Sunflower genomics & breeding
Helianthus annuus · Russia, Ukraine, Argentina, Romania, China
Sunflower oil profile, Sclerotinia resistance, and downy mildew control define commercial value. Our genomic-selection module lets you stack oil traits without sacrificing field performance.
~55 million tonnes of seed, a major edible-oil crop.
Typical breeding goals
- •Seed yield and oil content
- •Oil profile (high-oleic, mid-oleic)
- •Sclerotinia and downy mildew resistance
- •Drought tolerance
Common challenges
- •Sclerotinia
- •Downy mildew
- •Broomrape
- •Drought
Pre-loaded trait library
When you upload sunflower data, our phenotype column picker pre-suggests these standard traits so you don't start from a blank slate.
What you can run on sunflower data
Every module below works on your uploaded sunflower 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 sunflower data
Upload a CSV, run a real GWAS or genomic-selection model, and get publication-ready output in minutes.
Get started