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

As a pivotal crop in cereal, rice provides the staple food for more than 50% of the world’s population. It supplies 23 per cent of global per capita energy and 16 per cent of protein. The consumption of rice is declining in developing countries because of its own limitation viz., low protein, fat and micronutrients especially Iron and Zinc. Globally, rice is grown on about 150 m ha and Asian countries account for 90 percent of its area. India ranks first in area (44.8 m ha) and second in production (90 mt) among rice producing countries, in terms of productivity India ranks 9 th (Anonymous, 2007). Grain Protein content (GPC) is the macro nutrients essential for building up the human body. They are called macro nutrients because they form the bulk of the food. Many proteins are enzymes that catalyze biochemical reactions and are vital to metabolism. Proteins also have structural or mechanical functions, such as actin and myosin in muscle and the proteins in the cytoskeleton, which form a system of scaffolding that maintains cell shape.

/pdf/qualitative-and-quantitative-genetic-variations-in-the-f2-2iq9d27d9k.pdf

Qualitative and Quantitative Genetic Variations in the F2 Inter Varietal Cross of Rice (Oryza sativa L.) under Aerobic Condition and Parental Polymorphism Survey

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

In the present study, F2 segregating population was developed from the crosses between the parental lines K343 and DHMAS rice during Kharif 2016 and evaluated for statistical description, genetic variability components i.e. GCV & PCV, heritability (broad sense) and genetic advance of various agronomical traits. The experiment was conducted using augmented design-I, analysis of variance (ANOVA) revealed statistically significant differences (p<0.05) indicating the existence of genetic variability amongst the population. The mean value and range in days to maturity, duration of grain filling and spikelet fertility % age was greater than that of other traits. The values recorded for genotypic variance was less than those of the phenotypic variance. In the present study spikelet density (7.24) and yield per plant (7.02) showed the highest GCV values whereas days to maturity showed the lowest GCV value (2.61). The low GCV and PCV values, indicates that trait expression was less influenced by the environment. Heritability was estimated from medium to high ranging from 56.00 to 97.00 percent for all the traits, except duration of grain filling which showed the heritability estimate of 25 per cent only. Heritability coupled with genetic advance was also recorded for all the given traits in the population. The estimation of these parameters helps the breeder in achieving the required crop improvement by selection.

https://www.phytojournal.com/archives/2018/vol7issue3/PartN/7-2-563-284.pdf

Statistical description, genetic variability, heritability and genetic advance assessment for various agronomical traits in F2 population of rice (Oryza sativa L.)

The experiment was conducted to investigate the parental diversity along the rice genome and to understand and screen out the SSR markers-indicated polymorphism between two indica rice (Oryza sativa L.) cultivars. Namely K343, the most well-liked rice variety in the hill zone of the Jammu Region, and RML22, a rice line created at IRRI, Philippines. The study is to select polymorphic markers (Simple Sequence Repeat- SSR) associated with hill ecologies rice cultivars and additional research projects like gene pyramiding and background selection to recover the recurrent parent genome (RPG) in blast gene introgression in elite lines. 450 SSR markers, evenly distributed throughout the rice genome, were used to assess the parental polymorphism between these genotypes. Of these two cultivars, 51 markers (11.33%) showed polymorphism with bands in different spectrums throughout the genome. The study has been used to Marker Assisted Backcross (MAB) breeding to integrate rice blast resistance genes in the parental genotype. The pool of polymorphic markers has the potential to use in similar studies and work, with a high probability of polymorphism for the cultivars of hill ecologies, and thus increase the chance of selection of probability in marker selection.

Screening microsatellite markers for establishing parental polymorphism in Indian rice (Oryza sativa L.)

In hill rice ecologies, Magnaporthe oryzae causes rice blasts and a significant biotic constraint. The study was aimed to develop rice blast-resistant lines/varieties through marker-assisted selection (MAS) by using the Pi54 gene, which provides resistance against the most prominent blast fungus isolate (PLP-1). The blast resistance gene Pi54 from the indica rice genotype DHMAS has been inserted into the genetic makeup of the temperate rice variety K343 in the current study. Three SSR markers (TRS26, TRS33, and RM206) are closely linked to the Pi54 locus used in this study. Marker RM206, located 0.7cM from the Pi54 gene, distinguished between donor and recipient alleles and co-segregated with the target gene. Thus, RM206 is used for Pi54 gene foreground selection. Sixty-one plants have the homozygous allele for the Pi54 in BC1F2, accounting for roughly 50% of the homozygous population. Polymorphic genome-wide SSR assessment of the BC1F2 genetic stock (K343*2/DHMAS) revealed recurrent parent genome (RPG) recovery above 75% in 4 plants. Therefore, M. oryzae race PLP-1, the dominant race used in this study, showed high resistance to the resistant response after inoculation.

Blast resistance (Pi54) introgression in temperate rice (Oryza sativa L.) K343 using marker assisted backcrossing

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

Statistical description, genetic variability, heritability and genetic advance assessment for various agronomical traits in F2 population of rice (Oryza sativa L.)

Screening microsatellite markers for establishing parental polymorphism in Indian rice (Oryza sativa L.)

Blast resistance (Pi54) introgression in temperate rice (Oryza sativa L.) K343 using marker assisted backcrossing