The objective of this research was to identify single nucleotide polymorphisms (SNPs) and to develop an Illumina Infinium BeadChip that contained over 50,000 SNPs from soybean (Glycine max L. Merr.). A total of 498,921,777 reads 35–45 bp in length were obtained from DNA sequence analysis of reduced representation libraries from several soybean accessions which included six cultivated and two wild soybean (G. soja Sieb. et Zucc.) genotypes. These reads were mapped to the soybean whole genome sequence and 209,903 SNPs were identified. After applying several filters, a total of 146,161 of the 209,903 SNPs were determined to be ideal candidates for Illumina Infinium II BeadChip design. To equalize the distance between selected SNPs, increase assay success rate, and minimize the number of SNPs with low minor allele frequency, an iteration algorithm based on a selection index was developed and used to select 60,800 SNPs for Infinium BeadChip design. Of the 60,800 SNPs, 50,701 were targeted to euchromatic regions and 10,000 to heterochromatic regions of the 20 soybean chromosomes. In addition, 99 SNPs were targeted to unanchored sequence scaffolds. Of the 60,800 SNPs, a total of 52,041 passed Illumina’s manufacturing phase to produce the SoySNP50K iSelect BeadChip. Validation of the SoySNP50K chip with 96 landrace genotypes, 96 elite cultivars and 96 wild soybean accessions showed that 47,337 SNPs were polymorphic and generated successful SNP allele calls. In addition, 40,841 of the 47,337 SNPs (86%) had minor allele frequencies ≥10% among the landraces, elite cultivars and the wild soybean accessions. A total of 620 and 42 candidate regions which may be associated with domestication and recent selection were identified, respectively. The SoySNP50K iSelect SNP beadchip will be a powerful tool for characterizing soybean genetic diversity and linkage disequilibrium, and for constructing high resolution linkage maps to improve the soybean whole genome sequence assembly.
Citation: Song Q, Hyten DL, Jia G, Quigley CV, Fickus EW, Nelson RL, et al. (2013) Development and Evaluation of SoySNP50K, a High-Density Genotyping Array for Soybean. PLoS ONE 8(1): e54985. https://doi.org/10.1371/journal.pone.0054985
Editor: Tianzhen Zhang, Nanjing Agricultural University, China
Received: September 10, 2012; Accepted: December 18, 2012; Published: January 25, 2013
This is an open-access article, free of all copyright, and may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose. The work is made available under the Creative Commons CC0 public domain dedication.
Funding: This study was partially funded by the United Soybean Board (http://www.unitedsoybean.org/) as part of Project #8265 entitled: Whole Genome Analysis of the USDA Soybean Germplasm Collection and Applications for New Gene Discovery (50,000 SNPs). The United Soybean Board has read the manuscript and has indicated their approval of the manuscript. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Competing interests: The authors have declared that no competing interests exist.
Even with the extensive linkage disequilibrium (LD) in the soybean [Glycine max (L). Merr.] genome, a large number of molecular markers are required to cover the genome for the purpose of genome-wide genetic analysis. Single nucleotide polymorphisms (SNPs) are the most abundant form of DNA polymorphism in eukaryotic genomes , and are suitable for the development of high-throughput, easy-to-automate genotyping methods because most SNPs have only two alleles, thereby simplifying genotyping approaches and analysis –. Previous procedures of SNP discovery in plants involved primer selection, PCR amplification and sequencing of amplicons from a small set of diverse genotypes –. This approach is time-consuming and expensive. With the advent of second generation sequencing technology, large amounts of DNA sequence data can be generated in a short period of time. Coupled with the availability of the soybean whole genome sequence , large numbers of sequence variants can be efficiently identified via the alignment of the short sequence reads from diverse genotypes to the whole genome sequence –. High throughput SNP discovery is a necessity in light of the availability of high throughput SNP assay systems such as the Infinium platform from Illumina that allows the assay of large numbers of SNPs per DNA sample in parallel on a single silicon slide (http://www.illumina.com/). Thus, the objective of this research was to efficiently identify large numbers of SNPs in soybean using advanced sequencing technologies and SNP identification pipelines; to develop a custom high resolution Illumina Infinium BeadChip containing SNPs with high minor allele frequency, even distribution across the euchromatic and heterochromatic regions of the soybean genome; to evaluate the performance of the chip in a set of diverse landrace and elite soybean genotypes as well as in a diverse set of wild soybean [G. soja (Sieb. et Zucc.)] genotypes and to identify candidate regions that may be associated with domestication and recent selection imposed by plant breeding.
