GGE biplot analysis to evaluate genotype, environment and their interactions in sorghum multi-location data

Sorghum [Sorghum bicolor (L.) Moench] is a very important crop in the arid and semi-arid tropics of India and African subcontinent. In the process of release of new cultivars using multi-location data major emphasis is being given on the superiority of the new cultivars over the ruling cultivars, while very less importance is being given on the genotype × environment interaction (GEI). In the present study, performance of ten Indian hybrids over 12 locations across the rainy seasons of 2008 and 2009 was investigated using GGE biplot analysis. Location attributed higher proportion of the variation in the data (59.3–89.9%), while genotype contributed only 3.9–16.8% of total variation. Genotype × location interaction contributed 5.8–25.7% of total variation. We could identify superior hybrids for grain yield, fodder yield and for harvest index using biplot graphical approach effectively. Majority of the testing locations were highly correlated. ‘Which-won-where’ study partitioned the testing locations into three mega-environments: first with eight locations with SPH 1606/1609 as the winning genotypes; second mega-environment encompassed three locations with SPH 1596 as the winning genotype, and last mega-environment represented by only one location with SPH 1603 as the winning genotype. This clearly indicates that though the testing is being conducted in many locations, similar conclusions can be drawn from one or two representatives of each mega-environment. We did not observe any correlation of these mega-environments to their geographical locations. Existence of extensive crossover GEI clearly suggests that efforts are necessary to identify location-specific genotypes over multi-year and -location data for release of hybrids and varieties rather focusing on overall performance of the entries

Genetic diversity revealed utility of SSR markers in classifying parental lines and elite genotypes of sorghum (Sorghum bicolor L. Moench)

Abstract Genetic diversity among 82 rainy and post-rainy sorghum genotypes from India was studied using a set of 35 SSR markers distributed across all the linkage groups. A total of 198 alleles were recorded with an average of 5.71 per primer pair. The polymorphism information content (PIC) values ranged from 0.02 (Xisep 0310) to 0.86 (sb5-206) with a mean of 0.49, indicating high discriminating ability of the SSR markers used. Jaccard's similarity coefficients and cluster analysis revealed substantial diversity among the genotypes. Very high estimate of fixation index (F ST = 0.35, P= 0.001) was obtained when genotypes were structured as rainy and post-rainy season adaptation, and a much higher estimate (F ST = 0.40, P= 0.001) was obtained when the genotypes were classified as varieties, maintainers, restorers and germplasm lines. This indicates strong distinction based on usage groups. Pairwise F ST values based on usage groups corresponded well with the Nei's genetic distances. We were able to distinguish parental lines of hybrids based on their fertility groups, especially lines from rainy season sorghum, using SSR markers. To the best of our knowledge, this is the first report in sorghum demonstrating the utility of SSR markers in classifying lines based on their fertility groups. Our results have significant implications in classifying parental lines into heterotic groups for their use in heterosis breeding. The divergent maintainer and restorer lines identified based on Jaccard's similarity coefficients could serve as effective candidates for hybrid development

Genetic dissection of thousand-seed weight in linseed (Linum usitatissimum L.) using multi-locus genome-wide association study

Flaxseed/linseed is an important oilseed crop having applications in the food, nutraceutical, and paint industry. Seed weight is one of the most crucial determinants of seed yield in linseed. Here, quantitative trait nucleotides (QTNs) associated with thousand-seed weight (TSW) have been identified using multi-locus genome-wide association study (ML-GWAS). Field evaluation was carried out in five environments in multi-year-location trials. SNP genotyping information of the AM panel of 131 accessions comprising 68,925 SNPs was employed for ML-GWAS. From the six ML-GWAS methods employed, five methods helped identify a total of 84 unique significant QTNs for TSW. QTNs identified in ≥ 2 methods/environments were designated as stable QTNs. Accordingly, 30 stable QTNs have been identified for TSW accounting up to 38.65% trait variation. Alleles with positive effect on trait were analyzed for 12 strong QTNs with r2 ≥ 10.00%, which showed significant association of specific alleles with higher trait value in three or more environments. A total of 23 candidate genes have been identified for TSW, which included B3 domain-containing transcription factor, SUMO-activating enzyme, protein SCARECROW, shaggy-related protein kinase/BIN2, ANTIAUXIN-RESISTANT 3, RING-type E3 ubiquitin transferase E4, auxin response factors, WRKY transcription factor, and CBS domain-containing protein. In silico expression analysis of candidate genes was performed to validate their possible role in different stages of seed development process. The results from this study provide significant insight and elevate our understanding on genetic architecture of TSW trait in linseed

