IDEAS home Printed from https://ideas.repec.org/p/osf/osfxxx/g83ed.html
   My bibliography  Save this paper

A review of software tools for statistical tests of genetic association with rare variants using next generation sequence data

Author

Listed:
  • Shankar, Ravi Girikematha
  • Jorgensen, Andrea
  • Morris, Andrew

Abstract

Various statistical methods can be applied when undertaking analyses of association of human disease and traits with rare genetic variants in next generation sequence (NGS) datasets, depending on the phenotype of interest. The choice of open source software tools and packages to implement such methods are plentiful with varying features and available options, which makes it challenging for the end users to choose between different alternatives. The literature was searched to identify tools developed for rare variant association analysis in NGS datasets and a summary compiled. Each tool has its own features, advantages and limitations when considering factors such as efficiency, stability, user documentation and support. EPACTS, RVtests, and VAT are widely used standalone packages that offer flexible, efficient implementation of many statistical tests together with quality control procedures. R packages including SKAT and SAIGE-GENE are useful as they can utilise freely available libraries for data analysis and visualization. It is important for any existing packages to implement tests newly proposed in the literature to stay relevant in terms of use, but very few of them are updated actively. The results of this comparative review will provide guidance to researchers wishing to undertake genetic association analyses with rare variants using NGS data, and allow them to choose a suitable tool.

Suggested Citation

  • Shankar, Ravi Girikematha & Jorgensen, Andrea & Morris, Andrew, 2022. "A review of software tools for statistical tests of genetic association with rare variants using next generation sequence data," OSF Preprints g83ed, Center for Open Science.
  • Handle: RePEc:osf:osfxxx:g83ed
    DOI: 10.31219/osf.io/g83ed
    as

    Download full text from publisher

    File URL: https://osf.io/download/6310b150c87b6b03c2128f18/
    Download Restriction: no

    File URL: https://libkey.io/10.31219/osf.io/g83ed?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:osf:osfxxx:g83ed. See general information about how to correct material in RePEc.

    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.

    We have no bibliographic references for this item. You can help adding them by using 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 RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: OSF (email available below). General contact details of provider: https://osf.io/preprints/ .

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

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.