markers (cytokines: quantitative phenotypes) involved in the allergic responses in asthma through genome-wide study. This work aims:

– To assess strategies of association analysis allowing studying multiple phenotypes jointly. We will considered: 1) a principal component analysis (PCA) based strategy3 applied to the phenotypes of interest, 2) an approach testing by an ordinal regression the linear combination of the most associated phenotypes with genotypes of each SNP,4 and 3) an approach based on the combination of GWAS results (p-values) of each phenotype at each genetic marker,5

– To identify the common and specific genetic factors influencing levels of these biological markers by considering first each biological marker at a time, and then several phenotypes simultaneously using the optimal strategy defined above,

– To characterize gene-environment interactions with environmental factors (air pollution exposures) that can modulate the genetic factor effects on biological markers using a joint test and a “cocktail” method,6,7

– To study whether these gene-environment (GxE) interaction effects on biological markers are mediated through epigenetic mechanisms (DNA methylation) using causal inference test approaches.8

To note that the exact contribution of the student to this project is open to discussion and will depend on his/her background.

Tools and methodologies useful to the project:

  1. Statistical modelling and statistical software (STATA, SAS..)
  2. Genetic data analysis (R or ad-hoc software)
  3. Programming language

References :
1. Bouzigon E, Corda E, Aschard H, et al. Effect of 17q21 variants and smoking exposure in early-onset asthma. N Engl J Med. 2008; 359(19): 1985-94.
2. Moffatt MF, Gut IG, Demenais F, et al. A large-scale, consortium-based genomewide association study of asthma. N Engl J Med. 2010 Sep 23;363 13):1211-21.
3. Aschard H, Vilhjálmsson BJ, Greliche N, et al. Maximizing the power of principal-component analysis of correlated phenotypes in genome-wide association studies. Am J Hum Genet. 2014; 94(5): 662-76.
4. O’Reilly PF, Hoggart CJ, Pomyen Y, et al. MultiPhen: joint model of multiple phenotypes can increase discovery in GWAS. PLoS One 2012; 7:e34861.
5. van der Sluis S, Posthuma D, Dolan CV. TATES: efficient multivariate genotype-phenotype analysis for genome-wide association studies. PLoS Genet. 2013; 9:e1003235.
6. Kraft P, Yen YC, Stram DO, et al. Exploiting gene-environment interaction to detect genetic associations. Hum Hered. 2007; 63(2): 111-9.
7. Hsu L, Jiao S, Dai JY, et al. Powerful cocktail methods for detecting genome-wide gene-environment interaction.Genet Epidemiol. 2012; 36: 183-94.
8. Liu Y, Aryee MJ, Padyukov L, et al. Epigenome-wide association data implicate DNA methylation as an intermediary of genetic risk in rheumatoid arthritis. Nat Biotechnol. 2013; 31(2): 142-7.

Lab environment: We are a lab of genetic epidemiologists and the main objectives of the research program are to identify the genetic factors involved in human diseases, to understand their mode of action and to characterize the other factors (environment, lifestyle..) which may modulate their effect on disease. Our work includes data collection and data production, as well as data analysis and modeling approaches. Currently, we have different projects focused on asthma, cancer, neurodegenerative or cardiovascular related traits in large outbred populations and in population isolates. These projects are part of international collaborations. Lab website: http://genestat.cephb.fr

Working language: French or English

Application: Interested candidates should send a C.V., the names of three references and a letter of motivation to emmanuelle.bouzigon@inserm.fr