SNP Discovery and Randomness of SNP Distribution in the Soybean Genome
The total number of short reads and total bases obtained from the eight genotypes were 498,921,777 and 18,313 Mb, respectively and the total bases obtained from the eight genotypes ranged from 277 Mb (0.3×) to 6,473 Mb (6.8×) (Table 1). Following alignment of the short reads to the Williams 82 (Glyma1.01) genome sequence, 209,903 SNPs were identified by MAQ and/or CASAVA. The average distance between adjacent SNPs was 4.5 kb. Only 18, 193 and 1575 of the 209,863 gaps between adjacent SNPs had sequence length >200 kb, 100 kb and 50 kb, respectively and the largest gap was 328 kb. Thus, in general, the identified SNPs were fairly well distributed throughout the soybean genome.
Of the 209,903 SNPs, a total of 28,891 were A vs. T or G vs. C SNPs and thus, the remaining 188,272 SNPs were a potential source for Infinium II assay (duel color channel, single bead). After eliminating SNPs with N’s or ambiguity codes in the 60 nt of flanking sequence and SNPs residing within 25 nt of another SNP, a total of 150,171 of the 188,272 SNPs for Infinium II assay remained. After elimination of the SNPs with flanking sequences that were not unique in the genome, a total of 146,161 SNPs, including 785 SNPs from unanchored scaffolds, was retained and these SNPs were ideal candidates for Infinium II assay design.
Initial SNP Verification and Calculation of Priority Scores
Of the 146,161 SNPs, a total of 23,721, 50,229, 58,281 and 13,930 were assigned to priority groups of A, B, C, and D, respectively. Initial validation of 767 SNPs via Sanger sequencing gave validation rates of 100%, 83%, 77% and 67% for the SNPs with the A, B, C, and D scores, respectively (Table 2). These rates were used as priority scores (Ri) for the corresponding priority groups for calculation of the SNP selection index.
Selection of a Set of 60,800 SNPs
Because Illumina only allowed 60,800 SNPs in the bead pool at the time the chip was developed, a total of 60,701 SNPs were selected from the 20 chromosomes (Gm01–Gm20) and 99 SNPs were selected from 71 unanchored sequence scaffolds (Table S1). Of the 60,701 SNPs, a total of 50,701were selected from the euchromatic regions and 10,000 SNPs were selected from heterochromatic regions with approximately a 5∶1 ratio. Thus, the average density of SNPs was approximately 110.5 SNPs per Mb and 20.4 SNPs per Mb in euchromatic and heterochromatic regions, respectively (Table 3). The number of SNPs chosen from each chromosome was the product of the chromosome sequence length by the average SNP density in the euchromatic and heterochromatic regions, respectively. The SNPs along each chromosome were then selected with the iteration algorithm. Genetic mapping of the 99 SNPs from the 71 unanchored sequence scaffolds is expected to anchor 6.5 Mb of the 23 Mb of sequence that was not anchored in the Glyma1.01 build.
Index Score and Evenness of Spacing between the 60,800 Selected SNPs
After SNP selection, the SNP selection index score of the 60,800 selected SNPs was increased versus the source set of 146,161 SNPs. The average SNP selection index score of the selected SNPs was 0.69 vs. 0.60 for the complete set of 146,161 SNPs. Of the 146,161 SNPs, 73,084 were located in euchromatic DNA and 72,272 were located in heterochromatic regions. A total of 50,701 SNPs were selected from the euchromatic regions and 10,000 were selected from the heterochromatic regions (Table 3). In the pre-selected set, the average distance between SNPs was 6.3 kb and 6.8 kb with a standard deviation of 10.7 kb and 14.9 kb in the euchromatic and heterochromatic regions, respectively. Of the 73,084 SNPs identified in the euchromatic regions, 21,721 (30%) were at distances of ≤2 kb between adjacent SNPs. In the heterochromatic regions, a total of 58,766 SNPs were at distances of ≤10 kb, which was 81% of the 72,272 SNPs in the heterochromatic regions. Of these, 62,272 were eliminated via the application of the iteration algorithm. After elimination of SNPs via the application of the iteration algorithm, the result was an average distance between adjacent SNPs in the selected set of 60,800 SNPs of 9.1 kb and 49.1 kb with a standard deviation of 9.1 kb and 33.3 kb in the euchromatic and heterochromatic regions, respectively. Thus, the uniformity of the distance between selected SNPs was enhanced in the euchromatic regions and clustered SNPs were greatly reduced in the heterochromatic regions as a result of SNP selection via the application of the iteration algorithm (Fig. 1).
