Loading…

TRIO RVEMVS: A Bayesian framework for rare variant association analysis with expectation-maximization variable selection using family trio data

It is commonly reported that rare variants may be more functionally related to complex diseases than common variants. However, individual rare variant association tests remain challenging due to low minor allele frequency in the available samples. This paper proposes an expectation maximization vari...

Full description

Saved in:
Bibliographic Details
Published in:PloS one 2024-12, Vol.19 (12), p.e0314502
Main Authors: Yu, Duo, Koslovsky, Matthew, Steiner, Margaret C, Mohammadi, Kusha, Zhang, Chenguang, Swartz, Michael D
Format: Article
Language:English
Subjects:
Citations: Items that this one cites
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
cited_by
cites cdi_FETCH-LOGICAL-c5234-4922b6a04a2542700242c1dbfb51d07c22320f81df61f8de199442eb7e9fa5e13
container_end_page
container_issue 12
container_start_page e0314502
container_title PloS one
container_volume 19
creator Yu, Duo
Koslovsky, Matthew
Steiner, Margaret C
Mohammadi, Kusha
Zhang, Chenguang
Swartz, Michael D
description It is commonly reported that rare variants may be more functionally related to complex diseases than common variants. However, individual rare variant association tests remain challenging due to low minor allele frequency in the available samples. This paper proposes an expectation maximization variable selection (EMVS) method to simultaneously detect common and rare variants at the individual variant level using family trio data. TRIO_RVEMVS was assessed in both large (1500 families) and small (350 families) datasets based on simulation. The performance of TRIO_RVEMVS was compared with gene-level kernel and burden association tests that use pedigree data (PedGene) and rare-variant extensions of the transmission disequilibrium test (RV-TDT). At the region level, TRIO_RVEMVS outperformed PedGene and RV-TDT when common variants were included. TRIO_RVEMVS performed competitively with PedGene and outperformed RV-TDT when the analysis was only restricted to rare variants. At the individual variants level, with 1,500 trios, the average true positive rate of individual rare variants that were polymorphic across 500 datasets was 12.20%, and the average false positive rate was 0.74%. In the datasets with 350 trios, the average true and false positive rates of individual rare variants were 13.10% and 1.30%, respectively. When applying TRIO_RVEMVS to real data from the Gabriella Miller Kids First Pediatric Research Program, it identified 3 rare variants in q24.21 and q24.22 associated with the risk of orofacial clefts in the Kids First European population.
doi_str_mv 10.1371/journal.pone.0314502
format article
fullrecord <record><control><sourceid>gale_plos_</sourceid><recordid>TN_cdi_plos_journals_3141016128</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><galeid>A818939105</galeid><doaj_id>oai_doaj_org_article_57876a1bd3bc4eeeb41a9a22e4bc1dda</doaj_id><sourcerecordid>A818939105</sourcerecordid><originalsourceid>FETCH-LOGICAL-c5234-4922b6a04a2542700242c1dbfb51d07c22320f81df61f8de199442eb7e9fa5e13</originalsourceid><addsrcrecordid>eNqNk9tu00AQhi0EoiXwBghWQkJwkbAHH7lBoSoQqShSWnK7GtuzyQbbG3bttuEleGU2iVslqBfIF7Zmvv_f2fFMELxkdMREwj6sTGcbqEZr0-CIChZGlD8KTlkm-DDmVDw--D4Jnjm3ojQSaRw_DU5EFgsap9lp8OdqNpmS2fz8-_zyIxmTz7BBp6EhykKNN8b-JMpYYsEiuQbrMy0B50yhodWmIeBL2DjtyI1ulwRv11i0u8ywhltd6997bCfNKyQOK09sQ53TzYIoqHW1Ia3VhpTQwvPgiYLK4Yv-PQh-fDm_Ovs2vJh-nZyNL4ZFxEU4DDPO8xhoCDwKeUIpD3nBylzlEStpUnAuOFUpK1XMVFoiy7Iw5JgnmCmIkIlB8Hrvu66Mk30vnfRtZJTFjKeemOyJ0sBKrq2uwW6kAS13AWMXEmyriwpllKRJDCwvRV6EiJiHDDLgHMPcF1WC9_rUn9blNZYFNq2F6sj0ONPopVyYa8l8LXHKM-_wrnew5leHrpW1dgVWFTRoul3hccQSHlGPvvkHffh6PbUAfwPdKOMPLramcpyyNBMZ8-MyCEYPUP4psdaFnzylffxI8P5I4JkWb9sFdM7JyeXs_9np_Jh9e8AuEap26UzVbUfJHYPhHiyscc6iuu8yo3K7OHfdkNvFkf3ieNmrwz90L7rbFPEXCCgU1g</addsrcrecordid><sourcetype>Open