Dear all,
Benoit Liquet (https://sites.google.com/site/benoitliquet/home) is advertising a postdoc position in Statistics, located in the South-West of France, near Biarritz.
More details below, and in attachment. Please forward if you know any interested candidate.
Best,
Guillaume KON KAM KING Università degli Studi di Torino
*https://sites.google.com/site/guillaumekonkamking/home https://sites.google.com/site/guillaumekonkamking/home*
Recruitment grade: young researcher (i.e. with PhD) Location: Anglet, France Duration: 12 to 24 months, starting late 2018 (December or January 2019) Deadline: position open until filled Gross Salary Range: 2699 euros / month
Postdoc description:
Recently, several approaches were developed for completing the agnostic
genome-wide association studies (GWAS) in the discovery of new genetic risk factors. One such approach is gene set analysis (GSA) that incorporates the available biological knowledge of genes in order to provide additional insights into the mechanisms involved in carcinogenesis. Another approach proposed to test variants for association for multiple phenotypes as over the years multiple loci have been found to be associated to several distinct traits revealing the existence of pleiotropic effects. Although a number of statistical approaches have been proposed in the literature for identifying the existence of pleiotropy at SNP-level, not a lot of work has been conducted for investigating pleiotropy at gene- and pathway-levels. We aim to fill this gap by developing advanced statistical methods that test for pleiotropic effects at a pathway level. Also, we will extend these methods for the analysis of GxE interactions, as to the best of our knowledge, pleiotropy methods have not adapted for testing for GxE interactions. Therefore, our first objective is to develop novel statistical methods that will identify shared pathways between different diseases. Our second aim is to extend these methods for testing GxE in general or in the context of pleiotropy. These methods will be applied to explore the genetic relationship between differentiated thyroid cancer (DTC) and breast cancer (BC) types. It has been shown that these two cancers are linked as women with a prior history of DTC are at an increased risk for BC and conversely. Both tumors have been shown to be associated to common hormonal risk factors and shared genetic susceptibility is suspected. We will use data from the CECILE study (1019 BC cases and 999 controls) and from EPITHYR (1345 DTC cases and 1399 controls). All subjects were genotyped using the OncoArray chip that includes more than 500 000 variants. Through the developments of novel statistical methods, we aim to elucidate the common mechanisms between BC and DTC. This project will lead to the development of new statistical methods that will benefit both the epidemiology and statistical communities. These methods will permit to gain novel insights into the inherited genetic basis of BC and DTC by identifying the common biological elements that contribute to their aetiology.