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Professor Jayashankar Telangana State Agricultural University, Hyderabad (Telangana State)
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ThesisItem Open Access MOLECULAR MAPPING FOR YELLOW MOSAIC VIRUS RESISTANCE IN BLACKGRAM (Vigna mungo (L.) HEPPER)(PROFFESSOR JAYASHANKAR TELANGANA STATE AGRICULTURAL UNIVERSITY, 2022-12-19) Rambabu, E.; Anuradha, Ch.Blackgram (Vigna mungo (L). Hepper) (2n=22) is one of the most highly valuable pulse crop, cultivated in almost all parts of India. It is a good source of easily digestible proteins, carbohydrates and other nutritional factors. Beside different biotic and abiotic constraints, viral diseases mostly yellow mosaic disease is the prime threat for massive economic loss in areas of production. The Yellow Mosaic disease (YMD) caused by Yellow Mosaic Virus (YMV), a Gemini virus transmitted by whitefly (Bemesia tabaci Genn) is one of the most downfall disease that has the ability to cause yield loss upto 85%. The advancements in the field of biotechnology and molecular biology such as QTL mapping, marker-assisted selection and genetic transformation can be utilized in developing MYMV resistance urad beans. Improved resistance to Mungbean Yellow Mosaic Virus (MYMV) is now the major goal for breeding programs in blackgram. Hence, identification of quantitative trait loci (QTLs) for disease resistance followed by marker-assisted selection (MAS) is highly useful for genetic improvement of crops. With this background, the present study was made for molecular mapping of yellow mosaic virus resistance in blackgram. In the present investigation a mapping population (F2) was developed by crossing the MBG 207 (susceptible parent for YMV) and PU 31 (resistant parent for YMV). Sowing of parental material and crossing was performed during the season Kharif 2017 at Institute of Biotechnology (IBT), College of Agriculture, PJTSAU, Rajendranagar, Hyderabad, Telangana. True F1 plants were confirmed by polymorphic markers and F2 mapping population was developed from the progeny produced by selfing Fl individuals. F2 population was advanced to produce F2:3 (RIL) population. Phenotyping for various morphological traits like plant height, number of branches per plant, number of clusters per plant, pod length, number of pods per plant, number of seeds per plant, days to 50% flowering, days to maturity, 100 seed weight and single plant yield and for YMV resistance was carried out during Rabi 2018-19 at Agriculture Research Station (ARS), Madhira, Khammam, PJTSAU, Telangana which is a hotspot. Genetic parameters like mean, range, variance, heritability and genetic advance as percent of mean were studied. Results of variability parameters revealed that the extent of PCV was higher than GCV for most of the traits studied indicated that the expression of the trait is considerably influenced by the environment. Screening of mapping population along with parents was done and disease scoring was given for each line by following the disease scale of 0 (highly resistant) to 5 (highly susceptible). The disease scoring was recorded from initial flowering to harvesting at weekly intervals. A total of 413 simple sequence repeats (SSR’s) markers were used for parental polymorphic survey between the parents and each marker is optimized for its annealing temperature using a gradient PCR. A total of 22 (5.32%) SSR markers only have shown the parental polymorphism. These 22 polymorphic SSR markers were used for genotyping of 177 F2 individuals and their allelic pattern was recorded. The linkage map consisted of 11 linkage groups (LGs) with a total genetic distance of 707.05 cM with an average marker interval of 32.13 cM between the markers using the Kosambi mapping function. The longest linkage group was LG3 (243.94) and the shortest were LG1, LG4, LG6, LG7, LG8, and LG11 (0.0). The maximum and minimum number of markers per linkage group was 5 for LG3 and one for LG1, LG4, LG6, LG7, LG8, and LG11. QTL analysis was performed using QTL IciMapping software 4.1 at a LOD threshold of 3.0 and significance level of 0.01 to detect the QTLs for YMV resistance as well as for yield related traits using inclusive composite interval mapping (ICIM) approach. A total of two major QTLs governing YMV resistance were identified on the linkage group 3. One QTL, qYMV_3-1 is flanked by the marker interval of CEDG305 and CEDG010 with a phenotypic variance (PVE %) of 18.749 and a LOD score of 4.14 and another QTL, qYMV_3-2 is flanked by the marker interval of VR155 and VR0169 with a PVE of 11.181 and a LOD value of 3.19. There are five different major QTLs detected for various morphological traits like number of pods per plant (NP), days to maturity (M), and 100-seed weight (100- SW). For number of pods per plant (NP), two QTLs namely qNP_3-1 on chromosome 3 and qNP_10-1 on chromosome 10 were identified. The