Website</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>3141016128</pqid></control><display><type>article</type><title>TRIO RVEMVS: A Bayesian framework for rare variant association analysis with expectation-maximization variable selection using family trio data</title><source>PubMed Central (Open Access)</source><source>Publicly Available Content Database (Proquest) (PQ_SDU_P3)</source><creator>Yu, Duo ; Koslovsky, Matthew ; Steiner, Margaret C ; Mohammadi, Kusha ; Zhang, Chenguang ; Swartz, Michael D</creator><contributor>Kachroo, Priyadarshini</contributor><creatorcontrib>Yu, Duo ; Koslovsky, Matthew ; Steiner, Margaret C ; Mohammadi, Kusha ; Zhang, Chenguang ; Swartz, Michael D ; Kachroo, Priyadarshini</creatorcontrib><description>It is commonly reported that rare variants may be more functionally related to complex diseases than common variants. However, individual rare variant association tests remain challenging due to low minor allele frequency in the available samples. This paper proposes an expectation maximization variable selection (EMVS) method to simultaneously detect common and rare variants at the individual variant level using family trio data. TRIO_RVEMVS was assessed in both large (1500 families) and small (350 families) datasets based on simulation. The performance of TRIO_RVEMVS was compared with gene-level kernel and burden association tests that use pedigree data (PedGene) and rare-variant extensions of the transmission disequilibrium test (RV-TDT). At the region level, TRIO_RVEMVS outperformed PedGene and RV-TDT when common variants were included. TRIO_RVEMVS performed competitively with PedGene and outperformed RV-TDT when the analysis was only restricted to rare variants. At the individual variants level, with 1,500 trios, the average true positive rate of individual rare variants that were polymorphic across 500 datasets was 12.20%, and the average false positive rate was 0.74%. In the datasets with 350 trios, the average true and false positive rates of individual rare variants were 13.10% and 1.30%, respectively. When applying TRIO_RVEMVS to real data from the Gabriella Miller Kids First Pediatric Research Program, it identified 3 rare variants in q24.21 and q24.22 associated with the risk of orofacial clefts in the Kids First European population.</description><identifier>ISSN: 1932-6203</identifier><identifier>EISSN: 1932-6203</identifier><identifier>DOI: 10.1371/journal.pone.0314502</identifier><identifier>PMID: 39630689</identifier><language>eng</language><publisher>United States: Public Library of Science</publisher><subject>Analysis ; Association analysis ; Bayes Theorem ; Bayesian analysis ; Bayesian statistical decision theory ; Biology and Life Sciences ; Birth defects ; Children &amp; youth ; Computer Simulation ; Datasets ; Diagnosis ; Disease ; DNA nucleotidylexotransferase ; Evaluation ; Families &amp; family life ; Feature selection ; Gene Frequency ; Genetic aspects ; Genetic Predisposition to Disease ; Genetic Variation ; Genomes ; Genomics ; Haplotypes ; Humans ; Linkage Disequilibrium ; Maximization ; Medicine and Health Sciences ; Models, Genetic ; Mortality ; Optimization ; Orofacial clefts ; Parents &amp; parenting ; Pediatrics ; Pedigree ; People and Places ; Polymorphism, Single Nucleotide ; Regression analysis ; Research and Analysis Methods</subject><ispartof>PloS one, 2024-12, Vol.19 (12), p.e0314502</ispartof><rights>Copyright: © 2024 Yu et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.