Background: For most complex traits, heritability, the proportion of the variation in the trait that can be attributed to genetics, generally ranges between ~0.2 and ~0.8. Identifying the genetic variants that contribute to these traits has been a major focus of human genetics, especially since the onset of cheap, accurate and fast genotyping methods, which has led to huge investments in a type of study known as Genome-Wide Association Studies (GWAS). However, most of the genetic regions underlying traits remain unknown, and this phenomenon has been termed “missing heritability”1. That is, the identified genetic variants so far found to be linked to important traits, such as intelligence and body weight, can only explain a comparatively small proportion of the estimated total contribution of genetics to these traits.
Hypothesis: One hypothesis suggests that the missing heritability2 problem arises from a combination of problems associated with the genotyping arrays used. Most studies only testing a subset of genetic variants in the genome for associations with a given trait. This suggests, that high quality whole genome sequence data, where all genetic variants are identified, could help fill in the gap3, and therefore explain where the missing heritability lies. The student will test this hypothesis using a novel, large cohort of whole genome sequences of Scottish Individuals.
Aim: The project will focus on modelling whole-genome sequencing data of clinical quality from circa 1,500 people from the Lothian Birth Cohort 1921 and 1936 to understand how much more genetic variation is captured by sequencing data compared to standard commercial genotyping arrays. The preferred traits to be studied will be intelligence and height, however, there is a wide range of other traits available that might interest you.
Student: You will learn to work in a high performance computing environment (ARCHER, the UK National Supercomputer), handling cutting-edge genomic data, and statistical methodology such as REML. Programming skills, or a willingness to learn to program in C++, python or a similar programming language, is essential, as this project will involve handling huge volumes of data.
Applications including a full CV with names and addresses (including email addresses) of two academic referees, should be sent to: Liz Archibald, Postgraduate Research Student Administration, The Roslin Institute and R(D)SVS, The University of Edinburgh, Easter Bush, Midlothian, EH25 9RG. Or emailed to [email protected]
When applying for the studentship please state clearly the title of the studentship and the supervisors in your covering letter.