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

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 F 2 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 (GE Control and GE Drought ) 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.

New interactive machine learning tool for marine image analysis

“…This study focused on RootPainter's application to M. lingua individuals only. However, the success demonstrated with this notoriously complex species of interest, paired with previous terrestrial examples of model success [11,[67][68][69], gives confidence that RootPainter will be capable of segmenting other marine species. The applicability of RootPainter to species identification and biodiversity investigations may be increased by the introduction of multi-annotation capabilities.…”

New Interactive Machine Learning Tool for Marine Image Analysis

“…This study focused on RootPainter's application to M. lingua individuals only. However, the success demonstrated with this notoriously complex species of interest, paired with previous terrestrial examples of model success [31,[52][53][54], gives confidence that RootPainter will be capable of segmenting other marine species. The applicability of RootPainter to species identification and biodiversity investigations may be increased by introduction of multiannotation capabilities.…”

Automated Seminal Root Angle Measurement with Corrective Annotation

“…Our proposed root angle measurement method offers speed, accuracy (depending on root visibility) and is freely available as open-source software with a user-accessible GUI. The segmentation component of our pipeline was implemented using RootPainter [34], which previous studies have demonstrated to be a flexible, accessible and efficient way to train segmentation models for a variety of root datasets [33, 3, 11, 12, 2, 21], including for images obtained from rhizobox experiments [1, 4].…”