GIS-FA: an approach to integrating thematic maps, factor-analytic, and envirotyping for cultivar targeting.

KEY MESSAGE: We propose an "enviromics" prediction model for recommending cultivars based on thematic maps aimed at decision-makers. Parsimonious methods that capture genotype-by-environment interaction (GEI) in multi-environment trials (MET) are important in breeding programs. Understanding the causes and factors of GEI allows the utilization of genotype adaptations in the target population of environments through environmental features and factor-analytic (FA) models. Here, we present a novel predictive breeding approach called GIS-FA, which integrates geographic information systems (GIS) techniques, FA models, partial least squares (PLS) regression, and enviromics to predict phenotypic performance in untested environments. The GIS-FA approach enables: (i) the prediction of the phenotypic performance of tested genotypes in untested environments, (ii) the selection of the best-ranking genotypes based on their overall performance and stability using the FA selection tools, and (iii) the creation of thematic maps showing overall or pairwise performance and stability for decision-making. We exemplify the usage of the GIS-FA approach using two datasets of rice [Oryza sativa (L.)] and soybean [Glycine max (L.) Merr.] in MET spread over tropical areas. In summary, our novel predictive method allows the identification of new breeding scenarios by pinpointing groups of environments where genotypes demonstrate superior predicted performance. It also facilitates and optimizes cultivar recommendations by utilizing thematic maps.

Population improvement via recurrent selection drives genetic gain in upland rice breeding.

One of the main challenges of breeding programs is to identify superior genotypes from a large number of candidates. By gradually increasing the frequency of favorable alleles in the breeding population, recurrent selection improves the population mean for target traits, increasing the chance to identify promising genotypes. In rice, population improvement through recurrent selection has been used very little to date, except in Latin America. At Embrapa (Brazilian Agricultural Research Corporation), the upland rice breeding program is conducted in two phases: population improvement followed by product development. In this study, the CNA6 population, evaluated over five cycles (3 to 7) of selection, including 20 field trials, was used to assess the realized genetic gain. A high rate of genetic gain was observed for grain yield, at 215 kg.ha-1 per cycle or 67.8 kg.ha-1 per year (3.08%). The CNA6 population outperformed the controls only for the last cycle, with a yield difference of 1128 kg.ha-1. An analysis of the product development pipeline, based on 29 advanced yield trials with lines derived from cycles 3 to 6, showed that lines derived from the CNA6 population had high grain yield, but did not outperform the controls. These results demonstrate that the application of recurrent selection to a breeding population with sufficient genetic variability can result in significant genetic gains for quantitative traits, such as grain yield. The integration of this strategy into a two-phase breeding program also makes it possible to increase quantitative traits while selecting for other traits of interest.

Genome and Environment Based Prediction Models and Methods of Complex Traits Incorporating Genotype × Environment Interaction.

Genomic-enabled prediction models are of paramount importance for the successful implementation of genomic selection (GS) based on breeding values. As opposed to animal breeding, plant breeding includes extensive multienvironment and multiyear field trial data. Hence, genomic-enabled prediction models should include genotype × environment (G × E) interaction, which most of the time increases the prediction performance when the response of lines are different from environment to environment. In this chapter, we describe a historical timeline since 2012 related to advances of the GS models that take into account G × E interaction. We describe theoretical and practical aspects of those GS models, including the gains in prediction performance when including G × E structures for both complex continuous and categorical scale traits. Then, we detailed and explained the main G × E genomic prediction models for complex traits measured in continuous and noncontinuous (categorical) scale. Related to G × E interaction models this review also examine the analyses of the information generated with high-throughput phenotype data (phenomic) and the joint analyses of multitrait and multienvironment field trial data that is also employed in the general assessment of multitrait G × E interaction. The inclusion of nongenomic data in increasing the accuracy and biological reliability of the G × E approach is also outlined. We show the recent advances in large-scale envirotyping (enviromics), and how the use of mechanistic computational modeling can derive the crop growth and development aspects useful for predicting phenotypes and explaining G × E.

Breeding implications of drought stress under future climate for upland rice in Brazil.

