ArticleBMC genomics2014
Genome-enabled prediction of quantitative traits in chickens using genomic annotation.
Article in BMC genomics, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers.
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Who cites it
26 citing papers in PubMed.
- Genomic selection: Essence, applications, and prospects.The plant genome · 2025Review
- Genomic variance partitioning of carcass and meat quality traits in Angus beef cattle.Frontiers in veterinary science · 2025Article
- Transcriptome-based prediction for polygenic traits in rice using different gene subsets.BMC genomics · 2024Article
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- Genomic prediction based on preselected single-nucleotide polymorphisms from genome-wide association study and imputed whole-genome sequence data annotation for growth traits in Duroc pigs.Evolutionary applications · 2024Article
- A comprehensive study on size and definition of the core group in the proven and young algorithm for single-step GBLUP.Genetics, selection, evolution : GSE · 2022Article
- Genic and non-genic SNP contributions to additive and dominance genetic effects in purebred and crossbred pig traits.Scientific reports · 2022Article
- Single-Trait and Multiple-Trait Genomic Prediction From Multi-Class Bayesian Alphabet Models Using Biological Information.Frontiers in genetics · 2021Article
- Accurate Prediction of a Quantitative Trait Using the Genes Controlling the Trait for Gene-Based Breeding in Cotton.Frontiers in plant science · 2020Article
- Genomic Prediction Based on SNP Functional Annotation Using Imputed Whole-Genome Sequence Data in Korean Hanwoo Cattle.Frontiers in genetics · 2020Article
- Incorporating Genome Annotation Into Genomic Prediction for Carcass Traits in Chinese Simmental Beef Cattle.Frontiers in genetics · 2020Article
- Genomic Prediction Including SNP-Specific Variance Predictors.G3 (Bethesda, Md.) · 2019Article
- A targeted genotyping approach to enhance the identification of variants for lactation persistency in dairy cows.Journal of animal science · 2019Article
- Predicting male fertility in dairy cattle using markers with large effect and functional annotation data.BMC genomics · 2019Article
- Predictive ability of genome-assisted statistical models under various forms of gene action.Scientific reports · 2018Article
- Genomic Prediction of Complex Phenotypes Using Genic Similarity Based Relatedness Matrix.Frontiers in genetics · 2018Article
- Functional Partitioning of Genomic Variance and Genome-Wide Association Study for Carcass Traits in Korean Hanwoo Cattle Using Imputed Sequence Level SNP Data.Frontiers in genetics · 2018Article
- Article
- A predictive assessment of genetic correlations between traits in chickens using markers.Genetics, selection, evolution : GSE · 2017Article
- Whole-genome sequence-based genomic prediction in laying chickens with different genomic relationship matrices to account for genetic architecture.Genetics, selection, evolution : GSE · 2017Article
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4 authors.
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No grant is acknowledged in the PubMed record.
Abstract
backgroundGenome-wide association studies have been deemed successful for identifying statistically associated genetic variants of large effects on complex traits. Past studies have found enrichment of trait-associated SNPs in functionally annotated regions, while depletion was reported for intergenic regions (IGR). However, no systematic examination of connections between genomic regions and predictive ability of complex phenotypes has been carried out.
resultsIn this study, we partitioned SNPs based on their annotation to characterize genomic regions that deliver low and high predictive power for three broiler traits in chickens using a whole-genome approach. Additive genomic relationship kernels were constructed for each of the genic regions considered, and a kernel-based Bayesian ridge regression was employed as prediction machine. We found that the predictive performance for ultrasound area of breast meat from using genic regions marked by SNPs was consistently better than that from SNPs in IGR, while IGR tagged by SNPs were better than the genic regions for body weight and hen house egg production. We also noted that predictive ability delivered by the whole battery of markers was close to the best prediction achieved by one of the genomic regions.
conclusionsWhole-genome regression methods use all available quality filtered SNPs into a model, contrary to accommodating only validated SNPs from exonic or coding regions. Our results suggest that, while differences among genomic regions in terms of predictive ability were observed, the whole-genome approach remains as a promising tool if interest is on prediction of complex traits.
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