Edilberto D. Redoña

Genomic selection and association mapping in rice (Oryza sativa): effect of trait genetic architecture, training population composition, marker number and statistical model on accuracy of rice genomic selection in elite, tropical rice breeding lines.

Genomic Selection (GS) is a new breeding method in which genome-wide markers are used to predict the breeding value of individuals in a breeding population. GS has been shown to improve breeding efficiency in dairy cattle and several crop plant species, and here we evaluate for the first time its efficacy for breeding inbred lines of rice. We performed a genome-wide association study (GWAS) in conjunction with five-fold GS cross-validation on a population of 363 elite breeding lines from the International Rice Research Institutes (IRRI) irrigated rice breeding program and herein report the GS results. The population was genotyped with 73,147 markers using genotyping-by-sequencing. The training population, statistical method used to build the GS model, number of markers, and trait were varied to determine their effect on prediction accuracy. For all three traits, genomic prediction models outperformed prediction based on pedigree records alone. Prediction accuracies ranged from 0.31 and 0.34 for grain yield and plant height to 0.63 for flowering time. Analyses using subsets of the full marker set suggest that using one marker every 0.2 cM is sufficient for genomic selection in this collection of rice breeding materials. RR-BLUP was the best performing statistical method for grain yield where no large effect QTL were detected by GWAS, while for flowering time, where a single very large effect QTL was detected, the non-GS multiple linear regression method outperformed GS models. For plant height, in which four mid-sized QTL were identified by GWAS, random forest produced the most consistently accurate GS models. Our results suggest that GS, informed by GWAS interpretations of genetic architecture and population structure, could become an effective tool for increasing the efficiency of rice breeding as the costs of genotyping continue to decline.

Multi-parent advanced generation inter-cross (MAGIC) populations in rice: progress and potential for genetics research and breeding

BackgroundThis article describes the development of Multi-parent Advanced Generation Inter-Cross populations (MAGIC) in rice and discusses potential applications for mapping quantitative trait loci (QTLs) and for rice varietal development. We have developed 4 multi-parent populations: indica MAGIC (8 indica parents); MAGIC plus (8 indica parents with two additional rounds of 8-way F1 inter-crossing); japonica MAGIC (8 japonica parents); and Global MAGIC (16 parents – 8 indica and 8 japonica). The parents used in creating these populations are improved varieties with desirable traits for biotic and abiotic stress tolerance, yield, and grain quality. The purpose is to fine map QTLs for multiple traits and to directly and indirectly use the highly recombined lines in breeding programs. These MAGIC populations provide a useful germplasm resource with diverse allelic combinations to be exploited by the rice community.ResultsThe indica MAGIC population is the most advanced of the MAGIC populations developed thus far and comprises 1328 lines produced by single seed descent (SSD). At the S4 stage of SSD a subset (200 lines) of this population was genotyped using a genotyping-by-sequencing (GBS) approach and was phenotyped for multiple traits, including: blast and bacterial blight resistance, salinity and submergence tolerance, and grain quality. Genome-wide association mapping identified several known major genes and QTLs including Sub1 associated with submergence tolerance and Xa4 and xa5 associated with resistance to bacterial blight. Moreover, the genome-wide association study (GWAS) results also identified potentially novel loci associated with essential traits for rice improvement.ConclusionThe MAGIC populations serve a dual purpose: permanent mapping populations for precise QTL mapping and for direct and indirect use in variety development. Unlike a set of naturally diverse germplasm, this population is tailor-made for breeders with a combination of useful traits derived from multiple elite breeding lines. The MAGIC populations also present opportunities for studying the interactions of genome introgressions and chromosomal recombination.

Genome-wide prediction models that incorporate de novo GWAS are a powerful new tool for tropical rice improvement.

