Plant Physiol BiochemJClub
Integrating GWAS, transcriptomics, and machine learning enables genomic prediction of grain cadmium content in malting barley.
Plant Physiology and Biochemistry · · Journal Article · Open access
Xu
Abstract ↗AI summary
The abstract is read at the publisher; the summary is JClub's.
XGBoost trained on 1000 markers predicts grain cadmium content in malting barley with r=0.86 across 100 barley accessions, enabling early Cd risk evaluation.
- Why it matters: Cadmium contamination threatens malting barley quality, yet predictive tools and a clear genetic basis for grain Cd levels remain limited.
- What they did: The study integrated GWAS, RNA-seq with cis-eQTL mapping, and multi-omics machine learning, benchmarking five genomic prediction algorithms and nominating candidate genes.
- The result: GWAS identified 3902 Cd-associated SNPs and prioritization yielded 67 metal transporter genes, including HvACA8a as a likely negative modulator and HvAHA9 and HvYSL15 as positive regulators.
The findingWhy it mattersWhat they didThe result
AI summary of the abstract · may contain errors · not medical advice · read the paper.
- Open access