</rights><rights>COPYRIGHT 2024 Public Library of Science</rights><rights>2024 Yu 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>2024 Yu et al 2024 Yu et al</rights><rights>2024 Yu 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><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><cites>FETCH-LOGICAL-c5234-4922b6a04a2542700242c1dbfb51d07c22320f81df61f8de199442eb7e9fa5e13</cites><orcidid>0000-0002-8988-1646 ; 0000-0003-3953-8775</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.proquest.com/docview/3141016128/fulltextPDF?pq-origsite=primo$$EPDF$$P50$$Gproquest$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.proquest.com/docview/3141016128?pq-origsite=primo$$EHTML$$P50$$Gproquest$$Hfree_for_read</linktohtml><link.rule.ids>230,314,727,780,784,885,25752,27923,27924,37011,37012,44589,53790,53792,74897</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/39630689$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><contributor>Kachroo, Priyadarshini</contributor><creatorcontrib>Yu, Duo</creatorcontrib><creatorcontrib>Koslovsky, Matthew</creatorcontrib><creatorcontrib>Steiner, Margaret C</creatorcontrib><creatorcontrib>Mohammadi, Kusha</creatorcontrib><creatorcontrib>Zhang, Chenguang</creatorcontrib><creatorcontrib>Swartz, Michael D</creatorcontrib><title>TRIO RVEMVS: A Bayesian framework for rare variant association analysis with expectation-maximization variable selection using family trio data</title><title>PloS one</title><addtitle>PLoS One</addtitle><description>It is commonly reported that rare variants may be more functionally related to complex diseases than common variants. However, individual rare variant association tests remain challenging due to low minor allele frequency in the available samples. This paper proposes an expectation maximization variable selection (EMVS) method to simultaneously detect common and rare variants at the individual variant level using family trio data. TRIO_RVEMVS was assessed in both large (1500 families) and small (350 families) datasets based on simulation. The performance of TRIO_RVEMVS was compared with gene-level kernel and burden association tests that use pedigree data (PedGene) and rare-variant extensions of the transmission disequilibrium test (RV-TDT). At the region level, TRIO_RVEMVS outperformed PedGene and RV-TDT when common variants were included. TRIO_RVEMVS performed competitively with PedGene and outperformed RV-TDT when the analysis was only restricted to rare variants. At the individual variants level, with 1,500 trios, the average true positive rate of individual rare variants that were polymorphic across 500 datasets was 12.20%, and the average false positive rate was 0.74%. In the datasets with 350 trios, the average true and false positive rates of individual rare variants were 13.10% and 1.30%, respectively. When applying TRIO_RVEMVS to real data from the Gabriella Miller Kids First Pediatric Research Program, it identified 3 rare variants in q24.21 and q24.22 associated with the risk of orofacial clefts in the Kids First European population.</description><subject>Analysis</subject><subject>Association analysis</subject><subject>Bayes Theorem</subject><subject>Bayesian analysis</subject><subject>Bayesian statistical decision theory</subject><subject>Biology and Life Sciences</subject><subject>Birth defects</subject><subject>Children &amp; youth</subject><subject>Computer Simulation</subject><subject>Datasets</subject><subject>Diagnosis</subject><subject>Disease</subject><subject>DNA nucleotidylexotransferase</subject><subject>Evaluation</subject><subject>Families &amp; family life</subject><subject>Feature selection</subject><subject>Gene Frequency</subject><subject>Genetic aspects</subject><subject>Genetic Predisposition to Disease</subject><subject>Genetic Variation</subject><subject>Genomes</subject><subject>Genomics</subject><subject>Haplotypes</subject><subject>Humans</subject><subject>Linkage Disequilibrium</subject><subject>Maximization</subject><subject>Medicine and Health Sciences</subject><subject>Models, Genetic</subject><subject>Mortality</subject><subject>Optimization</subject><subject>Orofacial clefts</subject><subject>Parents &amp; parenting</subject><subject>Pediatrics</subject><subject>Pedigree</subject><subject>People and Places</subject><subject>Polymorphism, Single Nucleotide</subject><subject>Regression analysis</subject><subject>Research and Analysis