(A) Selected SNPs (50,701) in euchromatic regions of SoySNP50K and the pre-selected SNPs (73,084) in euchromatic regions. (B) Selected SNPs (10,000) in heterochromatic regions and pre-selected SNPs (72,272) in heterochromatic regions.
Distribution of SNPs in Genic Regions
A total of 16,381 (27%) of the 60,800 SNPs resided in genes and, of these, 6,820 SNPs were located in coding DNA sequence, 8,103 SNPs in introns, 752 SNPs in 5′untranslated regions and 706 SNPs in 3′UTR (Table S1). Within 2 kb upstream of genes there were a total of 7,707 SNPs and 6,723 SNPs were within 2 kb downstream of genes. Overall, more than 50% of the 60,800 SNPs resided within genes or within 2 kb upstream or downstream of genes.
Infinium Chip Performance
Of the 60,800 SNPs submitted to Illumina for inclusion in the SNP assay, 52,041 SNPs, or 86% of the 60,800 passed Illumina manufacturing quality control and were included in the final bead pool of the SoySNP50K Infinium BeadChip. Validation of the SoySNP50K Infinium BeadChip was performed via the analysis of diverse germplasm including 96 landraces, 96 elite cultivars and 96 wild soybean accessions. Of the 52,041 SNPs, 47,337 (91%) were polymorphic and yielded successful SNP allele calls based upon clear separation between the fluorescent signals from the two homozygous genotypic classes for a given SNP as assessed with the normalized theta analysis of the Illumina GenomeStudio SNP analysis software.
The rate of SNPs producing successful SNP allele calls was associated with the SNP selection index score, the Illumina Infinium SNP design score and the priority group (Table 4). The correlation coefficients of the SNP selection index score and SNP score vs. the rate of SNPs with successful allele calls were 0.93 and 0.94, respectively, and both were significant at the 1% level. Correlations with priority group were not calculated because priority group is not a continuous variable and the number of priority groups is small. However, it appeared that higher priority group resulted in an increased rate of SNPs with successful allele calls (Table 4).
Estimate of SNP Minor Allele Frequency in Diverse Germplasm
Of the 47,337 SNP assays that produced successful allele calls, 40,841 (86%) had a minor allele frequency ≥10% when tested on the 96 landraces, 96 elite cultivars and 96 wild soybeans. Within the individual groups, the landraces had the most SNPs with a minor allele frequency ≥10% while the elite cultivars had the fewest SNPs with a minor allele frequency ≥10% (Table 5). Only 3,294 of the 47,337 had a minor allele frequency ≤5% within the diverse germplasm (Table 5).
Estimate of Genetic Distance and Number of Polymorphic SNPs among Pairs of Genotypes within the 96 Landraces, the 96 Elite Cultivars and the 96 Wild Accessions
Genetic distances of 47.6% of the pairs of genotypes among the elite cultivars, 68.1% among the landraces and 48.1% among the wild accessions were greater than 0.3 based on the SNPs with minor allele frequency (MAF) >0.05 (Table 6). The mean genetic distance and the standard deviation were 0.294 and 0.0393 among pairs of elite cultivars, 0.322 and 0.0518 among pairs of landraces, and 0.292 and 0.0528 among pairs of wild accessions, respectively. On average, 9,974 and 18,151 SNPs were expected to be polymorphic between a random pair of elite cultivars or landraces, respectively. A total of 9,094 to15,565, 9,240 to 17,761 and 7,889 to 16,576 SNPs were projected to be polymorphic between any randomly selected pair of elite cultivars, landraces and wild soybean accessions at the 95% probability level, respectively.
Identification of Regions with Signatures of Selection
A total of 620 and 42 regions (100 kb windows), contained loci with high average fixation indices (Fst≥0.6) between G. soja and landrace and between landrace and elite populations, respectively (Table 7, Table S2). These regions may contain loci associated with domestication from the wild soybean or with selection imposed during 70 years of North American soybean breeding, respectively. The examination of these regions will provide the basis for much further investigation aimed at the genic content of regions that underwent selection during domestication and during the genetic improvement of soybean adapted to the diverse soybean producing environments of North America. At this time we thought it appropriate to briefly describe one of the genome regions that was apparently under selection during soybean domestication and one with the selection imposed by North American soybean breeding. In the case of a region associated with domestication, we observed that the region flanked by Sat_355 and Satt474 of soybean linkage group B2 , i.e., position13,784,007–33,076,661 of the Gm14 in Glyma1.01 , has been reported to contain major quantitative trait loci (QTL) controlling seed yield and plant height , oil , seed size –, and stem strength . Most of these traits were very likely affected during domestication, accordingly, we observed that most of the Fst values between G. soja and the landraces in this region were greater than 0.6. In contrast, most of the Fst values between the elite and landrace populations were less than 0.1 (Fig. 2a).
