scholarly journals Genetic Mapping of QTL Associated with Seed Macronutrients Accumulation in ‘MD 96-5722’ by ‘Spencer’ Recombinant Inbred Lines of Soybean

2017 ◽  
Vol 3 (2) ◽  
pp. 224-235 ◽  
Author(s):  
Nacer Bellaloui ◽  
Laila Khandaker ◽  
Masum Akond ◽  
Stella K. Kantartzi ◽  
Khalid Meksem ◽  
...  
Keyword(s):  
Recombinant Inbred ◽  
Genetic Relationships ◽  
Recombinant Inbred Lines ◽  
Soybean Seed ◽  
Peak Position ◽  
Inbred Lines ◽  
Snp Markers ◽  
Nutrition Quality ◽  
C And N ◽  
N:S Ratio

Research of quantitative trait loci (QTL) for macronutrient accumulation in soybean seed is limited. Therefore, the objective of this research was to identify QTL related to macronutrients (N, C, S, P, K, Ca, and Mg) in seeds in 92 F5:7 recombinant inbred lines (RILs) developed from a cross between MD 96-5722 (MD) and Spencer using a total 5,376 Single Nucleotide Polymorphism (SNP) markers. A genetic linkage map based on SNP markers was constructed using the Illumina Infinium SoySNP6K BeadChip Array. The RILs were genotyped using 537 polymorphic, reliably segregating SNP markers. A total of 8 QTL for K (qPOT001-qPOT008) were identified on LGs D1b (Chr 1), N (Chr 3), A1 (Chr 5), O (Chr 10), F (Chr 13), B2 (Chr 14), and J (Chr 16). Four QTL for Mg (qMAG001-qMAG004) were identified on LGs N (Chr 3), A1 (Chr 5), J (Chr 16), and G (Chr 18). One QTL for P (qPHO001), one for C on LG J (Chr 16), one for N (qNIT001) and S (qSUL001) on the same LG J (Chr 16), and one QTL for Ca (qCAL001) on LG G (Chr 18). K and Mg QTL were clustered together on LG A1 (Chr 5) with a peak position of 9.50 cM and  LOD support interval of 8.50-9.50 cM. Similar observation was noticed for P, K, Mg, C, N, and S, where the QTL were clustered on LG J (Chr 16) with peak position of 11 cM for K, P, and S, and 10 cM for C and N, and 12 cM for Mg. The LOD support intervals for all these clustered QTL were between 8.90 and 12.30 cM. The QTL clustering of these nutrients suggests possible common physiological and genetic relationships, suggesting possible similar metabolic processes and pathways  for these nutrients. The inverse relationships between N:S ratio and all nutrients suggest possible use of N:S ratio as a measure for higher nutrients accumulation in seed. Since most of QTL identified in this study were not previously reported, this research will further help breeders to improve nutrient accumulation in seeds and contribute to our understanding of the physiological and genetic bases of seed nutrition quality.

scholarly journals Identification of QTL Underlying Seed Micronutrients Accumulation in ‘MD 96-5722’ by ‘Spencer’ Recombinant Inbred Lines of Soybean

2017 ◽  
Vol 1 (3) ◽  
pp. 39-49 ◽  
Author(s):  
Nacer Bellaloui ◽  
Laila Khandaker ◽  
Masum Akond ◽  
Stella K. Kantartzi ◽  
Khalid Meksem ◽  
...  
Keyword(s):  
Recombinant Inbred ◽  
Recombinant Inbred Lines ◽  
Inbred Lines ◽  
Snp Markers ◽  
Linkage Groups ◽  
Seed Industry ◽  
Similar Region ◽  
Nutritional Qualities ◽  
Genetic Mechanisms ◽  
Nutritional Needs

