摘要

由于人口增长和更的天气事件，世界上对粮食和饲料作物的需求不断增长，需要高产和有韧性的作物。许多农业上重要的性状是多基因的，由多个调控层控制，并与环境有很强的相互作用。在这项研究中，120个多年生黑麦草（Lolium perenne L.）F2家族在地下灌溉的半农田设施中跨越水梯度生长。基因组（单核苷酸多态性[SNP]）、转录组（基因表达[GE]）和DNA甲基组（MET）数据与通过半田间设施的对照和干旱部分收集的饲料质量性状数据相结合，获得了处理效果。使用卷积神经网络图像分析评估110cm以下的深根长度（DRL）。贝叶斯预测模型用于将表型方差划分为组成部分，并评估不同调控层（SNP、GE和MET）捕获的所有性状中表型方差的比例。研究了SNP、GE、MET的空间效应和效应，以及GE和MET之间的相互作用（GE×MET）和GE×处理（GE控制和GE干旱）的相互作用。基因表达解释了所有研究表型的遗传和空间变异的很大一部分原因，而MET解释了SNPs或GE未解释的残留变异。对于DRL，MET也有助于解释空间差异。该研究提供了一个统计上优雅的分析范式，它整合了基因组、转录组和MET信息，以了解复杂性状的多基因效应的调节机制。

Relative importance of genotype, gene expression, and DNA methylation on complex traits in perennial ryegrass

Plant Genome

Abstract

The growing demａnd for food and feed crops in the world because of growing population and more extreme weather events requires high-yielding and resilient crops. Many agriculturally important traits are polygenic, controlled by multiple regulatory layers, and with a strong interaction with the environment. In this study, 120 F2 families of perennial ryegrass (Lolium perenne L.) were grown across a water gradient in a semifield facility with subsoil irrigation. Genomic (single-nucleotide polymorphism [SNP]), transcriptomic (gene expression [GE]), and DNA methylomic (MET) data were integrated with feed quality trait data collected from control and drought sections in the semifield facility, providing a treatment effect. Deep root length (DRL) below 110 cm was assessed with convolutional neural network image analysis. Bayesian prediction models were used to partition phenotypic variance into its components and evaluated the proportion of phenotypic variance in all traits captured by different regulatory layers (SNP, GE, and MET). The spatial effects and effects of SNP, GE, MET, the interaction between GE and MET (GE × MET) and GE × treatment (GEControl ａnd GEDrought ) interaction were investigated. Gene expression explained a substantial part of the genetic and spatial variance for all the investigated phenotypes, whereas MET explained residual variance not accounted for by SNPs or GE. For DRL, MET also contributed to explaining spatial variance. The study provides a statistically elegant analytical paradigm that integrates genomic, transcriptomic, and MET information to understａnd the regulatory mechanisms of polygenic effects for complex traits.