(A) Significant Fst values on Gm14 between SSR markers Sat_355 and Satt474 may contain loci associated with soybean domestication (B) Significant Fst values between SSR markers Satt396 and Sat_140 may contain loci that were under selection in N. American soybean breeding programs.
In the case of selection imposed during the years of North America soybean breeding, an example of a strong peak of Fst between the landrace and elite populations was observed between 2.7–5.0 mbp of Gm04 in Glyma1.01 (Fig. 2b). QTL for resistance to soybean cyst nematode (SCN) race 2 ,  seed size , pods per nod  and seed protein concentration  have been reported in the region flanked by Satt396 and Sat_140 of soybean linkage group C1 , i.e., position 2,719,766–5,221,447 of Gm04 in Glyma1.01 .
A number of options exist for high throughput SNP genotyping and the Illumina GoldenGate platform is a commonly used assay in plants. Examples of its application in plants include 1536-SNP assays in barley , , maize , Aegilops tauschii , common bean , and a 96-SNP assay in wheat . In soybean, a universal linkage panel containing 1536 SNPs for high-throughput soybean QTL mapping was developed . The Illumina Infinium assay provides a significantly higher level of SNP genotyping capacity and recent Infinium assays with successful allele calls for nearly 50,000 SNPs were reported in maize (Zea mays L.)  and assays for nearly 8,000 SNPs have been reported in sunflower (Helianthus annuus L.) , apple (Malus×domestica)  and tomato (Solanum lycopersicum) . The SoySNP50K is the first Infinium beadchip containing >50 k SNPs in soybean. This assay is being applied to genotype more than 19,000 cultivated and wild soybean accessions in the USDA Soybean Germplasm Collection and more than 960 recombinant inbred lines in each of two mapping populations at the USDA-ARS, Beltsville, MD. The resulting datasets will assist in the application of genome-wide association studies of important traits and to detect signatures of selection and evolution, pinpoint genes or regions controlling phenotypic variants and define whole-genome LD patterns and regions associated with soybean domestication. The SoySNP50K BeadChip will expedite the development of high resolution linkage maps to improve the previous whole genome sequence assembly and to position unanchored scaffolds to the current assembly (Glyma1.01).
The justification for the reduced representation library used in the SNP discovery was to reduce the complexity of the subset of the genome that was sequenced for SNP discovery and to generate large sets of common fragments for alignment. This approach may leave portions of the genome without sequence and thus without SNPs. However, due to the use of 10 different restriction enzymes and the resulting diversity of restriction sites, the identified SNPs were, for the most part, randomly distributed in the soybean genome.
The density of genes and the recombination rate in euchromatic and heterochromatic regions differ dramatically with much lower recombination and a lower gene density in the heterochromatic vs. the euchromatic regions . Low recombination rate would be expected to result in more extensive LD in the heterochromatic regions, although the actual extent is unknown. Thus, we attempted to design a beadchip with higher SNP density in the euchromatic than in the heterochromatic regions i.e., 1 SNP every 9.1 kb in euchromatic regions and one SNP every 49.1 kb in heterochromatic regions. To avoid clustering, SNPs were selected such that the distance between SNPs was as near as possible to 9.1 kb in the euchromatic and 49.1 kb in the heterochromatic regions. Thus, the selected SNPs would evenly cover both the euchromatic and heterochromatic regions. The very different SNP densities in euchromatic and heterochromatic regions and their association with the anticipated difference in the magnitude of LD in the two regions were done in anticipation of the application of genome wide association studies in diverse populations of accessions selected from the USDA Soybean Germplasm Collection.
