Quantitative genetics and systems genetics
Much of my work to date has been centred around the use of genetic reference panels to identify genes that modulate adult neurogenesis. An extensive dataset of hippocampal transcript expression in the BXD panel consisting of 99 mouse strains (Overall et al., 2009) has provided a core resource for the investigation of co-expression networks—an approach which has been both enlightening in itself as well as being the basis for a broader multiscalar research program. The idea that whole-genome scale datasets can enable us to move away from traditional ‘one-gene-at-a-time’ quantitative trait locus (QTL) mapping to analyses where genes are considered within their molecular environment (often referred to as ‘systems genetics’) has grabbed my attention and driven me to explore the methods further. The realisation that not only gene expression, but also expression of physiological phenotypes can be integrated into network models has inspired studies looking at the molecular neighbourhood of several core neurogenesis traits. I have also investigated modifications to standard QTL mapping techniques to incorporate transcript expression networks and expression QTLs (eQTLs) in a two-step approach to identify genomic loci associated with highly complex phenotypes. I am now working on applying quantitative genetics methods to unpick the molecular changes associated with age-related decline in brain function.
Molecular processes of ageing in the brain
My focus has recently moved from gene expression dynamics during adult hippocampal neurogenesis to how the molecular make-up of the hippocampus changes with age. It is important to understand the detailed processes occurring at the cellular level in order to build useful models of how the neurons function—and thus how they affect behaviour.
Environmental regulation of brain function and behaviour
There is a complex interplay between how an organism reacts to its environment, how this reaction is affected by genetics and how the reaction shapes the organism's use of the environment (or even how it directly shapes the environment itself). I am interested in observing animals while they interact with their environments and relating their behaviour to known differences in the molecular make-up of their brains (due, for example, to genetics or ageing). I am particularly intrigued by the possibility of using tracking data to infer behavioural states, as this offers a way to gather large quantities of data for extended periods of time without disturbing the animals. Such a longitudinal study approach is particularly valuable when following age-related effects. I have also been investigating the use of machine learning to extract informative behavioural meta-traits from tracking data (using, for example, my software Rtrack) and am adapting this approach to more complex datasets. Ultimately, brain function, like any phenotype, is a combination of genes and environment, and I believe that both of these aspects need to be studied together to make real progress in the field.
Integrative systems biology
I am also extremely interested in the synthesis of diverse datasets (Overall, 2017) which includes making use of resources in the public domain (Overall et al., 2015). I believe it is important that the hard-won results of research are analysed to their fullest potential—and this includes reanalysing datasets outside of their original context. To this end, I have initiated a project for the adult neurogenesis community which collates all the literature on gene expression and function into a searchable database and ontology (MANGO; Overall et al., 2012).