Untargeted metabolomic genome-wide association study reveals genetic and biochemical insights into polyphenols of apple fruit
Why this work is in the frame
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Bibliographic record
Abstract
) is one of the most popular fruits grown and consumed worldwide, contributing to human health with significant amounts of polyphenols and other bioactive compounds, and providing positive impacts to the economy and society. Understanding the diversity and inheritance of health-active compounds in apple can provide novel selection criteria for future breeding and cultivar development, as consumers increasingly prioritize the health benefits of their food choices. We therefore conducted an untargeted metabolomic analysis using ultra-high-performance liquid chromatography-mass spectrometry (UPLC-MS) to investigate thousands of semipolar chemicals, mainly phenolic compounds, in 439 diverse apple accessions, and quantified 2066 features in positive ion mode. To identify key areas of genetic control for apple metabolite abundance, we performed a metabolomic genome-wide association study (mGWAS) on the quantified mass features using ~280 000 single nucleotide polymorphisms (SNPs). The mGWAS revealed >630 significant loci with hotspots for various groups of known and unknown phenolic compounds including flavonols on Chromosome 1, dihydrochalcones on Chromosome 5, and flavanols on Chromosomes 15 and 16. The most significant hotspot on Chromosome 16 included bHLH and C2H2 transcription factors that may play a role in controlling the abundance and complexity of phenolic compounds through regulation of the flavonoid biosynthesis pathway. Our analysis links the apple metabolome with candidate genes and biosynthetic mechanisms and establishes a foundation for marker-assisted breeding and gene editing to improve and modify phenolic compounds in apple for marketability and the benefit of human health.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it