Computational

Genetic Analysis of Mendelian and Complex Disorders

July 18th - 24th 2018

Wellcome Genome Campus, Hinxton, UK

Summary

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.”

Programme

The programme will discuss fundamental issues needed to increase success
in gene mapping studies including:

  • Why families?
    -Contrasting family and population study designs
    -Practical aspects of collecting family data
  • Association analysis in samples of unrelated and related individuals
    -Linear mixed models (LMM, aka variance components)
  • Linkage analysis as an effective tool for gene mapping in the post-GWAS era
  • Quality control strategies:
    -When only using unrelateds
    -When families are included
  • Using families in order to move beyond simple genetic models
  • Haplotyping using GWAS and sequencing data
  • Analysis of rare traits using sequencing data from families
  • Risk prediction, meta-analysis, and other post-GWAS analysis using families

Download the draft 2018 course timetable here>

Teaching will take the form of lectures by instructors and invited expert
speakers, informal tutorials, hands-on computer sessions, and analysis of
example disease data sets. Our interactive and intensive educational
program will enable researchers to better carry out sophisticated
statistical analyses of genetic data, and will also improve their
interpretation and understanding of the results. All the software used is
freely available, so that skills learned can be easily applied after the
course.  After the course, participants will be provided with a
virtual machine copy of the computer used during the course, so they can
easily explore back at their home institutions the computer exercises and
example data sets in greater detail.

To ensure that participants get personalized constructive advice,
particularly pertaining to the study design and analysis plan for their
own research, each participant will give a short presentation about his
or her own research project, either planned or in progress.

Learning Outcomes
On completion of the course, participants should be able to:

  • Evaluate the use of family and population data in genetic analyses and determine in what ways family data would be useful in their own research projects.
  • Have a deeper appreciation of optimal study design and power, and to be able to critically evaluate the design and power of their own research projects.
  • Evaluate current best statistical approaches and conditions under which their use is appropriate or inappropriate, and thus determine the most suitable statistical methods for their own research projects.
  • Be able to use current software to analyse real family and population data and to interpret the results, including quality control, association and linkage testing, and fine mapping approaches.

Instructors and speakers

Course Organiser
Daniel E.
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 Instructors
Najaf Amin Erasmus Medical Centre, The Netherlands
Jin Zhou University of Arizona, USA

Guest Speakers
Carl
Anderson

Wellcome Trust Sanger Institute, UK
Inês Barroso Wellcome Trust Sanger
Institute, UK
Takis Benos University of Pittsburgh, USA

How to apply

Prerequisites
Applicants should be 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/).

Applications
Applications
can be submitted online
. Places are limited and will be awarded on
merit. If you have any problems with the online application process,
please contact
us
.

Please note: Applications
must be supported by a
recommendation from a scientific or clinical
sponsor (e.g. supervisor,
line manager, or head of department). A
request for a supporting
statement will be sent to your nominated sponsor automatically during
the application process. Applicants must ensure that their sponsor
provides this supporting statement by the application deadline.
Applications without a supporting statement cannot be
considered.

Deadlines
Deadline for Applications: Closed

Travel visas
Please contact the
event organiser if you require a letter to support a travel visa
application to the UK. Note that letters will only be provided to confirmed attendees.

Non-European Economic Area or Swiss nationals may be required to have a
visa to enter the UK.
Early application is strongly advised, as this process can take 6-8 weeks
or longer.

Please visit the following websites for further information:
UK Border Agency website and information for general visitors and business
visitors.

Cost and bursaries

Cost
The course is subsidised by the Wellcome Genome Campus Advanced Courses
and Scientific Conferences Programme. This is a residential course and
the fee is £995, including all accommodation and meals.
This subsidised fee is available to all non-commercial applicants. Please
contact us
for the commercial fee.

Bursaries
Advanced Courses are subsidised for non-commercial applicants from
anywhere in the world. Additional, limited bursaries are
available (up
to a 50% reduction of the course fee) and are awarded on merit. If you
would like to apply for a
bursary, please complete the bursary section
of the online application
form.

Please note that both the applicant
and sponsor are required to provide
a justification for the
bursary as part of the application.

Additional funding opportunities
Visit
our Funding webpage
for additional funding opportunities currently
available.


Bursary terms and conditions

UK Courses (held at the Wellcome Genome Campus, Hinxton,
Cambridge)
A
limited number of bursaries are available for each course. These are
awarded by the selection committee according to merit. The bursary
covers a maximum of 50% of the course fee, though in exceptional
circumstances an application for the total course fee may be considered.
Where there are many bursary applications, the selection committee may
issue smaller amounts. We cannot assist with travel costs to attend UK
courses.

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