Evaluating the detection ability of a range of epistasis detection methods on simulated data for pure and impure epistatic models
Numerous approaches have been proposed for the detection of epistatic interactions within GWAS datasets in order to better understand the drivers of disease and genetics. A selection of state-of-the-art approaches were assessed. These included the statistical tests, fast-epistasis, BOOST, logistic r...
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| Veröffentlicht in: | PloS one 2022-02, Vol.17 (2), p.e0263390-e0263390 |
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| creator | Russ, Dominic Williams, John A Cardoso, Victor Roth Bravo-Merodio, Laura Pendleton, Samantha C Aziz, Furqan Acharjee, Animesh Gkoutos, Georgios V |
| description | Numerous approaches have been proposed for the detection of epistatic interactions within GWAS datasets in order to better understand the drivers of disease and genetics.
A selection of state-of-the-art approaches were assessed. These included the statistical tests, fast-epistasis, BOOST, logistic regression and wtest; swarm intelligence methods, namely AntEpiSeeker, epiACO and CINOEDV; and data mining approaches, including MDR, GSS, SNPRuler and MPI3SNP. Data were simulated to provide randomly generated models with no individual main effects at different heritabilities (pure epistasis) as well as models based on penetrance tables with some main effects (impure epistasis). Detection of both two and three locus interactions were assessed across a total of 1,560 simulated datasets. The different methods were also applied to a section of the UK biobank cohort for Atrial Fibrillation.
For pure, two locus interactions, PLINK's implementation of BOOST recovered the highest number of correct interactions, with 53.9% and significantly better performing than the other methods (p = 4.52e - 36). For impure two locus interactions, MDR exhibited the best performance, recovering 62.2% of the most significant impure epistatic interactions (p = 6.31e - 90 for all but one test). The assessment of three locus interaction prediction revealed that wtest recovered the highest number (17.2%) of pure epistatic interactions(p = 8.49e - 14). wtest also recovered the highest number of three locus impure epistatic interactions (p = 6.76e - 48) while AntEpiSeeker ranked as the most significant the highest number of such interactions (40.5%). Finally, when applied to a real dataset for Atrial Fibrillation, most notably finding an interaction between SYNE2 and DTNB. |
| doi_str_mv | 10.1371/journal.pone.0263390 |
| format | Article |
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A selection of state-of-the-art approaches were assessed. These included the statistical tests, fast-epistasis, BOOST, logistic regression and wtest; swarm intelligence methods, namely AntEpiSeeker, epiACO and CINOEDV; and data mining approaches, including MDR, GSS, SNPRuler and MPI3SNP. Data were simulated to provide randomly generated models with no individual main effects at different heritabilities (pure epistasis) as well as models based on penetrance tables with some main effects (impure epistasis). Detection of both two and three locus interactions were assessed across a total of 1,560 simulated datasets. The different methods were also applied to a section of the UK biobank cohort for Atrial Fibrillation.
