Medical and Developmental Genetics
Quantitative Trait Locus (QTL) Identification
The aims of the study are (i) to genetically map and identify quantitative trait loci (QTL) concerned with common human disorders; (ii) to characterise the genetic architecture of complex traits; (iii) to evaluate the use of genetic markers in trait prediction and treatment.
CCACE (Centre for Cognitive Ageing and Cognitive Epidemiology)
The QTL in Health & Disease group has a long and productive collaboration with Prof Ian Deary and his team at The University of Edinburgh CCACE (http://www.ccace.ed.ac.uk/). Participants in the CROATIA cohorts, the Viking Health Study and the Generation Scotland Scottish Family Health Study (SFHS) have undergone a range of tests using the same validated methods. The data collected includes quantitative information on important domains of cognition such as short and long term memory, speed of processing, executive function and verbal intelligence in addition to derived traits such as the general intelligence factor (g) and a measure of cognitive decline. The general cognitive ability (g) factor is strongly correlated with the other cognitive traits examined and shows high heritability (0.44) in SFHS. It is strongly predictive of many health related outcomes, including cognitive decline, dementia, cardiovascular disease and age at death and is a major focus of the current genome-wide association analyses being carried out in collaboration between the QTL and CCACE research groups.
European Consortium on Urinary Traits (ECUT)
There is compelling evidence for strong associations of kidney disease with adverse outcomes, yet the number of clinical trials in nephrology continues to lag behind most other medical specialities. New biomarkers are needed both to predict the risk of kidney disease in unaffected individuals and outcomes in people with disease. Here, a human population genetics approach to analysis of quantitative trait measures in urine is resulting in novel genome-wide significant “hits” in functionally relevant genes. The laboratory analyses, currently on over 12,000 samples from a range of cohorts, are led by Prof Olivier Devuyst, University of Zurich, and use a variety of techniques including high-throughput ELISA assays, colorimetric methods and ion chromatography. ECUT is a collaboration between the MRC HGU QTL group and researchers in CCACE (LBC1936), Croatia, Italy and Switzerland.
The Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) Consortium was formed to facilitate genome-wide association study meta-analyses and replication opportunities among multiple large and well-phenotyped longitudinal cohort studies. A major goal of the CHARGE Consortium is to produce a series of high-impact publications that describe the collaborative results of genome-wide association scans for a number of cardiovascular, lung, blood and aging phenotypes. The focal point of the design, analysis, interpretation and publication of research results for each specific phenotype is the Phenotype Working Groups (PWGs). The MRC HGU QTL group, particularly through Caroline Hayward, has a long-standing collaboration with CHARGE, with data from the isolates and Generation Scotland cohorts contributing to a wide range of PWGs.
In collaboration with colleagues at the Centre for Global Health, University of Split (P.I. Prof Igor Rudan) and the University of Edinburgh (P.I. Prof Harry Campbell), participants have been recruited to three Croatian cohorts, collectively named the 10 001 Dalmatians study. In the CROATIA-Vis study (recruited in collaboration with the institute of Authropological Research in Zagreb as a population-based study during 2003 and 2004 in the Dalmatian island of Vis), all subjects visited the clinical research centre in the region where they were examined and samples were taken. Biochemical and physiological measurements were performed, detailed genealogies reconstructed, questionnaire of lifestyle and environmental exposures collected, and blood samples stored for further analyses. CROATIA-Korcula participants were recruited in the same manner from the Dalmatian island of Korcula in 2007 and CROATIA-Split from the mainland Croatian city of Split in 2009-2010. Dr Ozren Polasek (Croatian Centre for Global Health and The University of Edinburgh) provided excellent local leadership of the field study team to recruit a further 3,000 participants in Korcula, bringing the total size of the Croatian cohorts to 6,000.
We acknowledge the invaluable contributions of the recruitment teams in Vis, Korcula and Split (including those from the Institute of Anthropological Research in Zagreb and the Croatian Centre for Global Health at the University of Split), the administrative teams in Croatia and Edinburgh and the people of Vis, Korcula and Split. Data and samples from the CROATIA cohorts have been used in a wide range of research projects, many of which are still ongoing. The QTL Programme participates in the EU FP7-funded projects MIMOmics (http://www.mimomics.eu/) and HighGlycan (http://www.highglycan.eu/).
