{"doi":"10.1159/000375409","title":"A Sequence Kernel Association Test for Dichotomous Traits in Family Samples under a Generalized Linear Mixed Model","abstract":"<jats:p>&lt;b&gt;&lt;i&gt;Objective:&lt;/i&gt;&lt;/b&gt; The existing methods for identifying multiple rare variants underlying complex diseases in family samples are underpowered. Therefore, we aim to develop a new set-based method for an association study of dichotomous traits in family samples. &lt;b&gt;&lt;i&gt;Methods:&lt;/i&gt;&lt;/b&gt; We introduce a framework for testing the association of genetic variants with diseases in family samples based on a generalized linear mixed model. Our proposed method is based on a kernel machine regression and can be viewed as an extension of the sequence kernel association test (SKAT and famSKAT) for application to family data with dichotomous traits (F-SKAT). &lt;b&gt;&lt;i&gt;Results:&lt;/i&gt;&lt;/b&gt; Our simulation studies show that the original SKAT has inflated type I error rates when applied directly to family data. By contrast, our proposed F-SKAT has the correct type I error rate. Furthermore, in all of the considered scenarios, F-SKAT, which uses all family data, has higher power than both SKAT, which uses only unrelated individuals from the family data, and another method, which uses all family data. &lt;b&gt;&lt;i&gt;Conclusion:&lt;/i&gt;&lt;/b&gt; We propose a set-based association test that can be used to analyze family data with dichotomous phenotypes while handling genetic variants with the same or opposite directions of effects as well as any types of family relationships.</jats:p>","journal":"Human Heredity","year":2015,"id":627004,"datarank":0.515098080672772,"base_score":3.4339872044851463,"endowment":3.4339872044851463,"self_citation_contribution":0.515098080672772,"citation_network_contribution":0.0,"self_endowment_contribution":0.515098080672772,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":30,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":null,"is_data_producer":false,"deposit_databanks":null,"is_oa":false,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":null,"fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":218541,"name":"Hemant K. Tiwari","orcid":"0000-0003-4016-1856","position":1,"is_corresponding":false},{"id":455514,"name":"Nengjun Yi","orcid":"0000-0001-5857-5868","position":2,"is_corresponding":false},{"id":373622,"name":"Guimin Gao","orcid":"0000-0001-5556-7141","position":3,"is_corresponding":false},{"id":160197,"name":"Kui Zhang","orcid":null,"position":4,"is_corresponding":false},{"id":1622306,"name":"Wan-Yu Lin","orcid":null,"position":5,"is_corresponding":false},{"id":1622307,"name":"Xiang-Yang Lou","orcid":null,"position":6,"is_corresponding":false},{"id":418110,"name":"Xiangqin Cui","orcid":"0000-0003-0621-9313","position":7,"is_corresponding":false},{"id":373623,"name":"Nianjun Liu","orcid":"0000-0002-2565-8154","position":8,"is_corresponding":false},{"id":1298999,"name":"Qi Yan","orcid":"0000-0002-5963-9242","position":0,"is_corresponding":false}],"reference_count":0,"raw_metadata":{"has_enrichment":true,"resolved":true,"title":"A Sequence Kernel Association Test for Dichotomous Traits in Family Samples under a Generalized Linear Mixed Model","abstract":"<jats:p>&lt;b&gt;&lt;i&gt;Objective:&lt;/i&gt;&lt;/b&gt; The existing methods for identifying multiple rare variants underlying complex diseases in family samples are underpowered. Therefore, we aim to develop a new set-based method for an association study of dichotomous traits in family samples. &lt;b&gt;&lt;i&gt;Methods:&lt;/i&gt;&lt;/b&gt; We introduce a framework for testing the association of genetic variants with diseases in family samples based on a generalized linear mixed model. Our proposed method is based on a kernel machine regression and can be viewed as an extension of the sequence kernel association test (SKAT and famSKAT) for application to family data with dichotomous traits (F-SKAT). &lt;b&gt;&lt;i&gt;Results:&lt;/i&gt;&lt;/b&gt; Our simulation studies show that the original SKAT has inflated type I error rates when applied directly to family data. By contrast, our proposed F-SKAT has the correct type I error rate. Furthermore, in all of the considered scenarios, F-SKAT, which uses all family data, has higher power than both SKAT, which uses only unrelated individuals from the family data, and another method, which uses all family data. &lt;b&gt;&lt;i&gt;Conclusion:&lt;/i&gt;&lt;/b&gt; We propose a set-based association test that can be used to analyze family data with dichotomous phenotypes while handling genetic variants with the same or opposite directions of effects as well as any types of family relationships.