Coventry University Featured PhD Programmes
University of Edinburgh Featured PhD Programmes
Catalysis Hub Featured PhD Programmes
King’s College London Featured PhD Programmes
University of Reading Featured PhD Programmes

Identifying the genetic determinants of translation rate in blood cells and characterising their relevance to human traits

  • Full or part time
    Dr E Turro
    Dr W Astle
  • Application Deadline
    Friday, May 31, 2019
  • Funded PhD Project (Students Worldwide)
    Funded PhD Project (Students Worldwide)

Project Description

The central dogma of molecular biology states that genetically encoded information flows from DNA to mRNA to protein. The flow from DNA to mRNA occurs through a process called transcription and the flow from mRNA to protein through a process called translation. Interestingly, measurements of transcript (i.e. mRNA) and protein abundance do not exhibit particularly strong correlation across genes. This lack of correlation is no doubt partly due to measurement error and partly due to heterogeneity in biological processes such as transcript and protein degradation across genes (1). Nevertheless, there remains the intriguing possibility that the imperfect correlation can be explained in part by heterogeneity in the translation rate, defined as the rate at which protein is generated from a unit quantity of transcripts with a particular RNA sequence, across genes. In addition to heterogeneity in the translation rate between genes within individuals, imperfect correlation between transcript and protein abundance across individuals within genes suggests that the translation rate for a given gene may vary across individuals. It is likely that this variation is partly due to genetic factors inherited in the DNA sequence.
Recent research has identified several mechanisms by which variation in the mRNA sequence can influence the translation rate, including variation that leaves the amino acid sequence of the corresponding protein product intact (2,3). Refining these mechanisms may improve understanding of disease aetiology (4) and aid the development of new therapies (5). The group of Dr Mattia Frontini in the Department of Haematology has generated datasets of transcript abundances measured by high-throughput RNA sequencing, and protein abundance levels measured by mass spectrometry, from four different blood cell types—platelets, monocytes, neutrophils and CD4+ T cells—in approximately 50 individuals (400 datasets in total). This distinctive dataset provides a unique opportunity to develop statistical methods for elucidating the genetic determinants of translation rates in blood cells and to discern the relevance of translation rates to a wide array of human quantitative and disease traits measured in 500,000 genotyped individuals in UK Biobank (6). The student will develop methods to estimate the translation rate at the gene, isoform and individual level using paired RNA-seq and mass-spectrometric measurements of transcript and protein abundances. Genetic determinants of the translation rates will be identified using genetic association analysis. A general prediction model for translation rates will be developed. Using this model, rates will be imputed into 500,000 individuals from UK Biobank and tested for association with a wide array of measured traits..
This project will build on the experience of Drs William Astle and Ernest Turro in the domain of statistical genomics, notably using Bayesian modelling strategies (7,8,9) and the experience of Dr Mattia Frontini in blood genomics and cell biology (10,11). The successful candidate will have access to extensive computing facilities at the University’s high performance computing cluster. Any potential findings will be amenable to rapid experimental follow-up in the laboratory.

Funding Notes

The MRC Biostatistics Unit offers at least 6 fulltime PhDs funded by the Medical Research Council or NIHR for commencement in April 2019 or October 2019.

Academic and Residence eligibility criteria apply.

More details are available at
(View Website )

In order to be formally considered all applicants must also complete a University of Cambridge application form- full details can be found here (View Website )

However informal enquiries are welcome to

Projects will remain open until the studentships are filled but priority will be given to applications received by the 3rd January 2019

References

References
1. Correlation of mRNA and protein in complex biological samples. Maier et al (2009) FEBS Lett.
2. Synonymous but not the same: the causes and consequences of codon bias. Plotkin and Kudla (2011) Nat Rev Genet.
3. Synonymous mutations and ribosome stalling can lead to altered folding pathways and distinct minima. Tsai et al (2008) J Mol Biol.
4. Exposing synonymous mutations. Hunt et al (2014) Trends Genet.
5. A "Silent" Polymorphism in the MDR1 Gene Changes Substrate Specificity. Kimchi-Sarfaty et al (2007) Science.
6. UK Biobank: An Open Access Resource for Identifying the Causes of a Wide Range of Complex Diseases of Middle and Old Age. Sudlow et al (2015) PLoS Medicine.
7. Haplotype and isoform specific expression estimation using multi-mapping RNA-seq reads. Turro et al (2011) Genome Biol.
8. Flexible analysis of RNA-seq data using mixed effects models. Turro, Astle and Tavaré (2014) Bioinformatics.
9. A Bayesian model of NMR spectra for the deconvolution and quantification of metabolites in complex biological mixtures. Astle et al (2012) J Am Stat Assoc.
10. Transcriptional diversity during lineage commitment of human blood progenitors. Chen et al (2014) Science.
11. Platelet function is modified by common sequence variation in megakaryocyte super enhancers. Petersen et al (2017) Nat Commun.

Email Now

Insert previous message below for editing? 
You haven’t included a message. Providing a specific message means universities will take your enquiry more seriously and helps them provide the information you need.
Why not add a message here
* required field
Send a copy to me for my own records.

Your enquiry has been emailed successfully





FindAPhD. Copyright 2005-2019
All rights reserved.