Using omics data to improve approaches for discovering the genomic loci underlying human and animal traits

Supervisors: James Prendergast, Colin Semple

Project description:

In recent years a considerable amount of effort has been put into trying to uncover the genetic basis of important animal and human phenotypes. In humans over 50,000 links between genetic variants and the risk of developing a disease or carrying a trait have been uncovered. In cattle this number is almost 100,000. However, for the majority of important phenotypes only a small fraction of the complete set of genomic variants involved are known. A number of potential explanations have been put forward for this “missing heritability” including undetected interactions between variants, rarer variants that have so far been poorly studied or the effect of variation in intermediate phenotypes (such as gene expression) acting to obscure underlying genetic associations. The aim of this PhD project will be to develop new, improved approaches for uncovering genetic loci underlying important human and animal phenotypes by taking into account background variation in the expression of associated genes and other intermediate phenotypes. Exciting new resources, measuring gene expression in multiple tissues across many individuals, make these approaches possible for the first time. Preliminary data from the Prendergast lab suggests that this is also tractable given current computational resources and can yield compelling results.

The student will benefit from supervision across world-leading human genetics and livestock institutes and the supervisors’ access to unparalleled datasets spanning human and animal cohorts. During the course of this PhD the student will use datasets such as the UK biobank cohort of 500,000 individuals ( and livestock cohorts spanning thousands of phenotyped cattle to test and develop their new approaches.


•    Blanco-Gómez et al. Missing heritability of complex diseases: Enlightenment by genetic variants from intermediate phenotypes. Bioessays. 38, 664-73 (2016)
•    Castel et al. Modified penetrance of coding variants by cis-regulatory variation shapes human traits biorxiv
•    Gamazon et al. A gene-based association method for mapping traits using reference transcriptome data Nature Genetics 47, 1091–1098 (2015)