Genetic dissection of thousand-seed weight in linseed (Linum usitatissimum L.) using multi-locus genome-wide association study

Flaxseed/linseed is an important oilseed crop having applications in the food, nutraceutical, and paint industry. Seed weight is one of the most crucial determinants of seed yield in linseed. Here, quantitative trait nucleotides (QTNs) associated with thousand-seed weight (TSW) have been identified using multi-locus genome-wide association study (ML-GWAS). Field evaluation was carried out in five environments in multi-year-location trials. SNP genotyping information of the AM panel of 131 accessions comprising 68,925 SNPs was employed for ML-GWAS. From the six ML-GWAS methods employed, five methods helped identify a total of 84 unique significant QTNs for TSW. QTNs identified in ≥ 2 methods/environments were designated as stable QTNs. Accordingly, 30 stable QTNs have been identified for TSW accounting up to 38.65% trait variation. Alleles with positive effect on trait were analyzed for 12 strong QTNs with r2 ≥ 10.00%, which showed significant association of specific alleles with higher trait value in three or more environments. A total of 23 candidate genes have been identified for TSW, which included B3 domaincontaining transcription factor, SUMO-activating enzyme, protein SCARECROW, shaggy-related protein kinase/BIN2, ANTIAUXIN-RESISTANT 3, RING-type E3 ubiquitin transferase E4, auxin response factors, WRKY transcription factor, and CBS domain-containing protein. In silico expression analysis of candidate genes was performed to validate their possible role in different stages of seed development process. The results from this study provide significant insight and elevate our understanding on genetic architecture of TSW trait in linseed

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Not AvailableSorghum [Sorghum bicolor (L.) Moench] grown in India is of two adaptive types: rainy and post-rainy. The postrainy sorghum is predominantly consumed by humans. While releasing new cultivars through multi-location testing, major emphasis is given to the superiority of new cultivars over existing cultivars, with very little emphasis on the genotype × environment interaction (GEI). To understand the complexity of GEI in post-rainy sorghum testing location trials, the multi-location evaluation data of two post-rainy seasons (2009/10 and 2010/11) under the All India Coordinated Sorghum Improvement Project were analysed. In both years, location explained the highest proportion of total sum of squares followed by the GEI effect and main effect of genotype. Additive main effects and multiplicative interaction (AMMI), stability values (ASV) and genotype + genotype × environment interaction (GGE) instability values recorded high correlation resulting in identification of the best performing cultivars. However, the rank correlations were lower, though still significant. A mixture of crossover and non-crossover GEI was a common occurrence in both years. ‘Which-won-where’ analysis suggested the existence of four possible mega-environments (ME) among post-rainy testing locations, with a few non-informative locations within ME. Mega-environments are characterized by soil type, rainfall pattern and moisture conservation practices. The present study indicated the possibility of reducing the number of test locations by eliminating non-representative highly correlated locations and suggested the need to breed for location-specific genotypes rather than genotypes with wider adaptability.Not Availabl

Not Available

Not AvailableSorghum [Sorghum bicolor (L.) Moench] grown in India is of two adaptive types: rainy and post-rainy. The postrainy sorghum is predominantly consumed by humans. While releasing new cultivars through multi-location testing, major emphasis is given to the superiority of new cultivars over existing cultivars, with very little emphasis on the genotype x environment interaction (GEI). To understand the complexity of GEI in post-rainy sorghum testing location trials, the multi-location evaluation data of two post-rainy seasons (2009/10 and 2010/11) under the All India Coordinated Sorghum Improvement Project were analysed. In both years, location explained the highest proportion of total sum of squares followed by the GEI effect and main effect of genotype. Additive main effects and multiplicative interaction (AMMI), stability values (ASV) and genotype + genotype x environment interaction (GGE) instability values recorded high correlation resulting in identification of the best performing cultivars. However, the rank correlations were lower, though still significant. A mixture of crossover and non-crossover GEI was a common occurrence in both years. 'Which-won-where' analysis suggested the existence of four possible mega-environments (ME) among post-rainy testing locations, with a few non-informative locations within ME. Mega-environments are characterized by soil type, rainfall pattern and moisture conservation practices. The present study indicated the possibility of reducing the number of test locations by eliminating non-representative highly correlated locations and suggested the need to breed for location-specific genotypes rather than genotypes with wider adaptability.Not Availabl

