An Efficient Approach to Screening Epigenome-Wide Data
Screening cytosine-phosphate-guanine dinucleotide (CpG) DNA methylation sites in association with some covariate(s) is desired due to high dimensionality. We incorporate surrogate variable analyses (SVAs) into (ordinary or robust) linear regressions and utilize training and testing samples for neste...
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| Veröffentlicht in: | BioMed research international 2016-01, Vol.2016 (2016), p.1-16 |
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| creator | Zhang, Hongmei Lockett, Gabrielle A. Tong, Xin Ray, Meredith A. Karmaus, Wilfried J. |
| description | Screening cytosine-phosphate-guanine dinucleotide (CpG) DNA methylation sites in association with some covariate(s) is desired due to high dimensionality. We incorporate surrogate variable analyses (SVAs) into (ordinary or robust) linear regressions and utilize training and testing samples for nested validation to screen CpG sites. SVA is to account for variations in the methylation not explained by the specified covariate(s) and adjust for confounding effects. To make it easier to users, this screening method is built into a user-friendly R package, ttScreening, with efficient algorithms implemented. Various simulations were implemented to examine the robustness and sensitivity of the method compared to the classical approaches controlling for multiple testing: the false discovery rates-based (FDR-based) and the Bonferroni-based methods. The proposed approach in general performs better and has the potential to control both types I and II errors. We applied ttScreening to 383,998 CpG sites in association with maternal smoking, one of the leading factors for cancer risk. |
| doi_str_mv | 10.1155/2016/2615348 |
| format | Article |
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We incorporate surrogate variable analyses (SVAs) into (ordinary or robust) linear regressions and utilize training and testing samples for nested validation to screen CpG sites. SVA is to account for variations in the methylation not explained by the specified covariate(s) and adjust for confounding effects. To make it easier to users, this screening method is built into a user-friendly R package, ttScreening, with efficient algorithms implemented. Various simulations were implemented to examine the robustness and sensitivity of the method compared to the classical approaches controlling for multiple testing: the false discovery rates-based (FDR-based) and the Bonferroni-based methods. The proposed approach in general performs better and has the potential to control both types I and II errors. We applied ttScreening to 383,998 CpG sites in association with maternal smoking, one of the leading factors for cancer risk.</description><identifier>ISSN: 2314-6133</identifier><identifier>EISSN: 2314-6141</identifier><identifier>DOI: 10.1155/2016/2615348</identifier><identifier>PMID: 27034928</identifier><language>eng</language><publisher>Cairo, Egypt: Hindawi Publishing Corporation</publisher><subject>Algorithms ; Comparative analysis ; Computational Biology ; CpG Islands - genetics ; DNA methylation ; DNA Methylation - genetics ; Epidemiology ; Epigenetics ; Epigenomics - statistics & numerical data ; Genome, Human ; Health aspects ; Humans ; Linear Models ; Medical research ; Methods ; Methylation ; Neoplasms - genetics ; Oligonucleotide Array Sequence Analysis ; Public health ; Pyrimidines ; Reproducibility ; Risk Factors ; Variables</subject><ispartof>BioMed research international, 2016-01, Vol.2016 (2016), p.1-16</ispartof><rights>Copyright © 2016 Meredith A. Ray et al.</rights><rights>COPYRIGHT 2016 John Wiley & Sons, Inc.</rights><rights>Copyright © 2016 Meredith A. Ray et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.</rights><rights>Copyright © 2016 Meredith A. Ray et al. 2016</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c532t-c0634c2c8fa3f7cb72f88480592271363505bb40f48930f11bf0ae13676775b3</citedby><cites>FETCH-LOGICAL-c532t-c0634c2c8fa3f7cb72f88480592271363505bb40f48930f11bf0ae13676775b3</cites><orcidid>0000-0001-5312-3774 ; 0000-0003-3557-0364</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC4808532/pdf/$$EPDF$$P50$$Gpubmedcentral$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC4808532/$$EHTML$$P50$$Gpubmedcentral$$Hfree_for_read</linktohtml><link.rule.ids>230,314,727,780,784,885,27924,27925,53791,53793</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/27034928$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><contributor>Zhai, Weiwei</contributor><creatorcontrib>Zhang, Hongmei</creatorcontrib><creatorcontrib>Lockett, Gabrielle A.