During the SoySNP50K BeadChip development, account was taken of the factors that were essential for a successful assay design i.e., high allele call success rate, high minor allele frequency and the even distribution of SNPs in heterochromatic and euchromatic regions across the soybean genome. The success rate of a beadchip is subject to the bead type success rate during manufacture and the rate of successful allele calls in genotyping. The bead type success rate is the proportion of the total number of attempted assays present in the final product. Illumina guarantees a bead type success rate of >80% but not 100% due to the imperfect efficiency of manufacturing steps such as synthesizing oligonucleotide probes, attaching probes to silica beads, pooling beads, loading bead pools and decoding the beads identified on the beadchip (http://www.illumina.com/Documents/products/technotes/technote_iselect_design.pdf). The 86% bead type success rate for the SoySNP50K beadchip is in the normal range defined by Illumina, Inc. The allele call success rate is the proportion of the total number of assays generating reliable allele calls among the total number of assays in the final product. Reliability of the SNP allele call is dependent on the intensity of fluorescent signals from both alleles in the SNP Graph displayed by the GenomeStudio software and the distinct separation of the clusters of the allelic classes i.e., the two homozygotes and the heterozygotes, as displayed in the normalized theta output of GenomeStudio. Because the fluorescent signal of SNPs may vary with DNA samples and assays, the incidence of poor allele clustering such as multiple or indistinguishable clusters of samples may occur and lead to inaccurate allele calls. Based on our standard to define SNPs with successful allele calls, the rate of successful allele calls using the SoySNP50K beadchip was 91%, which is comparable to the rate of 77–91% reported in human populations using the Illumina Infinium 550 K beadchip , 92% in cattle using Illumina Infinium assay BovineSNP50  and 81% in horse using the Illumina commercial assay EquineSNP50 .
Rare alleles can produce spurious associations between SNP markers and phenotypes and are therefore often not used in certain genetic analyses. Therefore, SNPs with minor allele frequency below a certain threshold should be avoided. In order to detect SNPs with high minor allele frequency, we sequenced mixed DNAs from six diverse cultivated and two wild soybean genotypes and were able to identify SNPs by aligning reads from the mixed DNA to the reference sequence. Because the SNPs with one allele identical to the reference allele and another to the alternative allele that was present in at least one of the genotypes as determined using short reads of individual genotypes were given the highest priority score, these SNPs were most likely retained during subsequent selection. Statistically, in the mixed DNA, reads containing the minor allele were less likely to be present than reads containing the common allele. We observed that only 7% of the SNPs assayed with SoySNP50K had a minor allele frequency <0.05 in the 288 G. max and G. soja accessions analyzed. This proportion is generally lower than that reported in various populations of dog, cattle, horse, pig and sheep using Illumina BeadChips , , .
Numerous candidate regions associated with domestication and selection imposed by soybean breeding in North America were identified by examining the Fst between the populations. The example of the high fixation indices (Fst≥0.6) between G. soja and landrace in the 13–30 Mb region on Gm14 (Fig. 2a) provides one such region that is likely the result of a domestication sweep. While the high Fst between the elite cultivars and landraces on Gm04 (Fig. 2b) identifies a region that was likely under selection imposed by North American soybean breeders, it is not immediately clear why this region was the target of strong selection. As indicated earlier, QTL for resistance to soybean cyst nematode (SCN) race 2 was been reported in this region , . However, the QTL was present in PI 438498B and was not reported until 2001 and is thus unlikely to have been used in the breeding of the 96 elite cultivars included in this study that were released between 1990 and 2000. Relative to the other QTL reported in this region i.e., seed size , pods per node  and seed protein concentration  there is no suggestion that breeders have specifically targeted this region in any type of marker assisted selection program. However, it is possible that selection for improved agronomic traits and higher yields over many years of N. American soybean breeding has altered allele frequency in this region on Gm04. There are data available in the USDA Germplasm Resources Information Network (GRIN) database for seed size (g/100 seeds) for some of the 96 landrace and 96 elite cultivars we analyzed. Gizlice et al.  analyzed the pedigrees of modern N. American soybean cultivars and identified 25 landraces that were the ancestors of modern N. American cultivars that contributed 90% of the genetic base of modern cultivars. Seed size data for 23 of the 25 ancestors were available in GRIN and had a mean seed size of 16.4 g/100 seeds. GRIN also contained data for 39 of the 96 elite cultivars we analyzed. These had a mean seed size of 14.7 g/100 seeds. This suggests that selection over the many years of N. American soybean breeding has reduced seed size. The soybean decade’s study that is comparing soybean ancestors along with cultivars developed over the years supports the contention of reduced seed size of modern cultivars versus ancestral cultivars (Personal communication – R. Nelson). Further analysis of this genome region is clearly of interest.
