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Software update: Moving the R package sommer to multivariate mixed models for genome-assisted prediction
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What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
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The Elements of Statistical Learning: Data Mining, Inference, and Prediction
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Joint Analysis of Psychiatric Disorders Increases Accuracy of Risk Prediction for Schizophrenia, Bipolar Disorder, and Major Depressive Disorder
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Generalized linear mixed models: a practical guide for ecology and evolution
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Variance component model to account for sample structure in genome-wide association studies
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Genome-wide association studies: theoretical and practical concerns
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An efficient variance component approach implementing an average information REML suitable for combined LD and linkage mapping with a general complex pedigree
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Linear models for the prediction of animal breeding values
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MTG2: an efficient algorithm for multivariate linear mixed model analysis based on genomic information
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Bayesian inference for generalized linear mixed models
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Testing the phenotypic gambit: phenotypic, genetic and environmental correlations of colour
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One Hundred Years of Statistical Developments in Animal Breeding
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An efficient variance component approach implementing an average information REML suitable for combined LD and linkage mapping with a general complex pedigree
inferredcites →https://doi.org/10.1186/1297-9686-43-26openalex-snapshot
Accuracy of multi-trait genomic selection using different methods
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Chapter 11: Genome-Wide Association Studies
inferredcites →https://doi.org/10.1371/journal.pone.0156744openalex-snapshot
Genome-Assisted Prediction of Quantitative Traits Using the R Package sommer
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Genome-Wide Association Studies: What Do They Teach Us about Asthma and Chronic Obstructive Pulmonary Disease?
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Efficient Control of Population Structure in Model Organism Association Mapping
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Multiple-Trait Genomic Selection Methods Increase Genetic Value Prediction Accuracy
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Regularization Paths for Generalized Linear Models via Coordinate Descent
inferredcites →https://doi.org/10.18637/jss.v033.i02openalex-snapshot
MCMC Methods for Multi-Response Generalized Linear Mixed Models: The MCMCglmm R Package
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Fitting Linear Mixed-Effects Models Using lme4
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Best Linear Unbiased Estimation and Prediction under a Selection Model
inferredcites →https://doi.org/10.2307/2533274openalex-snapshot
Average Information REML: An Efficient Algorithm for Variance Parameter Estimation in Linear Mixed Models
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Incoming claims: where this work appears in other papers’ reference lists and arguments.
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