</creatorcontrib><creatorcontrib>Tong, Xin</creatorcontrib><creatorcontrib>Ray, Meredith A.</creatorcontrib><creatorcontrib>Karmaus, Wilfried J.</creatorcontrib><title>An Efficient Approach to Screening Epigenome-Wide Data</title><title>BioMed research international</title><addtitle>Biomed Res Int</addtitle><description>Screening cytosine-phosphate-guanine dinucleotide (CpG) DNA methylation sites in association with some covariate(s) is desired due to high dimensionality. We incorporate surrogate variable analyses (SVAs) into (ordinary or robust) linear regressions and utilize training and testing samples for nested validation to screen CpG sites. SVA is to account for variations in the methylation not explained by the specified covariate(s) and adjust for confounding effects. To make it easier to users, this screening method is built into a user-friendly R package, ttScreening, with efficient algorithms implemented. Various simulations were implemented to examine the robustness and sensitivity of the method compared to the classical approaches controlling for multiple testing: the false discovery rates-based (FDR-based) and the Bonferroni-based methods. The proposed approach in general performs better and has the potential to control both types I and II errors. We applied ttScreening to 383,998 CpG sites in association with maternal smoking, one of the leading factors for cancer risk.</description><subject>Algorithms</subject><subject>Comparative analysis</subject><subject>Computational Biology</subject><subject>CpG Islands - genetics</subject><subject>DNA methylation</subject><subject>DNA Methylation - genetics</subject><subject>Epidemiology</subject><subject>Epigenetics</subject><subject>Epigenomics - statistics & numerical data</subject><subject>Genome, Human</subject><subject>Health aspects</subject><subject>Humans</subject><subject>Linear Models</subject><subject>Medical research</subject><subject>Methods</subject><subject>Methylation</subject><subject>Neoplasms - genetics</subject><subject>Oligonucleotide Array Sequence Analysis</subject><subject>Public health</subject><subject>Pyrimidines</subject><subject>Reproducibility</subject><subject>Risk Factors</subject><subject>Variables</subject><issn>2314-6133</issn><issn>2314-6141</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2016</creationdate><recordtype>article</recordtype><sourceid>RHX</sourceid><sourceid>EIF</sourceid><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><sourceid>GNUQQ</sourceid><recordid>eNqNkctrGzEQxkVIaUKaW89loZdAuo1G770EjOM-INBDAz0KrSzZCmtpo10n5L-PjF3ncYouI5gf33wzH0KfAX8H4PyCYBAXRACnTB2gY0KB1QIYHO7_lB6h02G4xeUpELgRH9ERkZiyhqhjJCaxmnkfbHBxrCZ9n5Oxy2pM1V-bnYshLqpZHxYuppWr_4W5q67MaD6hD950gzvd1RN082N2M_1VX__5-Xs6ua4tp2SsLRaUWWKVN9RL20rilWIK84YQCVRQjnnbMuyZaij2AK3HxpWGFFLylp6gy61sv25Xbm6Lx2w63eewMvlRJxP0604MS71I97rMUMVBETjbCeR0t3bDqFdhsK7rTHRpPWiQxQwVXL0HlUoCASUK-vUNepvWOZZDbCiBedmRPFML0zkdok_Fot2I6gknWAITghfq25ayOQ1Ddn6_HWC9yVhvMta7jAv-5eVF9vD_RAtwvgWWIc7NQ3innCuM8-YF3UgARZ8AbFCzRQ</recordid><startdate>20160101</startdate><enddate>20160101</enddate><creator>Zhang, Hongmei</creator><creator>Lockett, Gabrielle A.</creator><creator>Tong, Xin</creator><creator>Ray, Meredith A.</creator><creator>Karmaus, Wilfried J.</creator><general>Hindawi Publishing Corporation</general><general>John Wiley & Sons, Inc</general><general>Hindawi Limited</general><scope>ADJCN</scope><scope>AHFXO</scope><scope>RHU</scope><scope>RHW</scope><scope>RHX</scope><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>3V.</scope><scope>7QL</scope><scope>7QO</scope><scope>7T7</scope><scope>7TK</scope><scope>7U7</scope><scope>7U9</scope><scope>7X7</scope><scope>7XB</scope><scope>88E</scope><scope>8FD</scope><scope>8FE</scope><scope>8FG</scope><scope>8FH</scope><scope>8FI</scope><scope>8FJ</scope><scope>8FK</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>ARAPS</scope><scope>AZQEC</scope><scope>BBNVY</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>BHPHI</scope><scope>C1K</scope><scope>CCPQU</scope><scope>CWDGH</scope><scope>DWQXO</scope><scope>FR3</scope><scope>FYUFA</scope><scope>GHDGH</scope><scope>GNUQQ</scope><scope>H94</scope><scope>HCIFZ</scope><scope>K9.