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Computational Methods for Detection of Differentially Methylated Regions Using Kernel Distance and Scan Statistics
Motivation: Researchers in genomics are increasingly interested in epigenetic factors such as DNA methylation because they play an important role in regulating gene expression without changes in the sequence of DNA. Abnormal DNA methylation is associated with many human diseases. Results: We propose...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6523914/ https://www.ncbi.nlm.nih.gov/pubmed/31013791 http://dx.doi.org/10.3390/genes10040298 |
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author | Dunbar, Faith Xu, Hongyan Ryu, Duchwan Ghosh, Santu Shi, Huidong George, Varghese |
author_facet | Dunbar, Faith Xu, Hongyan Ryu, Duchwan Ghosh, Santu Shi, Huidong George, Varghese |
author_sort | Dunbar, Faith |
collection | PubMed |
description | Motivation: Researchers in genomics are increasingly interested in epigenetic factors such as DNA methylation because they play an important role in regulating gene expression without changes in the sequence of DNA. Abnormal DNA methylation is associated with many human diseases. Results: We propose two different approaches to test for differentially methylated regions (DMRs) associated with complex traits, while accounting for correlations among CpG sites in the DMRs. The first approach is a nonparametric method using a kernel distance statistic and the second one is a likelihood-based method using a binomial spatial scan statistic. The kernel distance method uses the kernel function, while the binomial scan statistic approach uses a mixed-effects model to incorporate correlations among CpG sites. Extensive simulations show that both approaches have excellent control of type I error, and both have reasonable statistical power. The binomial scan statistic approach appears to have higher power, while the kernel distance method is computationally faster. The proposed methods are demonstrated using data from a chronic lymphocytic leukemia (CLL) study. |
format | Online Article Text |
id | pubmed-6523914 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-65239142019-06-03 Computational Methods for Detection of Differentially Methylated Regions Using Kernel Distance and Scan Statistics Dunbar, Faith Xu, Hongyan Ryu, Duchwan Ghosh, Santu Shi, Huidong George, Varghese Genes (Basel) Article Motivation: Researchers in genomics are increasingly interested in epigenetic factors such as DNA methylation because they play an important role in regulating gene expression without changes in the sequence of DNA. Abnormal DNA methylation is associated with many human diseases. Results: We propose two different approaches to test for differentially methylated regions (DMRs) associated with complex traits, while accounting for correlations among CpG sites in the DMRs. The first approach is a nonparametric method using a kernel distance statistic and the second one is a likelihood-based method using a binomial spatial scan statistic. The kernel distance method uses the kernel function, while the binomial scan statistic approach uses a mixed-effects model to incorporate correlations among CpG sites. Extensive simulations show that both approaches have excellent control of type I error, and both have reasonable statistical power. The binomial scan statistic approach appears to have higher power, while the kernel distance method is computationally faster. The proposed methods are demonstrated using data from a chronic lymphocytic leukemia (CLL) study. MDPI 2019-04-12 /pmc/articles/PMC6523914/ /pubmed/31013791 http://dx.doi.org/10.3390/genes10040298 Text en © 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Dunbar, Faith Xu, Hongyan Ryu, Duchwan Ghosh, Santu Shi, Huidong George, Varghese Computational Methods for Detection of Differentially Methylated Regions Using Kernel Distance and Scan Statistics |
title | Computational Methods for Detection of Differentially Methylated Regions Using Kernel Distance and Scan Statistics |
title_full | Computational Methods for Detection of Differentially Methylated Regions Using Kernel Distance and Scan Statistics |
title_fullStr | Computational Methods for Detection of Differentially Methylated Regions Using Kernel Distance and Scan Statistics |
title_full_unstemmed | Computational Methods for Detection of Differentially Methylated Regions Using Kernel Distance and Scan Statistics |
title_short | Computational Methods for Detection of Differentially Methylated Regions Using Kernel Distance and Scan Statistics |
title_sort | computational methods for detection of differentially methylated regions using kernel distance and scan statistics |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6523914/ https://www.ncbi.nlm.nih.gov/pubmed/31013791 http://dx.doi.org/10.3390/genes10040298 |
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