Genetic architecture of protein and oil content in soybean seed and meal.

Soybean is grown primarily for the protein and oil extracted from its seed and its value is influenced by these components. The objective of this study was to map marker-trait associations (MTAs) for the concentration of seed protein, oil, and meal protein using the soybean nested association mapping (SoyNAM) population. The composition traits were evaluated on seed harvested from over 5000 inbred lines of the SoyNAM population grown in 10 field locations across 3 years. Estimated heritabilities were at least 0.85 for all three traits. The genotyping of lines with single nucleotide polymorphism markers resulted in the identification of 107 MTAs for the three traits. When MTAs for the three traits that mapped within 5 cM intervals were binned together, the MTAs were mapped to 64 intervals on 19 of the 20 soybean chromosomes. The majority of the MTA effects were small and of the 107 MTAs, 37 were for protein content, 39 for meal protein, and 31 for oil content. For cases where a protein and oil MTAs mapped to the same interval, most (94%) significant effects were opposite for the two traits, consistent with the negative correlation between these traits. A coexpression analysis identified candidate genes linked to MTAs and 18 candidate genes were identified. The large number of small effect MTAs for the composition traits suggest that genomic prediction would be more effective in improving these traits than marker-assisted selection.

Interactions Among Heterodera glycines, Macrophomina phaseolina, and Soybean Genotype.

Heterodera glycines, the soybean cyst nematode (SCN), and fungal pathogen Macrophomina phaseolina are economically important soybean pathogens that may coinfest fields. Resistance remains the most effective management tactic for SCN, and the rhg1-b resistance allele derived from plant introduction 88788 is most commonly deployed in the northern United States. The concomitant effects of SCN and M. phaseolina on soybean performance, as well as the effect of the rhg1-b allele in two different genetic backgrounds, were evaluated in three environments (during 2013 to 2015) and a greenhouse bioassay. Within two soybean populations, half of the lines had the rhg1-b allele, and the other half had the susceptible allele in the backgrounds of the cultivars IA3023 and LD00-3309. Significant interactions between soybean rhg1-b allele and M. phaseolina-infested plots were observed in 2014. In all experiments, initial SCN populations (Pi) and M. phaseolina in roots were associated with reduced soybean yield. SCN reproduction factor (RF = final population/Pi) was affected by SCN Pi, rhg1-b, and genetic background. A background-by-genotype interaction on yield was observed only in 2015, with a stronger rhg1-b effect in the LD00-3309 background, which suggested that the susceptible parent 'IA3023' is tolerant to SCN. SCN female index from greenhouse experiments was compared with field RF, and Lin's concordance and Pearson's correlation coefficients decreased with increasing field SCN Pi in soil. In this study, both SCN and M. phaseolina reduced soybean yield asymptomatically, and the impact of SCN rhg1-b resistance was dependent on SCN virulence but also population density.

High-throughput characterization, correlation, and mapping of leaf photosynthetic and functional traits in the soybean (Glycine max) nested association mapping population.

Photosynthesis is a key target to improve crop production in many species including soybean [Glycine max (L.) Merr.]. A challenge is that phenotyping photosynthetic traits by traditional approaches is slow and destructive. There is proof-of-concept for leaf hyperspectral reflectance as a rapid method to model photosynthetic traits. However, the crucial step of demonstrating that hyperspectral approaches can be used to advance understanding of the genetic architecture of photosynthetic traits is untested. To address this challenge, we used full-range (500-2,400 nm) leaf reflectance spectroscopy to build partial least squares regression models to estimate leaf traits, including the rate-limiting processes of photosynthesis, maximum Rubisco carboxylation rate, and maximum electron transport. In total, 11 models were produced from a diverse population of soybean sampled over multiple field seasons to estimate photosynthetic parameters, chlorophyll content, leaf carbon and leaf nitrogen percentage, and specific leaf area (with R2 from 0.56 to 0.96 and root mean square error approximately <10% of the range of calibration data). We explore the utility of these models by applying them to the soybean nested association mapping population, which showed variability in photosynthetic and leaf traits. Genetic mapping provided insights into the underlying genetic architecture of photosynthetic traits and potential improvement in soybean. Notably, the maximum Rubisco carboxylation rate mapped to a region of chromosome 19 containing genes encoding multiple small subunits of Rubisco. We also mapped the maximum electron transport rate to a region of chromosome 10 containing a fructose 1,6-bisphosphatase gene, encoding an important enzyme in the regeneration of ribulose 1,5-bisphosphate and the sucrose biosynthetic pathway. The estimated rate-limiting steps of photosynthesis were low or negatively correlated with yield suggesting that these traits are not influenced by the same genetic mechanisms and are not limiting yield in the soybean NAM population. Leaf carbon percentage, leaf nitrogen percentage, and specific leaf area showed strong correlations with yield and may be of interest in breeding programs as a proxy for yield. This work is among the first to use hyperspectral reflectance to model and map the genetic architecture of the rate-limiting steps of photosynthesis.

