Empirical threshold values for quantitative trait mapping.

Plant genetic resources (PGRs) are the total hereditary material, which includes all the alleles of various genes, present in a crop species and its wild relatives. They are a major resource that humans depend on to increase farming resilience and profit. Hence, the demand for genetic resources will increase as the world population increases. There is a need to conserve and maintain the genetic diversity of these valuable resources for sustainable food security. Due to environmental changes and genetic erosion, some valuable genetic resources have already become extinct. The landraces, wild relatives, wild species, genetic stock, advanced breeding material, and modern varieties are some of the important plant genetic resources. These diverse resources have contributed to maintaining sustainable biodiversity. New crop varieties with desirable traits have been developed using these resources. Novel genes/alleles linked to the trait of interest are transferred into the commercially cultivated varieties using biotechnological tools. Diversity should be maintained as a genetic resource for the sustainable development of new crop varieties. Additionally, advances in biotechnological tools, such as next-generation sequencing, molecular markers, in vitro culture technology, cryopreservation, and gene banks, help in the precise characterization and conservation of rare and endangered species. Genomic tools help in the identification of quantitative trait loci (QTLs) and novel genes in plants that can be transferred through marker-assisted selection and marker-assisted backcrossing breeding approaches. This article focuses on the recent development in maintaining the diversity of genetic resources, their conservation, and their sustainable utilization to secure global food security.

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Genetic Diversity, Conservation, and Utilization of Plant Genetic Resources

Rice is a major food crop that sustains approximately half of the world population. Recent worldwide improvements in the standard of living have increased the demand for high-quality rice. Accurate identification of quantitative trait loci (QTLs) for rice grain quality traits will facilitate rice quality breeding and improvement. In the present study, we performed high-resolution QTL mapping for rice grain quality traits using a genotyping-by-sequencing approach. An F2 population derived from a cross between an elite japonica variety, Koshihikari, and an indica variety, Nona Bokra, was used to construct a high-density genetic map. A total of 3,830 single nucleotide polymorphism markers were mapped to 12 linkage groups spanning a total length of 2,456.4 cM, with an average genetic distance of 0.82 cM. Seven grain quality traits—the percentage of whole grain, percentage of head rice, percentage of area of head rice, transparency, percentage of chalky rice, percentage of chalkiness area, and degree of chalkiness—of the F2 population were investigated. In total, 15 QTLs with logarithm of the odds (LOD) scores >4 were identified, which mapped to chromosomes 6, 7, and 9. These loci include four QTLs for transparency, four for percentage of chalky rice, four for percentage of chalkiness area, and three for degree of chalkiness, accounting for 0.01%–61.64% of the total phenotypic variation. Of these QTLs, only one overlapped with previously reported QTLs, and the others were novel. By comparing the major QTL regions in the rice genome, several key candidate genes reported to play crucial roles in grain quality traits were identified. These findings will expedite the fine mapping of these QTLs and QTL pyramiding, which will facilitate the genetic improvement of rice grain quality.

High-resolution quantitative trait locus mapping for rice grain quality traits using genotyping by sequencing

Identification of QTLs for Yield and Associated Traits in F2 Population of Rice

Abstract Rice is a staple crop providing a significant portion of the global food supply. It is then crucial to develop strategies for breeding high‐yield cultivars to meet global food security challenges, including the UN's zero‐hunger goal. In this study, QTL mapping was employed to pinpoint key genomic regions linked to traits influencing rice yield, with a focus on panicle structure—a critical determinant of grain number. Over two consecutive years, QTLs were identified using 88 JJ625LG/Namchan Recombinant Inbred Lines (JNRILs), revealing several candidate genes. Notably, Gn1a , a known regulator of grain number, was mapped within qNS1 and qNSSr1‐1 , while the sd1 gene, linked to plant height, was detected across multiple QTLs. Furthermore, a novel gene, OsNSMq3 ( Os03g0843800 ), encoding a methyltransferase, was identified in various QTLs, with haplotype and sequence homology analysis suggesting its role in enhancing yield by influencing panicle structure development. The increase in primary and secondary branches, driven by these genes, leads to a higher number of spikelets per panicle, thereby boosting yield. These findings underscore the potential of candidate genes from stable QTLs as valuable tools in molecular breeding to develop high‐yield rice cultivars, addressing global hunger and aiding food supply in refugee crises. 

<scp>SNP</scp>‐based <scp>QTL</scp> mapping and identification of panicle structure‐related genes in rice

Panicle traits are the most important agronomic characters which directly relate to yield in rice. Panicle length (PL) being one of the major components of rice panicle structure is controlled by quantitative trait loci (QTLs). In our research, conducted at Research Farm of SKUAST-J, crosses of parental lines K343 and DHMAS were made for generating F2 mapping population, which were then transplanted into the field using augmented design-I. The F2 population was used for phenotypic evaluation, development of linkage map and identification of QTLs on the chromosomes by using SSR markers. A total of 450 SSR markers were used for screening both the parents of which 53 highly polymorphic markers were selected and used for genotyping of 233 genotypes of F2population. Linkage map was generated using MAPMAKER/EXP3.0 software, seven linkage groups were found distributed on 11 chromosomes of rice. QTLs were detected using QTL Cartographer (v2.5) software. Based on 1000 permutation tests, a logarithm of odds (LOD) threshold value 2.0 and 3.0 was set. Composite interval mapping was used to map QTLs in populations derived from bi-parental crosses. The phenotypic data, genotypic data and the genetic linkage map generated identified total three QTLs of which one was identified for PL qPL2, located at 85.01 cM position with 2.1 LOD value and in between the marker intervals RM324-RM208, this QTL explained the phenotype variation by 4.36%. The other two QTLs were identified for spikelet density (SD) qSD3.1 and qSD3.2, located at 28.91 and 39.51 cM, respectively, both with a flanking marker RM6832 on chromosome 3. The LOD value and phenotypic variation explained for qSD3.1 and qSD3.2 was 3.00 and 3.25; 9.70 and 12.34% respectively. The reported QTLs identified in the study suggested a less diversity in the parents used and also the rejection of not so useful markers from the used set of markers for PL and SD.

Detection of QTL for panicle architecture in $$\hbox {F}_{2}$$ F 2 population of rice

Breeding and Improvement

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Detection of QTL for panicle architecture in $$\hbox {F}_{2}$$ F 2 population of rice

Breeding and Improvement