Methods</subject><issn>1932-6203</issn><issn>1932-6203</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2024</creationdate><recordtype>article</recordtype><sourceid>PIMPY</sourceid><sourceid>DOA</sourceid><recordid>eNqNk9tu00AQhi0EoiXwBghWQkJwkbAHH7lBoSoQqShSWnK7GtuzyQbbG3bttuEleGU2iVslqBfIF7Zmvv_f2fFMELxkdMREwj6sTGcbqEZr0-CIChZGlD8KTlkm-DDmVDw--D4Jnjm3ojQSaRw_DU5EFgsap9lp8OdqNpmS2fz8-_zyIxmTz7BBp6EhykKNN8b-JMpYYsEiuQbrMy0B50yhodWmIeBL2DjtyI1ulwRv11i0u8ywhltd6997bCfNKyQOK09sQ53TzYIoqHW1Ia3VhpTQwvPgiYLK4Yv-PQh-fDm_Ovs2vJh-nZyNL4ZFxEU4DDPO8xhoCDwKeUIpD3nBylzlEStpUnAuOFUpK1XMVFoiy7Iw5JgnmCmIkIlB8Hrvu66Mk30vnfRtZJTFjKeemOyJ0sBKrq2uwW6kAS13AWMXEmyriwpllKRJDCwvRV6EiJiHDDLgHMPcF1WC9_rUn9blNZYFNq2F6sj0ONPopVyYa8l8LXHKM-_wrnew5leHrpW1dgVWFTRoul3hccQSHlGPvvkHffh6PbUAfwPdKOMPLramcpyyNBMZ8-MyCEYPUP4psdaFnzylffxI8P5I4JkWb9sFdM7JyeXs_9np_Jh9e8AuEap26UzVbUfJHYPhHiyscc6iuu8yo3K7OHfdkNvFkf3ieNmrwz90L7rbFPEXCCgU1g</recordid><startdate>20241204</startdate><enddate>20241204</enddate><creator>Yu, Duo</creator><creator>Koslovsky, Matthew</creator><creator>Steiner, Margaret C</creator><creator>Mohammadi, Kusha</creator><creator>Zhang, Chenguang</creator><creator>Swartz, Michael D</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/0000-0002-8988-1646</orcidid><orcidid>https://orcid.org/0000-0003-3953-8775</orcidid></search><sort><creationdate>20241204</creationdate><title>TRIO RVEMVS: A Bayesian framework for rare variant association analysis with expectation-maximization variable selection using family trio data</title><author>Yu, Duo ; Koslovsky, Matthew ; Steiner, Margaret C ; Mohammadi, Kusha ; Zhang, Chenguang ; Swartz, Michael D</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c5234-4922b6a04a2542700242c1dbfb51d07c22320f81df61f8de199442eb7e9fa5e13</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2024</creationdate><topic>Analysis</topic><topic>Association analysis</topic><topic>Bayes Theorem</topic><topic>Bayesian analysis</topic><topic>Bayesian statistical decision theory</topic><topic>Biology and Life Sciences</topic><topic>Birth defects</topic><topic>Children &amp; youth</topic><topic>Computer Simulation</topic><topic>Datasets</topic><topic>Diagnosis</topic><topic>Disease</topic><topic>DNA nucleotidylexotransferase</topic><topic>Evaluation</topic><topic>Families &amp; family life</topic><topic>Feature selection</topic><topic>Gene Frequency</topic><topic>Genetic aspects</topic><topic>Genetic Predisposition to Disease</topic><topic>Genetic Variation</topic><topic>Genomes</topic><topic>Genomics</topic><topic>Haplotypes</topic><topic>Humans</topic><topic>Linkage Disequilibrium</topic><topic>Maximization</topic><topic>Medicine and Health Sciences</topic><topic>Models, Genetic</topic><topic>Mortality</topic><topic>Optimization</topic><topic>Orofacial clefts</topic><topic>Parents &amp; parenting</topic><topic>Pediatrics</topic><topic>Pedigree</topic><topic>People and Places</topic><topic>Polymorphism, Single Nucleotide</topic><topic>Regression analysis</topic><topic>Research and Analysis Methods</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Yu, Duo</creatorcontrib><creatorcontrib>Koslovsky, Matthew</creatorcontrib><creatorcontrib>Steiner, Margaret C</creatorcontrib><creatorcontrib>Mohammadi, Kusha</creatorcontrib><creatorcontrib>Zhang, Chenguang</creatorcontrib><creatorcontrib>Swartz, Michael D</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>Opposing Viewpoints in Context (Gale)</collection><collection>Gale In Context: Science</collection><collection>ProQuest Central (Corporate)</collection><collection>Animal Behavior Abstracts</collection><collection>Bacteriology Abstracts (Microbiology B)</collection><collection>Biotechnology Research Abstracts</collection><collection>Nursing &amp; Allied Health Database</collection><collection>Ecology Abstracts</collection><collection>Entomology Abstracts (Full archive)</collection><collection>Immunology Abstracts</collection><collection>Meteorological &amp; Geoastrophysical Abstracts</collection><collection>Nucleic