Jim Wilson Research Group (Affiliate)
Quantitative traits in health and disease
Research in a Nutshell
Research in my group spans the interface of population and disease genetics, with a focus on the genetic architecture of complex traits and the identification of genetic variants influencing quantitative risk factors for common diseases such as heart disease and diabetes. I am particularly interested in high kinship isolate populations which have increased utility for rare variant discovery, but we also make use of the UK Biobank and Generation Scotland resources. I am principal investigator of the Viking Genes studies (including ORCADES, VIKING and VIKING II), platform resources for health research to which we have recruited 10,000 participants from the Northern and Western Isles of Scotland. I am also the local PI for the CROATIA isolate cohorts.
My major research interest is in homozygosity and the potential role of recessive genetic variants in determining disease risk – I steer an international consortium (ROHgen) of >200 cohort studies and >1.4 M research participants, which seeks to understand the effect of inbreeding on complex traits in humans. After developing the methods to measure homozygosity and describing the global distribution, we demonstrated an effect of genome-wide homozygosity on height and cognition. More recently we showed for the first time that parental relatedness has a profound effect on fertility of the individual, and smaller effects on risk-taking traits. Moreover, we could show using sibling comparisons that this effect was causally genetic and not confounded, and that this effect is likely due to rare variants in the genome.
Other research themes in my group include the genetics of lifespan, COVID severity, homozygous knockouts from sequence data, the genetics of proteomics, the contribution of the Y chromosome to complex trait variation and genetic architecture more generally in terms of heritability, pleiotropy, etc. I also run a study of Multiple Sclerosis in Orkney and Shetland, focussing both on genetics and the role of vitamin D.
A final strand of activity is in population genetics, particularly focussed on the genetic history of the British Isles, where I was the first to discover genetic evidence for Norse Viking ancestry. I recently described the fine-scale genetic structure of Britain and Ireland, focussing on Scotland. An important SW to NE divide in Scottish genetics was apparent, as well as a number of isolated populations with their own distinct gene pools – the Northern Isles, the Hebrides, the Isle of Man and County Donegal. Detailed analysis in Shetland has shown that 10% of all genetic variants found there are either unique to the islands or at least tenfold enriched there, compared to Edinburgh.
Our recent collaborations with clinical geneticists in NHS Grampian have started to reveal the consequences of these unique gene pools for the people of the Northern Isles, as certain actionable variants are at much higher frequency than elsewhere. After our initial study of a Long QT Syndrome variant in Shetland, we are now moving on to broader surveys of actionable and Mendelian variants in Orkney and Shetland. We are at the forefront of applying genomic medicine in Scotland through the approved return of actionable genetic findings in our VIKING Genes study.
|Professor Jim Wilson||Group Leader|
|Dr Shona Kerr||Project Manager|
|Marisa Muckian||PhD student|
|Jurgis Kuliesius||PhD student|
|Shenyi Zhang||PhD student|
|Dr Rob Young||Academic Track Lecturer (Zhejiang)|
Partners and Funders
- Medical Research Council
- Chief Scientist Office of Scottish Government
- Shetland and Orkney Multiple Sclerosis Research Project
genetic architecture, rare variants, GWAS, isolated populations, cohort studies, inbreeding depression, runs of homozygosity, Multiple Sclerosis, vitamin D, bodyfat, retinal vessels, Y chromosome, mtDNA, multi-omics, lifespan, food preferences
The Wilson group is a dry group with expertise in population and quantitative genetics, including genome-wide association, mixed models, polygenic risk scores, quantitative traits, whole genome sequence analysis, pipelining, running cohort studies