Quantitative Traits in Health and Disease
The QTL (Quantitative Trait Locus) programme leverages the special population structures in our Scottish and Croatian cohorts to deliver biological understanding of the causes of variation in complex traits. A substantial part of the genetic component of disease remains hidden in rare variants that cannot be easily detected in traditional genome-wide association studies (GWAS), which have identified many common variants contributing to complex disease. Rare variants tend to have stronger effects, therefore are critical to risk stratification in precision medicine and more amenable to functional follow-up studies. Moreover, otherwise rare variants can increase in frequency by drift in isolated populations, facilitating their detection. The QTL group leads collaborative research integrating biochemical measures with “omics” data to elucidate uric acid metabolism, kidney function and control of body fat distribution. Discovering the genes affecting these and other traits pinpoints important biological pathways and molecular mechanisms. The kinship-based structure of our cohorts allows identification of both the contribution of genetic variation associated with the pedigree and the role of family environments and specific environmental variables, understanding of which will be crucial for future predictive medicine.
|Dr Caroline Hayward||
|Dr Veronique Vitart||
|Professor Jim Wilson||
|Susan Campbell||Technical Support|
|Shona Kerr||Project Manager|
|Chloe Stanton||Postdoctoral Scientist|
|Camilla Drake||Technical Support|
|Jonathan Marten||PhD Student|
|Thibaud Boutin||Data Analyst|
|Reka Nagy||PhD Student|
|Lucija Klaric||PhD Student|
Exploration of haplotype research consortium imputation for genome-wide association studies in 20,032 Generation Scotland participants
Genome Med. 2017; 9: 23 (link to paper on PubMedCentral)
Directional dominance on stature and cognition in diverse human populations
Nature. 2015; 523:459 (link to paper on PubMedCentral)
Homozygous loss-of-function variants in European cosmopolitan and isolate populations. Hum Mol Genet.2015. 24:5464-74 (link to paper on PubMedCentral)
Genome-wide association analyses identify 18 new loci associated with serum urate concentrations. Nat Genet. 2013. 45:145 (link to paper on PubMedCentral)
Loci Associated with N-Glycosylation of Human Immunoglobulin G Show Pleiotropy with Autoimmune Diseases and Haematological Cancers. PLoS Genet. 2013. 9(1): ):e1003225 (link to paper on PubMedCentral)
The CROATIA, ORCADES, VIKING and GS:SFHS study data have been the subject of many internal (within the University of Edinburgh) and external collaborations, which are encouraged. Written informed consent was obtained from all participants and is broad and enduring. Summary data from specific projects have been deposited in the University of Edinburgh DataShare repository.
All exome sequence data generated to date on the cohorts described here, along with whole genome sequence data for ORCADES, have been deposited in the European Genome-phenome Archive (EGA, https://www.ebi.ac.uk/ega/home). The procedure for those who wish to work with any of the datasets is to email:
The data and sample sharing process is facilitated by a full-time project manager and agreed proposals are conducted in collaboration with appropriate members of the QTL team.
University of Dundee, Dundee, UK, Prof Colin Palmer, Professor Blair Smith (School of Medicine).
Genetic risk factors, quantitative traits, isolate populations, disease mechanisms, DNA sequence analysis, deep phenotyping, linkage, omics, genetic architecture, homozygosity, Y chromosome
GWAS, genomics, imputation, statistical genetics, population genetics, electronic health record linkage, populations cohort recruitment and biobanking