</jats:p>","is_dataset_classified":null,"base_score":3.4339872044851463,"endowment":3.4339872044851463,"datacite_reuse_total":0,"file_count":0,"downloads":0,"views":0,"has_version_chain":false,"is_dataset":false,"is_oa":false,"pmid":"25791389","pmcid":"PMC4825859","openalex_id":"https://openalex.org/W2084747252","authors":[],"funders":[{"funder_name":"NIDA NIH HHS","grant_id":"R01 DA025095","title":null},{"funder_name":"NIAMS NIH HHS","grant_id":"P60 AR064172","title":null},{"funder_name":"NIGMS NIH HHS","grant_id":"R01 GM073766","title":null},{"funder_name":"NHLBI NIH HHS","grant_id":"R01HL092173","title":null},{"funder_name":"NIGMS NIH HHS","grant_id":"R01 GM069430","title":null},{"funder_name":"NIGMS NIH HHS","grant_id":"5R01GM069430-08","title":"Bayesian Methods for Genome-Wide Interacting QTL Mapping"},{"funder_name":"NIGMS NIH HHS","grant_id":"GM073766","title":null},{"funder_name":"NIGMS NIH HHS","grant_id":"R01 GM081488","title":null},{"funder_name":"NIDA NIH HHS","grant_id":"5R01DA025095","title":null},{"funder_name":"NIGMS NIH HHS","grant_id":"GM081488","title":null},{"funder_name":"Wellcome Trust","grant_id":"unidentified","title":"unidentified"},{"funder_name":"National Institutes of Health","grant_id":"5P60AR064172-03","title":"Adaptive Immune Response to Gut Microbiota in Juvenile &amp; Adult Spondyloarthritis"},{"funder_name":"National Institutes of Health","grant_id":"5R01GM081488-05","title":"Genome Wide Haplotype Association Analysis"},{"funder_name":"National Institutes of Health","grant_id":"5R01DA025095-04","title":"Detection of multifactor interactions with application to nicotine dependence"},{"funder_name":"National Science Foundation","grant_id":"1158862","title":"RII: Enhancing Alabama's Research Capacity in Nano/Bio Science and Sensors"}],"total_grants":15,"fwci":3.1515,"citation_percentile":0.92039772,"influential_citations":0,"citation_trend":[{"year":2015,"count":3},{"year":2016,"count":1},{"year":2017,"count":1},{"year":2018,"count":9},{"year":2019,"count":3},{"year":2020,"count":4},{"year":2021,"count":1},{"year":2022,"count":2},{"year":2023,"count":2},{"year":2024,"count":2},{"year":2025,"count":1}],"oa_status":"bronze","license":"https://www.karger.com/Services/SiteLicenses","oa_locations":[{"url":"https://www.karger.com/Article/Pdf/375409","host_type":"journal"},{"url":"https://www.karger.com/Article/Pdf/375409","host_type":"publisher"},{"url":"https://doi.org/10.1159/000375409","host_type":"journal"},{"url":"https://pubmed.ncbi.nlm.nih.gov/25791389","host_type":"repository"},{"url":"https://www.ncbi.nlm.nih.gov/pmc/articles/4825859","host_type":"repository"},{"url":"https://dx.doi.org/10.1159/000375409","host_type":""}],"fields_of_study":["Genetic Associations and Epidemiology","Statistical Methods and Inference","Bioinformatics and Genomic Networks","0301 basic medicine","0303 health sciences","03 medical and health sciences","Algorithms","Computer Simulation","Genetic Variation","Humans","Linear Models","Phenotype","Quantitative Trait, Heritable"],"mesh_terms":["Algorithms","Computer Simulation","Humans","Phenotype","Genetic Variation","Linear Models","Quantitative Trait, Heritable"],"keywords":["Type I and type II errors","Association test","Kernel (algebra)","Association (psychology)","Statistics","Genetic association","Set (abstract data type)","Mathematics","Sequence (biology)","Kernel method","Linear model","Computer science","Machine learning","Genetics","Biology","Psychology","Genotype","Support vector machine","Phenotype","Quantitative Trait, Heritable","Linear Models","Genetic Variation","Humans","Computer Simulation","Algorithms"],"sdg_mappings":[{"sdg_number":4,"sdg_label":"4. 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