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 Institute's (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.

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.Electronic supplementary materialThe online version of this article (doi:10.1186/1939-8433-6-11) contains supplementary material, which is available to authorized users.

No abstract

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.

To identify quantitative trait loci (QTL) controlling heat tolerance in rice, the progeny of BC 1 F 1 and F 2 populations derived from an IR64 · N22 cross were exposed to 38/24°C for 14 days at the flowering stage, and spikelet fertility was assessed. A custom 384-plex Illumina GoldenGate genotyping assay was used to genotype the F 2 and selected BC 1 F 1 plants. Four single nucleotide polymorphisms were associated with heat tolerance in the BC 1 F 1 population using selective genotyping and single marker analysis, and four putative QTL were found to be associated with heat tolerance in the F 2 population. Two major QTL were located on chromosome 1 (qHTSF1.1) and chromosome 4 (qHTSF4.1). These two major QTL could explain 12.6% (qHTSF1.1) and 17.6% (qHTSF4.1) of the variation in spikelet fertility under high temperature. Tolerant allele of qHTSF1.1 was from the susceptible parent IR64, and that of qHTSF4.1 was from tolerant parent N22. The effect of qHTSF4.1 on chromosome 4 was confirmed in selected BC 2 F 2 progeny from the same IR64 · N22 cross, and the plants with qHTSF4.1 showed significantly higher spikelet fertility than other genotypes.

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.Electronic supplementary materialThe online version of this article (doi:10.1186/s12863-015-0199-7) contains supplementary material, which is available to authorized users.

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.

No abstract