A SNP-Based Genetic Linkage Map of Soybean Using the SoyS - NP6K Illumina Infinium BeadChip Genotyping Array

Akond, Masum; Liu, Shiming; Schoener, Lauren; Anderson, James A.; Kantartzi, Stella K.; Meksem, Khalid; Song, Qijian; Wang, Dechun; Zhang, Wen; Lightfoot, David A.; Kassem, My Abdelmajid

doi:10.5147/jpgs.2013.0090

Cited by 29 publications

(22 citation statements)

References 32 publications

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“…BeadChip (Akond et al, 2013) • SoySNP50K array (Song et al, 2013) • 384 SNP GoldenGate assay (Hyten et al, 2008 (Varshney et al, 2012a) • 292 lines • 20 (crossing parentals of recombinant inbred lines, introgression lines, MAGIC and NAM population; Kumar et al, 2016) • 60K Axiom®Cajanus SNP array (Saxena et al, 2017 and unpublished) (Gupta et al, 2017) • 35 (parental genotypes of mapping populations; Thudi et al, 2016a) • 129 released varieties (Thudi et al, 2016b) • 300 lines (ICRISAT, unpublished) • 3000 lines (ICRISAT, unpublished) • GoldenGate assays based on VeraCode technology (Roorkiwal et al, 2013) • 60K Axiom®Cicer SNP array (Roorkiwal et al, 2017 (Chen et al, 2016) • 11 genotypes including synthetics and their diploid parents (Chen et al, 2016) • 41 diverse genotypes (30 tetraploids and 11 diploids) (Clevenger et al, 2017;Pandey et al, 2017a) • 58K Axiom®Arachis SNP array (Pandey et al, 2017a) •1536 SNP GoldenGate assay (Nagy et al, 2012) Common bean • 80.57% of Phaseolus vulgaris var G19833 genome (587 Mb); 26 279 protein coding genes (Schmutz et al, 2014) • 17 varieties • BARCBean6K_1, BARCBean6K_2 chips, BARCBean6K_3 SNP chips (Song et al, 2013 (Deulvot et al, 2010) • Combines custom-made growth vessels and new image analysis algorithms to non-destructively monitor RSA development over space (2D) and time • Allows information on soil properties (e.g. moisture)…”

Section: High-density and Precise Phenotypingmentioning

confidence: 99%

Accelerating genetic gains in legumes for the development of prosperous smallholder agriculture: integrating genomics, phenotyping, systems modelling and agronomy

Varshney

Thudi

Pandey

et al. 2018

Journal of Experimental Botany

105

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Grain legumes form an important component of the human diet, provide feed for livestock, and replenish soil fertility through biological nitrogen fixation. Globally, the demand for food legumes is increasing as they complement cereals in protein requirements and possess a high percentage of digestible protein. Climate change has enhanced the frequency and intensity of drought stress, posing serious production constraints, especially in rainfed regions where most legumes are produced. Genetic improvement of legumes, like other crops, is mostly based on pedigree and performance-based selection over the past half century. To achieve faster genetic gains in legumes in rainfed conditions, this review proposes the integration of modern genomics approaches, high throughput phenomics, and simulation modelling in support of crop improvement that leads to improved varieties that perform with appropriate agronomy. Selection intensity, generation interval, and improved operational efficiencies in breeding are expected to further enhance the genetic gain in experimental plots. Improved seed access to farmers, combined with appropriate agronomic packages in farmers' fields, will deliver higher genetic gains. Enhanced genetic gains, including not only productivity but also nutritional and market traits, will increase the profitability of farming and the availability of affordable nutritious food especially in developing countries.

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Section: High-density and Precise Phenotypingmentioning

confidence: 99%

Accelerating genetic gains in legumes for the development of prosperous smallholder agriculture: integrating genomics, phenotyping, systems modelling and agronomy

Varshney

Thudi

Pandey

et al. 2018

Journal of Experimental Botany

105

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“…To date, different molecular techniques including, amplified fragment length polymorphisms (AFLPs), restriction fragment length polymorphisms (RFLPs), simple sequence repeats (SSRs or microsatellites), and single nucleotide polymorphisms (SNPs) were used for constructing map and subsequently QTL identification. Updated genetic maps have been created for the soybean genome and the recent consensus map was created using 1,536 SNP markers, which showed that the soybean genome is approximately 2,300 centimorgans (cM) in length (Hyten et al, 2010) The creations of genetic linkage maps for soybeans have proved useful for identifying QTL (Hyten et al, 2010) and for this purpose recently we developed a genetic linkage map of soybean from RILs of MD 96-5722 by 'Spencer' using the SoySNP6K Illumina Infinium BeadChip Genotyping Array (Akond et al, 2013). Another high density genetic linkage maps was constructed using the 1,536 Universal Soy Linkage Panel 1.0 SNP markers in the 'PI 438489B' by 'Hamilton' populations and 31 linkage groups were obtained and used in this study .…”

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confidence: 99%

Additional Quantitative Trait Loci and Candidate Genes for Seed Isoflavone Content in Soybean