The Generation Scotland (www.generationscotland.org) Scottish Family Health Study (GS:SFHS) is a family-based genetic epidemiology cohort with DNA, other biological samples (serum, urine and cryopreserved whole blood) and socio-demographic and clinical data from approximately 24,000 volunteers, aged 18-98 years, in ~7,000 family groups [Smith BH et al, International Journal of Epidemiology 2013;42:689–700]. Generation Scotland operates as a biobank and as of 2015, over 200 applications from investigators had been processed by the GS Access Committee. GS:SFHS has a breadth of phenotype information, including detailed data on cognitive function, personality traits and mental health. Although data collection was cross-sectional, GS:SFHS becomes a longitudinal cohort as a result of the ability to link to routine NHS data, using the community health index (CHI) number.
DNA from 20,000 GS:SFHS participants has been analysed by high density genome-wide chip genotyping, with low failure and high call rates. QC analyses were performed, data cleaned using quality scores and proportions typed, sample identity verified against recorded gender and pedigree and data checked for unknown relationships based on estimated identity-by-descent. Population stratification was assessed by analysis of principal components and imputing to the 1000 Genome data set. The quality of the checked data was assessed by GWAS analysis on anthropometric and lipid phenotypic quantitative traits, correcting for family relationships using a polygenic kinship matrix and for population stratification using principal component analysis. These results are being compared with those in published meta-analyses and the genome-wide data are being used by the QTL group in a range of research projects involving both international consortia and local experts. For example, mental health research is being led by colleagues at the Division of Psychiatry, University of Edinburgh.
The Viking Health Study
The Northern Isles of Scotland (Orkney and Shetland) have been isolated from the rest of the British Isles by their geographic position at the extreme northern periphery. They have a closely shared settlement history from Viking times to the modern era. The high degree of haplotype sharing is evident in principal components analyses of Y chromosome variation: Orkney and Shetland stand side by side, isolated from all other sampling sites in the British Isles. These island groups represent one of the most isolated populations in the UK, with a shared Scottish and Scandinavian inheritance.
A team led by Dr Jim Wilson (University of Edinburgh and IGMM) has from 2013-2015 recruited over 2,000 volunteers from the archipelago of Shetland to increase the sample size from the Northern Isles. The ORCADES study has been running successfully since 2005 in the neighbouring Orkney Islands and provides a rich resource of 2,000 deeply phenotyped subjects, including data on essentially the same traits and questionnaires as used in the Croatian studies. All subjects have at least two grandparents from Orkney and there is a high degree of kinship. Genome-wide scans are available for all 4,000 participants. The group has also established the Multiple Sclerosis in the Northern Isles (NIMS) study – an MS case-control collection from both Orkney and Shetland. The ORCADES, NIMS and Shetland studies form The Viking Health Study.
Oher Collaborative Programmes
- IGMM, University of Edinburgh: Professor Andrew Jackson (MRC Programme Leader), Dr Toby Hurd (Chancellor’s Fellow), Dr Colin Semple (MRC Programme Leader) Professor Malcolm Dunlop (MRC Programme Leader) Dr Andy Finch (Chancellor’s Fellow), Professor David Porteous.
- University of Edinburgh, Edinburgh, UKProfessor Sarah Wild, Professor Brian Walker, Professor Andrew McIntosh, Professor Ian Deary
- University of Glasgow, Glasgow, UKProfessor Sandosh Padmanabhan and Professor Christian Delles(Institute of Cardiovascular and Medical Sciences), Professor Ruth Jarrett (Institute of Infection, Immunity and Inflammation).
- University of Dundee, Dundee, UK Prof Colin Palmer, Professor Blair Smith (School of Medicine).
- University of Aberdeen, Aberdeen UK Dr Lynne Hocking, Institute of Medical Sciences.
- University of Split, Croatia Professor Igor Rudan, Dr Ozren Polasek and Dr Ivana Kolcic (Public Health Sciences)
- University of Zagreb, Croatia Professor Gordan Lauc.
- Institute for Anthropological Research, Zagreb, Croatia, Dr. Branca Janicijevic, Dr Nina Smolej-Narancic.
- University of Zurich, Professor Olivier Devuyst, Professor Murielle Bochud.
- University of Leicester, Professor Martin Tobin.
- University of Lausanne, Swiss Institute of Bioinformatics, Dr Zoltan Kutalik.
Quantitative Trait Locus (QTL) Identification in Isolate Populations
The aims are (i) to map genetically and identify quantitative trait loci (QTL) concerned with common human disorders in isolated and general populations; (ii) to characterise the genetic architecture of complex traits; (iii) to evaluate the use of genetic markers in trait prediction and treatment.