Rice is the most important food crop in the developing world. For rice production systems to address the challenges of increasing demand and climate change, potential and on-farm yield increases must be increased. Breeding is one of the main strategies toward such aim. Here, we hypothesize that climatic and atmospheric changes for the upland rice growing period in central Brazil are likely to alter environment groupings and drought stress patterns by 2050, leading to changing breeding targets during the 21st century. As a result of changes in drought stress frequency and intensity, we found reductions in productivity in the range of 200-600 kg/ha (up to 20%) and reductions in yield stability throughout virtually the entire upland rice growing area (except for the southeast). In the face of these changes, our crop simulation analysis suggests that the current strategy of the breeding program, which aims at achieving wide adaptation, should be adjusted. Based on the results for current and future climates, a weighted selection strategy for the three environmental groups that characterize the region is suggested. For the highly favorable environment (HFE, 36%-41% growing area, depending on RCP), selection should be done under both stress-free and terminal stress conditions; for the favorable environment (FE, 27%-40%), selection should aim at testing under reproductive and terminal stress, and for the least favorable environment (LFE, 23%-27%), selection should be conducted for response to reproductive stress only and for the joint occurrence of reproductive and terminal stress. Even though there are differences in timing, it is noteworthy that stress levels are similar across environments, with 40%-60% of crop water demand unsatisfied. Efficient crop improvement targeted toward adaptive traits for drought tolerance will enhance upland rice crop system resilience under climate change.

Genome wide association study (GWAS) for grain yield in rice cultivated under water deficit.

The identification of rice drought tolerant materials is crucial for the development of best performing cultivars for the upland cultivation system. This study aimed to identify markers and candidate genes associated with drought tolerance by Genome Wide Association Study analysis, in order to develop tools for use in rice breeding programs. This analysis was made with 175 upland rice accessions (Oryza sativa), evaluated in experiments with and without water restriction, and 150,325 SNPs. Thirteen SNP markers associated with yield under drought conditions were identified. Through stepwise regression analysis, eight SNP markers were selected and validated in silico, and when tested by PCR, two out of the eight SNP markers were able to identify a group of rice genotypes with higher productivity under drought. These results are encouraging for deriving markers for the routine analysis of marker assisted selection. From the drought experiment, including the genes inherited in linkage blocks, 50 genes were identified, from which 30 were annotated, and 10 were previously related to drought and/or abiotic stress tolerance, such as the transcription factors WRKY and Apetala2, and protein kinases.

Traditional and modern plant breeding methods with examples in rice (Oryza sativa L.).

Plant breeding can be broadly defined as alterations caused in plants as a result of their use by humans, ranging from unintentional changes resulting from the advent of agriculture to the application of molecular tools for precision breeding. The vast diversity of breeding methods can be simplified into three categories: (i) plant breeding based on observed variation by selection of plants based on natural variants appearing in nature or within traditional varieties; (ii) plant breeding based on controlled mating by selection of plants presenting recombination of desirable genes from different parents; and (iii) plant breeding based on monitored recombination by selection of specific genes or marker profiles, using molecular tools for tracking within-genome variation. The continuous application of traditional breeding methods in a given species could lead to the narrowing of the gene pool from which cultivars are drawn, rendering crops vulnerable to biotic and abiotic stresses and hampering future progress. Several methods have been devised for introducing exotic variation into elite germplasm without undesirable effects. Cases in rice are given to illustrate the potential and limitations of different breeding approaches.

Association mapping for yield and grain quality traits in rice (Oryza sativa L.).

Association analysis was applied to a panel of accessions of Embrapa Rice Core Collection (ERiCC) with 86 SSR and field data from two experiments. A clear subdivision between lowland and upland accessions was apparent, thereby indicating the presence of population structure. Thirty-two accessions with admixed ancestry were identified through structure analysis, these being discarded from association analysis, thus leaving 210 accessions subdivided into two panels. The association of yield and grain-quality traits with SSR was undertaken with a mixed linear model, with markers and subpopulation as fixed factors, and kinship matrix as a random factor. Eight markers from the two appraised panels showed significant association with four different traits, although only one (RM190) maintained the marker-trait association across years and cultivation. The significant association detected between amylose content and RM190 was in agreement with previous QTL analyses in the literature. Herein, the feasibility of undertaking association analysis in conjunction with germplasm characterization was demonstrated, even when considering low marker density. The high linkage disequilibrium expected in rice lines and cultivars facilitates the detection of marker-trait associations for implementing marker assisted selection, and the mining of alleles related to important traits in germplasm.

