Genome-wide association studies (GWAS) have been effective at beginning to reveal the role of common genetic variants in the control of diseases and underlying traits. Such common variants have very small effects, however, making them refractory to detailed molecular dissection and determination of causal pathways and, hence, potential drug development. Rare variants affecting the same diseases often have much larger effects and are therefore amenable to molecular dissection but are difficult to detect as a consequence of their rarity. The resolution to this problem is to identify genes influenced by the combined effect of several rare and other variants that cannot be individually detected by classical GWAS approaches. We previously developed regional heritability mapping approaches1 that have been effective at detecting clusters of rare and common variants that have escaped detection in standard GWAS2, including for diseases that have been challenging for standard GWAS such as depression3. By taking advantage of the particular characteristics of such variants and newly available sequence information, both from local cohorts and the UK Biobank, this project will exploit the effectiveness of Regional Heritability Mapping (RHM) using recently collected real population, high-density exome sequence data. We will exploit our recently enhanced approach that takes advantage of the insight that rare variants are associated with the relatively large haplotype in which they first arose and restricted to pedigrees descended in the relatively recent past from a common ancestor4. This is particularly relevant to the family-based population VIKING cohorts available in Edinburgh which enable detection of relatively recently arisen variants segregating within families.
The Northern Isles VIKING cohort data includes 4000 participants and is rich in terms of relevant phenotypes (including BMI, whole body fat measures, DXA body scans and extensive metabolomics data) and high density genomic data including high quality whole exome data on all participants recently generated by collaborators Regeneron. UK Biobank also has obesity-related measures and increasing amounts of DXA body scans and will commence release of its sequence data in 2019. We will follow-up the initial research in this much larger resource for a selection of overlapping traits, with its relative lack of family data ameliorated by the size of the dataset.
To explore regional heritability mapping approaches for the analysis of sequence data to identify genes with rare variants contributing to disease risk and understand the basis of genetic variation in obesity related traits. The main components are to
1. investigate existing associations not detected by GWAS (for example gene based) to determine the extent to which they can be explained by rare variants
2. identify previously undetected regions associated with obesity by performing whole exome scans
3. further understand the basis of the observed variation by integrating results with publicly available and local resources including gene expression, proteomic and methylation data
4. explore potential value of identified loci in the drug discovery pipeline with collaborators Regeneron following guidelines in the "Collaboration and Material Transfer Agreement" agreed between Regeneron and UoE
5. optimise RHM for use with exome data
• An understanding of quantitative genetics
• Development of computational skills including scripting and R programming, use of genomics software and experience with high-performance computation
• Experience in complex statistical analyses and interpretation of results of large scale genetic analysis
• Expertise in dealing with large data sets including sequence data
• An understanding of the impact of obesity at the population and individual level and the potential application of genomics to alleviate the problem
• Communication of complex research outcomes to scientific and general audiences
• Experience working in a multidisciplinary team and in collaboration with Regeneron
This MRC programme is joint between the Universities of Edinburgh and Glasgow. You will be registered at the host institution of the primary supervisor detailed in your project selection.
All applications should be made via the University of Edinburgh, irrespective of project location: http://www.ed.ac.uk/studying/postgraduate/degrees/index.php?r=site/view&id=919
Please note, you must apply to one of the projects and you should contact the primary supervisor prior to making your application. Additional information on the application process if available from the link above.
For more information about Precision Medicine visit: http://www.ed.ac.uk/usher/precision-medicine https://www.ed.ac.uk/usher/precision-medicine/project-opportunities
1. Nagamine Y, Pong-Wong R, Navarro P, Vitart V, Hayward C, Rudan I, Campbell H, Wilson J, Wild S, Hicks AA, Pramstaller PP, Hastie N, Wright AF and Haley CS (2012) Localising Loci underlying Complex Trait Variation Using Regional Genomic Relationship Mapping. PLoS One, 7 (10), e46501
2. Shirali M, Pong-Wong R, Navarro P, Knott S, Hayward C, Vitart V, Rudan I, Campbell H, Hastie N, Wright A & Haley C (2015), 'Regional heritability mapping method helps explain missing heritability of blood lipid traits in isolated populations' Heredity, vol 116, pp. 333–338. DOI: 10.1038/hdy.2015.107
3. Zeng Y, Navarro P, Fernandez-pujals AM, Hall LS, Clarke T, Thomson PA, Smith BH, Hocking LJ, Padmanabhan S, Hayward C, Macintyre DJ, Wray NR, Deary I, Porteous DJ, Haley CS & Mcintosh AM (2017), 'A Combined Pathway and Regional Heritability Analysis Indicates NETRIN1 Pathway is Associated with Major Depressive Disorder' Biological Psychiatry, vol 81, no. 4, pp. 336-346. DOI: 10.1016/j.biopsych.2016.04.017
4. Shirali M, Knott S, Pong-Wong R, Navarro P and Haley C (2018) Haplotype Heritability Mapping Method Uncovers Missing Heritability of Complex Traits. Scientific Reports, 8, 4982. DOI: 10.1038/s41598-018-23307-4