Optimizing Genomic-Enabled Prediction in Small-Scale Maize Hybrid Breeding Programs: A Roadmap Review

The usefulness of genomic prediction (GP) for many animal and plant breeding programs has been highlighted for many studies in the last 20 years. In maize breeding programs, mostly dedicated to delivering more highly adapted and productive hybrids, this approach has been proved successful for both large- and small-scale breeding programs worldwide. Here, we present some of the strategies developed to improve the accuracy of GP in tropical maize, focusing on its use under low budget and small-scale conditions achieved for most of the hybrid breeding programs in developing countries. We highlight the most important outcomes obtained by the University of São Paulo (USP, Brazil) and how they can improve the accuracy of prediction in tropical maize hybrids. Our roadmap starts with the efforts for germplasm characterization, moving on to the practices for mating design, and the selection of the genotypes that are used to compose the training population in field phenotyping trials. Factors including population structure and the importance of non-additive effects (dominance and epistasis) controlling the desired trait are also outlined. Finally, we explain how the source of the molecular markers, environmental, and the modeling of genotype–environment interaction can affect the accuracy of GP. Results of 7 years of research in a public maize hybrid breeding program under tropical conditions are discussed, and with the great advances that have been made, we find that what is yet to come is exciting. The use of open-source software for the quality control of molecular markers, implementing GP, and envirotyping pipelines may reduce costs in an efficient computational manner. We conclude that exploring new models/tools using high-throughput phenotyping data along with large-scale envirotyping may bring more resolution and realism when predicting genotype performances. Despite the initial costs, mostly for genotyping, the GP platforms in combination with these other data sources can be a cost-effective approach for predicting the performance of maize hybrids for a large set of growing conditions

snpReady e BGGE: pacotes do R para preparar dados genômicos e realizar predições genômicas

The use of molecular markers allows an increase in efficiency of the selection as well as better understanding of genetic resources in breeding programs. However, with the increase in the number of markers, it is necessary to process it before it can be ready to use. Also, to explore Genotype x Environment (GE) in the context of genomic prediction some covariance matrices needs to be set up before the prediction step. Thus, aiming to facilitate the introduction of genomic practices in the breeding program pipelines, we developed two R-packages. The former is called snpReady, which is set to prepare data sets to perform genomic studies. This package offers three functions to reach this objective, from organizing and apply the quality control, build the genomic relationship matrix and a summary of a population genetics. Furthermore, we present a new imputation method for missing markers. The latter is the BGGE package that was built to generate kernels for some GE genomic models and perform predictions. It consists of two functions (getK and BGGE). The former is helpful to create kernels for the GE genomic models, and the latter performs genomic predictions with some features for GE kernels that decreases the computational time. The features covered in the two packages presents a fast and straightforward option to help the introduction and usage of genome analysis in the breeding program pipeline.O uso de marcadores moleculares permite um aumento na eficiência da seleção, bem como uma melhor compreensão dos recursos genéticos em programas de melhoramento. No entanto, com o aumento do número de marcadores, é necessário o processamento deste antes de deixa-lo disponível para uso. Além disso, para explorar a interação genótipo x ambiente (GA) no contexto da predição genômica, algumas matrizes de covariância precisam ser obtidas antes da etapa de predição. Assim, com o objetivo de facilitar a introdução de práticas genômicas nos programa de melhoramento, dois pacotes em R foram desenvolvidos. O primeiro, snpReady, foi criado para preparar conjuntos de dados para realizar estudos genômicos. Este pacote oferece três funções para atingir esse objetivo, organizando e aplicando o controle de qualidade, construindo a matriz de parentesco genômico e com estimativas de parâmetros genéticos populacionais. Além disso, apresentamos um novo método de imputação para marcas perdidas. O segundo pacote é o BGGE, criado para gerar kernels para alguns modelos genômicos de interação GA e realizar predições genômicas. Consiste em duas funções (getK e BGGE). A primeira é utilizada para criar kernels para os modelos GA, e a última realiza predições genômicas, com alguns recursos especifico para os kernels GA que diminuem o tempo computacional. Os recursos abordados nos dois pacotes apresentam uma opção rápida e direta para ajudar a introdução e uso de análises genômicas nas diversas etapas do programa de melhoramento

