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Three-dimensional genome wide association study of cardiac motion

  • Full or part time
  • Application Deadline
    Sunday, December 01, 2019
  • Competition Funded PhD Project (Students Worldwide)
    Competition Funded PhD Project (Students Worldwide)

Project Description

Heart failure is a leading cause of death globally and, although being at least moderately heritable, the genetic determinants of cardiac function are very poorly defined. Phenotyping for genetic association studies relies on manual analysis of cardiac imaging to quantify explicit functional parameters such as ventricular mass, ejection fraction and left atrial size - but these are crude global measures that are only moderately reproducible and insensitive to the underlying disturbances of cardiovascular physiology, reducing power for genetic studies. A recent meta-analysis did not observing any significant associations between conventional echocardiographic (heart ultrasound) measurements of left ventricular function and common genetic variants – with limitations in phenotyping complex motion cited as a major factor.

We have recently shown how applying computer vision techniques to track the motion of the heart at thousands of points can be a powerful approach for quantifying cardiac function in very large populations. Using these data as input to deep learning networks can efficiently identify complex motion traits from human cardiac MRI studies that are salient for survival prediction. We propose applying this concept to genetic association studies in a powerful new paradigm for cardiac GWAS. Here we will extend our computer vision algorithms to the 100,000 participants in UK Biobank with cardiac MRI to create a unique “atlas” of 3D heart motion. We plan to develop semi-supervised dimensionality reduction techniques for complex cardiac traits, coupled with multi-trait genome-wide analyses that are computationally efficient, to reveal tractable mechanisms underlying heart failure.

This project is at the intersection of computer vision, machine learning dimensionality reduction and multi-trait GWAS – embedded in a vibrant multi-disciplinary team comprising computer scientists, bioinformaticians and clinicians offering strong support for translational research.

To Apply: Please visit our website (https://lms.mrc.ac.uk/study-here/phd-studentships/lms-3-5yr-studentships/) to download an application form.

Funding Notes

This project is one of multiple available projects potentially funded by the MRC. If successful the studentship would cover all tuition fee payments and includes a tax-free stipend amounting to £21,000pa (paid in monthly installments directly to the student) for 3.5 years.

Whilst this funding is available to students worldwide, due to the higher tuition fee rate of overseas students competition is higher and so only exceptional OS applicants will be considered.

References

Bello GA, Dawes TJW, Duan J, Biffi C, de Marvao A, Howard L, Gibbs JSR, Wilkins MR, Cook SA, Rueckert D, O'Regan DP. Deep learning cardiac motion analysis for human survival prediction. Nat Mach Intell. 2019;1:95-104

Duan J, Bello G, Schlemper J, Bai W, Dawes TJW, Biffi C, de Marvao A, Doumou G, O'Regan DP, Rueckert D. Automatic 3D bi-ventricular segmentation of cardiac images by a shape-refined multi-task deep learning approach. IEEE Trans Med Imaging. 2019;38:2151-2164

Biffi C, de Marvao A, Attard MI, Dawes TJW, Whiffin N, Bai W, Shi W, Francis C, Meyer H, Buchan R, Cook SA, Rueckert D, O'Regan DP. Three-dimensional cardiovascular imaging-genetics: a mass univariate framework. Bioinformatics. 2018;34:97-103

Meyer HV, Paolo Casale F, Stegle O, Birney E. LiMMBo: a simple, scalable approach for linear mixed models in high-dimensional genetic association studies. bioRxiv. 2018:255497

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