This Programme takes advantage of the reduced heterogeneity (genetic and environmental) of isolated populations to identify quantitative trait loci (QTL) of biomedical interest. This has been achieved firstly by contributing to the association mapping of over 700 common variants influencing quantitative traits (QTs) and diseases over the past five years, as shown in >200 publications. Secondly, we have elucidated new functions and causal pathways by taking advantage of the laboratory strengths that are available within the IGMM. For example, having found an association between variants in the SLC2A9 gene and serum uric acid, we carried out functional analysis of this putative hexose transporter and showed that it is a major uric acid transporter, influencing gout, the commonest inflammatory arthritis in men (Vitart et al. 2008). Another example of functional follow-up to a genome-wide association study (GWAS) is the HNF1A transcription factor, which we showed to be a master regulator of protein fucosylation (Lauc et al. 2010).
This provided a means of discriminating between different forms of early-onset diabetes and will help to deliver the most appropriate treatment (Thanabalasingham et al. 2013). In current work, we are building on the special properties of isolate populations which have advantages for detecting low frequency, rather than common, QTL variants. We have increased the size of our isolate population samples, by recruitment of 2,000 people in Shetland to the Viking Health Study and increasing the number of people in the Croatian cohorts to 6,000. We have performed genome-wide scans on all individuals and sequencing in a subset, with a view to accurate imputation of the exome-identified rare variants into all the genotyped samples. We will then carry out both association and linkage-based QTL mapping approaches for identifying low frequency variants, whether singly or clustered, and their effects on QTs. Finally, we are using a complementary approach in a large general population sample, the Generation Scotland Scottish Family Health Study, using a combination of extreme selection, genome-wide scans and exome sequencing to find QTL variants by genetic association and other methods.
Approach and Progress
In order to identify QTL influencing disease-related quantitative traits (QTs), we have performed genome-wide linkage and association analyses in samples obtained from adult volunteers from the islands of Vis and Korcula on the Dalmatian coast of Croatia (see figure), in collaboration with colleagues at the Centre for Global Health, University of Split (P.I. Prof. I. Rudan) and the University of Edinburgh (P.I. Prof. Harry Campbell) and a similar study in the islands of Orkney and Shetland (P.I. Dr Jim Wilson). The advantages of studying such populations include the high rates of participation, relatively uniform environment, suitable genetic characteristics of the population (e.g. increased relatedness) and ability to recruit families.
We have measured many hundreds of biomedically-relevant quantitative traits in all members of the Population isolates, and estimated the genetic parameters influencing each trait. We have carried out single nucleotide polymorphism (SNP) genome-wide association studies (GWAS). QT association studies were then carried out which identified a number of strong associations (see Publications).
The dense marker data is being used to estimate individual genome-wide heterozygosity which is being analysed in relation to trait values to test the hypothesis that these are, in some cases, correlated. This type of analysis provides information on which traits have dominance components, and on the number of loci involved. We are currently investigating the predictive value of genetic markers associated with complex traits.
Current and Future Work
Common variants identified in our own and many other GWAS studies have, in general, been of small effect and have not explained much of the total genetic variance for the traits examined. Efforts are now underway to examine the influence of rare variants on quantitative traits, using a variety of methods including whole genome and exome sequencing, regional heritability and haplotype analyses. Isolated populations have a potential advantage for this due to the availability of deeper coverage provided by next generation sequencing coupled with imputation.
In addition to identification of new associations, we are also following up confirmed GWAS associations by attempting to identify causal variants using both bioinformatic and laboratory based methods. Finally, we are performing assays to identify the functional consequences of identified associations at a molecular level.
Current staff involved in this work are:
- Dr Caroline Hayward (Joint PI)
- Dr Veronique Vitart (Joint PI)
- Professor Alan Wright (Visiting Professor)
- Dr Jim Wilson (Joint PI)
- Professor Nick Hastie (Collaborating PI)
- Professor Chris Haley (Collaborating PI)
- Professor Harry Campbell (Collaborating PI)
- Professor Igor Rudan (Collaborating PI)
- Dr Ozren Polasek (Collaborating PI)
- Susan Campbell BSc (Technical Support)
- Chloe Stanton PhD (Post-doctoral Research Fellow)
- Peter Wilcock BSc (Data Manager)
- Shona Kerr PhD (Project Manager)
- Jonathan Marten MSc (PhD Student)
- Thibaud Boutin PhD (Data Analyst)
- Camilla Drake (Technical Support)
- Reka Nagy (PhD Student)
- Lucija Klaric (PhD Student)