Advanced Search
MyIDEAS: Login to save this article or follow this journal

On the Optimal Design of Genetic Variant Discovery Studies

Contents:

Author Info

  • Ionita-Laza Iuliana

    (Columbia University)

  • Laird Nan M

    (Harvard School of Public Health)

Registered author(s):

    Abstract

    The recent emergence of massively parallel sequencing technologies has enabled an increasing number of human genome re-sequencing studies, notable among them being the 1000 Genomes Project. The main aim of these studies is to identify the yet unknown genetic variants in a genomic region, mostly low frequency variants (frequency less than 5%). We propose here a set of statistical tools that address how to optimally design such studies in order to increase the number of genetic variants we expect to discover. Within this framework, the tradeoff between lower coverage for more individuals and higher coverage for fewer individuals can be naturally solved.The methods here are also useful for estimating the number of genetic variants missed in a discovery study performed at low coverage.We show applications to simulated data based on coalescent models and to sequence data from the ENCODE project. In particular, we show the extent to which combining data from multiple populations in a discovery study may increase the number of genetic variants identified relative to studies on single populations.

    Download Info

    If you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.
    File URL: http://www.degruyter.com/view/j/sagmb.2010.9.1/sagmb.2010.9.1.1581/sagmb.2010.9.1.1581.xml?format=INT
    Download Restriction: For access to full text, subscription to the journal or payment for the individual article is required.

    As the access to this document is restricted, you may want to look for a different version under "Related research" (further below) or search for a different version of it.

    Bibliographic Info

    Article provided by De Gruyter in its journal Statistical Applications in Genetics and Molecular Biology.

    Volume (Year): 9 (2010)
    Issue (Month): 1 (August)
    Pages: 1-17

    as in new window
    Handle: RePEc:bpj:sagmbi:v:9:y:2010:i:1:n:33

    Contact details of provider:
    Web page: http://www.degruyter.com

    Order Information:
    Web: http://www.degruyter.com/view/j/sagmb

    Related research

    Keywords:

    References

    No references listed on IDEAS
    You can help add them by filling out this form.

    Citations

    Lists

    This item is not listed on Wikipedia, on a reading list or among the top items on IDEAS.

    Statistics

    Access and download statistics

    Corrections

    When requesting a correction, please mention this item's handle: RePEc:bpj:sagmbi:v:9:y:2010:i:1:n:33. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Peter Golla).

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If references are entirely missing, you can add them using this form.

    If the full references list an item that is present in RePEc, but the system did not link to it, you can help with this form.

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your profile, as there may be some citations waiting for confirmation.

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.