QTL Group: Quantitative Traits in Health and Disease
Quantitative Trait Locus (QTL) Identification
The aims are
- To map genetically and identify quantitative trait loci (QTL) influencing common disorders in isolated and general populations
- To characterise the genetic architecture of complex traits
- To evaluate the use of genetic markers in trait prediction.
This Programme (co-PIs Haley, Hayward, Vitart and Wilson) takes advantage of the reduced genetic and environmental heterogeneity of isolated populations to identify quantitative trait loci (QTL) of biomedical interest. This has been achieved firstly by contributing to the association mapping of common variants influencing quantitative traits and diseases, as shown in more than 300 publications. Isolated populations have features that continue to make them relevant in the era of 500,000 plus cohorts (e.g. this blog). Secondly, we have elucidated functions and causal pathways by taking advantage of the laboratory strengths available within the Institute of Genetics and Cancer (formerly the Institute of Genetics and Molecular Medicine). 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, (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 (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 to approximately 10,000. We have performed genome-wide scans on all individuals and sequencing in a subset, for accurate imputation of the WGS and exome-identified rare variants into all the genotyped samples. We will carry out both association and linkage-based QTL mapping approaches for identifying low frequency variants, and their effects on a range of traits. Finally, we are using a complementary approach in a large general population sample, the Generation Scotland Scottish Family Health Study, and in the UK Biobank.
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 1), 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) and a similar study in the islands of Orkney and Shetland (P.I. Professor 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, with “omics” in a subset, and estimated the genetic parameters influencing each trait. We have carried out single nucleotide polymorphism (SNP) genome-wide association studies (GWAS) and led international GWAS consortia.
Current and Future Work
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, MR and haplotype analyses. Isolated populations have a potential advantage due to the availability of next generation sequencing coupled with imputation. We will recruit 4,000 new participants to the “VIKING II” Study, starting in 2019.
In addition to identification of new rare variants and QTL, we are also collaborating with other research groups in functional following up of confirmed GWAS associations. The Vitart group in particular is attempting to identify causal variants using both a combination of bioinformatic and laboratory based methods, including performing assays to identify the functional consequences of identified associations at a molecular level.
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. In the CROATIA-Vis study (recruited in collaboration with the Institute of Anthropological 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 from 2012 - 2015, 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.
The Generation Scotland Scottish Family Health Study (GS:SFHS) is a family-based genetic epidemiology cohort with DNA, other biological samples and socio-demographic and clinical data from approximately 24,000 adult volunteers, in ~7,000 family groups. Generation Scotland operates as a biobank and over 300 applications from investigators have 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 became a longitudinal cohort as a result of the ability to link to routine NHS electronic health record data, using the community health index (CHI) number.
Over twenty thousand GS:SFHS participants were selected for genotyping using a large genome-wide array. GWAS results replicate known associations and additionally reveal novel findings, with rare variants, validating the use of the Haplotype Reference Consortium imputation panel. The genome-wide data are being used by the QTL group in a range of research projects involving both international consortia and local experts.
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. A 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.
The Orkney Complex Disease Study (ORCADES) began in 2005 in the Orkney Islands and consists of a rich resource of deeply phenotyped subjects. Over 2,000 volunteers from the archipelago of Shetland were recruited to the Viking Health Study Shetland in 2013-2015. All of these participants, collectively termed “VIKING”, have at least two grandparents from the Northern Isles, more than 90% with three or four such grandparents. VIKING 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. Genome-wide scans are available for all 4,300 participants.