Acids Abstracts</collection><collection>Virology and AIDS Abstracts</collection><collection>Agricultural Science Collection</collection><collection>Health &amp; Medical Collection</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>Medical Database (Alumni Edition)</collection><collection>ProQuest Pharma Collection</collection><collection>Public Health Database</collection><collection>Technology Research Database</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>ProQuest Natural Science Collection</collection><collection>Hospital Premium Collection</collection><collection>Hospital Premium Collection (Alumni Edition)</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>Materials Science &amp; Engineering Collection</collection><collection>ProQuest Central (Alumni)</collection><collection>ProQuest One Sustainability</collection><collection>ProQuest Central</collection><collection>Advanced Technologies &amp; Aerospace Collection</collection><collection>Agricultural &amp; Environmental Science Collection</collection><collection>ProQuest Central Essentials</collection><collection>Biological Science Collection</collection><collection>ProQuest Central</collection><collection>Technology Collection</collection><collection>Natural Science Collection</collection><collection>Environmental Sciences and Pollution Management</collection><collection>ProQuest One Community College</collection><collection>ProQuest Materials Science Collection</collection><collection>ProQuest Central Korea</collection><collection>Engineering Research Database</collection><collection>Health Research Premium Collection</collection><collection>Health Research Premium Collection (Alumni)</collection><collection>ProQuest Central Student</collection><collection>AIDS and Cancer Research Abstracts</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Health &amp; Medical Complete (Alumni)</collection><collection>Materials Science Database</collection><collection>Nursing &amp; Allied Health Database (Alumni Edition)</collection><collection>Meteorological &amp; Geoastrophysical Abstracts - Academic</collection><collection>ProQuest Engineering Collection</collection><collection>Biological Sciences</collection><collection>Agriculture Science Database</collection><collection>Health &amp; Medical Collection (Alumni Edition)</collection><collection>PML(ProQuest Medical Library)</collection><collection>Algology Mycology and Protozoology Abstracts (Microbiology C)</collection><collection>Biological Science Database</collection><collection>Engineering Database</collection><collection>Nursing &amp; Allied Health Premium</collection><collection>Advanced Technologies &amp; Aerospace Database</collection><collection>ProQuest Advanced Technologies &amp; Aerospace Collection</collection><collection>Biotechnology and BioEngineering Abstracts</collection><collection>Environmental Science Database</collection><collection>Materials Science Collection</collection><collection>Publicly Available Content Database (Proquest) (PQ_SDU_P3)</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central China</collection><collection>Engineering Collection</collection><collection>Environmental Science Collection</collection><collection>Genetics Abstracts</collection><collection>MEDLINE - Academic</collection><collection>PubMed Central (Full Participant titles)</collection><collection>Directory of Open Access Journals</collection><jtitle>PloS one</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Yu, Duo</au><au>Koslovsky, Matthew</au><au>Steiner, Margaret C</au><au>Mohammadi, Kusha</au><au>Zhang, Chenguang</au><au>Swartz, Michael D</au><au>Kachroo, Priyadarshini</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>TRIO RVEMVS: A Bayesian framework for rare variant association analysis with expectation-maximization variable selection using family trio data</atitle><jtitle>PloS one</jtitle><addtitle>PLoS