qNP_3-1 QTL is flanked by CEDG305 and CEDG010 with a PVE of 14.17% and LOD score of 3.23 and one more QTL, qNP_10-1 is flanked by CEDG021 and VR147 with a PVE and LOD score of 13.61% and 3.78 respectively. For days to maturity (M), two QTL were detected on chromosome 2 and 9. The QTL on chromosome 2, qM_2-1 is flanked by the markers CEDG225 and CEDG006 with a PVE of 15.99% and LOD score of 3.04 and another QTL on chromosome 9, qM_9-1 is flanked by the markers DMBSSR059 and CEDG267 with a PVE and LOD value of 16.65% and 3.65 respectively. One QTL, q100-SW_3-1 was identified for 100- seed weight (100-SW) on chromosome 3 which is flanked by the markers VR0169 and CEDG176 with a PVE of 14.74%. Many workers reported that the YMV resistance on other linkage groups except 3, but the present study has mapped the genomic loci for YMV resistance on chromosome 3, which might be a novel. This study has also mapped various QTLs for different yield attributing traits, which can be used in breeding programmes to increase the yield. The identified QTLs for YMV resistance could be used in breeding programmes to introgress the YMV resistance into the high yielding, YMV susceptible cultivars through marker-assisted selection (MAS).ThesisItem Open Access TRANSCRIPTOME PROFILING AND ANALYSIS OF Fe AND Zn RESPONSIVE GENES IN PEARL MILLET (Pennisetum glaucum L.)(PROFESSOR JAYASHANKAR TELANGANA STATE AGRICULTURAL UNIVERSITY, 2022-02-07) ANJALI, C; VANISRI, SMicronutrient malnutrition because of Fe and Zn deficiencies is posing a serious health issue in developing countries worldwide. To combat the micronutrient-based hidden hunger, biofortification is an efficient and cost-effective method to enhance the nutrient contents of crops by breeding techniques. Spearheaded by the HarvestPlus Program of Consultative Group for International Agricultural Research (CGIAR), global crop biofortification research was initiated and focused primarily on seven major staple crops including pearl millet, targeting three important micronutrients (Fe, Zn, and vitamin A). Pearl millet (Pennisetum glaucum L.) is a staple for millions of families in dryland tropics and reported varying concentrations of Fe and Zn in germplasm. Studies on the functional characterization of differentially expressed genes (DEGs) and their dynamic role in Fe and Zn pathways will accelerate the biofortified cultivar development. Transcriptome profiling of leaf and root samples of a pearl millet inbred ICMB 1505 under Fe and Zn stress conditions were carried out wherein ten to twelve days old seedlings were exposed to Fe and Zn stress treatments (–Fe–Zn, –Fe+Zn, +Fe–Zn, and +Fe+Zn) for 12 days. Total RNA was extracted from the treated samples followed by cDNA synthesis, cDNA library preparation, and sequencing of the constructed cDNA libraries. A total of 37,093 DEGs under different combinations of leaf and root stress conditions were identified, of which, 7023 and 9996 DEGs were reported in leaf and root stress conditions, respectively. Among the 11,429 unique DEGs, 8605 were annotated to cellular, biological, molecular functions and 458 DEGs were assigned to 39 pathways. The results revealed the expression of major genes related to mugineic acid pathway, phytohormones, chlorophyll biosynthesis, photosynthesis, and carbohydrate metabolism during Fe and Zn starvation in pearl millet. The analysis reported the expression of nicotianamine synthase (NAS), S-adenosyl L- methionine (SAM) synthetase, 2'-deoxymugineic acid (DMA), 2'- deoxymugineic- acid 2'-dioxygenase (IDS3), FER-like transcription factor genes involved in uptake mechanism and oligopeptide transporter 3 (OPT3), Zn-transporter 3 & 5, Zinc induced facilitator 1 (ZIF1), heavy metal ATPases (HMA) genes for transport of Fe and Zn. The study also discussed the expressional changes of several cellular pathway genes and their regulation in Fe and Zn homeostasis under their deficient conditions. The cross-talks between the Fe and Zn provided information on their dual and opposite regulation of key uptake and transporter genes under deficiency conditions. The identified Fe and Zn homeostasis-related genes from the transcriptomic data were annotated onto the pearl millet genome wherein a high number of genes were distributed on chromosome 3 and a low number of genes were distributed on chromosome 7. The orthologues for the top 42 genes were identified in Oryza sativa, Zea mays and Sorghum bicolor along with their annotations and chromosomal positions in respective crops. Moreover, the orthologues of the uncharacterized genes among the top 42 genes, selected based on gene ontology (GO) terms (involved in Fe and Zn homeostasis) reported being major Fe and Zn uptake (NAS, DMAS) and transporter genes (zinc transporter 4 and 9, YSL transporters) in rice, maize, and sorghum. The orthologues identified in rice, maize, and sorghum aids in the genetic biofortification