Genotype imputation for soybean nested association mapping population to improve precision of QTL detection.

KEY MESSAGE: Software for high imputation accuracy in soybean was identified. Imputed dataset could significantly reduce the interval of genomic regions controlling traits, thus greatly improve the efficiency of candidate gene identification. Genotype imputation is a strategy to increase marker density of existing datasets without additional genotyping. We compared imputation performance of software BEAGLE 5.0, IMPUTE 5 and AlphaPlantImpute and tested software parameters that may help to improve imputation accuracy in soybean populations. Several factors including marker density, extent of linkage disequilibrium (LD), minor allele frequency (MAF), etc., were examined for their effects on imputation accuracy across different software. Our results showed that AlphaPlantImpute had a higher imputation accuracy than BEAGLE 5.0 or IMPUTE 5 tested in each soybean family, especially if the study progeny were genotyped with an extremely low number of markers. LD extent, MAF and reference panel size were positively correlated with imputation accuracy, a minimum number of 50 markers per chromosome and MAF of SNPs > 0.2 in soybean line were required to avoid a significant loss of imputation accuracy. Using the software, we imputed 5176 soybean lines in the soybean nested mapping population (NAM) with high-density markers of the 40 parents. The dataset containing 423,419 markers for 5176 lines and 40 parents was deposited at the Soybase. The imputed NAM dataset was further examined for the improvement of mapping quantitative trait loci (QTL) controlling soybean seed protein content. Most of the QTL identified were at identical or at similar position based on initial and imputed datasets; however, QTL intervals were greatly narrowed. The resulting genotypic dataset of NAM population will facilitate QTL mapping of traits and downstream applications. The information will also help to improve genotyping imputation accuracy in self-pollinated crops.

Detection of rare nematode resistance Rhg1 haplotypes in Glycine soja and a novel Rhg1 α-SNAP.

This study pursued the hypothesis that wild plant germplasm accessions carrying alleles of interest can be identified using available single nucleotide polymorphism (SNP) genotypes for particular alleles of other (unlinked) genes that contribute to the trait of interest. The soybean cyst nematode (SCN, Heterodera glycines [HG]) resistance locus Rhg1 is widely used in farmed soybean [Glycine max (L.) Merr.]. The two known resistance-conferring haplotypes, rhg1-a and rhg1-b, typically contain three or seven to 10 tandemly duplicated Rhg1 segments, respectively. Each Rhg1 repeat carries four genes, including Glyma.18G022500, which encodes unusual isoforms of the vesicle-trafficking chaperone α-SNAP. Using SoySNP50K data for NSFRAN07 allele presence, we discovered a new Rhg1 haplotype, rhg1-ds, in six accessions of wild soybean, Glycine soja Siebold & Zucc. (0.5% of the ∼1,100 G. soja accessions in the USDA collection). The α-SNAP encoded by rhg1-ds is unique at an important site of amino acid variation and shares with the rhg1-a and rhg1-b α-SNAP proteins the traits of cytotoxicity and altered N-ethylmaleimide sensitive factor (NSF) protein interaction. Copy number assays indicate three repeats of rhg1-ds. G. soja PI 507613 and PI 507623 exhibit resistance to HG type 2.5.7 SCN populations, in part because of contributions from other loci. In a segregating F2 population, rhg1-b and rhg1-ds made statistically indistinguishable contributions to resistance to a partially virulent HG type 2.5.7 SCN population. Hence, the unusual multigene copy number variation Rhg1 haplotype was present but rare in ancestral G. soja and was present in accessions that offer multiple traits for SCN resistance breeding. The accessions were initially identified for study based on an unlinked SNP.