Akond

Richard²,

Ragin³

et al. 2013

JAS

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Seed isoflavone content of soybean (Glycine max L. Merr.) is a trait of moderate heritablity and an ideal target for marker selection. To date over 20 QTL have been identified underlying this trait among seven populations. The objectives of this study were to identify additional QTL and candidate genes controlling isoflavone content in a set of recombinant inbred line (RIL) populations of soybean grown in two different seasons. Variations of isoflavones namely daidzein, glycitein and genistein contents over two growing seasons and locations suggests that isoflavones are influenced by both genes and environments. Six QTL were identified on five different chromosomes (Chr) or linkage groups (LG) that controlled daidzein (Chr_2/LG-M; Chr_17a/LG-D2), glycitein (Chr_2/LG-D1b; Chr_8/LG-A2) and genistein (Chr_8/LG-A2; Chr_12/LG-H) respectively in the seeds grown in season 2010. Two QTL were identified for daidzein (Chr_6/LG-C2; Chr_13b/LG-F), two QTLs for glycitein (Chr_1/LG-D1a; Chr_17c/LG-D2) and five QTLs for genistein (Chr_3/ LG-N; Chr_8/LG-A2; Chr_9/LG-K; Chr_18/LG-G) in the seeds of the 2011 growing season. Genes located within QTL confidence intervals were retrieved and gene ontology (GO) terms were used to identify those related to the flavonoid biosynthesis process. Twenty six candidate genes were identified that may be involved in isoflavones accumulation in soybean seeds.

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“…Seeds of MD96-5722 and "Spencer" were obtained from the National Soybean Research Laboratory (NSRL) in 2006 [13]. Details of development of RIL population can be found elsewhere [13].…”

Section: Plant Materialsmentioning

confidence: 99%

“…The 657 SNPs clearly associated with either of the parents were mapped. The genetic linkage map [13] was constructed through Join Map 4 (Kyazma BV, Wageningen, the Netherlands) [18]. Composite interval mapping (CIM) was used to detect QTL through WinQTLCart 2.5 software (http://statgen.ncsu.edu/qtlcart/WQTLCart.htm) [13] using the genetic linkage map [13] and phenotypic data.…”

Section: Genetic Map Construction and Qtl Identificationmentioning

confidence: 99%

“…The genetic linkage map [13] was constructed through Join Map 4 (Kyazma BV, Wageningen, the Netherlands) [18]. Composite interval mapping (CIM) was used to detect QTL through WinQTLCart 2.5 software (http://statgen.ncsu.edu/qtlcart/WQTLCart.htm) [13] using the genetic linkage map [13] and phenotypic data. The Model 6 with four parameters for forward and backward stepwise regression, 10 cM window size, 1 cM step size and five (5) control markers were chosen for running WinQTLCart [13].…”

Section: Genetic Map Construction and Qtl Identificationmentioning

confidence: 99%

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Mapping of QTL Associated with Seed Amino Acids Content in “MD96-5722” by “Spencer” RIL Population of Soybean Using SNP Markers

Khandaker¹,

Akond²,

Liu³

et al. 2015

FNS

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. (2015) Mapping of QTL Associated with Seed Amino Acids Content in "MD96-5722" by "Spencer" RIL Population of Soybean Using SNP Markers. AbstractLimited information is available on the genetic analysis of amino acid composition in soybean seeds. Previously, quantitative trait loci (QTLs) for seed isoflavones, protein, oil, and fatty acids were identified in the "MD96-5722" by "Spencer" and other RIL populations. There were wide variations for these seed constituents among the RIL populations. Therefore, the objective of this study was to identify QTLs controlling different amino acids content in soybean seeds. To achieve this objective, ninety-two F5:7 recombinant inbred lines (RIL), developed from a cross of MD96-5722 and Spencer, using a total 5376 Single Nucleotide Polymorphism (SNPs) markers, were used. The RILs were genotyped by using 537 polymorphic, reliably segregating SNP markers, developed from the Illumina Infinium SoySNP6K BeadChip array. A total of 13 QTLs were identified with three QTLs for threonine on the linkage group (LG) A1, C2, and B2. Two QTLs were identified for each of the amino acids proline on LG D1a and B2, serine on LG A1 and C2, tryptophan on LG K and G, and cysteine on LG A1 and K. One QTL was identified for arginine on LG N and histidine on LG J. The new QTLs findings for seed amino acid will facilitate the development of soybean cultivars with higher protein and amino acid quality to help meet the industry and consumer needs.* Corresponding author. L. Khandaker et al.975

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A SNP-Based Genetic Linkage Map of Soybean Using the SoyS - NP6K Illumina Infinium BeadChip Genotyping Array

Cited by 29 publications

References 32 publications

Accelerating genetic gains in legumes for the development of prosperous smallholder agriculture: integrating genomics, phenotyping, systems modelling and agronomy

Accelerating genetic gains in legumes for the development of prosperous smallholder agriculture: integrating genomics, phenotyping, systems modelling and agronomy

Additional Quantitative Trait Loci and Candidate Genes for Seed Isoflavone Content in Soybean

Mapping of QTL Associated with Seed Amino Acids Content in “MD96-5722” by “Spencer” RIL Population of Soybean Using SNP Markers

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