Learning from methylomes: epigenomic correlates of Populus balsamifera traits based on deep learning models of natural DNA methylation

Abstract

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.

Plant phenotyping relevance

DNAメチル化データと深層学習を用いて、木質形質や由来などの植物形質を推定するモデルを開発・検証しており、形質推定手法が研究の中心である。

Code and data availability

The supplied blocks describe WGBS methylomes, phenotypes (Table S1), and deep learning models built with h2o.ai, but contain no authors' public deposit of phenotype data, methylome data, code, or trained models. The only URL mentioned (h2o documentation) is a generic software library, not a paper-specific asset. TableS

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