Applications are invited for a fulltime PhD studentship in Statistical Genetics in the Departments of Health Sciences and Genetics at the University of Leicester. This is an exciting cross-disciplinary project and will run alongside a Medical Research Council funded grant on related issues involving collaboration with colleagues from computer science (York) and Forensic statistics (Oslo). It should thus provide an excellent training for a career in Bioinformatics/Genetic Epidemiology/Biostatistics.
Title: Statistical Methods for Large Population Biobank Data
Supervisors: Nuala Sheehan ([log in to unmask]) and Paul Burton (Health Sciences) and Mark Jobling (Genetics)
Project Description:
It is now well understood that the large-scale population Biobank studies that have been set up worldwide to investigate the genetic risk factors underlying the common complex diseases of major public health concern do not have sufficient statistical power to discover rarer genes with large effects, gene-gene and gene-environment interaction effects and the (common) genes with relatively modest effects. Even larger sample sizes are required and this is often only achievable by pooling data from multiple studies. Although scientifically desirable, the sharing of data and especially data on human subjects is frequently prohibited by the particular ethical and legal constraints governing the initial agreements and consents for the individual studies. Moreover, there are undoubtedly lots of related individuals in any large genetic study and some individuals may feature in more than one participating study in a collaborating consortium so existing analyses that assume unrelatedness will be incorrect. Novel statistical methods that enable analyses to be performed across studies without actually sharing individual level data that could be identifying are currently being considered [1] but need to be extended to more complex analyses and different data frameworks. More importantly, being able to identify sets of relatives in order to adjust for the resulting correlation is of key interest. Recent work indicates that we can exploit the ever-increasing availability of genome-wide SNP data for estimating relationships between individuals using a likelihood approach, and we can often improve the search by incorporating additional information such as age, sex, generation gap etc. as informative prior information [2,3]. Surname information can also be considered as part of the prior since it can be a surrogate for shared Y-chromosome data, for example [4].
This project is fully funded for three and a half years and will provide the student with an excellent grounding in state-of-the-art developments for statistical methods in genetic applications. Hard-working, independent-minded and enthusiastic students with a strong statistics background who can work well in such a cross-disciplinary environment are encouraged to apply. The project will include several real datasets including a study of genome-wide data available from surname-ascertained cohorts of males known to share patrilineal ancestry within the last few centuries.
1. Wolfson M, Wallace SE, Masca N. et al. DataSHIELD: resolving a conflict in contemporary bioscience—performing a pooled analysis of individual-level data without sharing the data. Int. J. Epidemiol. 2010; 39: 1372-1382.
2. Sheehan N A and T Egeland. Structured incorporation of prior information in relationship identification problems. Ann. Hum. Genet. 2007; 71: 501-518.
3. Ø Skare, N Sheehan, and T Egeland. (2009) Identification of distant family relationships. Bioinformatics, 25:2376–2382.
4. King TE, Jobling MA (2009) Founders, drift and infidelity: the relationship between Y chromosome diversity and patrilineal surnames. Molecular Biology and Evolution 26, 1093-1102.
Further information and application guidelines can be found on the College web site at URL
http://www2.le.ac.uk/departments/gradschool/finance/funding/scholarships/mbsp-2011
The project is listed as a PhD Studentship in Population Research (Reference - MBSP-11-05) on these pages.
Dr Nuala Sheehan
Reader in Statistical Genetics,
Department of Health Sciences,
University of Leicester,
2nd Floor Adrian Building,
University Road,
Leicester LE1 7RH, UK
Tel: +44 (0)116 2297271
Fax: +44 (0)116 2297250
Email: [log in to unmask]<mailto:[log in to unmask]>
www.le.ac.uk/people/nas11<http://www.le.ac.uk/people/nas11>
http://www2.le.ac.uk/colleges/medbiopsych
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