Epigenomes have remarkable potential for the estimation of plant traits. This study tested the hypothesis that natural variation in DNA methylation can be used to estimate industrially important traits in a genetically diverse population of Populus balsamifera L. (balsam poplar) trees grown at two common garden sites. Statistical learning experiments enabled by deep learning models revealed that plant traits in novel genotypes can be modelled transparently using small numbers of methylated DNA predictors. Using this approach, tissue type, a nonheritable attribute, from which DNA methylomes were derived was assigned, and provenance, a purely heritable trait and an element of population structure, was determined. Significant proportions of phenotypic variance in quantitative wood traits, including total biomass (57.5%), wood density (40.9%), soluble lignin (25.3%) and cell wall carbohydrate (mannose: 44.8%) contents, were also explained from natural variation in DNA methylation. Modelling plant traits using DNA methylation can capture tissue-specific epigenetic mechanisms underlying plant phenotypes in natural environments. DNA methylation-based models offer new insight into natural epigenetic influence on plants and can be used as a strategy to validate the identity, provenance or quality of agroforestry products.

中文翻译：

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从甲基化组学习：基于天然DNA甲基化的深度学习模型，苦瓜特征的表观遗传学相关性。

表观基因组具有估计植物性状的巨大潜力。这项研究检验了以下假设：DNA甲基化的自然变异可用于估计在两个常见花园场所种植的遗传多样性的胡杨（Balulus balsamifera L.）的工业上重要的性状。深度学习模型支持的统计学习实验表明，可以使用少量甲基化DNA预测因子透明地对新型基因型的植物性状进行建模。使用这种方法，确定了非遗传属性的组织类型，从中衍生出DNA甲基化组，并确定了起源，纯遗传性状和种群结构要素。在定量的木材性状中，表型变异的比例很大，包括总生物量（57.5％），木材密度（40.9％），DNA甲基化的自然变化也解释了可溶性木质素（25.3％）和细胞壁碳水化合物（甘露糖：44.8％）的含量。使用DNA甲基化对植物性状进行建模可以捕获自然环境中植物表型背后的组织特异性表观遗传机制。基于DNA甲基化的模型提供了对植物自然表观遗传影响的新见解，并且可以用作验证农林业产品的特性，来源或质量的策略。