of nutrient contents in these crops. The gene structures of the identified orthologues were represented which helps in understanding the exon-intron positions and evolutionary changes among the species. The knowledge of the position of the above-identified genes in pearl millet, rice, maize, and sorghum crops can be utilized in the genetic biofortification of these crops for Fe and Zn contents by genetic engineering and other breeding programs. Our results assist in developing Fe and Zn-efficient pearl millet varieties through development of genic-SNPs for Fe and Zn responsive genes and their utilization in an ongoing biofortification breeding program to ameliorate malnutrition in the dryland tropics of South Asia and Sub-Saharan Africa. Micronutrient malnutrition because of Fe and Zn deficiencies is posing a serious health issue in developing countries worldwide. To combat the micronutrient-based hidden hunger, biofortification is an efficient and cost-effective method to enhance the nutrient contents of crops by breeding techniques. Spearheaded by the HarvestPlus Program of Consultative Group for International Agricultural Research (CGIAR), global crop biofortification research was initiated and focused primarily on seven major staple crops including pearl millet, targeting three important micronutrients (Fe, Zn, and vitamin A). Pearl millet (Pennisetum glaucum L.) is a staple for millions of families in dryland tropics and reported varying concentrations of Fe and Zn in germplasm. Studies on the functional characterization of differentially expressed genes (DEGs) and their dynamic role in Fe and Zn pathways will accelerate the biofortified cultivar development. Transcriptome profiling of leaf and root samples of a pearl millet inbred ICMB 1505 under Fe and Zn stress conditions were carried out wherein ten to twelve days old seedlings were exposed to Fe and Zn stress treatments (–Fe–Zn, –Fe+Zn, +Fe–Zn, and +Fe+Zn) for 12 days. Total RNA was extracted from the treated samples followed by cDNA synthesis, cDNA library preparation, and sequencing of the constructed cDNA libraries. A total of 37,093 DEGs under different combinations of leaf and root stress conditions were identified, of which, 7023 and 9996 DEGs were reported in leaf and root stress conditions, respectively. Among the 11,429 unique DEGs, 8605 were annotated to cellular, biological, molecular functions and 458 DEGs were assigned to 39 pathways. The results revealed the expression of major genes related to mugineic acid pathway, phytohormones, chlorophyll biosynthesis, photosynthesis, and carbohydrate metabolism during Fe and Zn starvation in pearl millet. The analysis reported the expression of nicotianamine synthase (NAS), S-adenosyl L- methionine (SAM) synthetase, 2'-deoxymugineic acid (DMA), 2'- deoxymugineic- acid 2'-dioxygenase (IDS3), FER-like transcription factor genes involved in uptake mechanism and oligopeptide transporter 3 (OPT3), Zn-transporter 3 & 5, Zinc induced facilitator 1 (ZIF1), heavy metal ATPases (HMA) genes for transport of Fe and Zn. The study also discussed the expressional changes of several cellular pathway genes and their regulation in Fe and Zn homeostasis under their deficient conditions. The cross-talks between the Fe and Zn provided information on their dual and opposite regulation of key uptake and transporter genes under deficiency conditions. The identified Fe and Zn homeostasis-related genes from the transcriptomic data were annotated onto the pearl millet genome wherein a high number of genes were distributed on chromosome 3 and a low number of genes were distributed on chromosome 7. The orthologues for the top 42 genes were identified in Oryza sativa, Zea mays and Sorghum bicolor along with their annotations and chromosomal positions in respective crops. Moreover, the orthologues of the uncharacterized genes among the top 42 genes, selected based on gene ontology (GO) terms (involved in Fe and Zn homeostasis) reported being major Fe and Zn uptake (NAS, DMAS) and transporter genes (zinc transporter 4 and 9, YSL transporters) in rice, maize, and sorghum. The orthologues identified in rice, maize, and sorghum aids in the genetic biofortification of nutrient contents in these crops. The gene structures of the identified orthologues were represented which helps in understanding the exon-intron positions and evolutionary changes among the species. The knowledge of the position of the above-identified genes in pearl millet, rice, maize, and sorghum crops can be utilized in the genetic biofortification of these crops for Fe and Zn contents by genetic engineering and other breeding programs. Our results assist in developing Fe and Zn-efficient pearl millet varieties through development of genic-SNPs for Fe and Zn responsive genes and their utilization in an ongoing biofortification breeding program to ameliorate malnutrition in the dryland tropics