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Statistical methods for integrating intermediate quantitative phenotypes in rare disease genetic association analysis

  • Full or part time
    Dr E Turro
    Dr W Astle
    Dr D Greene
  • Application Deadline
    Thursday, January 03, 2019
  • Funded PhD Project (Students Worldwide)
    Funded PhD Project (Students Worldwide)

Project Description

Only half of the approximately 7,000 known rare heritable disorders of humans have an established molecular basis. The genetic determinants that are known have been found through linkage studies and, more recently, from associations between (case/control) disease phenotypes and genetic variants identified by genomic DNA sequencing. Recently, we have developed Bayesian methodology for modeling the mixture of pathogenic and non-pathogenic rare variants at a genomic locus in the context of dominant and recessive Mendelian inheritance of disease (Greene et al, Am. J. Hum. Genet., 2017). This novel approach has aided genetic discovery for several different classes of rare disease and has recapitulated and strengthened the evidence for many previously known associations.

Despite the recent success of whole genome sequencing approaches, a substantial fraction of the genetic aetiologies of rare diseases remain unknown. One way to improve statistical power, is to attempt to measure quantitative aspects of the molecular mechanisms causing the disease, in patients who already have both a clinical phenotype and whole-genome sequence data. This so-called “intermediate-phenotyping” approach is becoming increasingly popular. Because it is not usually clear a priori which subset of the vast set of molecular phenotypes might mediate rare disease risk, many studies attempt to screen a domain of biological variation exhaustively, for example making gene expression measurements in a disease relevant cell-type by high-throughput RNA sequencing. The integration of evidence from a quantitative trait can boost power to detect a case/control genetic association, if the trait exhibits a joint pattern of association consistent with being a statistical intermediate.

The aim of the proposed project is to develop innovative statistical methods for uncovering associations between rare variants and rare Mendelian diseases that make use of quantitative phenotypes measured across all cases and controls. This project will build on the experience of both teams (BSU and Haematology) in the domain of rare disease analysis and statistical genomics, notably using Bayesian modelling strategies. The successful candidate will have access to extensive computing facilities at the University’s high performance computing cluster and join the largest established rare disease research programme in Europe ( The initial focus will be on diseases of the blood stem cell and its progeny, including clotting and immune disorders. Quantitative phenotypes include gene expression, proteomics and flow cytometric measurements. Several thousand cases with a blood-related clinical phenotype have been whole genome sequenced and we have access to deep epigenetic and chromosomal conformation data from all the major mature and progenitor cells of the blood, as well as the results of blood-trait GWAS and blood cell eQTL studies. These data will assist in the development and assessment of emerging methodological ideas. The potential findings will be amenable to rapid experimental follow-up in the laboratory, through established collaborations with colleagues in other institutions and within the Department of Haematology.

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


Greene D, NIHR BioResource, Rirchardson S, Turro E. A fast association test for identifying pathogenic variants involved in rare diseases. American Journal of Human Genetics, 2017 Jul; 101:104–114.

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