Leveraging trait and QTL covariates to improve genomic prediction of resistance to Fusarium head blight in Central European winter wheat.

Fusarium head blight (FHB) is a devastating disease of wheat, causing yield losses, reduced grain quality, and mycotoxin contamination. Breeding can mitigate the severity of FHB epidemics, especially with genomics-assisted methods. The mechanisms underlying resistance to FHB in wheat have been extensively studied, including phenological traits and genome-wide markers associated with FHB severity.

An Expectation-Maximization Algorithm for Combining a Sample of Partially Overlapping Covariance Matrices.

The generation of unprecedented amounts of data brings new challenges in data management, but also an opportunity to accelerate the identification of processes of multiple science disciplines. One of these challenges is the harmonization of high-dimensional unbalanced and heterogeneous data. In this manuscript, we propose a statistical approach to combine incomplete and partially-overlapping pieces of covariance matrices that come from independent experiments.

Genome-wide association mapping of Fusarium langsethiae infection and mycotoxin accumulation in oat (Avena sativa L.).

Fusarium langsethiae is a symptomless pathogen of oat panicles that produces T-2 and HT-2 mycotoxins, two of the most potent trichothecenes produced by Fusarium fungi in cereals. In the last few years, the levels of these mycotoxin in oat grain has increased and the European commission have already recommended a maximum level for of 1000 μg kg for unprocessed oat for human consumption. The optimal and most sustainable way of combating infection and mycotoxin contamination is by releasing resistant oat varieties.

Design of training populations for selective phenotyping in genomic prediction.

Phenotyping is the current bottleneck in plant breeding, especially because next-generation sequencing has decreased genotyping cost more than 100.000 fold in the last 20 years. Therefore, the cost of phenotyping needs to be optimized within a breeding program.

Multi-objective optimized genomic breeding strategies for sustainable food improvement.

The purpose of breeding programs is to obtain sustainable gains in multiple traits while controlling the loss of genetic variation. The decisions at each breeding cycle involve multiple, usually competing, objectives; these complex decisions can be supported by the insights that are gained by applying multi-objective optimization principles to breeding. The discussion in this manuscript includes the definition of several multi-objective optimized breeding approaches within the phenotypic or genomic breeding frameworks and the comparison of these approaches with the standard multi-trait breeding schemes such as tandem selection, independent culling and index selection.

High-density genetic mapping of a major QTL for resistance to multiple races of loose smut in a tetraploid wheat cross.

Loose smut, caused by Ustilago tritici (Pers.) Rostr., is a systemic disease of tetraploid durum wheat (Triticum turgidum L.

Locally epistatic models for genome-wide prediction and association by importance sampling.

Background: In statistical genetics, an important task involves building predictive models of the genotype-phenotype relationship to attribute a proportion of the total phenotypic variance to the variation in genotypes. Many models have been proposed to incorporate additive genetic effects into prediction or association models. Currently, there is a scarcity of models that can adequately account for gene by gene or other forms of genetic interactions, and there is an increased interest in using marker annotations in genome-wide prediction and association analyses.

Genome-Wide Association Analysis Using R.

This chapter provides a practical overview of the statistical analysis using R [1] and genotype by sequencing (GBS) markers for genome-wide association studies (GWAS) in oats. Statistical analysis is performed by R package rrBLUP [2] and issues associated with the analysis are addressed along with the R code. The ultimate aim of this chapter is to provide a practical guideline to do GWAS analysis using R, rather than describe the theory in depth.

Chromatographic Methods to Evaluate Nutritional Quality in Oat.

Oats (A. sativa L.) have an important and positive role in human diet and health.