of South Asia and Sub-Saharan Africa.ThesisItem Open Access IDENTIFICATION OF QTLs FOR SEEDLING COLD TOLERANCE AND YIELD ATTRIBUTES IN RICE (Oryza sativa L.) THROUGH ASSOCIATION ANALYSIS(PROFESSOR JAYASHANKAR TELANGANA STATE AGRICULTURE UNIVERSITY, 2021-01-01) SANII LANAH; VANISRI, SRice is a temperature-sensitive crop and its production is severely affected by low temperatures in temperate and sub-tropical regions. In northern districts of Telangana state, rice is usually planted when the minimum air temperatures are below 150C particularly in rabi season. This temperature is lower than the optimum temperature needed for normal rice plant growth. Cold stress during the seedling stage and early establishment stage at the main field induces severe seedling mortality that increases the cost of cultivation and delays crop establishment and ultimately entails low yield. To understand the genetic basis of seedling cold tolerance in rice, we evaluated one hundred and two MAGIC indica lines along with six checks both in laboratory and field conditions. Significant variations in shoot length, root length, seedling height, seedling survival rate, cold stress tolerance index and chlorophyll content were observed among the genotypes. Genome-wide association analysis using GBS based SNP markers was performed with R based GAPIT software to identify the marker-trait associations for the rice seedling cold tolerance traits. After strict filtering of raw SNPs based on reading depth and minor allele frequency (MAF > 0.05), a final set of 27,041 SNPs were obtained, with an average of 2253 SNP markers per chromosome. GWAS using MLM analysis identified one hundred and three SNPs at P<0.0001 viz., two SNPs on chromosome 11 for leaf chlorosis, two SNPs on chromosome 7 for shoot length, two SNPs at CSI on chromosome 3 and one SNP on chromosome 2 for root length. Fourteen SNPs on chromosome 11 and two SNPs on chromosome 5 for seedling length, four SNPs on chromosome 11, two SNPs on chromosome 3 at CSI and three SNPs on chromosome 9 for cold stress tolerance index. Two SNPs at CSI on chromosome 11, three SNPs on chromosome 2 and one SNP on chromosome 5, forty-seven SNPs at on chromosome 8, fourteen SNPs and four SNPs on chromosome 5 for chlorophyll content under controlled cold stress conditions. In field stress conditions, thirty-six SNPs viz., one SNP on chromosome 11, two SNPs on chromosome 7 for shoot length, one SNP on chromosome 2 for root length, two SNPs on chromosome 5 for seedling length, twenty-two SNPs on chromosome 5 and eight SNPs on chromosome 5 for chlorophyll content. Further, two SNPs on chromosome 3, thirty-nine SNPs on chromosome 8, twelve SNPs on chromosome 11 have a pleiotropic effect on leaf chlorosis, seedling length, root length, shoot length, cold stress tolerance index and chlorophyll content under controlled cold stress conditions. The genetic diversity analysis using 135 polymorphic SSR markers amplified 699 alleles in 108 rice accessions with the mean major allele frequency, gene diversity and polymorphic information content of 0.5031, 0.6015 and 0.546 respectively. The phylogenetic tree was constructed using Unweighted Pair Group Method with Arithmetic Mean (UPGMA) cluster analysis based on Nei’s genetic distance which showed clustering of MAGIC indica lines into four distinct clusters with sub-clusters in each cluster. Based on an Evano test, the log likelihood revealed by structure showed the optimum value as 7 (K = 7), indicating that the entire 102 MAGIC indica lines and 6 checks were grouped into seven subgroups. Genome-wide association analysis using TASSEL identified a total of 627 and 159 marker-trait associations with GLM and MLM approaches, respectively at the different significance levels (P<0.05 and P<0.01). A false discovery per centage of 74.64 was observed for GLM compared to MLM approach. A total of 50 SSR marker trait association were found novel for rice seedling cold tolerance traits. Also, by comparative studies between CS, CN and FS, 8 markers viz., RM428, RM273, RM190, RM6356, RM25, RM310, RM72, RM271 were repeatedly found associated with different cold tolerance traits at CS and CN. Six markers viz., RM6365, RM11, RM6356, RM284, RM271, RM202 under FS and CN; four markers viz., RM507, RM6356, RM271, RM167 both under FS and CS and two markers viz., RM6356, RM271 under CS, CN and FS, were consistently identified for seedling cold tolerance related traits. Also, the marker-trait associations detected using SNPs were compared with those identified using SSR marker with their physical positions. A total of 45 marker-trait associations were common in both the analysis. Thus, the identified consistent markers for rice seedling cold tolerance can be further used in marker assisted breeding for seedling cold tolerance in rice after thorough validation in biparental populations developed for seedling cold tolerance in