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A quantile-based method for association mapping of quantitative phenotypes: an application to rheumatoid arthritis phenotypes
Genetic association of population-based quantitative trait data has traditionally been analyzed using analysis of variance (ANOVA). However, violations of certain statistical assumptions may lead to false-positive association results. In this study, we have explored model-free alternatives to ANOVA...
Autores principales: | , , , |
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Formato: | Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2009
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2795914/ https://www.ncbi.nlm.nih.gov/pubmed/20018007 |
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author | Ghosh, Saurabh Sanapala, Krishna Rao Ghosh, Abhik Chakladar, Sujatro |
author_facet | Ghosh, Saurabh Sanapala, Krishna Rao Ghosh, Abhik Chakladar, Sujatro |
author_sort | Ghosh, Saurabh |
collection | PubMed |
description | Genetic association of population-based quantitative trait data has traditionally been analyzed using analysis of variance (ANOVA). However, violations of certain statistical assumptions may lead to false-positive association results. In this study, we have explored model-free alternatives to ANOVA using correlations between allele frequencies in the different quantile intervals of the quantitative trait and the quantile values. We performed genome-wide association scans on anti-cyclic citrullinated peptide and rheumatoid factor-immunoglobulin M, two quantitative traits correlated with rheumatoid arthritis, using the data provided in Genetic Analysis Workshop 16. Both the quantitative traits exhibited significant evidence of association on Chromosome 6, although not in the human leukocyte antigen region which is known to harbor a major gene predisposing to rheumatoid arthritis. We found that while a majority of the significant findings using the asymptotic thresholds of ANOVA was not validated using permutations, a relatively higher proportion of the significant findings using the asymptotic cut-offs of the correlation statistic were validated using permutations. |
format | Text |
id | pubmed-2795914 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2009 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-27959142009-12-18 A quantile-based method for association mapping of quantitative phenotypes: an application to rheumatoid arthritis phenotypes Ghosh, Saurabh Sanapala, Krishna Rao Ghosh, Abhik Chakladar, Sujatro BMC Proc Proceedings Genetic association of population-based quantitative trait data has traditionally been analyzed using analysis of variance (ANOVA). However, violations of certain statistical assumptions may lead to false-positive association results. In this study, we have explored model-free alternatives to ANOVA using correlations between allele frequencies in the different quantile intervals of the quantitative trait and the quantile values. We performed genome-wide association scans on anti-cyclic citrullinated peptide and rheumatoid factor-immunoglobulin M, two quantitative traits correlated with rheumatoid arthritis, using the data provided in Genetic Analysis Workshop 16. Both the quantitative traits exhibited significant evidence of association on Chromosome 6, although not in the human leukocyte antigen region which is known to harbor a major gene predisposing to rheumatoid arthritis. We found that while a majority of the significant findings using the asymptotic thresholds of ANOVA was not validated using permutations, a relatively higher proportion of the significant findings using the asymptotic cut-offs of the correlation statistic were validated using permutations. BioMed Central 2009-12-15 /pmc/articles/PMC2795914/ /pubmed/20018007 Text en Copyright ©2009 Ghosh 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 | Proceedings Ghosh, Saurabh Sanapala, Krishna Rao Ghosh, Abhik Chakladar, Sujatro A quantile-based method for association mapping of quantitative phenotypes: an application to rheumatoid arthritis phenotypes |
title | A quantile-based method for association mapping of quantitative phenotypes: an application to rheumatoid arthritis phenotypes |
title_full | A quantile-based method for association mapping of quantitative phenotypes: an application to rheumatoid arthritis phenotypes |
title_fullStr | A quantile-based method for association mapping of quantitative phenotypes: an application to rheumatoid arthritis phenotypes |
title_full_unstemmed | A quantile-based method for association mapping of quantitative phenotypes: an application to rheumatoid arthritis phenotypes |
title_short | A quantile-based method for association mapping of quantitative phenotypes: an application to rheumatoid arthritis phenotypes |
title_sort | quantile-based method for association mapping of quantitative phenotypes: an application to rheumatoid arthritis phenotypes |
topic | Proceedings |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2795914/ https://www.ncbi.nlm.nih.gov/pubmed/20018007 |
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