Meta-GWAS for quantitative trait loci identification in soybean.

We report a meta-Genome Wide Association Study involving 73 published studies in soybean [Glycine max L. (Merr.)] covering 17,556 unique accessions, with improved statistical power for robust detection of loci associated with a broad range of traits. De novo GWAS and meta-analysis were conducted for composition traits including fatty acid and amino acid composition traits, disease resistance traits, and agronomic traits including seed yield, plant height, stem lodging, seed weight, seed mottling, seed quality, flowering timing, and pod shattering. To examine differences in detectability and test statistical power between single- and multi-environment GWAS, comparison of meta-GWAS results to those from the constituent experiments were performed. Using meta-GWAS analysis and the analysis of individual studies, we report 483 peaks at 393 unique loci. Using stringent criteria to detect significant marker-trait associations, 59 candidate genes were identified, including 17 agronomic traits loci, 19 for seed-related traits, and 33 for disease reaction traits. This study identified potentially valuable candidate genes that affect multiple traits. The success in narrowing down the genomic region for some loci through overlapping mapping results of multiple studies is a promising avenue for community-based studies and plant breeding applications.

Genetic Architecture of Soybean Yield and Agronomic Traits.

Soybean is the world's 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.

QTL mapping and epistatic interaction analysis of field resistance to sudden death syndrome (Fusarium virguliforme) in soybean.

KEY MESSAGE: Two interactive quantitative trait loci (QTLs) controlled the field resistance to sudden death syndrome (SDS) in soybean. The interaction between them was confirmed. Sudden death syndrome (SDS), caused by Fusarium virguliforme, is a major disease of soybean [Glycine max (L.) Merr.] in the United States. Breeding for soybean resistance to SDS is the most cost-effective method to manage the disease. The objective of this study was to identify and characterize quantitative trait loci (QTLs) underlying field resistance to SDS in a recombinant inbred line population from the cross GD2422 × LD01-5907. This population was genotyped with 1786 polymorphic single nucleotide polymorphisms (SNPs) using SoySNP6 K iSelect BeadChip and evaluated for SDS resistance in a naturally infested field. Four SDS resistance QTLs were mapped on Chromosomes 4, 8, 12 and 18. The resistant parent, LD01-5907, contributed the resistance alleles for the QTLs on Chromosomes 8 and 18 (qSDS-8 and qSDS-18), while the other parent, GD2422, provided the resistance alleles for the QTLs on Chromosomes 4 and 12 (qSDS-4 and qSDS-12). The minor QTL on Chromosome 12 (qSDS-12) is novel. The QTL on Chromosomes 8 and 18 (qSDS-8 and qSDS-18) overlapped with two soybean cyst nematode resistance-related loci, Rhg4 and Rhg1, respectively. A significant interaction between qSDS-8 and qSDS-18 was detected by disease incidence. Individual effects together with the interaction effect explained around 70% of the phenotypic variance. The epistatic interaction of qSDS-8 and qSDS-18 was confirmed by the field performance across multiple years. Furthermore, the resistance alleles at qSDS-8 and qSDS-18 were demonstrated to be recessive. The SNP markers linked to these QTLs will be useful for marker-assisted breeding to enhance the SDS resistance.

An atypical N-ethylmaleimide sensitive factor enables the viability of nematode-resistant Rhg1 soybeans.

