CWAS-Plus: estimating category-wide association of rare noncoding variation from whole-genome sequencing data with cell-type-specific functional data
Variants in cis-regulatory elements link the noncoding genome to human pathology; however, detailed analytic tools for understanding the association between cell-level brain pathology and noncoding variants are lacking. CWAS-Plus, adapted from a Python package for category-wide association testing (...
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creator | Kim, Yujin Jeong, Minwoo Koh, In Gyeong Kim, Chanhee Lee, Hyeji Kim, Jae Hyun Yurko, Ronald Kim, Il Bin Park, Jeongbin Werling, Donna M Sanders, Stephan J An, Joon-Yong |
description | Variants in cis-regulatory elements link the noncoding genome to human pathology; however, detailed analytic tools for understanding the association between cell-level brain pathology and noncoding variants are lacking. CWAS-Plus, adapted from a Python package for category-wide association testing (CWAS), enhances noncoding variant analysis by integrating both whole-genome sequencing (WGS) and user-provided functional data. With simplified parameter settings and an efficient multiple testing correction method, CWAS-Plus conducts the CWAS workflow 50 times faster than CWAS, making it more accessible and user-friendly for researchers. Here, we used a single-nuclei assay for transposase-accessible chromatin with sequencing to facilitate CWAS-guided noncoding variant analysis at cell-type-specific enhancers and promoters. Examining autism spectrum disorder WGS data (n = 7280), CWAS-Plus identified noncoding de novo variant associations in transcription factor binding sites within conserved loci. Independently, in Alzheimer's disease WGS data (n = 1087), CWAS-Plus detected rare noncoding variant associations in microglia-specific regulatory elements. These findings highlight CWAS-Plus's utility in genomic disorders and scalability for processing large-scale WGS data and in multiple-testing corrections. CWAS-Plus and its user manual are available at https://github.com/joonan-lab/cwas/ and https://cwas-plus.readthedocs.io/en/latest/, respectively. |
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CWAS-Plus, adapted from a Python package for category-wide association testing (CWAS), enhances noncoding variant analysis by integrating both whole-genome sequencing (WGS) and user-provided functional data. With simplified parameter settings and an efficient multiple testing correction method, CWAS-Plus conducts the CWAS workflow 50 times faster than CWAS, making it more accessible and user-friendly for researchers. Here, we used a single-nuclei assay for transposase-accessible chromatin with sequencing to facilitate CWAS-guided noncoding variant analysis at cell-type-specific enhancers and promoters. Examining autism spectrum disorder WGS data (n = 7280), CWAS-Plus identified noncoding de novo variant associations in transcription factor binding sites within conserved loci. Independently, in Alzheimer's disease WGS data (n = 1087), CWAS-Plus detected rare noncoding variant associations in microglia-specific regulatory elements. These findings highlight CWAS-Plus's utility in genomic disorders and scalability for processing large-scale WGS data and in multiple-testing corrections. CWAS-Plus and its user manual are available at https://github.com/joonan-lab/cwas/ and https://cwas-plus.readthedocs.io/en/latest/, respectively.</description><identifier>ISSN: 1467-5463</identifier><identifier>ISSN: 1477-4054</identifier><identifier>EISSN: 1477-4054</identifier><identifier>DOI: 10.1093/bib/bbae323</identifier><identifier>PMID: 38966948</identifier><language>eng</language><publisher>England: Oxford University Press</publisher><subject>Alzheimer Disease - genetics ; Autism Spectrum Disorder - genetics ; Chromatin - genetics ; Chromatin - metabolism ; Genetic Variation ; Genome, Human ; Genome-Wide Association Study - methods ; Humans ; Problem Solving Protocol ; Software ; Whole Genome Sequencing - methods</subject><ispartof>Briefings in bioinformatics, 2024-05, Vol.25 (4)</ispartof><rights>The Author(s) 2024. 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Published by Oxford University Press. 