Novel Methodology for predicting the Functional Effects of Genetic Variation

Rapid developments in next-generation sequencing technologies
has lead to a rapid increase in identified genetic variants,
which may be implicated in disease. Laboratory-based experiments
to investigate functional consequences of these genetic variants
is a costly and time-consuming process. The core application focus of the project
is to construct algorithmically-based methods for predicting
the functional consequence of genetic variants, a problem where we have already
achieved some state-of-the-art results in certain contexts. 50% of the project period will be
given over to the design of improved algorithms specific to this application.
The other 50% of the project period will be given over to achieving
state-of-the-art performance with these new methods, and existing methods,
in application to the stated goal of predicting which variants, insertions
and deletion events are associated with human disease. The pathways to impact
is briefly described in the Case and more fully detailed in the Pathways to Impact
statement.

This is a multidisciplinary project in which algorithm development (to attain optimal predictive performance) is strongly linked to the very important application of predicting which single nucleotide variants and indels are functional in disease. The project will have the following impacts:

1. The algorithms we will develop have a broader range of applications beyond the immediate project.
The stochastic edit code algorithms we propose to develop (Section 3.2) could be used in text processing,
in general. The MKL algorithms (Section 3.5) have potential applications in machine vision, bioinformatics,
supervised network inference, for example.

2. This project has an excellent tie-in with recently announced initiatives, such as Genomics England
(www.genomicsengland.co.uk) a 300 million pound project to gather 100,000 genomes (through
NHS facilities, ). The ability to functional annotate genomes is of obvious paramount importance, to
exploit this .

3. The developed tools would be useful in guiding experimentalists toward areas for investigation:

3.1. There is some prospect for the discovery of novel monogenic diseases, or highlighting the role of
SNVs in multi-factorial disease.

3.2. Interstitial deletion events leading to fusion genes are known disease-drivers in certain cancer contexts
and work in Section 3.1 [4.] and Section 3.2 could highlight novel drug targets.

In our benchmarking study (reported in Section 2.3), we have discovered 90,000 SNVs (at 90% or higher
confidence), indicated as functional in disease.

4. As reported in Pathways to Impact, we are in a good position to pursue any follow-up experimental studies
of identifies targets.