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Incorporating prior information into association studies
Summary: Recent technological developments in measuring genetic variation have ushered in an era of genome-wide association studies which have discovered many genes involved in human disease. Current methods to perform association studies collect genetic information and compare the frequency of vari...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2012
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3371867/ https://www.ncbi.nlm.nih.gov/pubmed/22689754 http://dx.doi.org/10.1093/bioinformatics/bts235 |
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author | Darnell, Gregory Duong, Dat Han, Buhm Eskin, Eleazar |
author_facet | Darnell, Gregory Duong, Dat Han, Buhm Eskin, Eleazar |
author_sort | Darnell, Gregory |
collection | PubMed |
description | Summary: Recent technological developments in measuring genetic variation have ushered in an era of genome-wide association studies which have discovered many genes involved in human disease. Current methods to perform association studies collect genetic information and compare the frequency of variants in individuals with and without the disease. Standard approaches do not take into account any information on whether or not a given variant is likely to have an effect on the disease. We propose a novel method for computing an association statistic which takes into account prior information. Our method improves both power and resolution by 8% and 27%, respectively, over traditional methods for performing association studies when applied to simulations using the HapMap data. Advantages of our method are that it is as simple to apply to association studies as standard methods, the results of the method are interpretable as the method reports p-values, and the method is optimal in its use of prior information in regards to statistical power. Availability: The method presented herein is available at http://masa.cs.ucla.edu Contact: eeskin@cs.ucla.edu |
format | Online Article Text |
id | pubmed-3371867 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2012 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-33718672012-06-11 Incorporating prior information into association studies Darnell, Gregory Duong, Dat Han, Buhm Eskin, Eleazar Bioinformatics Ismb 2012 Proceedings Papers Committee July 15 to July 19, 2012, Long Beach, Ca, Usa Summary: Recent technological developments in measuring genetic variation have ushered in an era of genome-wide association studies which have discovered many genes involved in human disease. Current methods to perform association studies collect genetic information and compare the frequency of variants in individuals with and without the disease. Standard approaches do not take into account any information on whether or not a given variant is likely to have an effect on the disease. We propose a novel method for computing an association statistic which takes into account prior information. Our method improves both power and resolution by 8% and 27%, respectively, over traditional methods for performing association studies when applied to simulations using the HapMap data. Advantages of our method are that it is as simple to apply to association studies as standard methods, the results of the method are interpretable as the method reports p-values, and the method is optimal in its use of prior information in regards to statistical power. Availability: The method presented herein is available at http://masa.cs.ucla.edu Contact: eeskin@cs.ucla.edu Oxford University Press 2012-06-15 2012-06-09 /pmc/articles/PMC3371867/ /pubmed/22689754 http://dx.doi.org/10.1093/bioinformatics/bts235 Text en © The Author(s) 2012. Published by Oxford University Press. http://creativecommons.org/licenses/by-nc/3.0 This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/3.0), which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Ismb 2012 Proceedings Papers Committee July 15 to July 19, 2012, Long Beach, Ca, Usa Darnell, Gregory Duong, Dat Han, Buhm Eskin, Eleazar Incorporating prior information into association studies |
title | Incorporating prior information into association studies |
title_full | Incorporating prior information into association studies |
title_fullStr | Incorporating prior information into association studies |
title_full_unstemmed | Incorporating prior information into association studies |
title_short | Incorporating prior information into association studies |
title_sort | incorporating prior information into association studies |
topic | Ismb 2012 Proceedings Papers Committee July 15 to July 19, 2012, Long Beach, Ca, Usa |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3371867/ https://www.ncbi.nlm.nih.gov/pubmed/22689754 http://dx.doi.org/10.1093/bioinformatics/bts235 |
work_keys_str_mv | AT darnellgregory incorporatingpriorinformationintoassociationstudies AT duongdat incorporatingpriorinformationintoassociationstudies AT hanbuhm incorporatingpriorinformationintoassociationstudies AT eskineleazar incorporatingpriorinformationintoassociationstudies |