2024</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><cites>FETCH-LOGICAL-c233t-b6ea37a3e6bdf854e76e89ecd0fcb5d866d909dfac52584c210bee67bef3adc83</cites><orcidid>0000-0001-9677-6599 ; 0000-0001-8839-6297</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/PMC11224609/pdf/$$EPDF$$P50$$Gpubmedcentral$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC11224609/$$EHTML$$P50$$Gpubmedcentral$$Hfree_for_read</linktohtml><link.rule.ids>230,314,723,776,780,881,27903,27904,53770,53772</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/38966948$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Kim, Yujin</creatorcontrib><creatorcontrib>Jeong, Minwoo</creatorcontrib><creatorcontrib>Koh, In Gyeong</creatorcontrib><creatorcontrib>Kim, Chanhee</creatorcontrib><creatorcontrib>Lee, Hyeji</creatorcontrib><creatorcontrib>Kim, Jae Hyun</creatorcontrib><creatorcontrib>Yurko, Ronald</creatorcontrib><creatorcontrib>Kim, Il Bin</creatorcontrib><creatorcontrib>Park, Jeongbin</creatorcontrib><creatorcontrib>Werling, Donna M</creatorcontrib><creatorcontrib>Sanders, Stephan J</creatorcontrib><creatorcontrib>An, Joon-Yong</creatorcontrib><title>CWAS-Plus: estimating category-wide association of rare noncoding variation from whole-genome sequencing data with cell-type-specific functional data</title><title>Briefings in bioinformatics</title><addtitle>Brief Bioinform</addtitle><description>Variants in cis-regulatory elements link the noncoding genome to human pathology; however, detailed analytic tools for understanding the association between cell-level brain pathology and noncoding variants are lacking. CWAS-Plus, adapted from a Python package for category-wide association testing (CWAS), enhances noncoding variant analysis by integrating both whole-genome sequencing (WGS) and user-provided functional data. With simplified parameter settings and an efficient multiple testing correction method, CWAS-Plus conducts the CWAS workflow 50 times faster than CWAS, making it more accessible and user-friendly for researchers. Here, we used a single-nuclei assay for transposase-accessible chromatin with sequencing to facilitate CWAS-guided noncoding variant analysis at cell-type-specific enhancers and promoters. Examining autism spectrum disorder WGS data (n = 7280), CWAS-Plus identified noncoding de novo variant associations in transcription factor binding sites within conserved loci. Independently, in Alzheimer's disease WGS data (n = 1087), CWAS-Plus detected rare noncoding variant associations in microglia-specific regulatory elements. These findings highlight CWAS-Plus's utility in genomic disorders and scalability for processing large-scale WGS data and in multiple-testing corrections. CWAS-Plus and its user manual are available at https://github.com/joonan-lab/cwas/ and https://cwas-plus.readthedocs.io/en/latest/, respectively.</description><subject>Alzheimer Disease - genetics</subject><subject>Autism Spectrum Disorder - genetics</subject><subject>Chromatin - genetics</subject><subject>Chromatin - metabolism</subject><subject>Genetic Variation</subject><subject>Genome, Human</subject><subject>Genome-Wide Association Study - methods</subject><subject>Humans</subject><subject>Problem Solving Protocol</subject><subject>Software</subject><subject>Whole Genome Sequencing - methods</subject><issn>1467-5463</issn><issn>1477-4054</issn><issn>1477-4054</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2024</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><recordid>eNpVkUtr3DAUhUVpaZJJV90XLQtBiWzZsp1NCUMehUACbehS6HE1o2JLE0nOMD-k_zd2MwntShfOx7m65yD0uaCnBe3YmXLqTCkJrGTv0GFRNQ2paF29n2fekLri7AAdpfSb0pI2bfERHbC247yr2kP0Z_nr4ge578d0jiFlN8js_AprmWEV4o5snQEsUwraTUrwOFgcZQTsg9fBzOyTjHvNxjDg7Tr0QFbgwwA4weMIXs-YkVnirctrrKHvSd5tgKQNaGedxnb0eraQ_V_uGH2wsk_waf8u0MPV5c_lDbm9u_6-vLglumQsE8VBskYy4MrYtq6g4dB2oA21WtWm5dx0tDNW6rqs20qXBVUAvFFgmTS6ZQv07cV3M6oBjAafo-zFJk45xJ0I0on_Fe_WYhWeRFGUZcWn9Bfo694hhunUlMXg0nyg9BDGJBht-BR7x2b05AXVMaQUwb7tKaiYmxRTk2Lf5ER_-fdrb-xrdewZGlWhFQ</recordid><startdate>20240523</startdate><enddate>20240523</enddate><creator>Kim, Yujin</creator><creator>Jeong, Minwoo</creator><creator>Koh, In Gyeong</creator><creator>Kim, Chanhee</creator><creator>Lee, Hyeji</creator><creator>Kim, Jae Hyun</creator><creator>Yurko, Ronald</creator><creator>Kim, Il Bin</creator><creator>Park, Jeongbin</creator><creator>Werling, Donna M</creator><creator>Sanders, Stephan J</creator><creator>An, Joon-Yong</creator><general>Oxford University Press</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>7X8</scope><scope>5PM</scope><orcidid>https://orcid.org/0000-0001-9677-6599</orcidid><orcidid>https://orcid.org/0000-0001-8839-6297</orcidid></search><sort><creationdate>20240523</creationdate><title>CWAS-Plus: estimating category-wide association of rare noncoding variation from whole-genome sequencing data with cell-type-specific functional data</title><author>Kim, Yujin ; Jeong, Minwoo ; Koh, In Gyeong ; Kim, Chanhee ; Lee, Hyeji ; Kim, Jae Hyun ; Yurko, Ronald ; Kim, Il Bin ; Park, Jeongbin ; Werling, Donna M ; Sanders, Stephan J ; An, Joon-Yong</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c233t-b6ea37a3e6bdf854e76e89ecd0fcb5d866d909dfac52584c210bee67bef3adc83</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2024</creationdate><topic>Alzheimer Disease - genetics</topic><topic>Autism Spectrum Disorder - genetics</topic><topic>Chromatin - genetics</topic><topic>Chromatin - metabolism</topic><topic>Genetic Variation</topic><topic>Genome, Human</topic><topic>Genome-Wide Association Study - methods</topic><topic>Humans</topic><topic>Problem Solving Protocol</topic><topic>Software</topic><topic>Whole Genome Sequencing - methods</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Kim, Yujin</creatorcontrib><creatorcontrib>Jeong, Minwoo</creatorcontrib><creatorcontrib>Koh, In Gyeong</creatorcontrib><creatorcontrib>Kim, Chanhee</creatorcontrib><creatorcontrib>Lee, Hyeji</creatorcontrib><creatorcontrib>Kim, Jae Hyun</creatorcontrib><creatorcontrib>Yurko, Ronald</creatorcontrib><creatorcontrib>Kim, Il Bin</creatorcontrib><creatorcontrib>Park, Jeongbin</creatorcontrib><creatorcontrib>Werling, Donna M</creatorcontrib><creatorcontrib>Sanders, Stephan J</creatorcontrib><creatorcontrib>An, Joon-Yong</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>MEDLINE - Academic</collection><collection>PubMed Central (Full Participant titles)</collection><jtitle>Briefings in bioinformatics</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Kim, Yujin</au><au>Jeong, Minwoo</au><au>Koh, In Gyeong</au><au>Kim, Chanhee</au><au>Lee, Hyeji</au><au>Kim, Jae Hyun</au><au>Yurko, Ronald</au><au>Kim, Il Bin</au><au>Park, Jeongbin</au><au>Werling, Donna M</au><au>Sanders, Stephan J</au><au>An, Joon-Yong</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>CWAS-Plus: estimating category-wide association of rare noncoding variation from whole-genome sequencing data with cell-type-specific functional data</atitle><jtitle>Briefings in bioinformatics</jtitle><addtitle>Brief Bioinform</addtitle><date>2024-05-23</date><risdate>2024</risdate><volume>25</volume><issue>4</issue><issn>1467-5463</issn><issn>1477-4054</issn><eissn>1477-4054</eissn><abstract>Variants in cis-regulatory elements link the noncoding genome to human pathology; however, detailed analytic tools for understanding the association between cell-level brain pathology and noncoding variants are lacking. CWAS-Plus, adapted from a Python package for category-wide association testing (CWAS), enhances noncoding variant analysis by integrating both whole-genome sequencing (WGS) and user-provided functional data. With simplified parameter settings and an efficient multiple testing correction method, CWAS-Plus conducts the CWAS workflow 50 times faster than CWAS, making it more accessible and user-friendly for researchers. Here, we used a single-nuclei assay for transposase-accessible chromatin with sequencing to facilitate CWAS-guided noncoding variant analysis at cell-type-specific enhancers and promoters. Examining autism spectrum disorder WGS data (n = 7280), CWAS-Plus identified noncoding de novo variant associations in transcription factor binding sites within conserved loci. Independently, in Alzheimer's disease WGS data (n = 1087), CWAS-Plus detected rare noncoding variant associations in microglia-specific regulatory elements. These findings highlight CWAS-Plus's utility in genomic disorders and scalability for processing large-scale WGS data and in multiple-testing corrections. CWAS-Plus and its user manual are available at https://github.com/joonan-lab/cwas/ and https://cwas-plus.readthedocs.io/en/latest/, respectively.</abstract><cop>England</cop><pub>Oxford University Press</pub><pmid>38966948</pmid><doi>10.1093/bib/bbae323</doi><orcidid>https://orcid.org/0000-0001-9677-6599</orcidid><orcidid>https://orcid.org/0000-0001-8839-6297</orcidid><oa>free_for_read</oa></addata></record> |
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subjects | Alzheimer Disease - genetics Autism Spectrum Disorder - genetics Chromatin - genetics Chromatin - metabolism Genetic Variation Genome, Human Genome-Wide Association Study - methods Humans Problem Solving Protocol Software Whole Genome Sequencing - methods |
title | CWAS-Plus: estimating category-wide association of rare noncoding variation from whole-genome sequencing data with cell-type-specific functional data |
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