Mixed Model Methods for Genomic Prediction and Variance Component Estimation of Additive and Dominance Effects Using SNP Markers
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{"title"=>"Mixed model methods for genomic prediction and variance component estimation of additive and dominance effects using SNP markers", "type"=>"journal", "authors"=>[{"first_name"=>"Yang", "last_name"=>"Da", "scopus_author_id"=>"7003284754"}, {"first_name"=>"Chunkao", "last_name"=>"Wang", "scopus_author_id"=>"55766589800"}, {"first_name"=>"Shengwen", "last_name"=>"Wang", "scopus_author_id"=>"54392350700"}, {"first_name"=>"Guo", "last_name"=>"Hu", "scopus_author_id"=>"35765644900"}], "year"=>2014, "source"=>"PLoS ONE", "identifiers"=>{"issn"=>"19326203", "scopus"=>"2-s2.0-84898934173", "pui"=>"373070849", "doi"=>"10.1371/journal.pone.0087666", "isbn"=>"10.1371/journal.pone.0087666", "sgr"=>"84898934173", "pmid"=>"24498162"}, "id"=>"03cfca92-133f-35b9-b2b8-613804af929d", "abstract"=>"We established a genomic model of quantitative trait with genomic additive and dominance relationships that parallels the traditional quantitative genetics model, which partitions a genotypic value as breeding value plus dominance deviation and calculates additive and dominance relationships using pedigree information. Based on this genomic model, two sets of computationally complementary but mathematically identical mixed model methods were developed for genomic best linear unbiased prediction (GBLUP) and genomic restricted maximum likelihood estimation (GREML) of additive and dominance effects using SNP markers. These two sets are referred to as the CE and QM sets, where the CE set was designed for large numbers of markers and the QM set was designed for large numbers of individuals. GBLUP and associated accuracy formulations for individuals in training and validation data sets were derived for breeding values, dominance deviations and genotypic values. Simulation study showed that GREML and GBLUP generally were able to capture small additive and dominance effects that each accounted for 0.00005-0.0003 of the phenotypic variance and GREML was able to differentiate true additive and dominance heritability levels. GBLUP of the total genetic value as the summation of additive and dominance effects had higher prediction accuracy than either additive or dominance GBLUP, causal variants had the highest accuracy of GREML and GBLUP, and predicted accuracies were in agreement with observed accuracies. Genomic additive and dominance relationship matrices using SNP markers were consistent with theoretical expectations. The GREML and GBLUP methods can be an effective tool for assessing the type and magnitude of genetic effects affecting a phenotype and for predicting the total genetic value at the whole genome level.", "link"=>"http://www.mendeley.com/research/mixed-model-methods-genomic-prediction-variance-component-estimation-additive-dominance-effects-usin", "reader_count"=>72, "reader_count_by_academic_status"=>{"Unspecified"=>5, "Professor > Associate Professor"=>1, "Researcher"=>11, "Student > Doctoral Student"=>5, "Student > Ph. D. Student"=>27, "Student > Postgraduate"=>3, "Other"=>8, "Student > Master"=>10, "Lecturer"=>1, "Professor"=>1}, "reader_count_by_user_role"=>{"Unspecified"=>5, "Professor > Associate Professor"=>1, "Researcher"=>11, "Student > Doctoral Student"=>5, "Student > Ph. D. Student"=>27, "Student > Postgraduate"=>3, "Other"=>8, "Student > Master"=>10, "Lecturer"=>1, "Professor"=>1}, "reader_count_by_subject_area"=>{"Unspecified"=>6, "Engineering"=>1, "Biochemistry, Genetics and Molecular Biology"=>1, "Mathematics"=>2, "Agricultural and Biological Sciences"=>54, "Medicine and Dentistry"=>1, "Physics and Astronomy"=>1, "Social Sciences"=>1, "Computer Science"=>4, "Earth and Planetary Sciences"=>1}, "reader_count_by_subdiscipline"=>{"Engineering"=>{"Engineering"=>1}, "Medicine and Dentistry"=>{"Medicine and Dentistry"=>1}, "Social Sciences"=>{"Social Sciences"=>1}, "Physics and Astronomy"=>{"Physics and Astronomy"=>1}, "Earth and Planetary Sciences"=>{"Earth and Planetary Sciences"=>1}, "Agricultural and Biological Sciences"=>{"Agricultural and Biological Sciences"=>54}, "Computer Science"=>{"Computer Science"=>4}, "Biochemistry, Genetics and Molecular Biology"=>{"Biochemistry, Genetics and Molecular Biology"=>1}, "Mathematics"=>{"Mathematics"=>2}, "Unspecified"=>{"Unspecified"=>6}}, "reader_count_by_country"=>{"Lebanon"=>1, "United States"=>1, "Brazil"=>3, "France"=>1, "Germany"=>1}, "group_count"=>3}

