Leveraging genome sequence for rare variant detection

Interested individuals must follow Steps 1, 2 and 3 at this link on how to apply
http://www.ed.ac.uk/biology/prospective-students/postgraduate/pgr/how-to-apply

In the quest to understand the genetic control of complex traits and diseases, genome-wide association studies (GWAS) have been effective at locating common variants of small effect but have so far had limited success at rare variant detection. Detection of rare variants of large effect would improve our ability to dissect causal pathways underlying disease as well as contribute to individual risk prediction. We previously developed regional heritability mapping approaches, which have been effective at detecting clusters of rare and common variants that have escaped detection in standard GWAS analyses.
This project will explore the effectiveness of regional heritability mapping using newly available sequence data. Our recent developments in mapping approaches have used haplotypes rather than individual SNP genotypes, and this, combined with the use of sequence data, exploits the characteristics of rare variants. These characteristics being that they are associated with the large haplotype in which they first arose and restricted to pedigrees descended in the relatively recent past from a common ancestor. The approach will be investigated and further developed using simulated data and application to real population exome and whole genome sequence data.
The project will focus on understanding the genetic control of obesity and related traits. The worldwide rise in obesity and associated metabolic and cardiovascular diseases is a major health concern and improved knowledge of the genetic architecture would identify factors important in the predisposition to obesity and enable targeted preventative measures to be considered. We have access to large population cohorts with phenotypes, genotypes and sequence data.

Aims
To explore regional heritability mapping approaches for the analysis of sequence data to identify rare variants contributing to disease risk. There are three components.
1. to explore and understand the haplotype structure in the sequence data
2. to investigate and identify promising strategies to analyse sequence data using a regional heritability approach with simulated data
3. to apply the optimum approach to obesity related traits using real population sequence data and phenotypes to identify regions harbouring rare variants affecting these traits and understand the consequence of these variants at the population and individual level

Training outcomes
• An understanding of quantitative genetics and its application in heritability estimation, association analyses and phenotypic prediction
• Development of computational skills including scripting language and R programming, use of genomics software and experience with high-performance and parallel computation.
• Experience in complex mixed linear model statistical analyses and interpretation of results of large scale genetic analysis.
• Expertise in dealing with large data sets including phenotypes, genotypes and sequence (exome and whole-genome) data
• An understanding of the impact of obesity at the population and individual level and the potential application of genomics to alleviate the problem
• Interpretation and communication of complex research outcomes to scientific and general audiences.
• Working in a multidisciplinary team.

This project will be jointly supervised by colleagues in the Institute of Evolutionary Biology and the MRC Human Genetics Unit, MRC Institute of Genetics and Molecular Medicine. Here are the links to our lab web pages:

http://knott.bio.ed.ac.uk/
http://www.ed.ac.uk/profile/pau-navarro
http://www.ed.ac.uk/mrc-human-genetics-unit/research/haley-group