rice. To identify the main genetic determinants governing yield traits, a total of 210 rice accessions were evaluated under irrigated conditions during kharif-2017 and kharif 2018. The phenotypic data was recorded for nine yield and yield-related characters. The pooled mean data (kharif 2017 & 2018) of days to 50 per cent flowering, plant height (cm), number of productive tillers per plant, panicle length (cm), number of filled grains per panicle, number of grains per panicle, 1000 grain weight (g), spikelet fertility (%) and grain yield per plant (g) for kharif 2017 & 2018 were found to be 104.18, 88.14cm, 12.39, 22.47cm, 133.30, 153.22, 23.31g, 86.56% and 26.89g respectively. Among the 210 rice accessions studied, 45 genotypes had shown superior yield performance over the local check variety. Analysis of variance of two season pooled data (kharif 2017 & 2018) showed highly significant differences among the genotypes for most of the traits studied. Studies of genetic variability revealed high PCV, GCV, heritability and genetic advance as per cent of the mean for grain yield per plant (g), number of filled grains, number of grains per plant and number of productive tillers, indicating the predominance of additive gene action in the inheritance of these characters. Detection of minor differences between GCV and PCV for all the characters indicated less influence of the environment on these characters. Marker-trait associations were identified using SSR markers in 210 rice accessions through GWAS. Out of 100 SSR markers used 76 were polymorphic with the mean major allele frequency, gene diversity and polymorphic information content of 0.4526, 0.6708 and 0.6232 respectively. Based on an Evano test using STRUCTURE analysis, maximum of Adhoc measure ΔK = 7, the entire 102 MAGIC indica lines, 106 Indian rice accessions and 4 checks were grouped into seven populations. Genome-wide association analysis using TASSEL identified a total of 229 and 75 marker-trait associations with GLM and MLM approaches, respectively at different significance levels (P<0.05 and P<0.01). A false discovery per centage of 67.25 was observed for GLM compared to MLM approach. Thirty-nine and 36 marker-trait associations during kharif-2017 and 2018 respectively, were identified. The markers viz., Gn1a-indel1, RM1, RM237, RM315 on chromosome 1; RM555 on chromosome 2; RM231, RM7, RM570 on chromosome 3; RM261 on chromosome 4; RM289 on chromosome 5; RM190, RM510, RM111, RM276, RM3 on chromosome 6; RM346 on chromosome 7; RM531 on chromosome 8; HvSSR10-34 on chromosome 10; RM224 on chromosome 11; GS5-03SNP, HD170007, RM511, RM277, RM260, RM17 on chromosome 10 were repeatedly associated across the years (kharif 2017 and 2018) with different yield attributes. Some of these markers were earlier identified to be linked with yield traits in rice through conventional QTL mapping efforts. These genomic regions may be potential candidates for application in marker-assisted breeding to develop resilient rice cultivars suitable for developing high yielding varieties after thorough validation and fine mapping. The results suggest that association mapping may be a viable alternative to conventional QTL mapping in detecting genomic regions associated with yield related traits using diverse germplasm inThesisItem Metadata only IDENTIFICATION OF QTLs FOR SEEDLING COLD TOLERANCE AND YIELD ATTRIBUTES IN RICE (Oryza sativa L.) THROUGH ASSOCIATION ANALYSIS(PROFESSOR JAYASHANKAR TELANGANA STATE AGRICULTURE UNIVERSITY, 2021-01-01) SANII LANAH; VANISRI, SRice is a temperature-sensitive crop and its production is severely affected by low temperatures in temperate and sub-tropical regions. In northern districts of Telangana state, rice is usually planted when the minimum air temperatures are below 150C particularly in rabi season. This temperature is lower than the optimum temperature needed for normal rice plant growth. Cold stress during the seedling stage and early establishment stage at the main field induces severe seedling mortality that increases the cost of cultivation and delays crop establishment and ultimately entails low yield. To understand the genetic basis of seedling cold tolerance in rice, we evaluated one hundred and two MAGIC indica lines along with six checks both in laboratory and field conditions. Significant variations in shoot length, root length, seedling height, seedling survival rate, cold stress tolerance index and chlorophyll content were observed among the genotypes. Genome-wide association analysis using GBS based SNP markers was performed with R based GAPIT software to identify the marker-trait associations for the rice seedling cold tolerance traits. After strict filtering of raw SNPs based on reading depth and minor allele frequency (MAF > 0.05), a final set of 27,041 SNPs were obtained, with an average of 2253 SNP markers per chromosome. GWAS using MLM analysis identified