To address the multiple challenges to food security posed by global climate change, population growth and rising incomes, plant breeders are developing new crop varieties that can enhance both agricultural productivity and environmental sustainability. Current breeding practices, however, are unable to keep pace with demand. Genomic selection (GS) is a new technique that helps accelerate the rate of genetic gain in breeding by using whole-genome data to predict the breeding value of offspring. Here, we describe a new GS model that combines RR-BLUP with markers fit as fixed effects selected from the results of a genome-wide-association study (GWAS) on the RR-BLUP training data. We term this model GS + de novo GWAS. In a breeding population of tropical rice, GS + de novo GWAS outperformed six other models for a variety of traits and in multiple environments. On the basis of these results, we propose an extended, two-part breeding design that can be used to efficiently integrate novel variation into elite breeding populations, thus expanding genetic diversity and enhancing the potential for sustainable productivity gains.

Varietal Improvement for Abiotic Stress Tolerance in Crop Plants: Special Reference to Salinity in Rice

Abiotic stresses are both serious in magnitude and widespread in occurrence and thus pose major hurdles to attaining higher crop productivity. In rice, salinity follows only drought stress in both extent and gravity. Tapping the potential of large salt-affected areas to increase rice production would contribute to food security and alleviate poverty in unfavorable rice growing environments where most resource poor farmers live. However, this would necessitate the development of salt tolerant varieties and their widespread adoption by farmers. Past progress in breeding new salt-tolerant varieties has been slow due to obvious reasons. Moreover, abiotic stresses seldom occur singly and are more severe when occur jointly. Progress, however, is being made in developing new salt tolerant genotypes using both conventional and non-conventional breeding methodologies. Robust screening techniques have been developed, the screening criteria and selection pressure are well elucidated, the genetics of salt tolerance are better understood and suitable genetic donors have been identified. Component traits for salinity tolerance are being pyramided, and recurrent selection methods such as the diallel selective mating are being employed to increase the frequency of the desired alleles in breeding populations specifically designed for deployment in specific target environments. Modern tools and techniques such as molecular marker-assisted selection is also being integrated into conventional breeding programs to increase the pace and efficiency of the varietal development process. Proven mechanisms of international collaboration are being harnessed to generate reliable research outputs while novel technology development and promotion approaches are employed to improve adoption levels and impact of new varieties. Various examples on these developments are provided in this chapter.

Identifying and confirming quantitative trait loci associated with heat tolerance at flowering stage in different rice populations.

BackgroundClimate change is affecting rice production in many countries. Developing new rice varieties with heat tolerance is an essential way to sustain rice production in future global warming. We have previously reported four quantitative trait loci (QTLs) responsible for rice spikelet fertility under high temperature at flowering stage from an IR64/N22 population. To further explore additional QTL from other varieties, two bi-parental F2 populations and one three-way F2 population derived from heat tolerant variety Giza178 were used for indentifying and confirming QTLs for heat tolerance at flowering stage.ResultsFour QTLs (qHTSF1.2, qHTSF2.1, qHTSF3.1 and qHTSF4.1) were identified in the IR64/Giza178 population, and two other QTLs (qHTSF6.1 and qHTSF11.2) were identified in the Milyang23/Giza178 population. To confirm the identified QTLs, another three-way-cross population derived from IR64//Milyang23/Giza178 was genotyped using 6K SNP chips. Five QTLs were identified in the three-way-cross population, and three of those QTLs (qHTSF1.2, qHTSF4.1 and qHTSF6.1) were overlapped with the QTLs identified in the bi-parental populations. The tolerance alleles of these QTLs were from the tolerant parent Giza178 except for qHTSF3.1. The QTL on chromosome 4 (qHTSF4.1) is the same QTL previously identified in the IR64/N22 population.ConclusionThe results from different populations suggest that heat tolerance in rice at flowering stage is controlled by several QTLs with small effects and stronger heat tolerance could be attained through pyramiding validated heat tolerance QTLs. QTL qHTSF4.1 was consistently detected across different genetic backgrounds and could be an important source for enhancing heat tolerance in rice at flowering stage. Polymorphic SNP markers in these QTL regions can be used for future fine mapping and developing SNP chips for marker-assisted breeding.

