Potato genomics & breeding
Solanum tuberosum · China, India, Russia, Ukraine, USA
Potato breeding fights late blight, viruses, and tuber-quality variation across heterogeneous environments. Our MET module decomposes GxE so you can release varieties that hold up everywhere they're sold.
~375 million tonnes, the world's most-grown non-cereal food crop.
Typical breeding goals
- •Tuber yield and dry matter
- •Late blight resistance (R-gene stacks)
- •Virus resistance (PVY, PLRV)
- •Processing quality (frying color, sugar)
Common challenges
- •Late blight
- •PVY virus
- •Heat-induced tuber disorders
- •Drought
Pre-loaded trait library
When you upload potato data, our phenotype column picker pre-suggests these standard traits so you don't start from a blank slate.
What you can run on potato data
Every module below works on your uploaded potato 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 potato data
Upload a CSV, run a real GWAS or genomic-selection model, and get publication-ready output in minutes.
Get started