one hundred and three SNPs at P<0.0001 viz., two SNPs on chromosome 11 for leaf chlorosis, two SNPs on chromosome 7 for shoot length, two SNPs at CSI on chromosome 3 and one SNP on chromosome 2 for root length. Fourteen SNPs on chromosome 11 and two SNPs on chromosome 5 for seedling length, four SNPs on chromosome 11, two SNPs on chromosome 3 at CSI and three SNPs on chromosome 9 for cold stress tolerance index. Two SNPs at CSI on chromosome 11, three SNPs on chromosome 2 and one SNP on chromosome 5, forty-seven SNPs at on chromosome 8, fourteen SNPs and four SNPs on chromosome 5 for chlorophyll content under controlled cold stress conditions. In field stress conditions, thirty-six SNPs viz., one SNP on chromosome 11, two SNPs on chromosome 7 for shoot length, one SNP on chromosome 2 for root length, two SNPs on chromosome 5 for seedling length, twenty-two SNPs on chromosome 5 and eight SNPs on chromosome 5 for chlorophyll content. Further, two SNPs on chromosome 3, thirty-nine SNPs on chromosome 8, twelve SNPs on chromosome 11 have a pleiotropic effect on leaf chlorosis, seedling length, root length, shoot length, cold stress tolerance index and chlorophyll content under controlled cold stress conditions. The genetic diversity analysis using 135 polymorphic SSR markers amplified 699 alleles in 108 rice accessions with the mean major allele frequency, gene diversity and polymorphic information content of 0.5031, 0.6015 and 0.546 respectively. The phylogenetic tree was constructed using Unweighted Pair Group Method with Arithmetic Mean (UPGMA) cluster analysis based on Nei’s genetic distance which showed clustering of MAGIC indica lines into four distinct clusters with sub-clusters in each cluster. Based on an Evano test, the log likelihood revealed by structure showed the optimum value as 7 (K = 7), indicating that the entire 102 MAGIC indica lines and 6 checks were grouped into seven subgroups. Genome-wide association analysis using TASSEL identified a total of 627 and 159 marker-trait associations with GLM and MLM approaches, respectively at the different significance levels (P<0.05 and P<0.01). A false discovery per centage of 74.64 was observed for GLM compared to MLM approach. A total of 50 SSR marker trait association were found novel for rice seedling cold tolerance traits. Also, by comparative studies between CS, CN and FS, 8 markers viz., RM428, RM273, RM190, RM6356, RM25, RM310, RM72, RM271 were repeatedly found associated with different cold tolerance traits at CS and CN. Six markers viz., RM6365, RM11, RM6356, RM284, RM271, RM202 under FS and CN; four markers viz., RM507, RM6356, RM271, RM167 both under FS and CS and two markers viz., RM6356, RM271 under CS, CN and FS, were consistently identified for seedling cold tolerance related traits. Also, the marker-trait associations detected using SNPs were compared with those identified using SSR marker with their physical positions. A total of 45 marker-trait associations were common in both the analysis. Thus, the identified consistent markers for rice seedling cold tolerance can be further used in marker assisted breeding for seedling cold tolerance in rice after thorough validation in biparental populations developed for seedling cold tolerance in rice. To identify the main genetic determinants governing yield traits, a total of 210 rice accessions were evaluated under irrigated conditions during kharif-2017 and kharif 2018. The phenotypic data was recorded for nine yield and yield-related characters. The pooled mean data (kharif 2017 & 2018) of days to 50 per cent flowering, plant height (cm), number of productive tillers per plant, panicle length (cm), number of filled grains per panicle, number of grains per panicle, 1000 grain weight (g), spikelet fertility (%) and grain yield per plant (g) for kharif 2017 & 2018 were found to be 104.18, 88.14cm, 12.39, 22.47cm, 133.30, 153.22, 23.31g, 86.56% and 26.89g respectively. Among the 210 rice accessions studied, 45 genotypes had shown superior yield performance over the local check variety. Analysis of variance of two season pooled data (kharif 2017 & 2018) showed highly significant differences among the genotypes for most of the traits studied. Studies of genetic variability revealed high PCV, GCV, heritability and genetic advance as per cent of the mean for grain yield per plant (g), number of filled grains, number of grains per plant and number of productive tillers, indicating the predominance of additive gene action in the inheritance of these characters. Detection of minor differences between GCV and PCV for all the characters indicated less influence of the environment on these characters. Marker-trait associations were identified using SSR markers in 210 rice accessions through GWAS. Out of 100 SSR markers used 76 were polymorphic with the mean major allele frequency, gene diversity and polymorphic information content of 