Simulation Data for Bayesian Genomic Selection of Threshold Traits

Encompassing simulation results from a study comparing Bayesian methods for estimating genomic breeding values of threshold traits. The study introduced three new methods (BayesTA, BayesTB, BayesTCπ) and evaluated their accuracy improvements over standard methods, with one scenario showing a 30.4 percentage point gain. It was authored by Qin Zhang and published in 2020.

Use Cases

• Compare the accuracy of the three BayesT methods (BayesTA, BayesTB, BayesTCπ) for genomic estimated breeding values (GEBVs) across different simulation scenarios.
• Analyze the impact of factors like number of phenotypic categories, incidence rate, number of QTLs, and heritability (h2) on the performance of threshold model methods.
• Validate the reported accuracy improvements, such as the 30.4, 2.4, and 5.7 percentage point gains in the standard scenario with 2 categories, 30% incidence, 50 QTLs, and h2=0.3.

Strengths

• The study provides specific, quantified accuracy improvements for the proposed methods, such as a 30.4 percentage point gain in one scenario.
• The simulation investigates multiple factors affecting method performance, including number of phenotypic categories, incidence, number of QTLs, and heritability.
• The dataset is associated with a peer-reviewed methodological extension of established Bayesian frameworks (BayesA, BayesB, BayesCπ) to threshold traits.

Limitations

• The dataset is based on simulation results, not real-world phenotypic or genomic data, limiting direct applicability to breeding programs.
• Specific details about the dataset's structure, such as row count, column names, and file formats, are unavailable from the input.
• The data's recency is limited as the last update was in 2020, and genomic selection methods may have advanced since.

Provenance

Source

Dryad digital repository.

Collection Method

Data generated from a computational simulation study comparing Bayesian genomic selection methods.

Freshness

Last updated 2020-06-24.