For pure, two locus interactions, PLINK's implementation of BOOST recovered the highest number of correct interactions, with 53.9% and significantly better performing than the other methods (p = 4.52e - 36). For impure two locus interactions, MDR exhibited the best performance, recovering 62.2% of the most significant impure epistatic interactions (p = 6.31e - 90 for all but one test). The assessment of three locus interaction prediction revealed that wtest recovered the highest number (17.2%) of pure epistatic interactions(p = 8.49e - 14). wtest also recovered the highest number of three locus impure epistatic interactions (p = 6.76e - 48) while AntEpiSeeker ranked as the most significant the highest number of such interactions (40.5%). Finally, when applied to a real dataset for Atrial Fibrillation, most notably finding an interaction between SYNE2 and DTNB.</description><identifier>ISSN: 1932-6203</identifier><identifier>EISSN: 1932-6203</identifier><identifier>DOI: 10.1371/journal.pone.0263390</identifier><identifier>PMID: 35180244</identifier><language>eng</language><publisher>United States: Public Library of Science</publisher><subject>Algorithms ; Alleles ; Analysis ; Atrial Fibrillation - genetics ; Biology ; Biology and Life Sciences ; Cancer ; Cardiac arrhythmia ; Computer and Information Sciences ; Data mining ; Data Mining - methods ; Datasets ; Dystrophin-Associated Proteins - genetics ; Epistasis ; Epistasis, Genetic ; Evaluation ; Fibrillation ; Fuzzy sets ; Gene Frequency ; Gene loci ; Generalized linear models ; Genetic epistasis ; Genetic Loci ; Genetics ; Genome-Wide Association Study - methods ; Genomes ; Genotype ; Humans ; Intelligence ; Linear Models ; Medicine ; Medicine and Health Sciences ; Methods ; Microfilament Proteins - genetics ; Models, Genetic ; Multifactor Dimensionality Reduction ; Nerve Tissue Proteins - genetics ; Neuropeptides - genetics ; Penetrance ; Polymorphism, Single Nucleotide ; ROC Curve ; Simulation ; Statistical analysis ; Statistical tests ; Swarm intelligence</subject><ispartof>PloS one, 2022-02, Vol.17 (2), p.e0263390-e0263390</ispartof><rights>COPYRIGHT 2022 Public Library of Science</rights><rights>2022 Russ et al. This is an open access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><rights>2022 Russ et al 2022 Russ et al</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c743t-748daebb6656366f92505140e728405c72e80d4aee7c9995dc19c5951c3c55103</citedby><cites>FETCH-LOGICAL-c743t-748daebb6656366f92505140e728405c72e80d4aee7c9995dc19c5951c3c55103</cites><orcidid>0000-0002-0906-1323 ; 0000-0002-2705-2068 ; 0000-0002-0357-5454 ; 0000-0002-2061-091X ; 0000-0002-9588-6304</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/PMC8856572/pdf/$$EPDF$$P50$$Gpubmedcentral$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC8856572/$$EHTML$$P50$$Gpubmedcentral$$Hfree_for_read</linktohtml><link.rule.ids>230,314,723,776,780,860,881,2096,2915,23845,27901,27902,53766,53768,79342,79343</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/35180244$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Russ, Dominic</creatorcontrib><creatorcontrib>Williams, John A</creatorcontrib><creatorcontrib>Cardoso, Victor Roth</creatorcontrib><creatorcontrib>Bravo-Merodio, Laura</creatorcontrib><creatorcontrib>Pendleton, Samantha C</creatorcontrib><creatorcontrib>Aziz, Furqan</creatorcontrib><creatorcontrib>Acharjee, Animesh</creatorcontrib><creatorcontrib>Gkoutos, Georgios V</creatorcontrib><title>Evaluating the detection ability of a range of epistasis detection methods on simulated data for pure and impure epistatic models</title><title>PloS one</title><addtitle>PLoS One</addtitle><description>Numerous approaches have been proposed for the detection of epistatic interactions within GWAS datasets in order to better understand the drivers of disease and genetics.
A selection of state-of-the-art approaches were assessed. These included the statistical tests, fast-epistasis, BOOST, logistic regression and wtest; swarm intelligence methods, namely AntEpiSeeker, epiACO and CINOEDV; and data mining approaches, including MDR, GSS, SNPRuler and MPI3SNP. Data were simulated to provide randomly generated models with no individual main effects at different heritabilities (pure epistasis) as well as models based on penetrance tables with some main effects (impure epistasis). Detection of both two and three locus interactions were assessed across a total of 1,560 simulated datasets. The different methods were also applied to a section of the UK biobank cohort for Atrial Fibrillation.