N-ethylmaleimide sensitive factor (NSF) and α-soluble NSF attachment protein (α-SNAP) are essential eukaryotic housekeeping proteins that cooperatively function to sustain vesicular trafficking. The "resistance to Heterodera glycines 1" (Rhg1) locus of soybean (Glycine max) confers resistance to soybean cyst nematode, a highly damaging soybean pest. Rhg1 loci encode repeat copies of atypical α-SNAP proteins that are defective in promoting NSF function and are cytotoxic in certain contexts. Here, we discovered an unusual NSF allele (Rhg1-associated NSF on chromosome 07; NSFRAN07 ) in Rhg1+ germplasm. NSFRAN07 protein modeling to mammalian NSF/α-SNAP complex structures indicated that at least three of the five NSFRAN07 polymorphisms reside adjacent to the α-SNAP binding interface. NSFRAN07 exhibited stronger in vitro binding with Rhg1 resistance-type α-SNAPs. NSFRAN07 coexpression in planta was more protective against Rhg1 α-SNAP cytotoxicity, relative to WT NSFCh07 Investigation of a previously reported segregation distortion between chromosome 18 Rhg1 and a chromosome 07 interval now known to contain the Glyma.07G195900 NSF gene revealed 100% coinheritance of the NSFRAN07 allele with disease resistance Rhg1 alleles, across 855 soybean accessions and in all examined Rhg1+ progeny from biparental crosses. Additionally, we show that some Rhg1-mediated resistance is associated with depletion of WT α-SNAP abundance via selective loss of WT α-SNAP loci. Hence atypical coevolution of the soybean SNARE-recycling machinery has balanced the acquisition of an otherwise disruptive housekeeping protein, enabling a valuable disease resistance trait. Our findings further indicate that successful engineering of Rhg1-related resistance in plants will require a compatible NSF partner for the resistance-conferring α-SNAP.

Field evaluation of three sources of genetic resistance to sudden death syndrome of soybean.

KEY MESSAGE: Despite numerous challenges, field testing of three sources of genetic resistance to sudden death syndrome of soybean provides information to more effectively improve resistance to this disease in cultivars. Sudden death syndrome (SDS) of soybean [Glycine max (L.) Merrill] is a disease that causes yield loss in soybean growing regions across the USA and worldwide. While several quantitative trait loci (QTL) for SDS resistance have been mapped, studies to further evaluate these QTL are limited. The objective of our research was to map SDS resistance QTL and to test the effect of mapped resistance QTL on foliar symptoms when incorporated into elite soybean backgrounds. We mapped a QTL from Ripley to chromosome 10 (CHR10) and a QTL from PI507531 to chromosomes 1 and 18 (CHR1 and 18). Six populations were then developed to test the following QTL: cqSDS-001, with resistance originating from PI567374, CHR10, CHR1, and CHR18. The populations which segregated for resistant and susceptible QTL alleles were field tested in multiple environments and evaluated for SDS foliar symptoms. While foliar disease development was variable across environments and populations, a significant effect of each QTL on disease was detected within at least one environment. This includes the detection of cqSDS-001 in three genetic backgrounds. The QTL allele from the resistant parents was associated with greater resistance than the susceptible alleles for all QTL and backgrounds with the exception of the allele for CHR18, where the opposite occurred. This study highlights the importance and difficulties of evaluating QTL and the need for multi-year SDS field testing. The information presented in this study can aid breeders in making decisions to improve resistance to SDS.

Genome-Wide Analysis of Grain Yield Stability and Environmental Interactions in a Multiparental Soybean Population.

