Dissecting the genetic architecture of seed-related traits in Brassica napus by integrating multi-omics analysis and VIS-NIR hyperspectral imaging.

Abstract

Background Brassica napus (B. napus) is globally important oilseed crop, yet traditional approaches for phenotyping of seed traits are labor-intensive and destructive. Results Here, we establish a non-destructive analytical framework integrating hyperspectral imaging (HSI) with machine learning for characterizing seed-related traits. We collect HSI data from seeds of 393 B. napus accessions over two consecutive years, generating 1,944 spectral indices per sample. We identify significant correlations between 1,293 hyperspectral indices and 956 seed metabolites. Flavonoid metabolites exhibit the most consistent interannual correlations with hyperspectral indices. Systematic benchmarking of 19 machine learning algorithms identifies nine optimal models for metabolite prediction, with 73.44% of metabolites achieving significant associations. Hyperspectral indices effectively predict nine key seed-related traits, including oil content, seed coat content, glucosinolate content and six fatty acid components. Genome-wide association studies (GWAS) of hyperspectral indices uncover three stable quantitative trait loci (QTL) hotspots, qHSI.hotA09, qHSI.hotA05 and qHSI.hotC05, that co-localize with QTLs for seed oil and seed coat content. Integration of GWAS with POCKET prioritization identifies BnaA09.MYB52 and BnaC05.PMT6 as candidate genes for the hotspots, qHSI.hotA09 and qHSI.hotC05, respectively. Functional validation using mutants demonstrates that both genes significantly influence seed flavonoid metabolites and hyperspectral profiles. BnaPMT6 is characterized as a novel positive regulator of seed coat content, similar to BnaMYB52. Conclusions This study establishes a novel, non-destructive approach for seed traits and metabolite assessment in B. napus seeds. It also provides a theoretical foundation and genetic basis for breeding of B. napus varieties with high oil content and improved nutritional quality.

Plant phenotyping relevance

種子形質を非破壊的に推定するハイパースペクトル画像と機械学習の分析フレームワークが研究の中心であり、多数の品種・複数年で検証されている。

Code and data availability

The paper's hyperspectral phenotype data are provided only as in-article supplementary tables (Additional file 2: Tables S2-3) with no authors' public repository URL; the deposited data (GSA PRJCA002835/PRJCA002836, OMIX004266) are molecular omics (resequencing, transcriptome, metabolome), which do not qualify. Code提及的

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