Quantitative Trait Loci Research Group
Quantitative Traits in Health and Disease
Section: Biomedical Genomics
Research in a Nutshell
- Understanding genetic architecture of complex traits, using kinship-structured and isolate populations with very rich phenotypes, particularly in terms of “omics.”
- Population cohorts include:
- CROATIA-Split, CROATIA-Korcula and CROATIA-Vis
- Orkney Complex Disease Study (ORCADES)
- VIKING Health Study – Shetland
- Generation Scotland
- Recruitment of a further 4,000 Northern Isles volunteers to our new population cohort, VIKING II.
- Coordination of and contribution to genetic analyses in international consortia including:
- For data and sample access, or if you have any questions, email: accessQTL@ed.ac.uk
|Professor Caroline Hayward||
|Dr Veronique Vitart||
|Professor Jim Wilson||
|Dr Shona Kerr||Project Manager|
|Dr Thibaud Boutin||Data Analyst|
|Dr Lucija Klaric||UKRI Rutherford Fund Fellow|
|Dr Carmen Amador||Research Scientist|
|Dr Andrew Bretherick||ECAT Fellow|
|Dr Pau Navarro||Research Scientist|
|Dr Chloe Stanton||Investigator Scientist|
|Dr Zuhair Mustafa||ECAT PhD student|
|Susan Campbell||Technical Support|
|David Buchanan||Data Manager|
|Anne Richmond||Data Analyst|
|Camilla Drake||Research assistant|
|Bailey Harrington||Research Student|
|Christina Joseph||PhD student|
|Xinyi Jiang||MSc student|
Research in Detail
The QTL (Quantitative Trait Locus) programme has recruited thousands of people to its Scottish and Croatian cohorts. Their special population structures help to improve biological understanding of the causes of variation in complex traits.
Genome – Wide Association Studies (GWAS) have identified many common variants contributing to complex disease. However, a substantial part of genetics in disease remains hidden in rare variants that cannot be easily detected through traditional GWAS.
Rare variants can increase in frequency by drift in isolated populations, facilitating their detection. Due to this, our cohorts focus on these isolated populations.
Rare variants tend to have strong effects, therefore are important for risk stratification in precision medicine. This means they are also well suited to functional follow-up studies.
Our cohorts have a kinship-based structure. This allows us to identify the contribution of genetic variation associated with extended pedigrees. This helps further understand the role of family environments and their environmental variables, which is crucial for future predictive medicine.
The QTL group also leads collaborative research. We integrate biochemical measures with “omics” data, to understand genes affecting a wide range of traits and disease, including uric acid metabolism, kidney function, eye disease and obesity. Discovering these genes and other traits pinpoints important biological pathways and molecular mechanisms.
All data and samples are generated in line with the Medical Research Council’s (MRC) policy on data sharing in human population cohorts.
- Parent of origin genetic effects on methylation in humans are common and influence complex trait variation. Nat Commun: 2019 Mar 27; 10(1):1383
- Genome-wide association meta-analysis highlights light-induced signaling as a driver for refractive error. Nature Genetics: 2018 Jun
- Regional variation in health is predominantly driven by lifestyle rather than genetics. Nat Commun. 2017 Oct 6; 8(1):801.
- Exploration of haplotype research consortium imputation for genome-wide association studies in 20,032 Generation Scotland participants. Genome Med. 2017; 9: 23
- Directional dominance on stature and cognition in diverse human populations Nature. 2015; 523:459
Data and Sample Access
The CROATIA, VIKING and Generation Scotland study data have been the subject of many internal and external collaborations. We welcome more applications to work with us on our datasets. Written and broad informed consent was obtained from all participants. Summary data from specific projects have been deposited in the University of Edinburgh DataShare repository.
We have generated exome sequence data across our cohorts and whole genome sequence data is available for ORCADES, Shetland and CROATIA-Korcula. These datasets have been deposited in the European Genome-phenome Archive under the management of the QTL data access committee.
If you wish to work with any of these datasets, you can email:
- accessQTL@ed.ac.uk (CROATIA, ORCADES, Viking Health Study – Shetland)
- firstname.lastname@example.org (Generation Scotland)
Sharing of data and samples is facilitated by a full-time project manager. All agreed proposals are conducted in collaboration with appropriate members of the QTL team.
- IGMM, University of Edinburgh: Prof Tim Aitman, Prof Wendy Bickmore, Prof Helen Colhoun, Prof Malcolm Dunlop, Dr Toby Hurd, Prof Andrew Jackson, Prof David Porteous, Dr Philip Riches, Prof Colin Semple, Prof Martin Taylor, Dr Pippa Thomson
- University of Edinburgh: Prof Harry Campbell, Prof Ian Deary, Prof Andrew McIntosh, Prof Nik Morton, Prof Igor Rudan
- University of Glasgow: Prof Ruth Jarrett (Institute of Infection, Immunity and Inflammation)
- University of Aberdeen and NHS Grampian: Prof Zosia Miedzybrodzka, Dr John Dean
- University of Split, Croatia: Dr Ozren Polasek and Dr Ivana Kolcic (Public Health Sciences)
- University of Zagreb, Croatia: Prof Gordan Lauc (Faculty of Pharmacy and Biochemistry)
- University of Zurich, Switzerland: Prof Olivier Devuyst, Prof Murielle Bochud
- University of Leicester, Leicester, UK: Prof Martin Tobin
- University of Lausanne, Switzerland: Prof Zoltan Kutalik (Swiss Institute of Bioinformatics)
- University of Otago, New Zealand: Prof Tony Merriman (School of Medical Sciences)
- University of Tartu, Estonia: Prof Tonu Esko, Dr Krista Fischer (Estonian Genome Centre)
- University of Helsinki, Finland: Prof Markus Perola, Dr Hannele Mattson (Finnish Institute of Molecular Medicine)
- University of Uppsala, Sweden: Prof Ulf Gyllensten, Dr Asa Johansson (Science for Life Laboratory)
Partners and Funders
- Generation Scotland
- Scottish Genomes Partnership
Quantitative traits, isolate populations, disease mechanisms, genetic risk factors, DNA sequence analysis, deep phenotyping, linkage, omics, genetic architecture, homozygosity, Y chromosome
GWAS, genomics, imputation, statistical genetics, population genetics, electronic health record linkage, population cohort recruitment and biobanking