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CCRaVAT and QuTie-enabling analysis of rare variants in large-scale case control and quantitative trait association studies.

Abstract:
BACKGROUND: Genome-wide association studies have been successful in finding common variants influencing common traits. However, these associations only account for a fraction of trait heritability. There has been a shift in the field towards studying low frequency and rare variants, which are now widely recognised as putative complex trait determinants. Despite this increasing focus on examining the role of low frequency and rare variants in complex disease susceptibility, there is a lack of user-friendly analytical packages implementing powerful association tests for the analysis of rare variants. RESULTS: We have developed two software tools, CCRaVAT (Case-Control Rare Variant Analysis Tool) and QuTie (Quantitative Trait), which enable efficient large-scale analysis of low frequency and rare variants. Both programs implement a collapsing method examining the accumulation of low frequency and rare variants across a locus of interest that has more power than single variant analysis. CCRaVAT carries out case-control analyses whereas QuTie has been developed for continuous trait analysis. CONCLUSIONS: CCRaVAT and QuTie are easy to use software tools that allow users to perform genome-wide association analysis on low frequency and rare variants for both binary and quantitative traits. The software is freely available and provides the genetics community with a resource to perform association analysis on rarer genetic variants.
Publication status:
Published
Peer review status:
Peer reviewed

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Publisher copy:
10.1186/1471-2105-11-527

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Institution:
University of Oxford
Role:
Author
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Institution:
University of Oxford
Role:
Author
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Institution:
University of Oxford
Division:
MSD
Department:
NDM
Sub department:
Human Genetics Wt Centre
Role:
Author
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Institution:
University of Oxford
Role:
Author


Publisher:
BioMed Central
Journal:
BMC bioinformatics More from this journal
Volume:
11
Issue:
1
Pages:
527
Publication date:
2010-10-21
DOI:
EISSN:
1471-2105
ISSN:
1471-2105


Language:
English
Keywords:
UUID:
uuid:52a2337b-64e1-415b-9409-628d2bc8dda8
Local pid:
pubs:93159
Source identifiers:
93159
Deposit date:
2012-12-19

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