Mots-Clés

quantitative genetics, genomics, genetic architecture, effect sizes

Description

Post-doctoral position

Distinguishing between the distribution of the effects of QTLs and markers

20 months temporary position, ISYEB, Paris, France

We offer a 20 months position, working as post-doctoral fellow to improve our ability to study the genetic architecture of complex traits. The candidate will join Pierre de Villemereuil’s team, at the Institute for Systematics, Evolution, Biodiversity (ISYEB), locate within the Muséum National d’Histoire Naturelle (MNHN) in Paris. This position is funded as part of the EvoGenArch Tremplin ANR grant. The position starts on 2024-01-08.

Scientific context

Most traits of ecological and evolutionary relevance are complex traits, i.e. influenced by a large number of loci (named Quantitative Trait Loci, or QTL) in the genomes. The features of the QTL are often referred to as the genetic architecture of the trait. One of the most important collective feature to characterise such architecture is the distribution of the effect sizes (i.e. the phenotypic impact of replacing one allele for the other) of QTL. Current methods allowing for the study of such distribution of effect sizes make the same core assumption: all QTL are a subset of the genetic markers (generally SNPs) available in the genome. However, QTL should come in all possible variety of genetic variants. This distinction between markers and QTL is generally not problematic if the aim is to locate a region of interest in the genome, or perform genomic predictions. However, if the goal is to infer the distribution of effect sizes from an evolutionary perspective, this distinction between markers and QTL becomes highly important. Indeed, if many, or most, markers are only linked to causal QTL, then the amount of linkage disequilibrium between each QTL and the markers will distort the inferred distribution of effect sizes. If we want to have a chance to empirically test the evolutionary predictions from the theoretical models, it is thus necessary to evaluate the breadth of the issue and work toward solutions.

Job description

The job will be mostly computational, based on data simulation, statistical inference and statistical modelling. Under the supervision of Pierre de Villemereuil, the candidate will have two missions:

• Task 1 (10 months): Perform simulations to evaluate the amount of distortion generated by linkage disequilibrium in a scenario where QTL are not a mere subset of the available markers, by comparing theoretical, known distribution of effect sizes and the ones yielded by sate-of-the-art methods in such scenario.

• Task 2 (10 months): Evaluate a solution for a statistical method that would distinguish between markers and QTL.

Requirements and profile

• PhD in evolutionary biology, genomics, bioinformatics or related fields.

• Track record of research activity and academic output (publications, conference communications, etc.).

• Demonstrated ability to work as part of a scientific team, take on research tasks

• Experience in and interest for computational biology, including e.g. data simulation in genomics or empirical statistical genetics.

• Technical skills in R, Python or Julia are a requirement.

• Background in even basic quantitative genetics will be highly appreciated.

Hosting lab & team

Pierre de Villemereuil’s research focuses on the genetics of adaptation, trying to understand how the levels of the genotype, phenotype and environment interacts to drive evolution in wild populations, notably in response to anthropic pressures. A combination of evolutionary ecology, quantitative genetics, population genomics and statistical modelling is necessary to unveil the patterns and processes of adaptation in the wild. The Institute for Systematics, Evolution, Biodiversity (ISYEB), located in the beautiful Jardin des Plantes of the Muséum National d’Histoire Naturelle in Paris, is one of the largest labs studying evolution in the city. ISYEB hosts worldwide leading science in evolution, systematics, phylogeny, genomics and ecology.