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Integrating Genetics and Genomics to Advance Soybean Research



Nested Association Mapping (NAM) of Genes Controlling Soybean Yield and Other Key Traits

Our goal is to improve the yield potential of soybean varieties. To this end we have mapped the chromosomal locations of genes that control yield and other important agronomic traits in both domestic and exotic germplasm using a Nested Association Panel composed of 40 important soybean varieties and cultivars crossed to a common hub parent. Further details about the SoyNAM Project are avaliable. Funding was provided by the North Central Soybean research Program (NCSRP) and the United Soybean Board (USB).

140 Recombinant Inbred Lines (RILs) were developed from each cross between the NAM parents and IA3023.

Click on a NAM Parent for details about that Population.
High Yielding
Lines
Lines With
Diverse Ancestry
PIs With High
Yields in Drought
Hub Parent
4J105-3-4LG03-2979PI 398881IA3023
5M20-2-5-2LG03-3191PI 427136
CL0J095-4-6LG00-3372PI 437169B
CL0J173-6-8LG04-4717PI 518751
HS6-3976LG04-6000PI 561370
LD00-3309LG05-4292PI 404188A
LD01-5907LG05-4317PI 574486
LD02-4485LG05-4464PI 507681B*
LD02-9050LG05-4832*Analysis of the progeny of SoyNAM population 46 (PI 507681B x IA3023) showed that the cross was not as described. No phenotypic data for SoyNAM 46 are available.
MagellanLG90-2550
MaverickLG92-1255
NE3001LG94-1128
ProhioLG94-1906
S06-13640LG97-7012
SkyllaLG98-1605
TN05-3027
U03-100612

You can also browse through images of the populations with our image browser.

Genetic Characterization of the Soybean Nested Association Mapping Population
Song, Q., L. Yan, C. Quigley, B. D. Jordan, E. Fickus, S. Schroeder, B. Song, Y. Charles An, D. Hyten, R. Nelson, K. Rainey, W. D. Beavis, J. Specht, B. Diers, and P. Cregan
Plant Genome 10 (2017). doi:10.3835/plantgenome2016.10.0109
Abstract: A set of nested association mapping (NAM) families was developed by crossing 40 diverse soybean [ (L.) Merr.] genotypes to the common cultivar. The 41 parents were deeply sequenced for SNP discovery. Based on the polymorphism of the single-nucleotide polymorphisms (SNPs) and other selection criteria, a set of SNPs was selected to be included in the SoyNAM6K BeadChip for genotyping the parents and 5600 RILs from the 40 families. Analysis of the SNP profiles of the RILs showed a low average recombination rate. We constructed genetic linkage maps for each family and a composite linkage map based on recombinant inbred lines (RILs) across the families and identified and annotated 525,772 high confidence SNPs that were used to impute the SNP alleles in the RILs. The segregation distortion in most families significantly favored the alleles from the female parent, and there was no significant difference of residual heterozygosity in the euchromatic vs. heterochromatic regions. The genotypic datasets for the RILs and parents are publicly available and are anticipated to be useful to map quantitative trait loci (QTL) controlling important traits in soybean.

Genome-wide Association Mapping of Qualitatively Inherited Traits in a Germplasm Collection
Bandillo, N. B., A. J. Lorenz, G. L. Graef, D. Jarquin, D. L. Hyten, R. L. Nelson, and J. E. Specht
Plant Genome 10 (2017). doi:10.3835/plantgenome2016.06.0054
Abstract: Genome-wide association (GWA) has been used as a tool for dissecting the genetic architecture of quantitatively inherited traits. We demonstrate here that GWA can also be highly useful for detecting many major genes governing categorically defined phenotype variants that exist for qualitatively inherited traits in a germplasm collection. Genome-wide association mapping was applied to categorical phenotypic data available for 10 descriptive traits in a collection of 13,000 soybean [Glycine max (L.) Merr.] accessions that had been genotyped with a 50,000 single nucleotide polymorphism (SNP) chip. A GWA on a panel of accessions of this magnitude can offer substantial statistical power and mapping resolution, and we found that GWA mapping resulted in the identification of strong SNP signals for 24 classical genes as well as several heretofore unknown genes controlling the phenotypic variants in those traits. Because some of these genes had been cloned, we were able to show that the narrow GWA mapping SNP signal regions that we detected for the phenotypic variants had chromosomal bp spans that, with just one exception, overlapped the bp region of the cloned genes, despite local variation in SNP number and nonuniform SNP distribution in the chip set.

Genetic Architecture of Soybean Yield and Agronomic Traits
Brian W. Diers, Jim Specht, Katy Martin Rainey, Perry Cregan, Qijian Song, Vishnu Ramasubramanian, George Graef, Randall Nelson, William Schapaugh, Dechun Wang, Grover Shannon, Leah McHale, Stella K. Kantartzi, Alencar Xavier, Rouf Mian, Robert M. Stupar, Jean-Michel Michno, Yong-Qiang Charles An, Wolfgang Goettel, Russell Ward, Carolyn Fox, Alexander E. Lipka, David Hyten, Troy Cary and William D. Beavis
G3: Genes, Genomes, Genetics October 1, 2018 vol. 8 no. 10 3367-3375. doi:10.1534/g3.118.200332
Abstract: Soybean is the worlds leading source of vegetable protein and demand for its seed continues to grow. Breeders have successfully increased soybean yield, but the genetic architecture of yield and key agronomic traits is poorly understood. We developed a 40-mating soybean nested association mapping (NAM) population of 5,600 inbred lines that were characterized by single nucleotide polymorphism (SNP) markers and six agronomic traits in field trials in 22 environments. Analysis of the yield, agronomic, and SNP data revealed 23 significant marker-trait associations for yield, 19 for maturity, 15 for plant height, 17 for plant lodging, and 29 for seed mass. A higher frequency of estimated positive yield alleles was evident from elite founder parents than from exotic founders, although unique desirable alleles from the exotic group were identified, demonstrating the value of expanding the genetic base of US soybean breeding.

Coming Soon

See SoyNAM QTL in SoyBase sequence browser

See SoyNAM QTL in SoyBase genetic maps

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