The genetic map of human molecular phenotypes

Rationale

Genome wide associations studies (GWASs) have discovered many genetic associations with a large range of human traits, but the functional consequences of GWAS signals often remain elusive, as most GWAS signals reside in non-coding genomic regions. However, GWAS signals are enriched in DNA regulatory elements and cell type specific annotations, and thus it is likely that GWAS signals confer their effects through modulating gene regulatory mechanisms.

Genetic factors for molecular traits (DNA methylation, gene expression, protein levels) are being discovered at an astonishing rate. A major hope for these genetic factors is that they can be used to identify causal mechanism of complex traits.[1] Fascinatingly, the dimensionality of molecular phenotyping is bound to surpass the density of human genetic variation, meaning that genetic pleiotropy (where one variant influences multiple phenotypes) is a necessary feature amongst molecular phenotypes. This has critical downstream implications for being able to use genetics to make valid causal inference of putative molecular targets on disease incidence and progression.

Aims & Objectives

This project will build a resource for storing and querying harmonized molecular QTL data in a computational efficient manner, and then use that resource to build pleiotropy maps of human molecular phenotypes. These maps will subsequently be used in evolutionary modelling and in collaboration with Illumina using machine learning and artificial intelligence approaches to understand the basis of molecular pleiotropy. This will include a research visit to Illumina AI lab in Germany.

1. Develop a computational framework for storing and querying molecular QTLs that will integrate with the OpenGWAS project

2. Generate pleiotropy maps using fine mapping and colocalization

3. Use evolutionary models to understand the impact of pleiotropy on natural selection processes

4. Use deep learning to predict disease mechanisms and disease progression from molecular pleiotropy maps

Methods

Currently summary statistics are stored for each GWA dataset separately. However this is not sustainable for QTL summary statistics with millions of molecular features. Therefore a new framework will be developed to store complete molecular QTL statistics for each dataset. Fine mapping and colocalization analysis will be used to integrate methylation QTL statistics from the Genetics of DNA Methylation Consortium, expression QTL statistics from eQTLGen and protein QTL statistics from SCALLOP and ALSPAC. This will result in maps of colocalized molecular traits. We will investigate biological models of pleiotropy, for example by using evolutionary models and gene-environmental interactions. We will use deep learning to identify molecular pleiotropy maps that correspond to distinct phenotypic patient subgroups.

How to apply for this project

This project will be based in Bristol Medical School - Population Health Sciences in the Faculty of Health Sciences at the University of Bristol.

If you have secured your own sponsorship or can self-fund this PhD please visit our information page here for further information on the department of Population Health Science and how to apply.