Genetic improvement toward optimized and stable agronomic performance of soybean genotypes is desirable for food security. Understanding how genotypes perform in different environmental conditions helps breeders develop sustainable cultivars adapted to target regions. Complex traits of importance are known to be controlled by a large number of genomic regions with small effects whose magnitude and direction are modulated by environmental factors. Knowledge of the constraints and undesirable effects resulting from genotype by environmental interactions is a key objective in improving selection procedures in soybean breeding programs. In this study, the genetic basis of soybean grain yield responsiveness to environmental factors was examined in a large soybean nested association population. For this, a genome-wide association to performance stability estimates generated from a Finlay-Wilkinson analysis and the inclusion of the interaction between marker genotypes and environmental factors was implemented. Genomic footprints were investigated by analysis and meta-analysis using a recently published multiparent model. Results indicated that specific soybean genomic regions were associated with stability, and that multiplicative interactions were present between environments and genetic background. Seven genomic regions in six chromosomes were identified as being associated with genotype-by-environment interactions. This study provides insight into genomic assisted breeding aimed at achieving a more stable agronomic performance of soybean, and documented opportunities to exploit genomic regions that were specifically associated with interactions involving environments and subpopulations.

A comparison of genotyping-by-sequencing analysis methods on low-coverage crop datasets shows advantages of a new workflow, GB-eaSy.

BACKGROUND: Genotyping-by-sequencing (GBS), a method to identify genetic variants and quickly genotype samples, reduces genome complexity by using restriction enzymes to divide the genome into fragments whose ends are sequenced on short-read sequencing platforms. While cost-effective, this method produces extensive missing data and requires complex bioinformatics analysis. GBS is most commonly used on crop plant genomes, and because crop plants have highly variable ploidy and repeat content, the performance of GBS analysis software can vary by target organism. Here we focus our analysis on soybean, a polyploid crop with a highly duplicated genome, relatively little public GBS data and few dedicated tools. RESULTS: We compared the performance of five GBS pipelines using low-coverage Illumina sequence data from three soybean populations. To address issues identified with existing methods, we developed GB-eaSy, a GBS bioinformatics workflow that incorporates widely used genomics tools, parallelization and automation to increase the accuracy and accessibility of GBS data analysis. Compared to other GBS pipelines, GB-eaSy rapidly and accurately identified the greatest number of SNPs, with SNP calls closely concordant with whole-genome sequencing of selected lines. Across all five GBS analysis platforms, SNP calls showed unexpectedly low convergence but generally high accuracy, indicating that the workflows arrived at largely complementary sets of valid SNP calls on the low-coverage data analyzed. CONCLUSIONS: We show that GB-eaSy is approximately as good as, or better than, other leading software solutions in the accuracy, yield and missing data fraction of variant calling, as tested on low-coverage genomic data from soybean. It also performs well relative to other solutions in terms of the run time and disk space required. In addition, GB-eaSy is built from existing open-source, modular software packages that are regularly updated and commonly used, making it straightforward to install and maintain. While GB-eaSy outperformed other individual methods on the datasets analyzed, our findings suggest that a comprehensive approach integrating the results from multiple GBS bioinformatics pipelines may be the optimal strategy to obtain the largest, most highly accurate SNP yield possible from low-coverage polyploid sequence data.

Impact of seed protein alleles from three soybean sources on seed composition and agronomic traits.

KEY MESSAGE: Evaluation of seed protein alleles in soybean populations showed that an increase in protein concentration is generally associated with a decrease in oil concentration and yield. Soybean [Glycine max (L.) Merrill] meal is one of the most important plant-based protein sources in the world. Developing cultivars high in seed protein concentration and seed yield is a difficult task because the traits have an inverse relationship. Over two decades ago, a protein quantitative trait loci (QTL) was mapped on chromosome (chr) 20, and this QTL has been mapped to the same position in several studies and given the confirmed QTL designation cqSeed protein-003. In addition, the wp allele on chr 2, which confers pink flower color, has also been associated with increased protein concentration. The objective of our study was to evaluate the effect of cqSeed protein-003 and the wp locus on seed composition and agronomic traits in elite soybean backgrounds adapted to the Midwestern USA. Segregating populations of isogenic lines were developed to test the wp allele and the chr 20 high protein QTL alleles from Danbaekkong (PI619083) and Glycine soja PI468916 at cqSeed protein-003. An increase in protein concentration and decrease in yield were generally coupled with the high protein alleles at cqSeed protein-003 across populations, whereas the effects of wp on protein concentration and yield were variable. These results not only demonstrate the difficulty in developing cultivars with increased protein and yield but also provide information for breeding programs seeking to improve seed composition and agronomic traits simultaneously.