For pure, two locus interactions, PLINK's implementation of BOOST recovered the highest number of correct interactions, with 53.9% and significantly better performing than the other methods (p = 4.52e - 36). For impure two locus interactions, MDR exhibited the best performance, recovering 62.2% of the most significant impure epistatic interactions (p = 6.31e - 90 for all but one test). The assessment of three locus interaction prediction revealed that wtest recovered the highest number (17.2%) of pure epistatic interactions(p = 8.49e - 14). wtest also recovered the highest number of three locus impure epistatic interactions (p = 6.76e - 48) while AntEpiSeeker ranked as the most significant the highest number of such interactions (40.5%). Finally, when applied to a real dataset for Atrial Fibrillation, most notably finding an interaction between SYNE2 and DTNB.</description><subject>Algorithms</subject><subject>Alleles</subject><subject>Analysis</subject><subject>Atrial Fibrillation - genetics</subject><subject>Biology</subject><subject>Biology and Life Sciences</subject><subject>Cancer</subject><subject>Cardiac arrhythmia</subject><subject>Computer and Information Sciences</subject><subject>Data mining</subject><subject>Data Mining - methods</subject><subject>Datasets</subject><subject>Dystrophin-Associated Proteins - genetics</subject><subject>Epistasis</subject><subject>Epistasis, Genetic</subject><subject>Evaluation</subject><subject>Fibrillation</subject><subject>Fuzzy sets</subject><subject>Gene Frequency</subject><subject>Gene loci</subject><subject>Generalized linear models</subject><subject>Genetic epistasis</subject><subject>Genetic Loci</subject><subject>Genetics</subject><subject>Genome-Wide Association Study - methods</subject><subject>Genomes</subject><subject>Genotype</subject><subject>Humans</subject><subject>Intelligence</subject><subject>Linear Models</subject><subject>Medicine</subject><subject>Medicine and Health Sciences</subject><subject>Methods</subject><subject>Microfilament Proteins - genetics</subject><subject>Models, Genetic</subject><subject>Multifactor Dimensionality Reduction</subject><subject>Nerve Tissue Proteins - genetics</subject><subject>Neuropeptides - genetics</subject><subject>Penetrance</subject><subject>Polymorphism, Single Nucleotide</subject><subject>ROC Curve</subject><subject>Simulation</subject><subject>Statistical analysis</subject><subject>Statistical tests</subject><subject>Swarm intelligence</subject><issn>1932-6203</issn><issn>1932-6203</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2022</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><sourceid>BENPR</sourceid><sourceid>DOA</sourceid><recordid>eNqNk99vFCEQxzdGY2v1PzBKYmL04U7YBXZ5MWmaqpc0aeKvV8LB7B0Nu5zANvbR_1y2t21uTR-EBybwme8MA1MULwlekqomH678EHrlljvfwxKXvKoEflQcE1GVC17i6vGBfVQ8i_EKY1Y1nD8tjipGGlxSelz8Ob9WblDJ9huUtoAMJNDJ-h6ptXU23SDfIoWC6jcwmrCzMalo4wHZQdp6E1E2o-0GpxIYZFRSqPUB7YYASPUG2e7W3Cskq1HnDbj4vHjSKhfhxbSeFD8-nX8_-7K4uPy8Oju9WOiaVmlR08YoWK85Z7zivBUlw4xQDHXZUMx0XUKDDVUAtRZCMKOJ0EwwoivNGMHVSfF6r7tzPsqpelHmwmEqRFbJxGpPGK-u5C7YToUb6ZWVtxs-bKQKOXEHUlDDedm2HHOaR6kavC5NQ7gmwKGhWevjFG1Yd2A09CkoNxOdn_R2Kzf-WjYN46wus8C7SSD4XwPEJDsbNTinevDDPm9BGlaPsd78gz58u4naqHwB27c-x9WjqDzlgpYEc8EytXyAytNAZ3X-aq3N-zOH9zOHzCT4nTZqiFGuvn39f_by55x9e8BuQbm0jd4N44-Lc5DuQR18jAHa-yITLMdOuauGHDtFTp2S3V4dPtC9011rVH8BR4IOMQ</recordid><startdate>20220218</startdate><enddate>20220218</enddate><creator>Russ, Dominic</creator><creator>Williams, John A</creator><creator>Cardoso, Victor Roth</creator><creator>Bravo-Merodio, Laura</creator><creator>Pendleton, Samantha C</creator><creator>Aziz, Furqan</creator><creator>Acharjee, Animesh</creator><creator>Gkoutos, Georgios V</creator><general>Public Library of Science</general><general>Public Library of Science (PLoS)</general><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>IOV</scope><scope>ISR</scope><scope>3V.