Evolution of the genetic architecture of quantitative traits (EvoGenArch)

A major challenge in evolutionary biology is to understand and predict the evolution of phenotypic traits influenced by many genes, a.k.a. quantitative traits, which represent the majority of adaptive traits.

For this, we require an accurate knowledge of the ‘genetic architecture’ of a trait, here defined as the statistical distribution of the effects of the genes on the phenotype.

However, it has not been possible to firmly check theoretical predictions against empirical data, due to a lack of method to accurately infer genetic architecture.

In this project, I will develop novel statistical methodology to accurately infer the genetic architecture of traits in the wild, by leveraging the statistical correlation between neighbouring sites in the genome, or linkage disequilibrium.

Using the power of a new linked-read sequencing to obtain information on recombination, I will apply this novel methodology to study the link between the genetic architecture of the traits, and the ‘evolutionary regime’, i.e. characteristics of selective and neutral factors.

First, I will perform an in-depth study of the link between selection and genetic architecture on a long-term-studied wild population of common lizards.

Second, I will apply my method to analogous traits across more than 20 species to infer their genetic architecture and use knowledge about the evolutionary regime and phylogenetic context, to assess the influence of those components on the variation in genetic architecture.

By combining novel methodology with analysis within and across species, this project will provide a firm empirical basis for thinking about genetic architecture.

In turn, this understanding of the expected distribution of the gene effects, depending on the evolutionary context, will improve our ability to forecast adaptation, predict phenotype from genomic data and locate genes in diverse fields such as evolution, agronomy, conservation and human health.