ArticleGenetics, selection, evolution : GSE2015
Partitioning of genomic variance reveals biological pathways associated with udder health and milk production traits in dairy cattle.
Article in Genetics, selection, evolution : GSE, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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Who cites it
19 citing papers in PubMed.
- Genomic selection: Essence, applications, and prospects.The plant genome · 2025Review
- Evaluation of heritability partitioning approaches in livestock populations.BMC genomics · 2024Article
- MPH: fast REML for large-scale genome partitioning of quantitative genetic variation.Bioinformatics (Oxford, England) · 2024Article
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- Application of Bayesian genomic prediction methods to genome-wide association analyses.Genetics, selection, evolution : GSE · 2022Review
- Incorporation of Trait-Specific Genetic Information into Genomic Prediction Models.Methods in molecular biology (Clifton, N.J.) · 2022Article
- Diversifying maize genomic selection models.Molecular breeding : new strategies in plant improvement · 2021Article
- Genomic Prediction Informed by Biological Processes Expands Our Understanding of the Genetic Architecture Underlying Free Amino Acid Traits in DryG3 (Bethesda, Md.) · 2020Article
- Prior Biological Knowledge Improves Genomic Prediction of Growth-Related Traits inFrontiers in genetics · 2020Article
- Pathway enrichment and protein interaction network analysis for milk yield, fat yield and age at first calving in a Thai multibreed dairy population.Asian-Australasian journal of animal sciences · 2019Article
- Functional Validation of Candidate Genes Detected by Genomic Feature Models.G3 (Bethesda, Md.) · 2018Article
- Use of biological priors enhances understanding of genetic architecture and genomic prediction of complex traits within and between dairy cattle breeds.BMC genomics · 2017Article
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- Multiple Trait Covariance Association Test Identifies Gene Ontology Categories Associated with Chill Coma Recovery Time in Drosophila melanogaster.Scientific reports · 2017Article
- Exploring the genetic architecture and improving genomic prediction accuracy for mastitis and milk production traits in dairy cattle by mapping variants to hepatic transcriptomic regions responsive to intra-mammary infection.Genetics, selection, evolution : GSE · 2017Article
- Integrating Sequence-based GWAS and RNA-Seq Provides Novel Insights into the Genetic Basis of Mastitis and Milk Production in Dairy Cattle.Scientific reports · 2017Article
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- Genomic Prediction for Quantitative Traits Is Improved by Mapping Variants to Gene Ontology Categories in Drosophila melanogaster.Genetics · 2016Article
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4 authors.
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Abstract
backgroundWe have used a linear mixed model (LMM) approach to examine the joint contribution of genetic markers associated with a biological pathway. However, with these markers being scattered throughout the genome, we are faced with the challenge of modelling the contribution from several, sometimes even all, chromosomes at once. Due to linkage disequilibrium (LD), all markers may be assumed to account for some genomic variance; but the question is whether random sets of markers account for the same genomic variance as markers associated with a biological pathway?
resultsWe applied the LMM approach to identify biological pathways associated with udder health and milk production traits in dairy cattle. A random gene sampling procedure was applied to assess the biological pathways in a dataset that has an inherently complex genetic correlation pattern due to the population structure of dairy cattle, and to linkage disequilibrium within the bovine genome and within the genes associated to the biological pathway.
conclusionsSeveral biological pathways that were significantly associated with health and production traits were identified in dairy cattle; i.e. the markers linked to these pathways explained more of the genomic variance and provided a better model fit than 95 % of the randomly sampled gene groups. Our results show that immune related pathways are associated with production traits, and that pathways that include a causal marker for production traits are identified with our procedure. We are confident that the LMM approach provides a general framework to exploit and integrate prior biological information and could potentially lead to improved understanding of the genetic architecture of complex traits and diseases.
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