Genetic and phenotypic characterization of rice grain quality traits to define research strategies for improving rice milling, appearance, and cooking qualities in Latin America and the Caribbean

Genotyping Error Detection and Customised Filtration for SNP Datasets

A major challenge in analysing single-nucleotide polymorphism (SNP) genotype datasets is detecting and filtering errors that bias analyses and misinterpret ecological and evolutionary processes. Here, we present a comprehensive method to estimate and minimise genotyping error rates (deviations from the 'true' genotype) in any SNP datasets using triplicates (three repeats of the same sample) in a four-step filtration pipeline. The approach involves: (1) SNP filtering by missing data; (2) SNP filtering by error rates; (3) sample filtering by missing data and (4) detection of recaptured individuals by using estimated SNP error rates. The modular pipeline is provided in an R script that allows customised adjustments. We demonstrate the applicability of the method using non-invasive sampling from the Asiatic wild ass (Equus hemionus) population in Israel. We genotyped 756 samples using 625 SNPs, of which 255 were triplicates of 85 samples. The average SNP error rate, calculated based on the number of mismatching genotypes across triplicates before filtration, was 0.0034 and was reduced to 0.00174 following filtration. Evaluating genetic distance (GD) and relatedness (r) between triplicates before and after filtration (expected to be at the minimum and maximum respectively) showed a significant reduction in the average GD, from 58.1 to 25.3 (p = 0.0002) and a significant increase in relatedness, from r = 0.98 to r = 0.991 (p = 0.00587). We demonstrate how error rate estimation enhances recapture detection and improves genotype quality.

Fine mapping of a major QTL, qECQ8, for rice taste quality

BACKGROUND

Rice ECQ (eating and cooking quality) is an important determinant of rice consumption and market expansion. Therefore, improvement of ECQ is one of the primary goals in rice breeding. However, ECQ-related quantitative trait loci (QTL) have not yet been fully revealed. The present study aimed to identify a major effect QTL for rice taste, an important component of ECQ via genotyping-by-sequencing, to reveal the associated molecular mechanisms, and to predict key candidate genes.

RESULTS

A population of F9 recombinant inbred lines resulting from a cross between R668 (national standard of high-quality third class) and R838 (common edible rice) was used to construct a high-density genetic map (2,295.062 cM). The map comprises 639,504 markers distributed on 12 linkage elements with an average genetic distance of 0.004 cM. We detected a major taste-related QTL, qECQ8, which explained 41.4% of phenotypic variance and had LOD values of 4.42-7.73. Using a five-generation NIL population from the backcross of "Ganxiangzhan No. 1" carrying qECQ8 with the recurrent parent R838 (without qECQ8), we narrowed qECQ8 to a 187.5 kb interval between markers M33 and M37 on Chr8. Comparative transcriptomic analysis revealed that photosynthesis, glyoxylate and dicarboxylate metabolism, carbon fixation in photosynthetic organisms, and alpha-linolenic acid metabolism were induced in developing seeds of lines containing qECQ8. Furthermore, we identified two candidate genes in the qECQ8 region, including LOC_Os08g30550 (zinc knuckle family protein), a major candidate for genetic-assisted breeding of high-quality rice.

CONCLUSION

Our findings provide important genetic resources for targeted improvement of rice taste quality and may facilitate the genetic breeding of rice ECQ.

Contrasting Alleles of OsNRT1.1b Fostering Potential in Improving Nitrogen Use Efficiency in Rice

Nitrogen use efficiency (NUE) is important for the growth and development of rice and is significant in reducing the costs of rice production. OsNRT1.1b is involved in nitrate assimilation, and the alleles at position 21,759,092 on chromosome 10 clearly separate indica (Pathum Thani 1 (PTT1) and Homcholasit (HCS)) and japonica (Azucena and Leum Pua (LP)) rice varieties. Rice morphological and physiological traits were collected at three nitrogen levels (N0 = 0 kg ha-1, N7 = 43.75 kg ha-1, and N14 = 87.5 kg ha-1). Leaf and tiller numbers in PTT1 and HCS at N7 and N14 were two to three times higher than those at N0. At harvest, the biomass yield in PTT1 was the highest, while the total grain number in HCS was the maximum. The leaf widths and total chlorophyll contents (SPAD units) of Azucena and LP increased with nitrogen application as well as photosynthetic pigment parameters; for example, plant senescence reflectance indices (PSRIs), structure-insensitive pigment indices (SIPIs), and modified chlorophyll absorption ratio indices (MCARIs) were highly related in the japonica varieties. PTT1 and HCS, both carrying the A allele at OsNRT1.1b, had better NUE than Azucena and LP with the G allele. HCS, overall, had better NUE than PTT1. The translation to grain yield of assimilates was remarkable in PTT1 and HCS compared with Azucena and LP. In addition, HCS converted biomass for a 75% higher yield than PTT1. The ability of HCS to produce high yields was achieved even at N7 nitrogen fertilization, manifesting efficient use of nitrogen.

