We study the regulation and evolution of gene expression using a combination of experimental and computational approaches.
Our work brings together quantitative genetics, genomics, epigenetics, and evolutionary biology to achieve a deeper understanding of how genetic variation within and between species affects genome-wide gene expression and ultimately shapes the phenotypic diversity of life.
Some of our long-term goals are to better understand:
Some specific projects in the lab:
Genome-wide scans for adaptive evolution of gene expression
Despite a great deal of work in this area, only a handful of cases of adaptive gene expression evolution have been discovered, each by a painstaking candidate gene approach. We have recently developed the first method that systematically scans genome-wide data to identify genes whose expression levels are under positive selection. We are currently extending this method in a variety of ways, and applying it to a wide range of species, including yeast, insects, plants, mice, and humans.Publications: Fraser et al 2010; Fraser et al 2011; Fraser 2011; Fraser et al 2012; Fraser 2013; Artieri & Fraser 2013.
Pinpointing mutations underlying gene expression adaptations
Even once gene expression adaptations have been identified (above), a major challenge is to identify the precise nucleotide changes that were selected. Knowing these will allow us to study a number of fundamental issues, such as the molecular mechanisms of gene expression adaptation, as well as the roles of epistasis and fitness trade-offs in adaptation. We are using yeast as a model for these studies, since it offers unparalleled advantages for engineering specific genetic alterations and measuring fitness.Publications: Fraser et al 2010; Chang et al 2013.
How genetic variation affects gene expression within species
Any genetic variant that contributes to the evolution of gene expression must first arise as a polymorphism within a species. Many studies have mapped these variants genome-wide, as gene expression quantitative trait loci (eQTL). We are expanding our understanding of how genetic variation can impact gene expression by mapping variants affecting different aspects of gene expression, such as its inter-individual variability or its temporal regulation. We also apply RNA-seq to hybrids between diverged strains, in order to generate genome-wide maps of all genes affected by cis-regulatory divergence.Publications: Fraser & Xie 2009; Babak et al 2010; Fraser & Schadt 2010; Fraser et al 2011.
Epigenetic evolution of gene expression
Gene expression is not only determined by the genome, but also by the epigenome (heritable information not encoded by DNA). However very little is known about how evolutionary changes in the epigenome may affect gene expression. We are focusing on two important epigenetic phenomena: DNA methylation and imprinting. For DNA methylation we are investigating both the causes and the consequences of its variation among humans. We are also exploring the evolutionary dynamics of imprinting, where only one allele of a gene (either the mother's or the father's) is expressed in any individual.Publications: Fraser et al 2012; Lam et al 2012.
Interpreting results from genome-wide association studies
In the past several years, hundreds of studies have scanned the human genome for genetic variants that influence disease risk. Unfortunately these do not implicate either the causal variants or the genes involved, so they provide very limited insight into the causes of disease. We are developing methods that allow us to systematically identify the affected genes and the molecular mechanisms underpinning these disease associations. We are also quantifying the selection pressures these disease-associated variants have experienced during human evolution.Publications: Fraser & Xie 2009.