An Efficient Method for Screening Rice Breeding Lines Against Races of Magnaporthe oryzae

Rice blast, caused by Magnaporthe oryzae, is the most destructive rice disease worldwide. The disease symptoms are usually expressed on the leaf and panicle. The leaf disease intensity in controlled environmental conditions is frequently quantified using a 0 to 5 scale, where 0 represents the absence of symptoms, and 5 represents large eyespot lesions. However, this scale restricts the qualitative classification of the varieties into intermediate resistant and susceptible categories. Here, we develop a 0 to 6 scale for blast disease that allows proper assignment of rice breeding lines and varieties into six resistance levels (highly resistant, resistant, moderately resistant, moderately susceptible, susceptible, and highly susceptible). We evaluated 40 common rice varieties against four major blast races (IB1, IB17, IB49, and IE1-K). Varieties carrying the Pi-ta gene were either highly resistant, resistant, or moderately resistant to IB17. The IE1-K race was able to break Pi-ta-mediated resistance of the rice varieties. The Pi-z gene conferred resistance to the IB17 and IE1-K races. The varieties M201, Cheniere, and Frontier were highly susceptible (score 6; 100% disease) to the race IE1-K. Moreover, varieties that were resistant or susceptible to all four blast races also showed similar levels of resistance/susceptibility to blast disease in the field. Taken together, our data proved that the 0 to 6 blast scale can efficiently determine the resistance levels of rice varieties against major blast races. This robust method will assist rice breeding programs to incorporate durable resistance against major and emerging blast races.[Formula: see text] Copyright © 2024 The Author(s). This is an open access article distributed under the CC BY-NC-ND 4.0 International license.

Molecular mapping and characterization of QTLs for grain quality traits in a RIL population of US rice under high nighttime temperature stress

Elevated nighttime temperatures resulting from climate change significantly impact the rice crop worldwide. The rice (Oryza sativa L.) plant is highly sensitive to high nighttime temperature (HNT) during grain-filling (reproductive stage). HNT stress negatively affects grain quality traits and has a major impact on the value of the harvested rice crop. In addition, along with grain dimensions determining rice grain market classes, the grain appearance and quality traits determine the rice grain market value. During the last few years, there has been a major concern for rice growers and the rice industry over the prevalence of rice grains opacity and the reduction of grain dimensions affected by HNT stress. Hence, the improvement of heat-stress tolerance to maintain grain quality of the rice crop under HNT stress will bolster future rice value in the market. In this study, 185 F₁₂-recombinant inbred lines (RILs) derived from two US rice cultivars, Cypress (HNT-tolerant) and LaGrue (HNT-sensitive) were screened for the grain quality traits grain length (GL), grain width (GW), and percent chalkiness (%chalk) under control and HNT stress conditions and evaluated to identify the genomic regions associated with the grain quality traits. In total, there were 15 QTLs identified; 6 QTLs represented under control condition explaining 3.33% to 8.27% of the phenotypic variation, with additive effects ranging from - 0.99 to 0.0267 on six chromosomes and 9 QTLs represented under HNT stress elucidating 6.39 to 51.53% of the phenotypic variation, with additive effects ranging from - 8.8 to 0.028 on nine chromosomes for GL, GW, and % chalk. These 15 QTLs were further characterized and scanned for natural genetic variation in a japonica diversity panel (JDP) to identify candidate genes for GL, GW, and %chalk. We found 6160 high impact single nucleotide polymorphisms (SNPs) characterized as such depending on their type, region, functional class, position, and proximity to the gene and/or gene features, and 149 differentially expressed genes (DEGs) in the 51 Mbp genomic region comprising of the 15 QTLs. Out of which, 11 potential candidate genes showed high impact SNP associations. Therefore, the analysis of the mapped QTLs and their genetic dissection in the US grown Japonica rice genotypes at genomic and transcriptomic levels provide deep insights into genetic variation beneficial to rice breeders and geneticists for understanding the mechanisms related to grain quality under heat stress in rice.

Genetic Characterization of the Partial Disease Resistance of Rice to Bacterial Panicle Blight and Sheath Blight by Combined QTL Linkage and QTL-seq Analyses

Bacterial panicle blight (BPB) and sheath blight (SB) are major diseases of rice and few cultivars have shown a high level of resistance to these diseases. A recombinant inbred line (RIL) population developed from the U.S. cultivars Jupiter (moderately resistant) and Trenasse (susceptible) was investigated to identify loci associated with the partial disease resistance to BPB and SB. Disease phenotypes in BPB and SB, as well as the days-to-heading (DTH) trait, were evaluated in the field. DTH was correlated to BPB and SB diseases, while BPB was positively correlated to SB in the field trials with this RIL population. Genotyping was performed using Kompetitive Allele Specific PCR (KASP) assays and whole-genome sequence (WGS) analyses. Quantitative trait locus (QTL) mapping and bulk segregant analysis using a set of WGS data (QTL-seq) detected a major QTL on the upper arm of chromosome 3 for BPB, SB, and DTH traits within the 1.0-1.9 Mb position. Additional QTLs associated with BPB and SB were also identified from other chromosomes by the QTL-seq analysis. The QTLs identified in this study contain at least nine candidate genes that are predicted to have biological functions in defense or flowering. These findings provide an insight into the complex nature of the quantitative resistance to BPB and SB, which may also be closely linked to the flowering trait.

An improved 7K SNP array, the C7AIR, provides a wealth of validated SNP markers for rice breeding and genetics studies

Single nucleotide polymorphisms (SNPs) are highly abundant, amendable to high-throughput genotyping, and useful for a number of breeding and genetics applications in crops. SNP frequencies vary depending on the species and populations under study, and therefore target SNPs need to be carefully selected to be informative for each application. While multiple SNP genotyping systems are available for rice (Oryza sativa L. and its relatives), they vary in their informativeness, cost, marker density, speed, flexibility, and data quality. In this study, we report the development and performance of the Cornell-IR LD Rice Array (C7AIR), a second-generation SNP array containing 7,098 markers that improves upon the previously released C6AIR. The C7AIR is designed to detect genome-wide polymorphisms within and between subpopulations of O. sativa, as well as O. glaberrima, O. rufipogon and O. nivara. The C7AIR combines top-performing SNPs from several previous rice arrays, including 4,007 SNPs from the C6AIR, 2,056 SNPs from the High Density Rice Array (HDRA), 910 SNPs from the 384-SNP GoldenGate sets, 189 SNPs from the 44K array selected to add information content for elite U.S. tropical japonica rice varieties, and 8 trait-specific SNPs. To demonstrate its utility, we carried out a genome-wide association analysis for plant height, employing the C7AIR across a diversity panel of 189 rice accessions and identified 20 QTLs contributing to plant height. The C7AIR SNP chip has so far been used for genotyping >10,000 rice samples. It successfully differentiates the five subpopulations of Oryza sativa, identifies introgressions from wild and exotic relatives, and is useful for quantitative trait loci (QTL) and association mapping in diverse materials. Moreover, data from the C7AIR provides valuable information that can be used to select informative and reliable SNP markers for conversion to lower-cost genotyping platforms for genomic selection and other downstream applications in breeding.