Fine Mapping of Resistance Genes from Five Brown Stem Rot Resistance Sources in Soybean.

Brown stem rot (BSR) of soybean [ (L.) Merr.] caused by (Allington & Chamb.) T.C. Harr. & McNew can be controlled effectively with genetic host resistance. Three BSR resistance genes , , and , have been identified and mapped to a large region on chromosome 16. Marker-assisted selection (MAS) will be more efficient and gene cloning will be facilitated with a narrowed genomic interval containing an gene. The objective of this study was to fine map the positions of genes from five sources. Mapping populations were developed by crossing the resistant sources 'Bell', PI 84946-2, PI 437833, PI 437970, L84-5873, and PI 86150 with either the susceptible cultivar Colfax or Century 84. Plants identified as having a recombination event near genes were selected and individually harvested to create recombinant lines. Progeny from recombinant lines were tested in a root-dip assay and evaluated for foliar and stem BSR symptom development. Overall, 4878 plants were screened for recombination, and progeny from 52 recombinant plants were evaluated with simple-sequence repeat (SSR) genetic markers and assessed for symptom development. Brown stem rot resistance was mapped to intervals ranging from 0.34 to 0.04 Mb in the different sources. In all sources, resistance was fine mapped to intervals inclusive of BARCSOYSSR_16_1114 and BARCSOYSSR_16_1115, which provides further evidence that one locus provides BSR resistance in soybean.

Genome-Wide Association Study of Brown Stem Rot Resistance in Soybean across Multiple Populations.

Genetic resistance to brown stem rot (BSR) of soybean [ (L.) Merr.], caused by (Allington & D.W. Chamb.) T.C. Harr. & McNew, has been identified and mapped with biparental populations. Although nearly 400 accessions have been identified with BSR resistance, this trait has been mapped in only 12 sources, and just two, PI84946-2 and PI88788, have been used to develop BSR resistant cultivars. Thus, there is a serious need to improve our knowledge of the genetic basis of BSR resistance in soybean so that resistance genes in cultivars can be diversified and markers close to resistance genes can be identified and used in marker-assisted selection (MAS). To this end, we conducted a genome-wide association study (GWAS) to identify novel genomic loci associated with BSR resistance and to gain further insight into a previously reported chromosome 16 region containing BSR resistance () genes. A total of 52,041 single-nucleotide polymorphisms (SNPs) were tested for association with BSR in a set of 4735 accessions from four diversity panels evaluated for resistance from 1989 to 2003. Using a unified mixed linear model and stepwise model selection, we refined the signals within the interval on chromosome 16 by finding associations that explain a substantial proportion of the total variation of BSR resistance. In combination with significant GWAS signals found elsewhere in the genome, our study will aid efforts to improve BSR resistance by providing new targets for MAS.

Impact of Rhg1 copy number, type, and interaction with Rhg4 on resistance to Heterodera glycines in soybean.

KEY MESSAGE: Evaluations of soybean populations showed that both Rhg1 copy number and type were important in determining soybean cyst nematode resistance with higher copy number within Rhg1 type conferring greater resistance. Rhg1 and Rhg4 are important loci conferring resistance to soybean cyst nematode (SCN; Heterodera glycines). Alleles at Rhg1 have been shown to vary for copy number and type and the importance of this variation in conferring resistance is not well defined. The repeat number ranges from one to 10 and there are three variant repeat sequence types [plant introduction (PI) 88788-'Fayette' type (F), 'Peking' type (P) and Williams 82 type (W)] across diverse soybean germplasm. We developed populations segregating for Rhg1 copy number and type and Rhg4 allele type to investigate the effect of these factors and their interaction on SCN resistance. F2 plants from each cross were evaluated for the segregation of Rhg1 and Rhg4 alleles and for SCN reproduction after infesting plants with HG type 2.5.7 and HG type 7 populations. Within repeat types, an increase in repeat number was associated with greater resistance. The P type Rhg1 showed an advantage over F + W type for SCN population HG type 2.5.7 but this was not observed for SCN HG type 7. While plants with P type Rhg1 required Rhg4 to achieve full resistance, Rhg4 did not increase resistance in the background of F + W type Rhg1 repeat. This study demonstrates the importance of both Rhg1 copy number and type in determining resistance and can assist soybean breeders in determining what alleles would best fit their breeding goals.