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Figshare

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  • {"files"=>["https://ndownloader.figshare.com/files/1368775", "https://ndownloader.figshare.com/files/1368776", "https://ndownloader.figshare.com/files/1368777", "https://ndownloader.figshare.com/files/1368778", "https://ndownloader.figshare.com/files/1368779", "https://ndownloader.figshare.com/files/1368780"], "description"=>"<div><p>We established a genomic model of quantitative trait with genomic additive and dominance relationships that parallels the traditional quantitative genetics model, which partitions a genotypic value as breeding value plus dominance deviation and calculates additive and dominance relationships using pedigree information. Based on this genomic model, two sets of computationally complementary but mathematically identical mixed model methods were developed for genomic best linear unbiased prediction (GBLUP) and genomic restricted maximum likelihood estimation (GREML) of additive and dominance effects using SNP markers. These two sets are referred to as the CE and QM sets, where the CE set was designed for large numbers of markers and the QM set was designed for large numbers of individuals. GBLUP and associated accuracy formulations for individuals in training and validation data sets were derived for breeding values, dominance deviations and genotypic values. Simulation study showed that GREML and GBLUP generally were able to capture small additive and dominance effects that each accounted for 0.00005–0.0003 of the phenotypic variance and GREML was able to differentiate true additive and dominance heritability levels. GBLUP of the total genetic value as the summation of additive and dominance effects had higher prediction accuracy than either additive or dominance GBLUP, causal variants had the highest accuracy of GREML and GBLUP, and predicted accuracies were in agreement with observed accuracies. Genomic additive and dominance relationship matrices using SNP markers were consistent with theoretical expectations. The GREML and GBLUP methods can be an effective tool for assessing the type and magnitude of genetic effects affecting a phenotype and for predicting the total genetic value at the whole genome level.</p></div>", "links"=>[], "tags"=>["Computational biology", "genomics", "Genome analysis tools", "Gene prediction", "Genome complexity", "population genetics", "Genetic polymorphism", "Population modeling", "genetics", "methods", "genomic", "variance", "estimation", "additive", "dominance", "snp"], "article_id"=>919825, "categories"=>["Biological Sciences"], "users"=>["Yang Da", "Chunkao Wang", "Shengwen Wang", "Guo Hu"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0087666.s001", "https://dx.doi.org/10.1371/journal.pone.0087666.s002", "https://dx.doi.org/10.1371/journal.pone.0087666.s003", "https://dx.doi.org/10.1371/journal.pone.0087666.s004", "https://dx.doi.org/10.1371/journal.pone.0087666.s005", "https://dx.doi.org/10.1371/journal.pone.0087666.s006"], "stats"=>{"downloads"=>8, "page_views"=>9, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Mixed_Model_Methods_for_Genomic_Prediction_and_Variance_Component_Estimation_of_Additive_and_Dominance_Effects_Using_SNP_Markers_/919825", "title"=>"Mixed Model Methods for Genomic Prediction and Variance Component Estimation of Additive and Dominance Effects Using SNP Markers", "pos_in_sequence"=>0, "defined_type"=>4, "published_date"=>"2014-01-30 04:21:50"}
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  • {"unique-ip"=>"6", "full-text"=>"5", "pdf"=>"3", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2020", "month"=>"7"}
  • {"unique-ip"=>"1", "full-text"=>"1", "pdf"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2020", "month"=>"8"}

Relative Metric

{"start_date"=>"2014-01-01T00:00:00Z", "end_date"=>"2014-12-31T00:00:00Z", "subject_areas"=>[{"subject_area"=>"/Biology and life sciences", "average_usage"=>[291]}, {"subject_area"=>"/Biology and life sciences/Agriculture", "average_usage"=>[282]}, {"subject_area"=>"/Biology and life sciences/Genetics", "average_usage"=>[306, 482]}]}
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