Association mapping for yield and grain quality traits in rice (Oryza sativa L. )

Association analysis was applied to a panel of accessions of Embrapa Rice Core Collection (ERiCC) with 86 SSR and field data from two experiments. A clear subdivision between lowland and upland accessions was apparent, thereby indicating the presence of population structure. Thirty-two accessions with admixed ancestry were identified through structure analysis, these being discarded from association analysis, thus leaving 210 accessions subdivided into two panels. The association of yield and grain-quality traits with SSR was undertaken with a mixed linear model, with markers and subpopulation as fixed factors, and kinship matrix as a random factor. Eight markers from the two appraised panels showed significant association with four different traits, although only one (RM190) maintained the marker-trait association across years and cultivation. The significant association detected between amylose content and RM190 was in agreement with previous QTL analyses in the literature. Herein, the feasibility of undertaking association analysis in conjunction with germplasm characterization was demonstrated, even when considering low marker density. The high linkage disequilibrium expected in rice lines and cultivars facilitates the detection of marker-trait associations for implementing marker assisted selection, and the mining of alleles related to important traits in germplasm.

QTL mapping of agronomic traits in tef [Eragrostis tef (Zucc) Trotter].

BACKGROUND: Tef [Eragrostis tef (Zucc.) Trotter] is the major cereal crop in Ethiopia. Tef is an allotetraploid with a base chromosome number of 10 (2n = 4x = 40) and a genome size of 730 Mbp. The goal of this study was to identify agronomically important quantitative trait loci (QTL) using recombinant inbred lines (RIL) derived from an inter-specific cross between E. tef and E. pilosa (30-5). RESULTS: Twenty-two yield-related and morphological traits were assessed across eight different locations in Ethiopia during the growing seasons of 1999 and 2000. Using composite interval mapping and a linkage map incorporating 192 loci, 99 QTLs were identified on 15 of the 21 linkage groups for 19 traits. Twelve QTLs on nine linkage groups were identified for grain yield. Clusters of more than five QTLs for various traits were identified on seven linkage groups. The largest cluster (10 QTLs) was identified on linkage group 8; eight of these QTLs were for yield or yield components, suggesting linkage or pleotrophic effects of loci. There were 15 two-way interactions of loci to detect potential epistasis identified and 75% of the interactions were derived from yield and shoot biomass. Thirty-one percent of the QTLs were observed in multiple environments; two yield QTLs were consistent across all agro-ecology zones. For 29.3% of the QTLs, the alleles from E. pilosa (30-5) had a beneficial effect. CONCLUSION: The extensive QTL data generated for tef in this study will provide a basis for initiating molecular breeding to improve agronomic traits in this staple food crop for the people of Ethiopia.

Association mapping of kernel size and milling quality in wheat (Triticum aestivum L.) cultivars.

Association mapping is a method for detection of gene effects based on linkage disequilibrium (LD) that complements QTL analysis in the development of tools for molecular plant breeding. In this study, association mapping was performed on a selected sample of 95 cultivars of soft winter wheat. Population structure was estimated on the basis of 36 unlinked simple-sequence repeat (SSR) markers. The extent of LD was estimated on chromosomes 2D and part of 5A, relative to the LD observed among unlinked markers. Consistent LD on chromosome 2D was <1 cM, whereas in the centromeric region of 5A, LD extended for approximately 5 cM. Association of 62 SSR loci on chromosomes 2D, 5A, and 5B with kernel morphology and milling quality was analyzed through a mixed-effects model, where subpopulation was considered as a random factor and the marker tested was considered as a fixed factor. Permutations were used to adjust the threshold of significance for multiple testing within chromosomes. In agreement with previous QTL analysis, significant markers for kernel size were detected on the three chromosomes tested, and alleles potentially useful for selection were identified. Our results demonstrated that association mapping could complement and enhance previous QTL information for marker-assisted selection.