An efficient method for measuring copy number variation applied to improvement of nematode resistance in soybean.

Copy number variation (CNV) is implicated in important traits in multiple crop plants, but can be challenging to genotype using conventional methods. The Rhg1 locus of soybean, which confers resistance to soybean cyst nematode (SCN), is a CNV of multiple 31.2-kb genomic units each containing four genes. Reliable, high-throughput methods to quantify Rhg1 and other CNVs for selective breeding were developed. The CNV genotyping assay described here uses a homeologous gene copy within the paleopolyploid soybean genome to provide the internal control for a single-tube TaqMan copy number assay. Using this assay, CNV in breeding populations can be tracked with high precision. We also show that extensive CNV exists within Fayette, a released, inbred SCN-resistant soybean cultivar with a high copy number at Rhg1 derived from a single donor parent. Copy number at Rhg1 is therefore unstable within a released variety over a relatively small number of generations. Using this assay to select for individuals with altered copy number, plants were obtained with both increased copy number and increased SCN resistance relative to control plants. Thus, CNV genotyping technologies can be used as a new type of marker-assisted selection to select for desirable traits in breeding populations, and to control for undesirable variation within cultivars.

Has photosynthetic capacity increased with 80 years of soybean breeding? An examination of historical soybean cultivars.

Crop biomass production is a function of the efficiencies with which sunlight can be intercepted by the canopy and then converted into biomass. Conversion efficiency has been identified as a target for improvement to enhance crop biomass and yield. Greater conversion efficiency in modern soybean [Glycine max (L.) Merr.] cultivars was documented in recent field trials, and this study explored the physiological basis for this observation. In replicated field trials conducted over three successive years, diurnal leaf gas exchange and photosynthetic CO2 response curves were measured in 24 soybean cultivars with year of release dates (YOR) from 1923 to 2007. Maximum photosynthetic capacity, mesophyll conductance and nighttime respiration have not changed consistently with cultivar release date. However, daily carbon gain was periodically greater in more recently released cultivars compared with older cultivars. Our analysis suggests that this difference in daily carbon gain primarily occurred when stomatal conductance and soil water content were high. There was also evidence for greater chlorophyll content and greater sink capacity late in the growing season in more recently released soybean varieties. Better understanding of the mechanisms that have improved conversion efficiency in the past may help identify new, promising targets for the future.

Evolution and selection of Rhg1, a copy-number variant nematode-resistance locus.

The soybean cyst nematode (SCN) resistance locus Rhg1 is a tandem repeat of a 31.2 kb unit of the soybean genome. Each 31.2-kb unit contains four genes. One allele of Rhg1, Rhg1-b, is responsible for protecting most US soybean production from SCN. Whole-genome sequencing was performed, and PCR assays were developed to investigate allelic variation in sequence and copy number of the Rhg1 locus across a population of soybean germplasm accessions. Four distinct sequences of the 31.2-kb repeat unit were identified, and some Rhg1 alleles carry up to three different types of repeat unit. The total number of copies of the repeat varies from 1 to 10 per haploid genome. Both copy number and sequence of the repeat correlate with the resistance phenotype, and the Rhg1 locus shows strong signatures of selection. Significant linkage disequilibrium in the genome outside the boundaries of the repeat allowed the Rhg1 genotype to be inferred using high-density single nucleotide polymorphism genotyping of 15 996 accessions. Over 860 germplasm accessions were found likely to possess Rhg1 alleles. The regions surrounding the repeat show indications of non-neutral evolution and high genetic variability in populations from different geographic locations, but without evidence of fixation of the resistant genotype. A compelling explanation of these results is that balancing selection is in operation at Rhg1.