Genome-wide association studies (GWAS) are revealing the role of common genetic variants in the control of diseases and underlying traits. Such common variants have very small effects, making their detailed molecular dissection and determination of causal pathways and, hence, potential drug development difficult. Rare variants affecting the same diseases often have much larger effects and are therefore amenable to molecular dissection but are difficult to detect because of their rarity. One resolution to this problem is to identify genes influenced by the combined effect of several rare variants that individually cannot be detected by classical GWAS approaches. We have developed regional heritability mapping approaches1 that are effective at detecting clusters of rare and common variants that escape detection in standard GWAS2, including for diseases that have been challenging for standard GWAS such as depression3. A recent enhancement takes advantage of the insight that rare variants are associated with the relatively large haplotype in which they first arose and are restricted to pedigrees descended in the relatively recent past from a common ancestor4. Promising first results indicate that this approach picks up novel regions associated with disease but requires further optimisation to make it amenable to large scale implementation.
Depression is the largest contributor to global disability with over 300 million people affected. The heritability is around 35% but only about 10% is attributed to common variants identified through GWAS. Rare variants could be a major contributor to this ‘missing’ heritability, making depression and related traits ideal for the development of analytical approaches to detect them.
This project will use high density SNP and sequence data from local cohorts and the UK Biobank to investigate and further develop regional heritability mapping approaches. Preliminary analyses have identified novel depression associated genomic regions, and the project will build on this research to consider the entire UK Biobank data set and investigate the nature of the associated regions by combining with other omics data, from our cohorts and publicly available databases.
The aims are to optimise regional heritability mapping approaches for the analysis of high density SNP and sequence data to identify genes with rare variants contributing to the risk of suffering from depression and understand the genetic basis of this disorder. The main components are to:
optimise size and choice of the genome regions, for example considering only functional elements, that are tested for the identification of trait associated variants
streamline regional heritability algorithms for large scale implementation with high density genomic data, considering, for example, computational issues to improve speed
perform whole genome scans in UK Biobank using regional heritability approaches to identify previously undetected regions associated with depression
investigate associations not detected by GWAS to determine the extent to which they can be explained by specific haplotypes and ultimately rare variants
further understand the basis of the observed variation by integrating results with publicly available and local resources including gene expression, proteomic and methylation data
This project will offer training in generic transferable and professional skills as well as the specialist areas of quantitative genetics and genomics. Training outcomes will include:
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 depression 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
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. For those applying to a University of Glasgow project, your application along with any supporting documents will be shared with University of Glasgow. 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 must contact the primary supervisor prior to making your application. Additional information on the application process is available from the link above.
For more information about Precision Medicine visit: http://www.ed.ac.uk/usher/precision-medicine
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, 116: 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, 81(4): 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