Genetic mapping of quantitative trait loci (QTL) associated with seed nutrition levels is almost non-existent. The objective of this study was to identify QTLs associated with seed micronutrients (iron, Fe; zinc, Zn; bororn, B; manganese, Mn; and copper, Cu) accumulation (concentration) in a population of 92 F5:7 recombinant inbred lines (RILs) that derived from a cross between MD 96-5722 (MD) and ‘Spencer’. For this purpose, a genetic linkage map based on 5,376 Single Nucleotide Polymorphism (SNP) markers was constructed using the Illumina Infinium SoySNP6K BeadChip array. The RILs were genotyped using 537 polymorphic, reliably segregating SNP markers. A total of 23 QTLs for micronutrients Fe, Zn, B, Mn, and Cu have been identified and mapped on eight linkage groups (LGs) of the soybean genome. Five QTLs were detected for Fe (qIRO001- qIRO005) on LGs N, A1, K, J, and G. Seven QTLs for Zn (qZIN001-qZIN007) on LGs D1a (Chr 1), N (Chr 3), F (Chr 5), B2 (Chr 14), J (Chr 16), A1 (Chr 5), and K (Chr 9). Two QTLs for B (qBOR001 and qBOR002) were detected on LGs N and A1. Four QTLs were detected for Mn (qMAN001-qMAN004) on LGs N, A1, K, and J, and five QTLs were detected for Cu (qCOP001- qCOP005) on LGs N, A1, K, J, and G). It was observed that the four QTLs for Zn, Cu, Fe, and Mn on LGs N (Chr 3), LG A1 (Chr 5), and LG J (Chr 16) were clustered in a similar region of the linkage groups, suggesting possible shared physiological and genetic mechanisms. The QTLs detected in this study are novel and will contribute to our understanding of the genetic basis of seed mineral nutrition. This research would allow breeders to efficiently select for higher seed nutritional qualities to meet the seed industry and human and livestock nutritional needs.

Analysis of QTLs for the micromorphology on the seed coat surface of soybean using recombinant inbred lines

2015 ◽  
Vol 25 (4) ◽  
pp. 409-415 ◽  
Author(s):  
Kazunori Otobe ◽  
Satoshi Watanabe ◽  
Kyuya Harada
Keyword(s):  
Seed Coat ◽  
Recombinant Inbred ◽  
Recombinant Inbred Lines ◽  
Soybean Seed ◽  
Coat Colour ◽  
Inbred Lines ◽  
Phenotypic Variance ◽  
Seed Coat Colour ◽  
Impermeable Layer ◽  
Phenotype Markers

AbstractThe seed coat of soybean (Glycine max (L.) Merrill) must protect the seed but allow water intake. Overprotection, causing impermeability, is assumed to be due to the presence of an impermeable layer in the seed coat, although validation of this assumption has relied on imbibition testing, which tends to be influenced by microfractures in the seed coat. Recent micromorphological analyses using laser-assisted topography microscopy revealed links to the surface roughness (SR) of the seed coat. To verify genetic links between hardseededness and SR, we analysed quantitative trait loci (QTLs) governing SR formation using 148 recombinant inbred lines (RILs) with a genetic linkage map covering 2663.6 cM of all 20 linkage groups of soybean, with 355 DNA markers and 5 phenotype markers. Five QTLs were detected, including previously identified hardseededness QTLs for ratio of seeds absorbing water, namely RAS1 and RAS2, which accounted for 20% of the phenotypic variance, and one near a locus inhibiting seed coat colour (I). These results indicate that the impermeability of soybean seed is genetically related to the reduction of SR.

scholarly journals Genetic analysis of agronomical traits in recombinant inbred lines of cowpea (Vignaunguiculata(L.) Walp.)under two water regimes.