Be-Breeder – an application for analysis of genomic data in plant breeding

Be-Breeder is an application directed toward genetic breeding of plants, developed through the Shiny package of the R software, which allows different phenotype and molecular (marker) analysis to be undertaken. The section for analysis of molecular data of the Be-Breeder application makes it possible to achieve quality control of genotyping data, to obtain genomic kinship matrices, and to analyze genomic selection, genome association, and genetic diversity in a simple manner on line. This application is available for use in a network through the site of the Allogamous Plant Breeding Laboratory of ESALQ-USP (http://www.genetica.esalq.usp.br/alogamas/R.html)

Tropical maize selection indexes genotypes for efficiency in use of nutrients: phosphorus

Brazil generates an annual demand for more than 2.83 million tons of phosphate fertilizers. Part of this is due to low P use efficiency (PUE) by plants, particularly in current maize cultivars. Thus, the aim of this study was to create indexes that allow accurate selection of maize genotypes with high PUE under conditions of either low or high P availability. The experiment was conducted in a greenhouse (20º45'14"S; 42º52'53"W) at the Universidade Federal de Viçosa in October 2010. We evaluated 39 experimental hybrid combinations and 14 maize inbred lines with divergent PUE under two conditions of P availability. The relative importance of the traits studied was analyzed and estimated by principal component analysis, factor analysis, and establishment of selection indexes. To obtain genotypes responsive to high P availability, the index SIHP (selection index for high phosphorus) = 0.3985 RDM + 0.3099 SDM + 0.5567 RLLAT + 0.2340 PUEb - 0.1139 SRS is recommended. To obtain genotypes tolerant to low P availability, the index SILP (selection index for low phosphorus) = 0.3548 RDM + 0.3996 RLLAT + 0.3344 SDM + 0.0041 SH/RS - 0.1019 SRS is suggestedO Brasil apresenta uma demanda anual de mais de 2,83 milhões de toneladas de fertilizantes fosfatados. Parte disto se deve a baixa eficiência no uso do fósforo (EUP) pelas plantas, especialmente nos atuais cultivares de milho. Assim, objetivou-se foi elaborar índices que permitam a seleção acurada de genótipos de milho eficientes no uso de P para condições de baixa e alta disponibilidade deste nutriente. O experimento foi conduzido em casa de vegetação (20º45'14"S; 42º52'53"W), na Universidade Federal de Viçosa, em outubro de 2010. Foram avaliadas 39 combinações híbridas experimentais e 14 linhagens de milho, divergentes para a eficiência no uso de P, em duas condições de disponibilidade deste nutriente. Foi realizada a análise de importância relativa dos caracteres estudados, estimada por meio do método dos componentes principais, a análise de fatores e confecção dos índices de seleção. Para obtenção de genótipos responsivos a alta disponibilidade de fósforo é recomendado o índice ISAP = 0,3985 MRS + 0,3099 MPS + 0,5567 CRLAT + 0,2340 EAbP - 0,1139 SER. Para obtenção de genótipos tolerantes a baixa disponibilidade deste nutriente é sugerido o índice ISBP = 0,3548 MRS + 0,3996 CRLAT + 0,3344 MSP + 0,0041 PA/SR - 0,1019 SE

BGGE: A New Package for Genomic-Enabled Prediction Incorporating Genotype × Environment Interaction Models

One of the major issues in plant breeding is the occurrence of genotype × environment (GE) interaction. Several models have been created to understand this phenomenon and explore it. In the genomic era, several models were employed to improve selection by using markers and account for GE interaction simultaneously. Some of these models use special genetic covariance matrices. In addition, the scale of multi-environment trials is getting larger, and this increases the computational challenges. In this context, we propose an R package that, in general, allows building GE genomic covariance matrices and fitting linear mixed models, in particular, to a few genomic GE models. Here we propose two functions: one to prepare the genomic kernels accounting for the genomic GE and another to perform genomic prediction using a Bayesian linear mixed model. A specific treatment is given for sparse covariance matrices, in particular, to block diagonal matrices that are present in some GE models in order to decrease the computational demand. In empirical comparisons with Bayesian Genomic Linear Regression (BGLR), accuracies and the mean squared error were similar; however, the computational time was up to five times lower than when using the classic approach. Bayesian Genomic Genotype × Environment Interaction (BGGE) is a fast, efficient option for creating genomic GE kernels and making genomic predictions

