GWAS of Post-Harvest Deterioration in Cassava Using Visual and AI Phenotyping

298 cassava accessions were genotyped to identify genomic regions linked to post-harvest physiological deterioration. The study used both human visual scoring and an AI-powered phenotyping method, identifying 6 significant SNPs from each approach. This dataset, authored by Kwame Obeng Dankwa and last updated in April 2026, provides genetic targets for breeding PPD-tolerant cassava varieties.

Use Cases

• Conducting genome-wide association studies based on SNP data from DArTag and GBS genotyping platforms
• Comparing traditional visual scoring with AI-powered phenotyping methods for trait assessment
• Identifying quantitative trait loci for post-harvest physiological deterioration tolerance in cassava
• Supporting marker-assisted and genomic selection programs for crop breeding

Strengths

• Includes data from a mapping population of 298 cassava accessions
• Employs two distinct genotyping platforms (DArTag and GBS) and two phenotyping methods (visual and AI)
• Identified consistent genomic associations on chromosomes 1 and 12 across methods

Limitations

• Dataset scale is limited to 121.3 KB, suggesting it may contain summary results rather than raw genomic data
• Column-level documentation is absent; field semantics must be inferred after download
• Row count is unknown, which may limit suitability assessment for certain analyses

Provenance

Source

figshare

Collection Method

Genome-wide association study conducted on a cassava mapping population, with genotyping and phenotyping data collection.

Time Range

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Freshness

Last updated 2026-04-22 05:35:21

Geography

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