Department of Horticulture and Crop Science, The Ohio State University, Columbus, OH, 43210, USA.
Department of Food Science and Technology, The Ohio State University, Columbus, OH, 43210, USA.
New Phytol. 2021 Dec;232(5):1944-1958. doi: 10.1111/nph.17693. Epub 2021 Oct 4.
Apple (Malus × domestica) has commercial and nutritional value, but breeding constraints of tree crops limit varietal improvement. Marker-assisted selection minimises these drawbacks, but breeders lack applications for targeting fruit phytochemicals. To understand genotype-phytochemical associations in apples, we have developed a high-throughput integration strategy for genomic and multiplatform metabolomics data. Here, 124 apple genotypes, including members of three pedigree-connected breeding families alongside diverse cultivars and wild selections, were genotyped and phenotyped. Metabolite genome-wide association studies (mGWAS) were conducted with c. 10 000 single nucleotide polymorphisms and phenotypic data acquired via LC-MS and H NMR untargeted metabolomics. Putative metabolite quantitative trait loci (mQTL) were then validated via pedigree-based analyses (PBA). Using our developed method, 519, 726 and 177 putative mQTL were detected in LC-MS positive and negative ionisation modes, and NMR, respectively. mQTL were indicated on each chromosome, with hotspots on linkage groups 16 and 17. A chlorogenic acid mQTL was discovered on chromosome 17 via mGWAS and validated with a two-step PBA, enabling discovery of novel candidate gene-metabolite relationships. Complementary data from three metabolomics approaches and dual genomics analyses increased confidence in validity of compound annotation and mQTL detection. Our platform demonstrates the utility of multiomic integration to advance data-driven, phytochemical-based plant breeding.
苹果（Malus × domestica）具有商业和营养价值，但树木作物的繁殖限制了品种的改良。标记辅助选择最小化了这些缺点，但育种者缺乏针对水果植物化学物质的应用。为了了解苹果中的基因型-植物化学物质关联，我们开发了一种用于基因组和多平台代谢组学数据的高通量集成策略。在这里，对 124 个苹果基因型进行了基因型和表型分析，包括三个谱系连接的育种家族的成员以及各种栽培品种和野生选择。对 c.进行了代谢物全基因组关联研究 (mGWAS) 10 000 个单核苷酸多态性和通过 LC-MS 和 1 H NMR 非靶向代谢组学获得的表型数据。然后通过基于系谱的分析 (PBA) 验证了假定的代谢物数量性状基因座 (mQTL)。使用我们开发的方法，在 LC-MS 正离子和负离子模式以及 NMR 中分别检测到 519、726 和 177 个假定的 mQTL。mQTL 指示在每个染色体上，连锁群 16 和 17 上有热点。通过 mGWAS 在第 17 号染色体上发现了一个绿原酸 mQTL，并通过两步 PBA 进行了验证，从而发现了新的候选基因-代谢物关系。三种代谢组学方法和双基因组分析的互补数据增加了化合物注释和 mQTL 检测有效性的可信度。我们的平台展示了多组学集成在推进基于数据驱动的植物化学物质的植物育种方面的实用性。
