This intensive, residential, computational course is aimed at scientists actively involved in genetic analysis of rare (Mendelian) or complex human traits who anticipate using state-of-the-art statistical analysis techniques on genetic data collected on related and unrelated individuals.

The programme provides a comprehensive overview of the statistical methods currently used to map disease susceptibility genes in humans and non-model organisms with an emphasis on data collected on families or populations (which should often be considered a collection of large families).

This is a small residential course, with a low student to instructor ratio, personalized attention, and the instructors actively involved throughout the week. Students present on their own research to the group and receive constructive criticism particularly pertaining to study design and analysis. This course is unique among statistical genetics courses in that it concentrates on approaches that capitalize on families or a combination of families and unrelated individuals in the post-GWAS era.

Why does this course emphasise family data?

In the GWAS and post-GWAS era, gene mapping has concentrated on analysis of unrelated individuals due to the simplicity and convenience. However, these approaches tend to treat any relatedness among individuals as a nuisance to be adjusted away rather than a benefit to be exploited.  Furthermore, researchers are increasingly aware that the use of unrelated individuals has limitations that family data overcome. Family studies have many advantages in gene mapping, such as:

  1. They are extremely powerful in situations where unrelated individuals lack power (e.g., when rare variants underlie the etiology) since related affecteds are more likely to share the same disease predisposing gene than unrelated affecteds
  2. They overcome confounding factors such as population stratification and allow better modeling of environmental factors
  3. They allow the examination of a wealth of nuanced genetic models; and (4) they provide ways to rule out artifacts and false associations that can plague genetic analyses.  This course will enable participants to make better use of their data that may include related individuals.

During this course, discussions of the latest statistical methodology are complemented by practical hands-on computer exercises using state-of-the-art software. The statistical principles behind each method will be carefully explained so that participants with a non-statistical background can understand and better interpret their results. Note, however, that the bioinformatics pipelines for calling variants from next generation sequencing data are not covered; the focus of this course is on the downstream analysis of the called variants.

Target Audience

This course is aimed primarily at advanced Ph.D. students and post-docs who are early in their careers, whose projects involve data that could be analysed by the methods covered in this course. Since we emphasize methods for handling family data, there is a preference for candidates who have some family data or who are likely to have access to family data in the future.  Programming experience is not required, but candidates without prior experience with the Unix/Linux/Mac command line will be expected to read through a tutorial on this topic prior to the course (available at http://www.ee.surrey.ac.uk/Teaching/Unix/).

Feedback from the 2017 course

"The amount of opportunity we had for discussion and interaction with one another was probably one of the best aspects - the opportunity to converse with so many great minds is priceless."
"The course was very well organized, the teachers are very easy going, friendly and accesible, and makes all much easier."
"Thank you for an informative and enjoyable course. It was very well organised and well taught. The instructors were very hands on and available throughout the course."
"This was a truly excellent course - all the instructors who were involved in the teaching sessions were excellent."
"Great course. I learned a lot, albeit not necessarily the thing I expected to learn. I feel like a better scientist, even more so than I feel like a better geneticist."
"The course was very useful as it helped place in a better context a lot of the genetic analysis techniques I had started learning about but was unsure about how to apply them in my research."

Application Deadline:
6 April 2018

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