Dataset Information

Molecular Breeding for Improving Productivity of Oryza sativa L. cv. Pusa 44 under Reproductive Stage Drought Stress through Introgression of a Major QTL, qDTY12.1.

ABSTRACT: Increasing rice production is quintessential to the task of sustaining global food security, as a majority of the global population is dependent on rice as its staple dietary cereal. Among the various constraints affecting rice production, reproductive stage drought stress (RSDS) is a major challenge, due to its direct impact on grain yield. Several quantitative trait loci (QTLs) conferring RSDS tolerance have been identified in rice, and qDTY12.1 is one of the major QTLs reported. We report the successful introgression of qDTY12.1 into Pusa 44, a drought sensitive mega rice variety of the northwestern Indian plains. Marker-assisted backcross breeding (MABB) was adopted to transfer qDTY12.1 into Pusa 44 in three backcrosses followed by four generations of pedigree selection, leading to development of improved near isogenic lines (NILs). Having a recurrent parent genome (RPG) recovery ranging from 94.7-98.7%, the improved NILs performed 6.5 times better than Pusa 44 under RSDS, coupled with high yield under normal irrigated conditions. The MABB program has been modified so as to defer background selection until BC3F4 to accelerate generational advancements. Deploying phenotypic selection alone in the early backcross generations could help in the successful recovery of RPG. In addition, the grain quality could be recovered in the improved NILs, leading to superior selections. Owing to their improved adaptation to drought, the release of improved NILs for regions prone to intermittent drought can help enhance rice productivity and production.

SUBMITTER: Oo KS

PROVIDER: S-EPMC8303740 | biostudies-literature | 2021 Jun

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Molecular Breeding for Improving Productivity of Oryza sativa L. cv. Pusa 44 under Reproductive Stage Drought Stress through Introgression of a Major QTL, qDTY12.1.

Oo Kyaw Swar KS Krishnan Subbaiyan Gopala SG Vinod Kunnummal Kurungara KK Dhawan Gaurav G Dwivedi Priyanka P Kumar Pankaj P Bhowmick Prolay Kumar PK Pal Madan M Chinnuswamy Viswanathan V Nagarajan Mariappan M Bollinedi Haritha H Ellur Ranjith Kumar RK Singh Ashok Kumar AK

Genes 20210624 7

Increasing rice production is quintessential to the task of sustaining global food security, as a majority of the global population is dependent on rice as its staple dietary cereal. Among the various constraints affecting rice production, reproductive stage drought stress (RSDS) is a major challenge, due to its direct impact on grain yield. Several quantitative trait loci (QTLs) conferring RSDS tolerance have been identified in rice, and qDTY12.1 is one of the major QTLs reported. We rep ...[more]