</scope><scope>7QG</scope><scope>7QL</scope><scope>7QO</scope><scope>7RV</scope><scope>7SN</scope><scope>7SS</scope><scope>7T5</scope><scope>7TG</scope><scope>7TM</scope><scope>7U9</scope><scope>7X2</scope><scope>7X7</scope><scope>7XB</scope><scope>88E</scope><scope>8AO</scope><scope>8C1</scope><scope>8FD</scope><scope>8FE</scope><scope>8FG</scope><scope>8FH</scope><scope>8FI</scope><scope>8FJ</scope><scope>8FK</scope><scope>ABJCF</scope><scope>ABUWG</scope><scope>AEUYN</scope><scope>AFKRA</scope><scope>ARAPS</scope><scope>ATCPS</scope><scope>AZQEC</scope><scope>BBNVY</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>BHPHI</scope><scope>C1K</scope><scope>CCPQU</scope><scope>D1I</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>KB.</scope><scope>KB0</scope><scope>KL.</scope><scope>L6V</scope><scope>LK8</scope><scope>M0K</scope><scope>M0S</scope><scope>M1P</scope><scope>M7N</scope><scope>M7P</scope><scope>M7S</scope><scope>NAPCQ</scope><scope>P5Z</scope><scope>P62</scope><scope>P64</scope><scope>PATMY</scope><scope>PDBOC</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>PTHSS</scope><scope>PYCSY</scope><scope>RC3</scope><scope>7X8</scope><scope>5PM</scope><scope>DOA</scope><orcidid>https://orcid.org/0000-0002-0906-1323</orcidid><orcidid>https://orcid.org/0000-0002-2705-2068</orcidid><orcidid>https://orcid.org/0000-0002-0357-5454</orcidid><orcidid>https://orcid.org/0000-0002-2061-091X</orcidid><orcidid>https://orcid.org/0000-0002-9588-6304</orcidid></search><sort><creationdate>20220218</creationdate><title>Evaluating the detection ability of a range of epistasis detection methods on simulated data for pure and impure epistatic models</title><author>Russ, Dominic ; Williams, John A ; Cardoso, Victor Roth ; Bravo-Merodio, Laura ; Pendleton, Samantha C ; Aziz, Furqan ; Acharjee, Animesh ; Gkoutos, Georgios V</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c743t-748daebb6656366f92505140e728405c72e80d4aee7c9995dc19c5951c3c55103</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2022</creationdate><topic>Algorithms</topic><topic>Alleles</topic><topic>Analysis</topic><topic>Atrial Fibrillation - 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Academic</collection><collection>PubMed Central (Full Participant titles)</collection><collection>DOAJ Directory of Open Access Journals</collection><jtitle>PloS one</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Russ, Dominic</au><au>Williams, John A</au><au>Cardoso, Victor Roth</au><au>Bravo-Merodio, Laura</au><au>Pendleton, Samantha C</au><au>Aziz, Furqan</au><au>Acharjee, Animesh</au><au>Gkoutos, Georgios V</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Evaluating the detection ability of a range of epistasis detection methods on simulated data for pure and impure epistatic models</atitle><jtitle>PloS one</jtitle><addtitle>PLoS One</addtitle><date>2022-02-18</date><risdate>2022</risdate><volume>17</volume><issue>2</issue><spage>e0263390</spage><epage>e0263390</epage><pages>e0263390-e0263390</pages><issn>1932-6203</issn><eissn>1932-6203</eissn><abstract>Numerous approaches have been proposed for the detection of epistatic interactions within GWAS datasets in order to better understand the drivers of disease and genetics.