Implementing multi-trait genomic selection to improve grain milling quality in oats (Avena sativa L.)

Oats (Avena sativa L.) provide unique nutritional benefits and contribute to sustainable agricultural systems. Breeding high-value oat varieties that meet milling industry standards is crucial for satisfying the demand for oat-based food products. Test weight, thins, and groat percentage are primary traits that define oat milling quality and the final price of food-grade oats. Conventional selection for milling quality is costly and burdensome. Multi-trait genomic selection (MTGS) combines information from genome-wide markers and secondary traits genetically correlated with primary traits to predict breeding values of primary traits on candidate breeding lines. MTGS can improve prediction accuracy and significantly accelerate the rate of genetic gain. In this study, we evaluated different MTGS models that used morphometric grain traits to improve prediction accuracy for primary grain quality traits within the constraints of a breeding program. We evaluated 558 breeding lines from the University of Illinois Oat Breeding Program across 2 years for primary milling traits, test weight, thins, and groat percentage, and secondary grain morphometric traits derived from kernel and groat images. Kernel morphometric traits were genetically correlated with test weight and thins percentage but were uncorrelated with groat percentage. For test weight and thins percentage, the MTGS model that included the kernel morphometric traits in both training and candidate sets outperformed single-trait models by 52% and 59%, respectively. In contrast, MTGS models for groat percentage were not significantly better than the single-trait model. We found that incorporating kernel morphometric traits can improve the genomic selection for test weight and thins percentage.

Multi-model genome-wide association studies for appearance quality in rice

Improving the quality of the appearance of rice is critical to meet market acceptance. Mining putative quality-related genes has been geared towards the development of effective breeding approaches for rice. In the present study, two SL-GWAS (CMLM and MLM) and three ML-GWAS (FASTmrEMMA, mrMLM, and FASTmrMLM) genome-wide association studies were conducted in a subset of 3K-RGP consisting of 198 rice accessions with 553,831 SNP markers. A total of 594 SNP markers were identified using the mixed linear model method for grain quality traits. Additionally, 70 quantitative trait nucleotides (QTNs) detected by the ML-GWAS models were strongly associated with grain aroma (AR), head rice recovery (HRR, %), and percentage of grains with chalkiness (PGC, %). Finally, 39 QTNs were identified using single- and multi-locus GWAS methods. Among the 39 reliable QTNs, 20 novel QTNs were identified for the above-mentioned three quality-related traits. Based on annotation and previous studies, four functional candidate genes (LOC_Os01g66110, LOC_Os01g66140, LOC_Os07g44910, and LOC_Os02g14120) were found to influence AR, HRR (%), and PGC (%), which could be utilized in rice breeding to improve grain quality traits.

Realized Genetic Gain in Rice: Achievements from Breeding Programs

Genetic improvement is crucial for ensuring food security globally. Indeed, plant breeding has contributed significantly to increasing the productivity of major crops, including rice, over the last century. Evaluating the efficiency of breeding strategies necessitates a quantification of this progress. One approach involves assessing the genetic gain achieved through breeding programs based on quantitative traits. This study aims to provide a theoretical understanding of genetic gain, summarize the major results of genetic gain studies in rice breeding, and suggest ways of improving breeding program strategies and future studies on genetic gain. To achieve this, we present the concept of genetic gain and the essential aspects of its estimation. We also provide an extensive literature review of genetic gain studies in rice (Oryza sativa L.) breeding programs to understand the advances made to date. We reviewed 29 studies conducted between 1999 and 2023, covering different regions, traits, periods, and estimation methods. The genetic gain for grain yield, in particular, showed significant variation, ranging from 1.5 to 167.6 kg/ha/year, with a mean value of 36.3 kg/ha/year. This translated into a rate of genetic gain for grain yield ranging from 0.1% to over 3.0%. The impact of multi-trait selection on grain yield was clarified by studies that reported genetic gains for other traits, such as plant height, days to flowering, and grain quality. These findings reveal that while breeding programs have achieved significant gains, further improvements are necessary to meet the growing demand for rice. We also highlight the limitations of these studies, which hinder accurate estimations of genetic gain. In conclusion, we offer suggestions for improving the estimation of genetic gain based on quantitative genetic principles and computer simulations to optimize rice breeding strategies.

Integrating genome-wide association study into genomic selection for the prediction of agronomic traits in rice (Oryza sativa L.)

