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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...
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Published in: | PloS one 2024-12, Vol.19 (12), p.e0314502 |
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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. |
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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 & 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</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. 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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. 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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 & youth</topic><topic>Computer Simulation</topic><topic>Datasets</topic><topic>Diagnosis</topic><topic>Disease</topic><topic>DNA nucleotidylexotransferase</topic><topic>Evaluation</topic><topic>Families & 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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> |
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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 |
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