A selection of state-of-the-art approaches were assessed. These included the statistical tests, fast-epistasis, BOOST, logistic regression and wtest; swarm intelligence methods, namely AntEpiSeeker, epiACO and CINOEDV; and data mining approaches, including MDR, GSS, SNPRuler and MPI3SNP. Data were simulated to provide randomly generated models with no individual main effects at different heritabilities (pure epistasis) as well as models based on penetrance tables with some main effects (impure epistasis). Detection of both two and three locus interactions were assessed across a total of 1,560 simulated datasets. The different methods were also applied to a section of the UK biobank cohort for Atrial Fibrillation.
For pure, two locus interactions, PLINK's implementation of BOOST recovered the highest number of correct interactions, with 53.9% and significantly better performing than the other methods (p = 4.52e - 36). For impure two locus interactions, MDR exhibited the best performance, recovering 62.2% of the most significant impure epistatic interactions (p = 6.31e - 90 for all but one test). The assessment of three locus interaction prediction revealed that wtest recovered the highest number (17.2%) of pure epistatic interactions(p = 8.49e - 14). wtest also recovered the highest number of three locus impure epistatic interactions (p = 6.76e - 48) while AntEpiSeeker ranked as the most significant the highest number of such interactions (40.5%). Finally, when applied to a real dataset for Atrial Fibrillation, most notably finding an interaction between SYNE2 and DTNB.</abstract><cop>United States</cop><pub>Public Library of Science</pub><pmid>35180244</pmid><doi>10.1371/journal.pone.0263390</doi><tpages>e0263390</tpages><orcidid>https://orcid.org/0000-0002-0906-1323</orcidid><orcidid>https://orcid.org/0000-0002-2705-2068</orcidid><orcidid>https://orcid.org/0000-0002-0357-5454</orcidid><orcidid>https://orcid.org/0000-0002-2061-091X</orcidid><orcidid>https://orcid.org/0000-0002-9588-6304</orcidid><oa>free_for_read</oa></addata></record> |
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| recordid | cdi_plos_journals_2630499728 |
| source | MEDLINE; DOAJ Directory of Open Access Journals; Elektronische Zeitschriftenbibliothek - Frei zugängliche E-Journals; PubMed Central; Free Full-Text Journals in Chemistry; Public Library of Science (PLoS) |
| subjects | Algorithms Alleles Analysis Atrial Fibrillation - genetics Biology Biology and Life Sciences Cancer Cardiac arrhythmia Computer and Information Sciences Data mining Data Mining - methods Datasets Dystrophin-Associated Proteins - genetics Epistasis Epistasis, Genetic Evaluation Fibrillation Fuzzy sets Gene Frequency Gene loci Generalized linear models Genetic epistasis Genetic Loci Genetics Genome-Wide Association Study - methods Genomes Genotype Humans Intelligence Linear Models Medicine Medicine and Health Sciences Methods Microfilament Proteins - genetics Models, Genetic Multifactor Dimensionality Reduction Nerve Tissue Proteins - genetics Neuropeptides - genetics Penetrance Polymorphism, Single Nucleotide ROC Curve Simulation Statistical analysis Statistical tests Swarm intelligence |
| title | Evaluating the detection ability of a range of epistasis detection methods on simulated data for pure and impure epistatic models |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-02-10T07%3A23%3A40IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-gale_plos_&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Evaluating%20the%20detection%20ability%20of%20a%20range%20of%20epistasis%20detection%20methods%20on%20simulated%20data%20for%20pure%20and%20impure%20epistatic%20models&rft.jtitle=PloS%20one&rft.au=Russ,%20Dominic&rft.date=2022-02-18&rft.volume=17&rft.issue=2&rft.spage=e0263390&rft.epage=e0263390&rft.pages=e0263390-e0263390&rft.issn=1932-6203&rft.eissn=1932-6203&rft_id=info:doi/10.1371/journal.pone.0263390&rft_dat=%3Cgale_plos_%3EA694210695%3C/gale_plos_%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=2630499728&rft_id=info:pmid/35180244&rft_galeid=A694210695&rft_doaj_id=oai_doaj_org_article_94d662ff60644442a80b2d816c1e6e84&rfr_iscdi=true |