0.4526, 0.6708 and 0.6232 respectively. Based on an Evano test using STRUCTURE analysis, maximum of Adhoc measure ΔK = 7, the entire 102 MAGIC indica lines, 106 Indian rice accessions and 4 checks were grouped into seven populations. Genome-wide association analysis using TASSEL identified a total of 229 and 75 marker-trait associations with GLM and MLM approaches, respectively at different significance levels (P<0.05 and P<0.01). A false discovery per centage of 67.25 was observed for GLM compared to MLM approach. Thirty-nine and 36 marker-trait associations during kharif-2017 and 2018 respectively, were identified. The markers viz., Gn1a-indel1, RM1, RM237, RM315 on chromosome 1; RM555 on chromosome 2; RM231, RM7, RM570 on chromosome 3; RM261 on chromosome 4; RM289 on chromosome 5; RM190, RM510, RM111, RM276, RM3 on chromosome 6; RM346 on chromosome 7; RM531 on chromosome 8; HvSSR10-34 on chromosome 10; RM224 on chromosome 11; GS5-03SNP, HD170007, RM511, RM277, RM260, RM17 on chromosome 10 were repeatedly associated across the years (kharif 2017 and 2018) with different yield attributes. Some of these markers were earlier identified to be linked with yield traits in rice through conventional QTL mapping efforts. These genomic regions may be potential candidates for application in marker-assisted breeding to develop resilient rice cultivars suitable for developing high yielding varieties after thorough validation and fine mapping. The results suggest that association mapping may be a viable alternative to conventional QTL mapping in detecting genomic regions associated with yield related traits using diverse germplasm in riceThesisItem Open Access IDENTIFICATION OF QTLs FOR SEEDLING COLD TOLERANCE AND YIELD ATTRIBUTES IN RICE (Oryza sativa L.) THROUGH ASSOCIATION ANALYSIS(PROFESSOR JAYASHANKAR TELANGANA STATE AGRICULTURAL UNIVERSITY, 2022-11-06) SANII LANAH; VANISRI, SRice is a temperature-sensitive crop and its production is severely affected by low temperatures in temperate and sub-tropical regions. In northern districts of Telangana state, rice is usually planted when the minimum air temperatures are below 150C particularly in rabi season. This temperature is lower than the optimum temperature needed for normal rice plant growth. Cold stress during the seedling stage and early establishment stage at the main field induces severe seedling mortality that increases the cost of cultivation and delays crop establishment and ultimately entails low yield. To understand the genetic basis of seedling cold tolerance in rice, we evaluated one hundred and two MAGIC indica lines along with six checks both in laboratory and field conditions. Significant variations in shoot length, root length, seedling height, seedling survival rate, cold stress tolerance index and chlorophyll content were observed among the genotypes. Genome-wide association analysis using GBS based SNP markers was performed with R based GAPIT software to identify the marker-trait associations for the rice seedling cold tolerance traits. After strict filtering of raw SNPs based on reading depth and minor allele frequency (MAF > 0.05), a final set of 27,041 SNPs were obtained, with an average of 2253 SNP markers per chromosome. GWAS using MLM analysis identified one hundred and three SNPs at P<0.0001 viz., two SNPs on chromosome 11 for leaf chlorosis, two SNPs on chromosome 7 for shoot length, two SNPs at CSI on chromosome 3 and one SNP on chromosome 2 for root length. Fourteen SNPs on chromosome 11 and two SNPs on chromosome 5 for seedling length, four SNPs on chromosome 11, two SNPs on chromosome 3 at CSI and three SNPs on chromosome 9 for cold stress tolerance index. Two SNPs at CSI on chromosome 11, three SNPs on chromosome 2 and one SNP on chromosome 5, forty-seven SNPs at on chromosome 8, fourteen SNPs and four SNPs on chromosome 5 for chlorophyll content under controlled cold stress conditions. In field stress conditions, thirty-six SNPs viz., one SNP on chromosome 11, two SNPs on chromosome 7 for shoot length, one SNP on chromosome 2 for root length, two SNPs on chromosome 5 for seedling length, twenty-two SNPs on chromosome 5 and eight SNPs on chromosome 5 for chlorophyll content. Further, two SNPs on chromosome 3, thirty-nine SNPs on chromosome 8, twelve SNPs on chromosome 11 have a pleiotropic effect on leaf chlorosis, seedling length, root length, shoot length, cold stress tolerance index and chlorophyll content under controlled cold stress conditions. The genetic diversity analysis using 135 polymorphic SSR markers amplified 699 alleles in 108 rice accessions with the mean major allele frequency, gene diversity and polymorphic information content of 0.5031, 0.6015 and 0.546 respectively. The phylogenetic tree was constructed using Unweighted Pair Group Method with Arithmetic Mean (UPGMA) cluster analysis based on Nei’s genetic distance which showed clustering of MAGIC