Human Genome Analysis: Genetic Analysis of Multifactorial Diseases
24-30 July 2013
Wellcome Trust Genome Campus, Hinxton, Cambridge
Now closed for applications. To be kept updated on future courses dates, please email us.
Course summary
An intensive, residential, computer-based course aimed at scientists actively involved in genetic analysis of multifactorial traits.
Programme
This advanced course covers statistical methods currently used to map disease susceptibility genes, with an emphasis on (but not limited to) methods that can analyse family data or a combination of families and individuals. Discussions of the latest statistical methodology are complemented by practical hands-on computer exercises using state-of-the-art software. The statistical basics behind each method will be carefully explained so that participants with a non-statistical background can understand.
With a focus on family data, we will discuss fundamental issues needed to increase success in gene mapping studies including: optimal study design, power to detect linkage and association, determining the most appropriate statistical methods and software, interpretation of statistical results and trouble shooting. We will also cover the basic principles of statistical inference, hypothesis testing, population and quantitative genetics and Mendelian inheritance. Our interactive and intensive educational program will enable one to better carry out sophisticated statistical analyses of genetic data, and will also improve one's interpretation and understanding of the results. All the software used is freely available, so that skills learned can be easily applied after the course.
Teaching will take the form of lectures by invited speakers, informal tutorials, hands-on computer sessions, and analysis of disease family data sets. There will also be an opportunity to discuss participants' own data sets.
For more information on course content, please refer to the 2012 timetable (pdf)
Course organiser
Daniel Weeks (University of Pittsburgh, USA)
Course instructors
Heather Cordell (Institute of Genetic Medicine, Newcastle University, UK)
Janet Sinsheimer (University of California, Los Angeles, USA)
Eric Sobel (University of California, Los Angeles, USA)
Joe Terwilliger (Columbia University, New York, USA)
Simon Heath (Centre Nacional d’Anàlisi Genòmica (CNAG), Barcelona, Spain)
Guest speakers
Hakon Hakonarson (Center for Applied Genomics, Children's Hospital of Philadelphia, USA)
Suzanne M. Leal (Center for Statistical Genetics, Baylor College of Medicine, USA)
David van Heel (Blizard Institute, Barts and The London School of Medicine and Dentistry, UK)
Kai Wang (University of Iowa, USA)
Krina Zondervan (Wellcome Trust Centre for Human Genetics, UK)
Feedback from previous courses
“The course was highly relevant for my current work and I am sure it will shape my next steps in my career.”
“I wish to thank the course instructors and the Wellcome trust for providing me the opportunity [to] gain knowledge and share thoughts.”
“Thanks a lot for this great course and the great time we have spent here!”
How to apply
Participants
The target audience is ideally postdoctoral researchers and advanced graduate students who have real data to analyse. Applications from senior established investigators are usually not considered. We aim to accept the member of a research group who is most likely to actually be analysing the data and who is most likely to share their training with other members. This course is aimed at the non-statistically trained researcher rather than those with advanced training in statistics.
Cost
The course tuition fees are subsidised by the Wellcome Trust for scientists based in non-commercial institutions anywhere in the world. This is a residential course, without exception, and there is a registration fee of £890 towards board and lodging for non-commercial applicants. The fee for commercial applicants is £3000.
Bursaries
Limited bursaries are available for non-commercial applicants (50% of fee) and are subject to open competition.
Deadlines
Now closed for applications.