ArticleGenetics2013
Dissecting high-dimensional phenotypes with bayesian sparse factor analysis of genetic covariance matrices.
Article in Genetics, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers.
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Who cites it
28 citing papers in PubMed.
- Gene expression evolution is predicted by stronger indirect selection at more pleiotropic genes.Evolution letters · 2025Article
- Measuring natural selection on the transcriptome.The New phytologist · 2025Review
- Multi-response phylogenetic mixed models: concepts and application.Biological reviews of the Cambridge Philosophical Society · 2025Review
- MegaLMM improves genomic predictions in new environments using environmental covariates.Genetics · 2025Article
- Principal component analysis revisited: fast multitrait genetic evaluations with smooth convergence.G3 (Bethesda, Md.) · 2024Article
- High-dimensional multi-omics measured in controlled conditions are useful for maize platform and field trait predictions.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2024Article
- Mega-scale Bayesian regression methods for genome-wide prediction and association studies with thousands of traits.Genetics · 2023Article
- Evolvability and constraint in the evolution of three-dimensional flower morphology.American journal of botany · 2022Article
- Article
- MegaLMM: Mega-scale linear mixed models for genomic predictions with thousands of traits.Genome biology · 2021Article
- Improving Genomic Prediction for Seed Quality Traits in Oat (Avena sativa L.) Using Trait-Specific Relationship Matrices.Frontiers in genetics · 2021Article
- Modeling multiple phenotypes in wheat using data-driven genomic exploratory factor analysis and Bayesian network learning.Plant direct · 2021Article
- Article
- Fast and flexible linear mixed models for genome-wide genetics.PLoS genetics · 2019Article
- Mouse Skull Mean Shape and Shape Robustness Rely on Different Genetic Architectures and Different Loci.Frontiers in genetics · 2019Article
- Article
- Genetic constraints on microevolutionary divergence of sex-biased gene expression.Philosophical transactions of the Royal Society of London. Series B, Biological sciences · 2018Article
- Article
- Mutational Pleiotropy and the Strength of Stabilizing Selection Within and Between Functional Modules of Gene Expression.Genetics · 2018Article
- The evolution of phenotypic integration: How directional selection reshapes covariation in mice.Evolution; international journal of organic evolution · 2017Article
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2 authors.
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Abstract
Quantitative genetic studies that model complex, multivariate phenotypes are important for both evolutionary prediction and artificial selection. For example, changes in gene expression can provide insight into developmental and physiological mechanisms that link genotype and phenotype. However, classical analytical techniques are poorly suited to quantitative genetic studies of gene expression where the number of traits assayed per individual can reach many thousand. Here, we derive a Bayesian genetic sparse factor model for estimating the genetic covariance matrix (G-matrix) of high-dimensional traits, such as gene expression, in a mixed-effects model. The key idea of our model is that we need consider only G-matrices that are biologically plausible. An organism's entire phenotype is the result of processes that are modular and have limited complexity. This implies that the G-matrix will be highly structured. In particular, we assume that a limited number of intermediate traits (or factors, e.g., variations in development or physiology) control the variation in the high-dimensional phenotype, and that each of these intermediate traits is sparse - affecting only a few observed traits. The advantages of this approach are twofold. First, sparse factors are interpretable and provide biological insight into mechanisms underlying the genetic architecture. Second, enforcing sparsity helps prevent sampling errors from swamping out the true signal in high-dimensional data. We demonstrate the advantages of our model on simulated data and in an analysis of a published Drosophila melanogaster gene expression data set.
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