Combining Limited Multiple Environment Trials Data with Crop Modeling to Identify Widely Adaptable Rice Varieties

Multi-Environment Trials (MET) are conventionally used to evaluate varietal performance prior to national yield trials, but the accuracy of MET is constrained by the number of test environments. A modeling approach was innovated to evaluate varietal performance in a large number of environments using the rice model ORYZA (v3). Modeled yields representing genotype by environment interactions were used to classify the target population of environments (TPE) and analyze varietal yield and yield stability. Eight Green Super Rice (GSR) and three check varieties were evaluated across 3796 environments and 14 seasons in Southern Asia. Based on drought stress imposed on rainfed rice, environments were classified into nine TPEs. Relative to the check varieties, all GSR varieties performed well except GSR-IR1-5-S14-S2-Y2, with GSR-IR1-1-Y4-Y1, and GSR-IR1-8-S6-S3-Y2 consistently performing better in all TPEs. Varietal evaluation using ORYZA (v3) significantly corresponded to the evaluation based on actual MET data within specific sites, but not with considerably larger environments. ORYZA-based evaluation demonstrated the advantage of GSR varieties in diverse environments. This study substantiated that the modeling approach could be an effective, reliable, and advanced approach to complement MET in the assessment of varietal performance on spatial and temporal scales whenever quality soil and weather information are accessible. With available local weather and soil information, this approach can also be adopted to other rice producing domains or other crops using appropriate crop models.

Effects of allelic variations in starch synthesis-related genes on grain quality traits of Korean nonglutinous rice varieties under different temperature conditions

Influences of allelic variations in starch synthesis-related genes (SSRGs) on rice grain quality were examined. A total of 187 nonglutinous Korean rice varieties, consisting of 170 Japonica and 17 Tongil-type varieties, were grown in the field and in two greenhouse conditions. The percentages of head rice and chalky grains, amylose content, alkali digestion value, and rapid visco-analysis characteristics were evaluated in the three different environments. Among the 10 previously reported SSRG markers used in this study, seven were polymorphic, and four of those showed subspecies-specific allele distributions. Six out of the seven polymorphic SSRG markers were significantly associated with at least one grain quality trait (R2 > 0.1) across the three different environments. However, the association level and significance were markedly lower when the analysis was repeated using only the 170 Japonica varieties. Similarly, the significant associations between SSRG allelic variations and changes in grain quality traits under increased temperature were largely attributable to the biased allele frequency between the two subpopulations. Our results suggest that within Korean Japonica varieties, these 10 major SSRG loci have been highly fixed during breeding history and variations in grain quality traits might be influenced by other genetic factors.

Proteome-wide Analysis of Lysine 2-hydroxyisobutyrylation in Developing Rice ( Oryza sativa ) Seeds

Lysine 2-hydroxyisobutyrylation is a recently identified protein post-translational modification that is known to affect the association between histone and DNA. However, non-histone protein lysine 2-hydroxyisobutyrylation remains largely unexplored. Utilizing antibody-based affinity enrichment and nano-HPLC/MS/MS analyses of 2-hydroxyisobutyrylation peptides, we efficaciously identified 9,916 2-hydroxyisobutyryl lysine sites on 2,512 proteins in developing rice seeds, representing the first lysine 2-hydroxyisobutyrylome dataset in plants. Functional annotation analyses indicated that a wide variety of vital biological processes were preferably targeted by lysine 2-hydroxyisobutyrylation, including glycolysis/gluconeogenesis, TCA cycle, starch biosynthesis, lipid metabolism, protein biosynthesis and processing. Our finding showed that 2-hydroxyisobutyrylated histone sites were conserved across plants, human, and mouse. A number of 2-hydroxyisobutyryl sites were shared with other lysine acylations in both histone and non-histone proteins. Comprehensive analysis of the lysine 2-hydroxyisobutyrylation sites illustrated that the modification sites were highly sequence specific with distinct motifs, and they had less surface accessibility than other lysine residues in the protein. Overall, our study provides the first systematic analysis of lysine 2-hydroxyisobutyrylation proteome in plants, and it serves as an important resource for future investigations of the regulatory mechanisms and functions of lysine 2-hydroxyisobutyrylation.