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

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

Estimation in a multiplicative mixed model involving a genetic relationship matrix

Genetic models partitioning additive and non-additive genetic effects for populations tested in replicated multi-environment trials (METs) in a plant breeding program have recently been presented in the literature. For these data, the variance model involves the direct product of a large numerator relationship matrix A, and a complex structure for the genotype by environment interaction effects, generally of a factor analytic (FA) form. With MET data, we expect a high correlation in genotype rankings between environments, leading to non-positive definite covariance matrices. Estimation methods for reduced rank models have been derived for the FA formulation with independent genotypes, and we employ these estimation methods for the more complex case involving the numerator relationship matrix. We examine the performance of differing genetic models for MET data with an embedded pedigree structure, and consider the magnitude of the non-additive variance. The capacity of existing software packages to fit these complex models is largely due to the use of the sparse matrix methodology and the average information algorithm. Here, we present an extension to the standard formulation necessary for estimation with a factor analytic structure across multiple environments

Functional selectivity of adenosine receptor ligands

Adenosine receptors are plasma membrane proteins that transduce an extracellular signal into the interior of the cell. Basically every mammalian cell expresses at least one of the four adenosine receptor subtypes. Recent insight in signal transduction cascades teaches us that the current classification of receptor ligands into agonists, antagonists, and inverse agonists relies very much on the experimental setup that was used. Upon activation of the receptors by the ubiquitous endogenous ligand adenosine they engage classical G protein-mediated pathways, resulting in production of second messengers and activation of kinases. Besides this well-described G protein-mediated signaling pathway, adenosine receptors activate scaffold proteins such as β-arrestins. Using innovative and sensitive experimental tools, it has been possible to detect ligands that preferentially stimulate the β-arrestin pathway over the G protein-mediated signal transduction route, or vice versa. This phenomenon is referred to as functional selectivity or biased signaling and implies that an antagonist for one pathway may be a full agonist for the other signaling route. Functional selectivity makes it necessary to redefine the functional properties of currently used adenosine receptor ligands and opens possibilities for new and more selective ligands. This review focuses on the current knowledge of functionally selective adenosine receptor ligands and on G protein-independent signaling of adenosine receptors through scaffold proteins

Factor analysis to investigate genotype and genotype X environment interaction effects on pro-vitamin A content and yield in maize synthetics

Vitamin A Deficiency (VAD) is a major public health problem in Sub-Saharan Africa affecting 33 million preschool-age children. Enrichment of maize varieties with provitamin A could provide sustainable and affordable solution to VAD. This study was conducted to understand the extent of GEI effects on both grain yield and provitamin A content in 21 maize synthetics and identify synthetics combining stable performance with high level provitamin A content across diverse environments in West Africa. Combined analysis of variance found significant (p < 0.01) GEI effects that prompted further investigation of the GEI magnitude using mixed model with factor analysis. Factors 1 and 2 explained 71% of the total variability. G5, G4, G12, G18, G2 and G14 were broadly adapted to a range of environments and considered the most stable and high yielding. G8, G1, and G10 were specifically adapted to a group of environments. Whereas, G21, G19 and G17 were found to be the worst and unstable genotypes. G4 combined stable performance with high provitamin A content, whereas G20 and G18 were stable but had low provitamin A contents. Three genotypes, G4, G12 and G14 were found to combine stability with high provitamin A contents. These genotypes can be recommended for production in the low-land tropics of West and Central Africa with similar environments

High-throughput method for ear phenotyping and kernel weight estimation in maize using ear digital imaging (vol 14, pg 49, 2018)

After the publication of our article [1], it was brought to our attention that in six places in the article we omitted to use quotation marks to show where the text has been directly used from the cited references

Genomic prediction models for grain yield of spring bread wheat in diverse agro-ecological zones

Genomic and pedigree predictions for grain yield and agronomic traits were carried out using high density molecular data on a set of 803 spring wheat lines that were evaluated in 5 sites characterized by several environmental co-variables. Seven statistical models were tested using two random cross-validations schemes. Two other prediction problems were studied, namely predicting the lines\u27 performance at one site with another (pairwise-site) and at untested sites (leave-one-site-out). Grain yield ranged from 3.7 to 9.0 t ha -1 across sites. The best predictability was observed when genotypic and pedigree data were included in the models and their interaction with sites and the environmental co-variables. The leave-one-site-out increased average prediction accuracy over pairwise-site for all the traits, specifically from 0.27 to 0.36 for grain yield. Days to anthesis, maturity, and plant height predictions had high heritability and gave the highest accuracy for prediction models. Genomic and pedigree models coupled with environmental co-variables gave high prediction accuracy due to high genetic correlation between sites. This study provides an example of model prediction considering climate data along-with genomic and pedigree information. Such comprehensive models can be used to achieve rapid enhancement of wheat yield enhancement in current and future climate change scenario