2015 ◽  
Vol 5 (1) ◽  
pp. 578-590
Author(s):  
Charlotte TONESSIA ◽  
N'Dri KOUASSI ◽  
Ndiaga CISSE ◽  
Severin Aké
Keyword(s):  
Genetic Variation ◽  
Recombinant Inbred ◽  
Agronomic Traits ◽  
Average Distance ◽  
Recombinant Inbred Lines ◽  
Inbred Lines ◽  
Snp Markers ◽  
Linkage Groups ◽  
Water Regimes ◽  
Agronomical Traits

Single nucleotide polymorphism (SNP) markers were used to develop a genetic-linkage map and to identify QTLsinvolved in the genetic variation of agronomical traits in cowpeaunder two water regimes. A total of 1536 SNP GoldenGate assay were used to screen for polymorphism in a cowpea population of recombinant inbred lines. A total of 299 SNP markers amplified polymorphic products of which 228 mapped to the 11 cowpea linkage groups with an average distance of 6.5 cM between markers. The new SNP genetic map with a total length of 1281,8 cM were aligned with the consensus cowpea map allowing filling some gaps, which will increase QTLs analysis. A total of 31 QTLs affecting agronomic traits were identified and mapped to cowpea genomic regions. Among them 45% explaining from 3 to 35% of genetic variation were detected for both water conditions. Co-locations between QTLs were identified on several linkage groups among them QTLs affecting harvest index (HI) and grain yield suggesting their common genetic bases. Because, HI has been shown as the most stable and highly correlated parameter with cowpea yield under stress; our results will enable the efficiency of MAS and enhance genetic progress in cowpea.

Diverse sources of resistance to Spodoptera litura (F.) in groundnut

2019 ◽  
Vol 79 (01S) ◽  
Author(s):  
M. A. Saleem ◽  
G. K. Naidu ◽  
H. L. Nadaf ◽  
P. S. Tippannavar
Keyword(s):  
Recombinant Inbred ◽  
Spodoptera Litura ◽  
Hot Spot ◽  
Recombinant Inbred Lines ◽  
Insect Pest ◽  
Inbred Lines ◽  
Leaf Damage ◽  
Botanical Variety ◽  
Resistant Genotypes ◽  
Botanical Varieties

Spodoptera litura an important insect pest of groundnut causes yield loss up to 71% in India. Though many effective chemicals are available to control Spodoptera, host plant resistance is the most desirable, economic and eco-friendly strategy. In the present study, groundnut mini core (184), recombinant inbred lines (318) and elite genotypes (44) were studied for their reaction to Spodoptera litura under hot spot location at Dharwad. Heritable component of variation existed for resistance to Spodoptera in groundnut mini core, recombinant inbred lines and elite genotypes indicating scope for selection of Spodoptera resistant genotypes. Only 29 (15%) genotypes belonging to hypogaea, fastigiata and hirsuta botanical varieties under mini core set, 15 transgressive segregants belonging to fastigiata botanical variety among 318 recombinant inbred lines and three genotypes belonging to hypogaea and fastigiata botanical varieties under elite genotypes showed resistance to Spodoptera litura with less than 10% leaf damage. Negative correlation existed between resistance to Spodoptera and days to 50 per cent flowering indicating late maturing nature of resistant genotypes. Eight resistant genotypes (ICG 862, ICG 928, ICG 76, ICG 2777, ICG 5016, ICG 12276, ICG 4412 and ICG 9905) under hypogaea botanical variety also had significantly higher pod yield. These diverse genotypes could serve as potential donors for incorporation of Spodoptera resistance in groundnut.

scholarly journals QTL analysis of seed dormancy in Arabidopsis using recombinant inbred lines and MQM mapping

Heredity ◽  
1997 ◽  
Vol 79 (2) ◽  
pp. 190-200 ◽  
Author(s):  
Wybe van der Schaar ◽  
Carlos Alonso-Blanco ◽  
Karen M Léon-Kloosterziel ◽  
Ritsert C Jansen ◽  
Johan W van Ooijen ◽  
...  
Keyword(s):  
Seed Dormancy ◽  
Qtl Analysis ◽  
Recombinant Inbred ◽  
Recombinant Inbred Lines ◽  
Inbred Lines
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