PMID: 34202818

Similar Datasets

Project description:BackgroundGrain size and weight are important target traits determining grain yield and quality in rice. Wild rice species possess substantial elite genes that can be served as an important resource for genetic improvement of rice. In this study, we identify and validate a novel QTL on chromosome 7 affecting the grain size and weight using introgression lines from cross of Oryza sativa and Oryza minuta.ResultsAn introgression line 'IL188' has been achieved from a wild species Oryza minuta (2n = 48, BBCC, W303) into O. sativa japonica Nipponbare. The F2 and F2:3 populations derived from a cross between IL188 and Nipponbare were used to map QTLs for five grain size traits, including grain length (GL), grain width (GW), grain length to width ratio (LWR), grain thickness (GT) and thousand grain weight (TGW). A total of 12 QTLs for the five grain traits were identified on chromosomes 1, 2, 3, 6, 7, and 8. The QTL-qGL7 controlling GL on chromosome 7 was detected stably in the F2 and F2:3 populations, and explained 15.09-16.30% of the phenotypic variance. To validate the effect of qGL7, eight residual heterozygous line (RHL) populations were developed through selfing four F2:3 and four F2:4 plants with different heterozygous segments for the target region. By further developing SSR and Indel markers in the target interval, qGL7 was delimited to a ~ 261 kb region between Indel marker Y7-12 and SSR marker Y7-38, which also showed significant effects on grain width and thousand grain weight. Comparing with the reference genome of Nipponbare, stop or frameshift mutations in the exon of the three putative genes LOC_Os07g36830, LOC_Os07g36900 and LOC_Os07g36910 encoding F-box domain-containing proteins may be the candidate genes for qGL7. Scanning electron microscopy analysis of the glume's epidermal cells showed that the cell length and width of NIL-qGL7IL188 was higher than NIL-qGL7Nip, indicating that qGL7 increases grain size and weight by regulating cell expansion.ConclusionsIn this study, we detected 12 QTLs regulating grain size and weight using an introgression line from a cross between Oryza sativa and Oryza minuta. Of these loci, we confirmed and delimited the qGL7 to a ~ 261 kb region. Three putative genes, LOC_Os07g36830, LOC_Os07g36900 and LOC_Os07g36910 encoding F-box domain-containing proteins may be the candidate genes for qGL7. These results provide a basis for map-based cloning of the qGL7 gene and useful information for marker assisted selection in rice grain quality improvement.

Project description:Rice is an important grain that is the staple food for most of the world's population. Drought is one of the major stresses that negatively affects rice yield. The nature of drought tolerance in rice is complex as it is determined by various components and has low heritability. Therefore, to ensure success in breeding programs for drought tolerant rice, QTLs (quantitative trait loci) of interest must be stable in a variety of plant genotypes and environments. This study identified stable QTLs in rice chromosomes in a variety of backgrounds and environments and conducted a meta-QTL analysis of stable QTLs that have been reported by previous research for use in breeding programs. A total of 653 QTLs for drought tolerance in rice from 27 genetic maps were recorded for analysis. The QTLs recorded were related to 13 traits in rice that respond to drought. Through the use of BioMercartor V4.2, a consensus map containing QTLs and molecular markers were generated using 27 genetic maps that were extracted from the previous 20 studies and meta-QTL analysis was conducted on the consensus map. A total of 70 MQTLs were identified and a total of 453 QTLs were mapped into the meta-QTL areas. Five meta-QTLs from chromosome 1 (MQTL 1.5 and MQTL 1.6), chromosome 2 (MQTL2.1 and MQTL 2.2) and chromosome 3 (MQTL 3.1) were selected for functional annotation as these regions have high number of QTLs and include many traits in rice that respond to drought. A number of genes in MQTL1.5 (268 genes), MQTL1.6 (640 genes), MQTL 2.1 (319 genes), MQTL 2.2 (19 genes) and MQTL 3.1 (787 genes) were annotated through Blast2GO. Few major proteins that respond to drought stress were identified in the meta-QTL areas which are Abscisic Acid-Insensitive Protein 5 (ABI5), the G-box binding factor 4 (GBF4), protein kinase PINOID (PID), histidine kinase 2 (AHK2), protein related to autophagy 18A (ATG18A), mitochondrial transcription termination factor (MTERF), aquaporin PIP 1-2, protein detoxification 48 (DTX48) and inositol-tetrakisphosphate 1-kinase 2 (ITPK2). These proteins are regulatory proteins involved in the regulation of signal transduction and gene expression that respond to drought stress. The meta-QTLs derived from this study and the genes that have been identified can be used effectively in molecular breeding and in genetic engineering for drought resistance/tolerance in rice.

Dataset Information

Molecular Breeding for Improving Productivity of Oryza sativa L. cv. Pusa 44 under Reproductive Stage Drought Stress through Introgression of a Major QTL, qDTY12.1.

Publications

Molecular Breeding for Improving Productivity of <i>Oryza sativa</i> L. cv. Pusa 44 under Reproductive Stage Drought Stress through Introgression of a Major QTL, <i>qDTY12.1</i>.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets