Automated seminal root angle measurement with corrective annotation.

Measuring seminal root angle is an important aspect of root phenotyping, yet automated methods are lacking. We introduce SeminalRootAngle, a novel open-source automated method that measures seminal root angles from images. To ensure our method is flexible and user-friendly we build on an established corrective annotation training method for image segmentation.

Compositionally aware estimation of cross-correlations for microbiome data.

In the field of microbiome studies, it is of interest to infer correlations between abundances of different microbes (here referred to as operational taxonomic units, OTUs). Several methods taking the compositional nature of the sequencing data into account exist. However, these methods cannot infer correlations between OTU abundances and other variables.

Nitrogen and Nod factor signaling determine Lotus japonicus root exudate composition and bacterial assembly.

Symbiosis with soil-dwelling bacteria that fix atmospheric nitrogen allows legume plants to grow in nitrogen-depleted soil. Symbiosis impacts the assembly of root microbiota, but it is unknown how the interaction between the legume host and rhizobia impacts the remaining microbiota and whether it depends on nitrogen nutrition. Here, we use plant and bacterial mutants to address the role of Nod factor signaling on Lotus japonicus root microbiota assembly.

Genetic architecture of inter-specific and -generic grass hybrids by network analysis on multi-omics data.

Background: Understanding the mechanisms underlining forage production and its biomass nutritive quality at the omics level is crucial for boosting the output of high-quality dry matter per unit of land. Despite the advent of multiple omics integration for the study of biological systems in major crops, investigations on forage species are still scarce.

Results: Our results identified substantial changes in gene co-expression and metabolite-metabolite network topologies as a result of genetic perturbation by hybridizing L.

Genetic analysis of global faba bean diversity, agronomic traits and selection signatures.

We identified marker-trait associations for key faba bean agronomic traits and genomic signatures of selection within a global germplasm collection. Faba bean (Vicia faba L.) is a high-protein grain legume crop with great potential for sustainable protein production.

The giant diploid faba genome unlocks variation in a global protein crop.

Increasing the proportion of locally produced plant protein in currently meat-rich diets could substantially reduce greenhouse gas emissions and loss of biodiversity. However, plant protein production is hampered by the lack of a cool-season legume equivalent to soybean in agronomic value. Faba bean (Vicia faba L.

Leveraging spatiotemporal genomic breeding value estimates of dry matter yield and herbage quality in ryegrass via random regression models.

Joint modeling of correlated multienvironment and multiharvest data of perennial crop species may offer advantages in prediction schemes and a better understanding of the underlying dynamics in space and time. The goal of the present study was to investigate the relevance of incorporating the longitudinal dimension of within-season multiple measurements of forage perennial ryegrass (Lolium perenne L.) traits in a reaction-norm model setup that additionally accounts for genotype × environment (G × E) interactions.

Major effect loci for plant size before onset of nitrogen fixation allow accurate prediction of yield in white clover.

Accurate genomic prediction of yield within and across generations was achieved by estimating the genetic merit of individual white clover genotypes based on extensive genetic replication using cloned material. White clover is an agriculturally important forage legume grown throughout temperate regions as a mixed clover-grass crop. It is typically cultivated with low nitrogen input, making yield dependent on nitrogen fixation by rhizobia in root nodules.

MeSCoT: the tool for quantitative trait simulation through the mechanistic modeling of genes' regulatory interactions.

This work represents a novel mechanistic approach to simulate and study genomic networks with accompanying regulatory interactions and complex mechanisms of quantitative trait formation. The approach implemented in MeSCoT software is conceptually based on the omnigenic genetic model of quantitative (complex) trait, and closely imitates the basic in vivo mechanisms of quantitative trait realization. The software provides a framework to study molecular mechanisms of gene-by-gene and gene-by-environment interactions underlying quantitative trait's realization and allows detailed mechanistic studies of impact of genetic and phenotypic variance on gene regulation.

Greenotyper: Image-Based Plant Phenotyping Using Distributed Computing and Deep Learning.

Image-based phenotype data with high temporal resolution offers advantages over end-point measurements in plant quantitative genetics experiments, because growth dynamics can be assessed and analysed for genotype-phenotype association. Recently, network-based camera systems have been deployed as customizable, low-cost phenotyping solutions. Here, we implemented a large, automated image-capture system based on distributed computing using 180 networked Raspberry Pi units that could simultaneously monitor 1,800 white clover () plants.

Effects of Different Strategies for Exploiting Genomic Selection in Perennial Ryegrass Breeding Programs.

Genomic selection (GS) is a potential pathway to accelerate genetic gain for perennial ryegrass ( L.). The main objectives of the present study were to investigate the level of genetic gain and accuracy by applying GS in commercial perennial ryegrass breeding programs.

Effect of genomic selection and genotyping strategy on estimation of variance components in animal models using different relationship matrices.

Background: The traditional way to estimate variance components (VC) is based on the animal model using a pedigree-based relationship matrix (A) (A-AM). After genomic selection was introduced into breeding programs, it was anticipated that VC estimates from A-AM would be biased because the effect of selection based on genomic information is not captured. The single-step method (H-AM), which uses an H matrix as (co)variance matrix, can be used as an alternative to estimate VC.

Genomic Selection Using Pedigree and Marker-by-Environment Interaction for Barley Seed Quality Traits From Two Commercial Breeding Programs.

With the current advances in the development of low-cost high-density array-based DNA marker technologies, cereal breeding programs are increasingly relying on genomic selection as a tool to accelerate the rate of genetic gain in seed quality traits. Different sources of genetic information are being explored, with the most prevalent being combined additive information from marker and pedigree-based data, and their interaction with the environment. In this, there has been mixed evidence on the performance of use of these data.

Use of multiple traits genomic prediction, genotype by environment interactions and spatial effect to improve prediction accuracy in yield data.

Genomic selection has been extensively implemented in plant breeding schemes. Genomic selection incorporates dense genome-wide markers to predict the breeding values for important traits based on information from genotype and phenotype records on traits of interest in a reference population. To date, most relevant investigations have been performed using single trait genomic prediction models (STGP).

Author Correction: Genomic prediction and GWAS of yield, quality and disease-related traits in spring barley and winter wheat.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Bayesian modeling reveals host genetics associated with rumen microbiota jointly influence methane emission in dairy cows.

Reducing methane emissions from livestock production is of great importance for the sustainable management of the Earth's environment. Rumen microbiota play an important role in producing biogenic methane. However, knowledge of how host genetics influences variation in ruminal microbiota and their joint effects on methane emission is limited.

Genomic prediction and GWAS of yield, quality and disease-related traits in spring barley and winter wheat.

Genome-wide association study (GWAS) and genomic prediction (GP) are extensively employed to accelerate genetic gain and identify QTL in plant breeding. In this study, 1,317 spring barley and 1,325 winter wheat breeding lines from a commercial breeding program were genotyped with the Illumina 9 K barley or 15 K wheat SNP-chip, and phenotyped in multiple years and locations. For GWAS, in spring barley, a QTL on chr.

Genome-wide association study on Fourier transform infrared milk spectra for two Danish dairy cattle breeds.

Background: Infrared spectral analysis of milk is cheap, fast, and accurate. Infrared light interacts with chemical bonds present inside the milk, which means that Fourier transform infrared milk spectra are a reflection of the chemical composition of milk. Heritability of Fourier transform infrared milk spectra has been analysed previously.

Breeding for dual-purpose wheat varieties using marker-trait associations for biomass yield and quality traits.

This study demonstrates that an active breeding nursery with rotation can be used to identify marker-trait associations for biomass yield and quality parameters that are important for biorefinery purposes. Wheat straw is a valuable feedstock for bioethanol production, but due to the recalcitrant nature of lignocellulose, its efficient use in biorefineries is limited by its low digestibility and difficult conversion of structural carbohydrates into free sugars. A genome-wide association study (GWAS) was conducted to search for significant SNP markers that could be used in a breeding programme to improve the value of wheat straw in a biorefinery setting.

Models with indirect genetic effects depending on group sizes: a simulation study assessing the precision of the estimates of the dilution parameter.

Background: In settings with social interactions, the phenotype of an individual is affected by the direct genetic effect (DGE) of the individual itself and by indirect genetic effects (IGE) of its group mates. In the presence of IGE, heritable variance and response to selection depend on size of the interaction group (group size), which can be modelled via a 'dilution' parameter (d) that measures the magnitude of IGE as a function of group size. However, little is known about the estimability of d and the precision of its estimate.

Improving genomic predictions by correction of genotypes from genotyping by sequencing in livestock populations.

Background: Genotyping by sequencing (GBS) is a robust method to genotype markers. Many factors can influence the genotyping quality. One is that heterozygous genotypes could be wrongly genotyped as homozygotes, dependent on the genotyping depths.

Genetic analysis of Fourier transform infrared milk spectra in Danish Holstein and Danish Jersey.

Fourier transform infrared milk spectral data are routinely used for milk quality control and have been revealed to be driven by genetics. This study aimed to (1) estimate heritability for 1,060 wavenumbers in the infrared region from 5,008 to 925 cm, (2) estimate genomic correlations between wavenumbers with increased heritability, and (3) compare results between Danish Holstein and Danish Jersey cows. For Danish Holstein, 3,275 cows and 19,656 milk records were available.

Genomic Prediction Using Multi-trait Weighted GBLUP Accounting for Heterogeneous Variances and Covariances Across the Genome.

Implicit assumption of common (co)variance for all loci in multi-trait Genomic Best Linear Unbiased Prediction (GBLUP) results in a genomic relationship matrix () that is common to all traits. When this assumption is violated, Bayesian whole genome regression methods may be superior to GBLUP by accounting for unequal (co)variance for all loci or genome regions. This study aimed to develop a strategy to improve the accuracy of GBLUP for multi-trait genomic prediction, using (co)variance estimates of SNP effects from Bayesian whole genome regression methods.

Genomic Prediction in Tetraploid Ryegrass Using Allele Frequencies Based on Genotyping by Sequencing.

Perennial ryegrass is an outbreeding forage species and is one of the most widely used forage grasses in temperate regions. The aim of this study was to investigate the possibility of implementing genomic prediction in tetraploid perennial ryegrass, to study the effects of different sequencing depth when using genotyping-by-sequencing (GBS), and to determine optimal number of single-nucleotide polymorphism (SNP) markers and sequencing depth for GBS data when applied in tetraploids. A total of 1,515 F tetraploid ryegrass families were included in the study and phenotypes and genotypes were scored on family-pools.

The Value of Expanding the Training Population to Improve Genomic Selection Models in Tetraploid Potato.

Genomic selection (GS) is becoming increasingly applicable to crops as the genotyping costs continue to decrease, which makes it an attractive alternative to traditional selective breeding based on observed phenotypes. With genome-wide molecular markers, selection based on predictions from genotypes can be made in the absence of direct phenotyping. The reliability of predictions depends strongly on the number of individuals used for training the predictive algorithms, particularly in a highly genetically diverse organism such as potatoes; however, the relationship between the individuals also has an enormous impact on prediction accuracy.