indica lines into four distinct clusters with sub-clusters in each cluster. Based on an Evano test, the log likelihood revealed by structure showed the optimum value as 7 (K = 7), indicating that the entire 102 MAGIC indica lines and 6 checks were grouped into seven subgroups. Genome-wide association analysis using TASSEL identified a total of 627 and 159 marker-trait associations with GLM and MLM approaches, respectively at the different significance levels (P<0.05 and P<0.01). A false discovery per centage of 74.64 was observed for GLM compared to MLM approach. A total of 50 SSR marker-trait association were found novel for rice seedling cold tolerance traits. Also, by comparative studies between CS, CN and FS, 8 markers viz., RM428, RM273, RM190, RM6356, RM25, RM310, RM72, RM271 were repeatedly found associated with different cold tolerance traits at CS and CN. Six markers viz., RM6365, RM11, RM6356, RM284, RM271, RM202 under FS and CN; four markers viz., RM507, RM6356, RM271, RM167 both under FS and CS and two markers viz., RM6356, RM271 under CS, CN and FS, were consistently identified for seedling cold tolerance related traits. Also, the marker-trait associations detected using SNPs were compared with those identified using SSR marker with their physical positions. A total of 45 marker-trait associations were common in both the analysis. Thus, the identified consistent markers for rice seedling cold tolerance can be further used in marker-assisted breeding for seedling cold tolerance in rice after thorough validation in biparental populations developed for seedling cold tolerance in rice. To identify the main genetic determinants governing yield traits, a total of 210 rice accessions were evaluated under irrigated conditions during kharif-2017 and kharif-2018. The phenotypic data was recorded for nine yield and yield-related characters. The pooled mean data (kharif 2017 & 2018) of days to 50 per cent flowering, plant height (cm), number of productive tillers per plant, panicle length (cm), number of filled grains per panicle, number of grains per panicle, 1000 grain weight (g), spikelet fertility (%) and grain yield per plant (g) for kharif 2017 & 2018 were found to be 104.18, 88.14cm, 12.39, 22.47cm, 133.30, 153.22, 23.31g, 86.56% and 26.89g respectively. Among the 210 rice accessions studied, 45 genotypes had shown superior yield performance over the local check variety. Analysis of variance of two season pooled data (kharif 2017 & 2018) showed highly significant differences among the genotypes for most of the traits studied. Studies of genetic variability revealed high PCV, GCV, heritability and genetic advance as per cent of the mean for grain yield per plant (g), number of filled grains, number of grains per plant and number of productive tillers, indicating the predominance of additive gene action in the inheritance of these characters. Detection of minor differences between GCV and PCV for all the characters indicated less influence of the environment on these characters. Marker-trait associations were identified using SSR markers in 210 rice accessions through GWAS. Out of 100 SSR markers used 76 were polymorphic with the mean major allele frequency, gene diversity and polymorphic information content of 0.4526, 0.6708 and 0.6232 respectively. Based on an Evano test using STRUCTURE analysis, maximum of Adhoc measure ΔK = 7, the entire 102 MAGIC indica lines, 106 Indian rice accessions and 4 checks were grouped into seven populations. Genome-wide association analysis using TASSEL identified a total of 229 and 75 marker-trait associations with GLM and MLM approaches, respectively at different significance levels (P<0.05 and P<0.01). A false discovery per centage of 67.25 was observed for GLM compared to MLM approach. Thirty-nine and 36 marker-trait associations during kharif-2017 and 2018 respectively, were identified. The markers viz., Gn1a-indel1, RM1, RM237, RM315 on chromosome 1; RM555 on chromosome 2; RM231, RM7, RM570 on chromosome 3; RM261 on chromosome 4; RM289 on chromosome 5; RM190, RM510, RM111, RM276, RM3 on chromosome 6; RM346 on chromosome 7; RM531 on chromosome 8; HvSSR10-34 on chromosome 10; RM224 on chromosome 11; GS5-03SNP, HD170007, RM511, RM277, RM260, RM17 on chromosome 10 were repeatedly associated across the years (kharif 2017 and 2018) with different yield attributes. Some of these markers were earlier identified to be linked with yield traits in rice through conventional QTL mapping efforts. These genomic regions may be potential candidates for application in marker-assisted breeding to develop resilient rice cultivars suitable for developing high yielding varieties after thorough validation and fine mapping. The results suggest that association mapping may be a viable alternative to conventional QTL mapping in detecting genomic regions associated with yield-related traits using diverse germplasm in rice
