Rapid and cost-effective genotyping of large mapping populations can be achieved by sequencing a reduced representation of the genome of every individual in a given population, and using that information to generate genetic markers. A customized genotyping-by-sequencing (GBS) pipeline was developed to genotype a rice F2 population from a cross of Oryza sativa ssp. japonica cv. Nipponbare and the African wild rice species O. longistaminata. While most GBS pipelines aim to analyze mainly homozygous populations, we attempted to genotype a highly heterozygous F2 population. We show how species- and population-specific improvements of established protocols can drastically increase sample throughput and genotype quality. Using as few as 50,000 reads for some individuals (134,000 reads on average), we were able to generate up to 8154 informative SNP markers in 1081 F2 individuals. Additionally, the effects of enzyme choice, read coverage, and data postprocessing are evaluated. Using GBS-derived markers, we were able to assemble a genetic map of 1536 cM. To demonstrate the usefulness of our GBS pipeline, we determined quantitative trait loci (QTL) for the number of tillers. We were able to map four QTL to chromosomes 1, 3, 4, and 8, and partially confirm their effects using introgression lines. We provide an example of how to successfully use GBS with heterozygous F2 populations. By using the comparatively low-cost MiSeq platform, we show that the GBS method is flexible and cost-effective, even for smaller laboratories.

The era of the green revolution has significantly improved rice yield productivity. However, with the growing population and decreasing arable land, rice scientists must find new ways to improve rice productivity. Although hundreds of rice yield-related QTLs were already mapped and some of them were cloned, only a few were utilized for actual systematic introgression breeding programs. In this study, the major yield QTLs Grain Number 1a (Gn1a) and Wealthy Farmer’s Panicle (WFP) were introgressed and stacked in selected NERICA cultivars by marker-assisted backcross breeding (MABB). The DNA markers RM3360, RM3452, and RM5493 were used for foreground selection. At BC₃F₄ and BC₃F₅ generation, a combination of marker-assisted selection and phenotypic evaluation were carried out to select lines with target alleles and traits. Further, genotyping-by-sequencing (GBS) was conducted to validate the introgression and determine the recurrent parent genome recovery (RPGR) of the selected lines. The Gn1a and/or WFP introgression lines showed significantly higher numbers of spikelets per panicle and primary branching compared to the recurrent parents. In addition, lines with Gn1a and/or WFP alleles were comparatively similar to the recurrent parents (RP) in most yield-related traits. This study demonstrates the success of utilizing yield QTLs and marker-assisted selection to develop and improve rice cultivars.

Rice grain yield prediction with UAV-driven multispectral images are re-emerging interests in precision agriculture, and an optimal sensing time is an important factor. The aims of this study were to (1) predict rice grain yield by using the estimated aboveground biomass (AGB) and leaf area index (LAI) from vegetation indices (VIs) and (2) determine the optimal sensing time in estimating AGB and LAI using VIs for grain yield prediction. An experimental trial was conducted in 2020 and 2021, involving two fertility conditions and five japonica rice cultivars (Aichinokaori, Asahi, Hatsushimo, Nakate Shinsenbon, and Nikomaru). Multi-temporal VIs were used to estimate AGB and LAI throughout the growth period with the extreme gradient boosting model and Gompertz model. The optimum time windows for predicting yield for each cultivar were determined using a single-day linear regression model. The results show that AGB and LAI could be estimated from VIs (R²: 0.56–0.83 and 0.57–0.73), and the optimum time window for UAV flights differed between cultivars, ranging from 4 to 31 days between the tillering stage and the initial heading stage. These findings help researchers to save resources and time for numerous UAV flights to predict rice grain yield.

A genetic resource for studying genetic architecture of agronomic traits and environmental adaptation is essential for crop improvements. Here, we report the development of a rice nested association mapping population (aus-NAM) using 7 aus varieties as diversity donors and T65 as the common parent. Aus-NAM showed broad phenotypic variations. To test whether aus-NAM was useful for quantitative trait loci (QTL) mapping, known flowering genes (Ehd1, Hd1, and Ghd7) in rice were characterized using single-family QTL mapping, joint QTL mapping, and the methods based on genome-wide association study (GWAS). Ehd1 was detected in all the seven families and all the methods. On the other hand, Hd1 and Ghd7 were detected in some families, and joint QTL mapping and GWAS-based methods resulted in weaker and uncertain peaks. Overall, the high allelic variations in aus-NAM provide a valuable genetic resource for the rice community.

DNA marker-assisted selection (MAS) has become an indispensable component of breeding. Single nucleotide polymorphisms (SNP) are the most frequent polymorphism in the rice genome. However, SNP markers are not readily employed in MAS because of limitations in genotyping platforms. Here the authors report a Golden Gate SNP array that targets specific genes controlling yield-related traits and biotic stress resistance in rice. As a first step, the SNP genotypes were surveyed in 31 parental varieties using the Affymetrix Rice 44K SNP microarray. The haplotype information for 16 target genes was then converted to the Golden Gate platform with 143-plex markers. Haplotypes for the 14 useful allele are unique and can discriminate among all other varieties. The genotyping consistency between the Affymetrix microarray and the Golden Gate array was 92.8%, and the accuracy of the Golden Gate array was confirmed in 3 F2 segregating populations. The concept of the haplotype-based selection by using the constructed SNP array was proofed.

Molecular markers play a crucial role in the improvement of rice. To benefit from these markers, genotyping is carried out to identify the differences at a specific position in the genome of individuals. The advances in sequencing technologies have led to the development of different genotyping techniques such as genotyping-by-sequencing. Unlike PCR-fragment-based genotyping, genotyping-by-sequencing has enabled the parallel sequencing and genotyping of hundreds of samples in a single run, making it more cost-effective. Currently, GBS is being used in several pre-breeding programs of rice to identify beneficial genes and QTL from different rice genetic resources. In this review, we present the current advances in the utilization of genotyping-by-sequencing for the development of rice pre-breeding materials and the improvement of existing rice cultivars. The challenges and perspectives of using this approach are also highlighted.