</scope><scope>LK8</scope><scope>M0S</scope><scope>M1P</scope><scope>M7N</scope><scope>M7P</scope><scope>P5Z</scope><scope>P62</scope><scope>P64</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>7X8</scope><scope>5PM</scope><orcidid>https://orcid.org/0000-0001-5312-3774</orcidid><orcidid>https://orcid.org/0000-0003-3557-0364</orcidid></search><sort><creationdate>20160101</creationdate><title>An Efficient Approach to Screening Epigenome-Wide Data</title><author>Zhang, Hongmei ; Lockett, Gabrielle A. ; Tong, Xin ; Ray, Meredith A. ; Karmaus, Wilfried J.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c532t-c0634c2c8fa3f7cb72f88480592271363505bb40f48930f11bf0ae13676775b3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2016</creationdate><topic>Algorithms</topic><topic>Comparative analysis</topic><topic>Computational Biology</topic><topic>CpG Islands - genetics</topic><topic>DNA methylation</topic><topic>DNA Methylation - genetics</topic><topic>Epidemiology</topic><topic>Epigenetics</topic><topic>Epigenomics - statistics & numerical data</topic><topic>Genome, Human</topic><topic>Health aspects</topic><topic>Humans</topic><topic>Linear Models</topic><topic>Medical research</topic><topic>Methods</topic><topic>Methylation</topic><topic>Neoplasms - genetics</topic><topic>Oligonucleotide Array Sequence Analysis</topic><topic>Public health</topic><topic>Pyrimidines</topic><topic>Reproducibility</topic><topic>Risk Factors</topic><topic>Variables</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Zhang, Hongmei</creatorcontrib><creatorcontrib>Lockett, Gabrielle A.</creatorcontrib><creatorcontrib>Tong, Xin</creatorcontrib><creatorcontrib>Ray, Meredith A.</creatorcontrib><creatorcontrib>Karmaus, Wilfried J.</creatorcontrib><collection>الدوريات العلمية والإحصائية - e-Marefa Academic and Statistical Periodicals</collection><collection>معرفة - 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Academic</collection><collection>PubMed Central (Full Participant titles)</collection><jtitle>BioMed research international</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Zhang, Hongmei</au><au>Lockett, Gabrielle A.</au><au>Tong, Xin</au><au>Ray, Meredith A.</au><au>Karmaus, Wilfried J.</au><au>Zhai, Weiwei</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>An Efficient Approach to Screening Epigenome-Wide Data</atitle><jtitle>BioMed research international</jtitle><addtitle>Biomed Res Int</addtitle><date>2016-01-01</date><risdate>2016</risdate><volume>2016</volume><issue>2016</issue><spage>1</spage><epage>16</epage><pages>1-16</pages><issn>2314-6133</issn><eissn>2314-6141</eissn><abstract>Screening cytosine-phosphate-guanine dinucleotide (CpG) DNA methylation sites in association with some covariate(s) is desired due to high dimensionality. We incorporate surrogate variable analyses (SVAs) into (ordinary or robust) linear regressions and utilize training and testing samples for nested validation to screen CpG sites. SVA is to account for variations in the methylation not explained by the specified covariate(s) and adjust for confounding effects. To make it easier to users, this screening method is built into a user-friendly R package, ttScreening, with efficient algorithms implemented. Various simulations were implemented to examine the robustness and sensitivity of the method compared to the classical approaches controlling for multiple testing: the false discovery rates-based (FDR-based) and the Bonferroni-based methods. The proposed approach in general performs better and has the potential to control both types I and II errors. We applied ttScreening to 383,998 CpG sites in association with maternal smoking, one of the leading factors for cancer risk.</abstract><cop>Cairo, Egypt</cop><pub>Hindawi Publishing Corporation</pub><pmid>27034928</pmid><doi>10.1155/2016/2615348</doi><tpages>16</tpages><orcidid>https://orcid.org/0000-0001-5312-3774</orcidid><orcidid>https://orcid.org/0000-0003-3557-0364</orcidid><oa>free_for_read</oa></addata></record> |
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| subjects | Algorithms Comparative analysis Computational Biology CpG Islands - genetics DNA methylation DNA Methylation - genetics Epidemiology Epigenetics Epigenomics - statistics & numerical data Genome, Human Health aspects Humans Linear Models Medical research Methods Methylation Neoplasms - genetics Oligonucleotide Array Sequence Analysis Public health Pyrimidines Reproducibility Risk Factors Variables |
| title | An Efficient Approach to Screening Epigenome-Wide Data |
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