Tropical maize selection indexes genotypes for efficiency in use of nutrients: phosphorus

ABSTRACT Brazil generates an annual demand for more than 2.83 million tons of phosphate fertilizers. Part of this is due to low P use efficiency (PUE) by plants, particularly in current maize cultivars. Thus, the aim of this study was to create indexes that allow accurate selection of maize genotypes with high PUE under conditions of either low or high P availability. The experiment was conducted in a greenhouse (20º45'14"S; 42º52'53"W) at the Universidade Federal de Viçosa in October 2010. We evaluated 39 experimental hybrid combinations and 14 maize inbred lines with divergent PUE under two conditions of P availability. The relative importance of the traits studied was analyzed and estimated by principal component analysis, factor analysis, and establishment of selection indexes. To obtain genotypes responsive to high P availability, the index SIHP (selection index for high phosphorus) = 0.3985 RDM + 0.3099 SDM + 0.5567 RLLAT + 0.2340 PUEb - 0.1139 SRS is recommended. To obtain genotypes tolerant to low P availability, the index SILP (selection index for low phosphorus) = 0.3548 RDM + 0.3996 RLLAT + 0.3344 SDM + 0.0041 SH/RS - 0.1019 SRS is suggested

Maize adaptation across temperate climates was obtained via expression of two florigen genes.

Expansion of the maize growing area was central for food security in temperate regions. In addition to the suppression of the short-day requirement for floral induction, it required breeding for a large range of flowering time that compensates the effect of South-North gradients of temperatures. Here we show the role of a novel florigen gene, ZCN12, in the latter adaptation in cooperation with ZCN8. Strong eQTLs of ZCN8 and ZCN12, measured in 327 maize lines, accounted for most of the genetic variance of flowering time in platform and field experiments. ZCN12 had a strong effect on flowering time of transgenic Arabidopsis thaliana plants; a path analysis showed that it directly affected maize flowering time together with ZCN8. The allelic composition at ZCN QTLs showed clear signs of selection by breeders. This suggests that florigens played a central role in ensuring a large range of flowering time, necessary for adaptation to temperate areas

Physiological adaptive traits are a potential allele reservoir for maize genetic progress under challenging conditions

Combined phenomic and genomic approaches are required to evaluate the margin of progress of breeding strategies. Here, we analyze 65 years of genetic progress in maize yield, which was similar (101 kg ha−1 year−1 ) across most frequent environmental scenarios in the European growing area. Yield gains were linked to physiologically simple traits (plant phenology and architecture) which indirectly affected reproductive development and light interception in all studied environments, marked by significant genomic signatures of selection. Conversely, studied physiological processes involved in stress adaptation remained phenotypically unchanged (e.g. stomatal conductance and growth sensitivity to drought) and showed no signatures of selection. By selecting for yield, breeders indirectly selected traits with stable effects on yield, but not physiological traits whose effects on yield can be positive or negative depending on environmental conditions. Because yield stability under climate change is desirable, novel breeding strategies may be needed for exploiting alleles governing physiological adaptive traits

Physiological adaptive traits are a potential allele reservoir for maize genetic progress under challenging conditions

International audienceCombined phenomic and genomic approaches are required to evaluate the margin of progress of breeding strategies. Here, we analyze 65 years of genetic progress in maize yield, which was similar (101 kg ha −1 year −1 ) across most frequent environmental scenarios in the European growing area. Yield gains were linked to physiologically simple traits (plant phenology and architecture) which indirectly affected reproductive development and light interception in all studied environments, marked by significant genomic signatures of selection. Conversely, studied physiological processes involved in stress adaptation remained phenotypically unchanged (e.g. stomatal conductance and growth sensitivity to drought) and showed no signatures of selection. By selecting for yield, breeders indirectly selected traits with stable effects on yield, but not physiological traits whose effects on yield can be positive or negative depending on environmental conditions. Because yield stability under climate change is desirable, novel breeding strategies may be needed for exploiting alleles governing physiological adaptive traits