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Professor Jayashankar Telangana State Agricultural University, Hyderabad (Telangana State)

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2021 - 20223
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Thesis Type: Ph.D × Author: SANII LANAH ×
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    ThesisItemOpen 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, S
    Rice 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
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    ThesisItemMetadata 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, S
    Rice 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
  • Thumbnail Image
    ThesisItemOpen 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, S
    Rice 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