PhD (M/F) in statistical genomics

Job Description

Thesis Subject

Context Dengue virus (DENV), a mosquito-borne flavivirus, represents the most prevalent arboviral disease globally, with 400 million annual infections and 40% of the world's population at risk. Its geographic expansion is accelerating, including autochthonous cases in Southern France since 2014 and endemic transmission in La Réunion since 2018. Clinical outcomes range from asymptomatic infection to severe dengue, a life-threatening condition characterized by plasma leakage and shock. Despite this variability, the genetic determinants of dengue severity remain poorly
understood. Current prevention strategies rely on ineffective vector control and vaccines like
Dengvaxia®, which carry risks of antibody-dependent enhancement (ADE), limiting their use in naïve populations. While host or viral genetic factors have been studied independently, the interactions between human and DENV genomes—critical for understanding pathogenicity—have been largely ignored.
Scientific Innovation This project addresses a major gap in infectious disease research by leveraging Dr. Pedergnana's pioneering Genome-to-Genome (G×G) methodology (Ansari et al., 2017, 2019). Unlike traditional genome-wide association studies (GWAS), which analyse host or pathogen genomes separately, this approach simultaneously examines paired human-viral genomic data to uncover interactions that define disease outcomes. By integrating evolutionary genetics, host susceptibility, and viral virulence, the project offers a holistic view of dengue pathogenicity. The method uses regression models that account for population structure, allowing the identification of specific genetic interactions between human and viral genomes. This approach avoids the
limitations of animal models and provides a direct, data-driven understanding of host-pathogen dynamics in natural infections.
Objectives and Hypothesis The central question of this project is: How do interactions between human and DENV genomes influence infection outcomes and severity? The hypothesis is that dengue severity is determined not by host or viral factors alone, but by specific G×G interactions—for example, a human allele may confer risk only in the presence of certain DENV strains. The project aims to: Identify G×G interactions in dengue-infected cohorts, focusing on plasma leakage, a hallmark of severe dengue. Link these interactions to clinical severity, providing insights into the genetic architecture of dengue pathogenicity. Validate findings using advanced statistical
methods, including mixed-effects models and Bayesian approaches, to ensure robustness and reproducibility.
Methods and Cohorts The study will analyse 1,400 patients from three well-characterized cohorts: 450 from Sri Lanka, 450 from La Réunion, and 500 from the Caribbean. For each patient, paired genomic data (human DNA and viral RNA) will be analysed alongside clinical metadata, including dengue severity, plasma leakage, viral load, serotype, and demographic information. The analytical pipeline includes: A G×G association analysis using regression models that incorporate viral and host principal components to control for population structure. A viral GWAS, the first of its kind for DENV, to explore viral variants associated with disease severity. Cross-cohort
validation and functional follow-up, with a focus on HLA region interactions and viral proteins like NS3 and NS5, which preliminary data suggest are linked to severity.
Preliminary results from 200 Sri Lankan patients have already revealed over 200 variable nucleotides in the DENV genome and significant G×G interactions, particularly in the HLA region and viral proteins associated with severe outcomes.
Expected Impact This project will deliver the first comprehensive G×G interaction map for DENV, revealing novel genetic drivers of severity. By identifying host-viral genetic interactions, it will enable the development of biomarkers for early prediction of severe dengue and inform stratified medical interventions tailored to individual genomic profiles.
From a public health perspective, the findings will support informed vaccine deployment, helping to mitigate risks like ADE, and enhance epidemic preparedness in high-risk regions such as France's overseas territories. Even if the G×G hypothesis is not fully validated, the project will still provide valuable GWAS results for host and viral SNPs, expand global dengue genomic datasets, and generate phylogenetic insights from full-length DENV sequences.

Your Work Environment

The PhD will be conducted at the MIVEGEC laboratory in Montpellier, in the Perturbation, Evolution and Virulence department under the supervision of Dr Vincent Pedergnana.

Compensation and benefits

Compensation

2300 € gross monthly

Annual leave and RTT

44 jours

Remote Working practice and compensation

Pratique et indemnisation du TT

Transport

Prise en charge à 75% du coût et forfait mobilité durable jusqu’à 300€

About the offer

About the CNRS

The CNRS is a major player in fundamental research on a global scale. The CNRS is the only French organization active in all scientific fields. Its unique position as a multi-specialist allows it to bring together different disciplines to address the most important challenges of the contemporary world, in connection with the actors of change.

CNRS

The research professions