This course type is computational

Genetic Analysis of Mendelian and Complex Disorders

18 - 24 July 2018Wellcome Genome Campus, Hinxton, UK
Deadlines (at 23:59 UTC):
  • Deadline for Applications Closed
  • Application Deadline Closed

  • 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 / 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 / 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.