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Data from: Across population genomic prediction scenarios in which Bayesian variable selection outperforms GBLUP
- 负责人：
- 关键词：
- GBLUP Genomic prediction across population Bayesian variable selection accuracy number of independent chromosome segments
- DOI：
- doi:10.5061/dryad.rq80k
- 摘要：
- rrently disappointing. It has been shown for within population genomic prediction that Bayesian variable selection models outperform GBLUP models
Data from: An upper bound for accuracy of prediction using GBLUP
- 负责人：
- 关键词：
- simulated genotypes
- DOI：
- doi:10.5061/dryad.3k8g5
- 摘要：
- of an estimability problem, and thereby provided an upper bound for reliability of prediction, and thus, for prediction R2. Genomic prediction methods GBLUP, BayesB
Data from: Incorporating single-step strategy into random regression model to enhance genomic prediction of longitudinal trait
- 负责人：
- DOI：
- doi:10.5061/dryad.8df69
- 摘要：
- with the pedigree-based RR-TDM and genomic best linear unbiased prediction (GBLUP) model. We performed extensive simulations to exploit potential advantages of SS RR
Data from: Efficiency of genomic prediction across two Eucalyptus nitens seed orchards with different selection histories
- 负责人：
- DOI：
- doi:10.5061/dryad.pf58510
- 摘要：
- le to build genomic prediction models using GBLUP which were compared to the traditional pedigree-based alternative using BLUP. GBLUP demonstrated tha
Data from: Multi-trait single-step genomic prediction accounting for heterogeneous (co)variances over the genome
- 负责人：
- 关键词：
- DOI：
- doi:10.5061/dryad.v4126t4
- 摘要：
- rdless of the size of data. Models such as BayesC or GBLUP assume a common (co)variance for a proportion (BayesC) or all (GBLUP) of the SNP effects. In this study
Data from: Factors affecting GEBV accuracy with single-step Bayesian models
- 负责人：
- Zhou, Lei
- 关键词：
- DOI：
- doi:10.5061/dryad.hk14j
- 摘要：
- with various numbers of QTL (5, 50 and 500) were simulated. Three models were implemented to analyze the simulated data: single-step GBLUP (SSGBLUP), single
Data from: Accurate genomic predictions for chronic wasting disease in U.S. white-tailed deer
- 负责人：
- 关键词：
- genome-wide association;chronic wasting disease;white-tailed deer;Genomic prediction ;heritability
- DOI：
- doi:10.5061/dryad.xd2547dcw
- 摘要：
- o demonstrated that more phenotypic variance was collectively explained by loci other than?PRNP.?Genomic best linear unbiased prediction (GBLUP
Data from: Assessing the expected response to genomic selection of individuals and families in Eucalyptus breeding with an additive-dominant model
- 负责人：
- DOI：
- doi:10.5061/dryad.ms580
- 摘要：
- We report a genomic selection (GS) study of growth and wood quality traits in an outbred F2 hybrid Eucalyptus population (n=768) using high-density single-nucleotide polymorphism (SNP) genotyping. Going beyond previous reports in forest trees, models were developed for different selection targets, namely, families, individuals within families and individuals across the entire population using a genomic model including dominance. To provide a more breeder-intelligible assessment of the performance of GS we calculated the expected response as the percentage gain over the population average expected genetic value (EGV) for different proportions of genomically selected individuals, using a rigorous cross-validation (CV) scheme that removed relatedness between training and validation sets. Predictive abilities (PAs) were 0.40–0.57 for individual selection and 0.56–0.75 for family selection. PAs under an additive+dominance model improved predictions by 5 to 14% for growth depending on the selection target, but no improvement was seen for wood traits. The good performance of GS with no relatedness in CV suggested that our average SNP density (~25?kb) captured some short-range linkage disequilibrium. Truncation GS successfully selected individuals with an average EGV significantly higher than the population average. Response to GS on a per year basis was ~100% more efficient than by phenotypic selection and more so with higher selection intensities. These results contribute further experimental data supporting the positive prospects of GS in forest trees. Because generation times are long, traits are complex and costs of DNA genotyping are plummeting, genomic prediction has good perspectives of adoption in tree breeding practice.
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