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(BHF Acc) Development of phenotype-specific models of polygenic contribution to cardiovascular disease


Project Description

A good understanding of the reasons that hinder our advances is an essential requirement in order to improve our capability for discovery and interpretation of causative variants. Researchers usually focus on identifying associations between specific single variants and the phenotype of interest. The main drawback to this approach is that it can only identify variants with a huge contribution to heritability. Another complication is that even in Mendelian diseases, there are various genes that can cause the exact same phenotype.

Hypothesis
Our hypothesis is that all phenotypes are caused by a number of variants, each of which has a different contribution to heritability. However, phenotypes would only be developed when the overall contribution of observed variants reached a particular threshold. Moreover, the overall contribution of all related variants exceeds by far the threshold for developing the phenotype.

Method
We will collate datasets of cases and controls, and for each gene we will infer its functionality based on the observed variants and their tissue-specific expression. Subsequently, we will define the disease phenotype as a function of the additive contribution of various variant-containing genes, and such phenotype is only observed when the contribution function reaches a particular threshold. Advanced optimization approaches will be used to calculate the contribution of each gene in order to maximise the differentiation of cases and controls. Therefore, we will be able to study the contribution of multiple genes concurrently. In more advanced stages we will explore multiplicative effects based on epistatic relationships.
We will start the method development using simulated data, but we will soon move to study specific diseases by collaborating with other researchers in the DCVS (e.g. Bernard Keavney) and beyond.

Entry Requirements:
Applications are invited from UK/EU nationals only. Applicants are expected to hold, or about to obtain, a minimum upper second class undergraduate degree (or equivalent) in Computer Science, Mathematics or Physics. A Masters degree in a relevant subject and/or experience in Data Analysis or Bioinformatics is desirable. We are willing to consider applicants with a background in Biological Sciences if they can prove extensive experience working with optimisation methods or machine learning approaches.

Applications are welcome for entry points in April 2020 or September 2020. Please select the appropriate entry point when applying.

Funding Notes

This project is to be funded under the BHF Accelerator Award. If you are interested in this project, please make direct contact with the Principal Supervisor to arrange to discuss the project further as soon as possible. You MUST also submit an online application form - full details on how to apply can be found on the BHF Accelerator Award website View Website

As an equal opportunities institution we welcome applicants from all sections of the community regardless of gender, ethnicity, disability, sexual orientation and transgender status. All appointments are made on merit.

References

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2.Boyle EA, Li YI, Pritchard JK. An Expanded View of Complex Traits: From Polygenic to Omnigenic. Cell. 2017 Jun 15;169(7):1177-1186. doi: 10.1016/j.cell.2017.05.038.
3.Yeaman S. Local Adaptation by Alleles of Small Effect. Am Nat. 2015 Oct;186 Suppl 1:S74-89. doi: 10.1086/682405.
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5.McCarthy MI, Abecasis GR, Cardon LR, Goldstein DB, Little J, Ioannidis JP, Hirschhorn JN. Genome-wide association studies for complex traits: consensus, uncertainty and challenges. Nat Rev Genet. 2008 May;9(5):356-69. doi: 10.1038/nrg2344.

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