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CCRaVAT and QuTie - enabling analysis of rare variants in large-scale case control and quantitative trait association studies
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 wi...
Autores principales: | , , , , |
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Formato: | Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2010
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2973964/ https://www.ncbi.nlm.nih.gov/pubmed/20964851 http://dx.doi.org/10.1186/1471-2105-11-527 |
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author | Lawrence, Robert Day-Williams, Aaron G Elliott, Katherine S Morris, Andrew P Zeggini, Eleftheria |
author_facet | Lawrence, Robert Day-Williams, Aaron G Elliott, Katherine S Morris, Andrew P Zeggini, Eleftheria |
author_sort | Lawrence, Robert |
collection | PubMed |
description | 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. |
format | Text |
id | pubmed-2973964 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2010 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-29739642010-11-05 CCRaVAT and QuTie - enabling analysis of rare variants in large-scale case control and quantitative trait association studies Lawrence, Robert Day-Williams, Aaron G Elliott, Katherine S Morris, Andrew P Zeggini, Eleftheria BMC Bioinformatics Software 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. BioMed Central 2010-10-21 /pmc/articles/PMC2973964/ /pubmed/20964851 http://dx.doi.org/10.1186/1471-2105-11-527 Text en Copyright ©2010 Lawrence et al; licensee BioMed Central Ltd. http://creativecommons.org/licenses/by/2.0 This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Software Lawrence, Robert Day-Williams, Aaron G Elliott, Katherine S Morris, Andrew P Zeggini, Eleftheria CCRaVAT and QuTie - enabling analysis of rare variants in large-scale case control and quantitative trait association studies |
title | CCRaVAT and QuTie - enabling analysis of rare variants in large-scale case control and quantitative trait association studies |
title_full | CCRaVAT and QuTie - enabling analysis of rare variants in large-scale case control and quantitative trait association studies |
title_fullStr | CCRaVAT and QuTie - enabling analysis of rare variants in large-scale case control and quantitative trait association studies |
title_full_unstemmed | CCRaVAT and QuTie - enabling analysis of rare variants in large-scale case control and quantitative trait association studies |
title_short | CCRaVAT and QuTie - enabling analysis of rare variants in large-scale case control and quantitative trait association studies |
title_sort | ccravat and qutie - enabling analysis of rare variants in large-scale case control and quantitative trait association studies |
topic | Software |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2973964/ https://www.ncbi.nlm.nih.gov/pubmed/20964851 http://dx.doi.org/10.1186/1471-2105-11-527 |
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