One</addtitle><date>2024-12-04</date><risdate>2024</risdate><volume>19</volume><issue>12</issue><spage>e0314502</spage><pages>e0314502-</pages><issn>1932-6203</issn><eissn>1932-6203</eissn><abstract>It is commonly reported that rare variants may be more functionally related to complex diseases than common variants. However, individual rare variant association tests remain challenging due to low minor allele frequency in the available samples. This paper proposes an expectation maximization variable selection (EMVS) method to simultaneously detect common and rare variants at the individual variant level using family trio data. TRIO_RVEMVS was assessed in both large (1500 families) and small (350 families) datasets based on simulation. The performance of TRIO_RVEMVS was compared with gene-level kernel and burden association tests that use pedigree data (PedGene) and rare-variant extensions of the transmission disequilibrium test (RV-TDT). At the region level, TRIO_RVEMVS outperformed PedGene and RV-TDT when common variants were included. TRIO_RVEMVS performed competitively with PedGene and outperformed RV-TDT when the analysis was only restricted to rare variants. At the individual variants level, with 1,500 trios, the average true positive rate of individual rare variants that were polymorphic across 500 datasets was 12.20%, and the average false positive rate was 0.74%. In the datasets with 350 trios, the average true and false positive rates of individual rare variants were 13.10% and 1.30%, respectively. When applying TRIO_RVEMVS to real data from the Gabriella Miller Kids First Pediatric Research Program, it identified 3 rare variants in q24.21 and q24.22 associated with the risk of orofacial clefts in the Kids First European population.</abstract><cop>United States</cop><pub>Public Library of Science</pub><pmid>39630689</pmid><doi>10.1371/journal.pone.0314502</doi><tpages>e0314502</tpages><orcidid>https://orcid.org/0000-0002-8988-1646</orcidid><orcidid>https://orcid.org/0000-0003-3953-8775</orcidid><oa>free_for_read</oa></addata></record>
fulltext fulltext
identifier ISSN: 1932-6203
ispartof PloS one, 2024-12, Vol.19 (12), p.e0314502
issn 1932-6203
1932-6203
language eng
recordid cdi_plos_journals_3141016128
source PubMed Central (Open Access); Publicly Available Content Database (Proquest) (PQ_SDU_P3)
subjects Analysis
Association analysis
Bayes Theorem
Bayesian analysis
Bayesian statistical decision theory
Biology and Life Sciences
Birth defects
Children & youth
Computer Simulation
Datasets
Diagnosis
Disease
DNA nucleotidylexotransferase
Evaluation
Families & family life
Feature selection
Gene Frequency
Genetic aspects
Genetic Predisposition to Disease
Genetic Variation
Genomes
Genomics
Haplotypes
Humans
Linkage Disequilibrium
Maximization
Medicine and Health Sciences
Models, Genetic
Mortality
Optimization
Orofacial clefts
Parents & parenting
Pediatrics
Pedigree
People and Places
Polymorphism, Single Nucleotide
Regression analysis
Research and Analysis Methods
title TRIO RVEMVS: A Bayesian framework for rare variant association analysis with expectation-maximization variable selection using family trio data
url http://sfxeu10.hosted.exlibrisgroup.com/loughborough?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-11T17%3A49%3A18IST&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=TRIO%20RVEMVS:%20A%20Bayesian%20framework%20for%20rare%20variant%20association%20analysis%20with%20expectation-maximization%20variable%20selection%20using%20family%20trio%20data&rft.jtitle=PloS%20one&rft.au=Yu,%20Duo&rft.date=2024-12-04&rft.volume=19&rft.issue=12&rft.spage=e0314502&rft.pages=e0314502-&rft.issn=1932-6203&rft.eissn=1932-6203&rft_id=info:doi/10.1371/journal.pone.0314502&rft_dat=%3Cgale_plos_%3EA818939105%3C/gale_plos_%3E%3Cgrp_id%3Ecdi_FETCH-LOGICAL-c5234-4922b6a04a2542700242c1dbfb51d07c22320f81df61f8de199442eb7e9fa5e13%3C/grp_id%3E%3Coa%3E%3C/oa%3E%3Curl%3E%3C/url%3E&rft_id=info:oai/&rft_pqid=3141016128&rft_id=info:pmid/39630689&rft_galeid=A818939105&rfr_iscdi=true