A reaction norm model for genomic selection using high-dimensional genomic and environmental data.

New methods that incorporate the main and interaction effects of high-dimensional markers and of high-dimensional environmental covariates gave increased prediction accuracy of grain yield in wheat across and within environments. In most agricultural crops the effects of genes on traits are modulated by environmental conditions, leading to genetic by environmental interaction (G × E). Modern genotyping technologies allow characterizing genomes in great detail and modern information systems can generate large volumes of environmental data. In principle, G × E can be accounted for using interactions between markers and environmental covariates (ECs). However, when genotypic and environmental information is high dimensional, modeling all possible interactions explicitly becomes infeasible. In this article we show how to model interactions between high-dimensional sets of markers and ECs using covariance functions. The model presented here consists of (random) reaction norm where the genetic and environmental gradients are described as linear functions of markers and of ECs, respectively. We assessed the proposed method using data from Arvalis, consisting of 139 wheat lines genotyped with 2,395 SNPs and evaluated for grain yield over 8 years and various locations within northern France. A total of 68 ECs, defined based on five phases of the phenology of the crop, were used in the analysis. Interaction terms accounted for a sizable proportion (16 %) of the within-environment yield variance, and the prediction accuracy of models including interaction terms was substantially higher (17-34 %) than that of models based on main effects only. Breeding for target environmental conditions has become a central priority of most breeding programs. Methods, like the one presented here, that can capitalize upon the wealth of genomic and environmental information available, will become increasingly important.

Cross-genome map based dissection of a nitrogen use efficiency ortho-metaQTL in bread wheat unravels concerted cereal genome evolution.

Monitoring nitrogen use efficiency (NUE) in plants is becoming essential to maintain yield while reducing fertilizer usage. Optimized NUE application in major crops is essential for long-term sustainability of agriculture production. Here, we report the precise identification of 11 major chromosomal regions controlling NUE in wheat that co-localise with key developmental genes such as Ppd (photoperiod sensitivity), Vrn (vernalization requirement), Rht (reduced height) and can be considered as robust markers from a molecular breeding perspective. Physical mapping, sequencing, annotation and candidate gene validation of an NUE metaQTL on wheat chromosome 3B allowed us to propose that a glutamate synthase (GoGAT) gene that is conserved structurally and functionally at orthologous positions in rice, sorghum and maize genomes may contribute to NUE in wheat and other cereals. We propose an evolutionary model for the NUE locus in cereals from a common ancestral region, involving species specific shuffling events such as gene deletion, inversion, transposition and the invasion of repetitive elements.

Exploiting the Repetitive Fraction of the Wheat Genome for High-Throughput Single-Nucleotide Polymorphism Discovery and Genotyping.

Transposable elements (TEs) account for more than 80% of the wheat genome. Although they represent a major obstacle for genomic studies, TEs are also a source of polymorphism and consequently of molecular markers such as insertion site-based polymorphism (ISBP) markers. Insertion site-based polymorphisms have been found to be a great source of genome-specific single-nucleotide polymorphism (SNPs) in the hexaploid wheat ( L.) genome. Here, we report on the development of a high-throughput SNP discovery approach based on sequence capture of ISBP markers. By applying this approach to the reference sequence of chromosome 3B from hexaploid wheat, we designed 39,077 SNPs that are evenly distributed along the chromosome. We demonstrate that these SNPs can be efficiently scored with the KASPar (Kompetitive allele-specific polymerase chain reaction) genotyping technology. Finally, through genetic diversity and genome-wide association studies, we also demonstrate that ISBP-derived SNPs can be used in marker-assisted breeding programs.