Research Scientist (CR1) at Inserm, Genetic Variation and Human Diseases Lab (U946), Paris.
Contribution of population genetics to the study of human diseases: statistical developments for mapping disease genes in isolated and/or consanguineous populations.
2006-2009: Research Scientist, Genetic Epidemiology and Human Population Structure Lab (Inserm U535), Villejuif. Genetic determinism of human diseases in consanguineous populations.
2003-2006: Post-doc, Central Nervous System Disorders Lab (Inserm U679), Paris. Mapping genes involved in neurodegenerative disorders using consanguineous populations.
1998-2001: Research assistant, dept. Biostatistics and Medicine, Univ of Washington, Seattle, USA. Segregation analysis of dyslexia, Linkage analysis of sib-pairs affected with autism.
1997-1998: Teaching assistant, dept. of Statistics, Biostatistics & Center for Quantitative Science, Univ of Washington, Seattle, USA.
1996-1997: Research assistant, dept. Statistics, Univ of Washington, Seattle, USA. Consulting in statistics and biostatistics.
2003: Ph.D. in Biostatistics, emphasis in Statistical Genetics - jointly between Univ of Washington & Univ Paris Sud. Co-advisors: Elizabeth Thompson (UW), Françoise Clerget-Darpoux (Inserm). Title: Estimation of random genome sharing; consequences for linkage detection
1998: M.S. in Biostatistics - Univ of Washington (Seattle, USA)
1997: Paris School of Statistics, Economics & Finance - ENSAE (FR)
1995: Maîtrise in Applied Mathematics & Social Sciences - Univ Paris Dauphine (FR)
Thesis award from the French Biometrics Society (2004)
My research interests lie at the interface of biostatistics, genetic epidemiology and population genetics. It has focused so far on developing methods for studying the genetic component of human diseases in populations where marriages between relatives are common (inbred and/or isolated populations).
Inbred populations are particularly interesting in the case of recessive or quasi-recessive diseases, such as Taybi-Linder syndrome or early forms of Parkinson’s disease, since patients are likely to carry two copies of the same ancestral mutation. For multifactorial diseases, like cancer or quantitative traits, studying isolated populations where inbreeding and relatedness result from a reduced population size and a limited flow of migrants can be a powerful strategy as these populations tend to have greater genetic and environmental homogeneity than large outbred populations.
However, statistical methods have been developed assuming that the patients were from large outbred populations. It is therefore necessary to continue developing methods that are tailored to the specificity of the studied populations.