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, however, making their detailed molecular dissection and the determination of causal pathways 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 depression. A recent enhancement takes advantage of the insight that rare variants are relatively recent and associated with the relatively large haplotype in which they first arose3. 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.
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 investigated height, neuroticism and depression to identify associated variants, and we will follow-up these to consider the entire UK Biobank data set and investigate the nature of the associated regions.
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 disease risk and understand the basis of genetic variation in complex traits. The main components are to
1. optimise size and structure of the genome regions, for example consider only functional elements, for the identification of trait associated variants
2. streamline regional heritability algorithms for large scale implementation with high density genomic data, considering, for example, computational issues to improve speed
3. perform whole genome scans in UK Biobank using regional heritability approaches to identify previously undetected regions associated with depression, height and neuroticism
4. investigate associations not detected by GWAS to determine the extent to which they can be explained by rare variants
5. 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 and development in generic transferable and professional skills as well as the specialist areas of genetics and genomics, including statistical and computational skills. Experience will be gained in state-of-the-art analytical methods and software used in the analysis of human data. Basic training is available through our MSc Programme in Quantitative Genetics and Genome Analysis (http://qgen.bio.ed.ac.uk
The project is relevant to students with a background in statistics or computational sciences and a keen interest in genetics as well as those with a training in quantitative or population genetics and related subjects. Experience in programming would be beneficial.
The position will be based in the Institute of Evolutionary Biology but supervision will be shared with colleagues, Pau Navarro and Chris Haley, in the MRC Human Genetics Unit, Institute of Genetics and Molecular Medicine. The successful applicant will join a group of jointly supervised PhD students and encouraged to participate in both Institutions. More about us can be found on our personal and group web-sites: https://www.ed.ac.uk/profile/sara-knott https://www.ed.ac.uk/profile/pau-navarro https://www.ed.ac.uk/profile/chris-haley
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. 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