Not Available

Not AvailableSorghum [Sorghum bicolor (L.) Moench] grown in India is of two adaptive types: rainy and post-rainy. The post-rainy sorghum is predominantly consumed by humans. While releasing new cultivars through multi-location testing, major emphasis is given to the superiority of new cultivars over existing cultivars, with very little emphasis on the genotype × environment interaction (GEI). To understand the complexity of GEI in post-rainy sorghum testing location trials, the multi-location evaluation data of two post-rainy seasons (2009/10 and 2010/11) under the All India Coordinated Sorghum Improvement Project were analysed. In both years, location explained the highest proportion of total sum of squares followed by the GEI effect and main effect of genotype. Additive main effects and multiplicative interaction (AMMI), stability values (ASV) and genotype + genotype × environment interaction (GGE) instability values recorded high correlation resulting in identification of the best performing cultivars. However, the rank correlations were lower, though still significant. A mixture of crossover and non-crossover GEI was a common occurrence in both years. ‘Which-won-where’ analysis suggested the existence of four possible mega-environments (ME) among post-rainy testing locations, with a few non-informative locations within ME. Mega-environments are characterized by soil type, rainfall pattern and moisture conservation practices. The present study indicated the possibility of reducing the number of test locations by eliminating non-representative highly correlated locations and suggested the need to breed for location-specific genotypes rather than genotypes with wider adaptability.Not Availabl

Genetic Diversity and Population Structure Analysis to Construct a Core Collection from Safflower (<i>Carthamus tinctorius</i> L.) Germplasm through SSR Markers

Genetic resources are the fundamental source of diversity available to plant breeders for the improvement of desired traits. However, a large germplasm set is difficult to preserve and use as a working collection in genetic studies. Hence, the present study evaluates the genetic diversity of 3115 safflower accessions from the Indian National Gene Bank, including Indian cultivars, to develop a manageable set of accessions, with similar genetic variations of germplasm studied. A total of 18 polymorphic SSR markers were used. The genetic diversity analysis revealed that germplasm accessions were highly diverse and there is no correlation between genetic diversity and the geographical collection of germplasm or sourcing of germplasm. A core set was developed using a core hunter software with different levels of composition, and it was found that 10% of the accessions showed maximum gene diversity and represented an equal number of alleles and major allele frequency in the germplasm studied. The developed core consisted of 351 accessions, including Indian cultivars, and they were validated with various genetic parameters to ascertain that they were a true core set for the studied accessions of safflower germplasm

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Not AvailableNature and complexity of genotype × environment interaction (GEI) was studied among eight rabi grain sorghum cultivars across 11 locations during rabi 2011–12 and 2012–13 using GGE biplot analysis. Location (L) contributed for 89.9% of variation for grain yield, while genotypes (G) and G × L interactions accounted for 1% and 9% of variation only. The first two principal components (PCs) of GGE biplot accounted for 50% of variation in data for grain yield, which not ideally explained overall variation in the data. However, the biplot clearly demonstrated that across environments, SPH 1721 was the highest yielding stable genotype followed by CSH 15R. High crossover GEI was recorded among the testing locations and close correlation among these locations was not detected. ‘Which-won-where’ analysis detected three mega-environments (ME) among the testing locations, with ME1 represented by 5 locations, ME2 with 4 locations and ME3 with 2 locations. The study indicated the possibility to reduce the number of testing locations.Not Availabl

Not Available

Not AvailableSorghum [ (L.) Moench] is a very important crop in the arid and semi-arid tropics of India and African subcontinent. In the process of release of new cultivars using multi-location data major emphasis is being given on the superiority of the new cultivars over the ruling cultivars, while very less importance is being given on the genotype x environment interaction (GEI). In the present study, performance of ten Indian hybrids over 12 locations across the rainy seasons of 2008 and 2009 was investigated using GGE biplot analysis. Location attributed higher proportion of the variation in the data (59.3-89.9%), while genotype contributed only 3.9-16.8% of total variation. Genotype x location interaction contributed 5.8-25.7% of total variation. We could identify superior hybrids for grain yield, fodder yield and for harvest index using biplot graphical approach effectively. Majority of the testing locations were highly correlated. 'Which-won-where' study partitioned the testing locations into three mega-environments: first with eight locations with SPH 1606/1609 as the winning genotypes; second mega-environment encompassed three locations with SPH 1596 as the winning genotype, and last mega-environment represented by only one location with SPH 1603 as the winning genotype. This clearly indicates that though the testing is being conducted in many locations, similar conclusions can be drawn from one or two representatives of each mega-environment. We did not observe any correlation of these mega-environments to their geographical locations. Existence of extensive crossover GEI clearly suggests that efforts are necessary to identify location-specific genotypes over multi-year and -location data for release of hybrids and varieties rather focusing on overall performance of the entries.Not Availabl