Multi-Trait Genomic Prediction for Miscanthus Biomass Traits

A study of 336 Miscanthus sacchariflorus genotypes evaluated across three environments for four biomass-related traits: biomass yield (YDY), total culm number (TCM), average internode length (AIL), and culm node number (CNN). The dataset supports analysis of multi-trait multi-environment (MTME) genomic prediction models, comparing their performance against single-trait models for predicting complex traits in plant breeding programs. Results indicate MTME models can improve predictive ability for certain traits and scenarios, potentially accelerating selection cycles.

Use Cases

• Benchmarking multi-trait versus single-trait genomic prediction models based on yield and morphological trait data
• Predicting performance of untested genotypes in new environments using cross-validation schemes
• Optimizing breeding program design by analyzing genotype-by-environment-by-trait interactions
• Training genomic selection models for complex biomass-related traits in perennial grasses

Strengths

• Includes data for 336 distinct genotypes, providing a substantial population for analysis
• Evaluates four distinct agronomic traits across three environments, enabling multi-environment study
• Uses three realistic cross-validation schemes (CV1, CVP, CV2) to test model predictive ability

Limitations

• Column names and exact data structure are not provided, limiting immediate usability
• The row count is not specified in any source, obscuring the dataset's granularity
• Metadata is sparse, with no information on the specific environments or measurement protocols

Provenance

Source

Diego Jarquin

Collection Method

Experimental evaluation of a Miscanthus sacchariflorus population, likely from field trials.

Freshness

Last updated on 2026-03-18