Rice genomics & breeding
Oryza sativa · China, India, Indonesia, Bangladesh, Vietnam
Rice feeds half the planet. Salt tolerance, blast resistance, and grain quality drive market value, and we make per-trait GWAS and multi-environment trial decomposition immediately accessible.
~520 million tonnes (milled) feed half of humanity.
Typical breeding goals
- •Grain yield and head rice recovery
- •Blast and bacterial blight resistance
- •Salinity and submergence tolerance
- •Grain quality (amylose, aroma)
Common challenges
- •Blast
- •Submergence
- •Salt stress
- •Brown planthopper
Pre-loaded trait library
When you upload rice data, our phenotype column picker pre-suggests these standard traits so you don't start from a blank slate.
What you can run on rice data
Every module below works on your uploaded rice dataset. The math is crop-agnostic; the defaults are crop-aware.
Find SNPs significantly associated with any trait you've measured.
Predict GEBVs and cross-validate accuracy before deploying in the program.
PCA and ancestry decomposition to control for stratification.
GxE heatmap, AMMI, and Finlay–Wilkinson stability for cross-location data.
Rank parents by weighted multi-trait scores.
Map top hits to genes via Ensembl Plants.
Historical weather, GDD, heat-stress days.
Whole-genome GBLUP yield predictions on your dataset.
Start analyzing your rice data
Upload a CSV, run a real GWAS or genomic-selection model, and get publication-ready output in minutes.
Get started