Accurately identifying varieties with targeted agronomic traits was thought to contribute to genetic selection and accelerate rice breeding progress. Genomic selection (GS) is a promising technique that uses markers covering the whole genome to predict the genomic-estimated breeding values (GEBV), with the ability to select before phenotypes are measured. To choose the appropriate GS models for breeding work, we analyzed the predictability of nine agronomic traits measured from a population of 459 diverse rice varieties. By the comparison of eight representative GS models, we found that the prediction accuracies ranged from 0.407 to 0.896, with reproducing kernel Hilbert space (RKHS) having the highest predictive ability in most traits. Further results demonstrated the predictivity of GS is altered by several factors. Moreover, we assessed the method of integrating genome-wide association study (GWAS) into various GS models. The predictabilities of GS combined peak-associated markers generated from six different GWAS models were significantly different; a recommendation of Mixed Linear Model (MLM)-RKHS was given for the GWAS-GS-integrated prediction. Finally, based on the above result, we experimented with applying the P-values obtained from optimal GWAS models into ridge regression best linear unbiased prediction (rrBLUP), which benefited the low predictive traits in rice.

Supplementary Information

The online version contains supplementary material available at 10.1007/s11032-023-01423-y.

Gene Genealogy-Based Mutation Analysis Reveals Emergence of Aus, Tropical japonica, and Aromatic of Oryza sativa during the Later Stage of Rice Domestication

Asian rice (Oryza sativa L.) has become a model for understanding gene functions and domestication in recent decades; however, its own diversification is still controversial. Although the division of indica and japonica and five subgroups (aus, indica (sensu stricto), japonica (sensu stricto), tropical japonica, and aromatic) are broadly accepted, how they are phylogenetically related is not transparent. To clarify their relationships, a sample of 121 diverse genes was chosen here from 12 Oryza genomes (two parental and ten O. sativa (Os)) in parallel to allow gene genealogy-based mutation (GGM) analysis. From the sample, 361 Os mutations were shared by two or more subgroups (referred to here as trans mutations) from 549 mutations identified at 51 Os loci. The GGM analysis and related tests indicates that aus diverged from indica at a time significantly earlier than when tropical japonica split from japonica. The results also indicate that aromatic was selected from hybrid progeny of aus and tropical japonica and that all five subgroups share a significant number of the early mutations identified previously. The results suggest that aus, tropical japonica, and aromatic emerged sequentially within the most recent 4-5 millennia of rice domestication after the split of indica and japonica.

GWAS Atlas: an updated knowledgebase integrating more curated associations in plants and animals

GWAS Atlas (https://ngdc.cncb.ac.cn/gwas/) is a manually curated resource of genome-wide genotype-to-phenotype associations for a wide range of species. Here, we present an updated implementation of GWAS Atlas by curating and incorporating more high-quality associations, with significant improvements and advances over the previous version. Specifically, the current release of GWAS Atlas incorporates a total of 278,109 curated genotype-to-phenotype associations for 1,444 different traits across 15 species (10 plants and 5 animals) from 830 publications and 3,432 studies. A collection of 6,084 lead SNPs of 439 traits and 486 experiment-validated causal variants of 157 traits are newly added. Moreover, 1,056 trait ontology terms are newly defined, resulting in 1,172 and 431 terms for Plant Phenotype and Trait Ontology and Animal Phenotype and Trait Ontology, respectively. Additionally, it is equipped with four online analysis tools and a submission platform, allowing users to perform data analysis and data submission. Collectively, as a core resource in the National Genomics Data Center, GWAS Atlas provides valuable genotype-to-phenotype associations for a diversity of species and thus plays an important role in agronomic trait study and molecular breeding.

Genome-wide association study reveals novel genomic regions governing agronomic and grain quality traits and superior allelic combinations for Basmati rice improvement

BACKGROUND

Basmati is a speciality segment in the rice genepool characterised by explicit grain quality. For the want of suitable populations, genome-wide association study (GWAS) in Basmati rice has not been attempted.

MATERIALS

To address this gap, we have performed a GWAS on a panel of 172 elite Basmati multiparent population comprising of potential restorers and maintainers. Phenotypic data was generated for various agronomic and grain quality traits across seven different environments during two consecutive crop seasons. Based on the observed phenotypic variation, three agronomic traits namely, days to fifty per cent flowering, plant height and panicle length, and three grain quality traits namely, kernel length before cooking, length breadth ratio and kernel length after cooking were subjected to GWAS. Genotyped with 80K SNP array, the population was subjected to principal component analysis to stratify the underlying substructure and subjected to the association analysis using Bayesian-information and Linkage-disequilibrium Iteratively Nested Keyway (BLINK) model.

RESULTS

We identified 32 unique MTAs including 11 robust MTAs for the agronomic traits and 25 unique MTAs including two robust MTAs for the grain quality traits. Six out of 13 robust MTAs were novel. By genome annotation, six candidate genes associated with the robust MTAs were identified. Further analysis of the allelic combinations of the robust MTAs enabled the identification of superior allelic combinations in the population. This information was utilized in selecting 77 elite Basmati rice genotypes from the panel.

CONCLUSION

This is the first ever GWAS study in Basmati rice which could generate valuable information usable for further breeding through marker assisted selection, including enhancing of heterosis.