Knowledge of the positions of the regions associated with domestication and with gene frequency changes associated with N. American soybean breeding is critical for the identification of genes controlling important agronomic traits. However, due to limited sample size, the span of the regions identified in the current study are still quite large. As the analysis of all of the more than 19,000 annual Glycine accessions in the USDA Soybean Germplasm Collection with the SoySNP50K proceeds it is anticipated that regions under selection will be detected with high statistical power and resolution. We can anticipate that the identification of such regions with strong signatures of selection will be the targets of investigation with the goal of identifying the impacted phenotype and the gene or genes that underlie the phenotypic change(s).
Materials and Methods
Sequencing a Panel of Eight Soybean Genotypes with the Illumina Genome Analyzer
Six soybean genotypes, Essex, Evans, Archer, Minsoy, Noir 1, and Peking and two wild soybean accessions, PI 468916 and PI 479752, were used for re-sequencing. The eight accessions are parents of soybean mapping populations. DNA of each of the eight genotypes was extracted from leaf or root tissues and each was digested separately with two sets of five restriction enzymes: Set 1: HaeIII, PsiI, SspI, RsaI; and Ms1I and Set 2: Cvik1–1, DraI, SnaB1, PshA1, and Stu1. The resulting digests were combined and the restriction fragments were size selected on a 1.0% agarose gel and the 110–140 bp fraction was purified and prepared for Illumina GA sequencing (Illumina Inc, San Diego, CA). Protocols for adapter ligation, cluster generation and sequencing by synthesis on the Illumina GAII were followed and read lengths of 35–45 bp were obtained. The Goat Pipeline (v 1.3) was used for base calling and quality assessment. A total of 56 lanes of sequence data were collected from the eight genotypes, in addition, 15 lanes of sequence data were collected from mixed DNA of Archer, Evans, Noir 1, Minsoy and Peking.
Sequence Alignment and SNP Identification
The whole genome sequence (WGS) of Williams 82 (Glyma1.01)  was used as the alignment reference and the short sequences from Essex, Evans, Archer, Minsoy, Noir 1, Peking, PI468916 and PI479752 were mapped to the reference sequence using MAQ (v0.6.8)  and Illumina ELAND software (Illumina Inc, San Diego, CA) of the Goat pipeline. SNPs were called using MAQ and Illumina CASAVA software (Illumina Inc, San Diego, CA). Default parameters of MAQ and CASAVA for SNP identification were applied except for the minimum read depth in MAQ, which was set to 1. Called SNPs were eliminated for Infinium design if any of the following conditions were met: both alleles were present in a genotype (indicative of paralogous sequence); the SNP allele from an individual genotype did not match the ambiguity code or the allele from the mixed DNA; 60 bases of SNP-flanking sequences contained N; there was another SNP residing within 25 bases of an identified SNP; the 25 nt SNP flanking sequences were not specific in the whole genome and the SNP type was A/T or G/C or an indel.
Initial Verification of SNP Calls from MAQ and CASAVA in Order to Set Parameters to Effectively Select SNPs for Infinium BeadChip Design
In order to optimize the selection criteria to effectively design the Infinium BeadChip, a total of 767 SNP-containing loci were randomly selected for preliminary validation of the accuracy of SNP calls. A PCR primer set targeting the SNP position was designed to each of the loci. Primers were designed using standalone Primer 3 (http://primer3.sourceforge.net/releases.php). The targeted PCR product length ranged from 400–600 bp, with the annealing temperature from 55–62°C and the primer length from 18–27 nucleotides. In order to reduce the likelihood of non-specific annealing of primers, all primer sequences were examined with e-PCR software  with the parameters of maximum number of mismatching nucleotides in one primer sequence of N = 3 and maximum number of gaps of one primer sequence of G = 1. Primer sets that were not uniquely aligned to the genome sequence under this stringency were discarded and reselected. The resulting amplification from genomic DNA of the genotypes containing the non-reference allele was sequenced with the ABI 3730 (Applied Biosystems, Foster City, CA) as described by Choi et al. , and the sequence was aligned to the soybean reference sequence. Variants were identified with PolyBayes SNP discovery software , .
Identification and Screening of Pre-existing SNPs at dbSNP of NCBI
A total of 5300 SNPs in soybean had previously been genetically mapped and assayed in multiple genotypes using Sanger sequencing , , . The flanking sequences of the SNPs were aligned to the WGS of Williams 82 using standalone Megablast software (http://www.ncbi.nlm.nih.gov/blast/megablast.shtml) and the specific positions of SNPs in the genome were determined with W = 50, cutoff percentage of alignment = 98 and low complexity filtered as described previously . A total of 2739 of the 5300 SNPs passed filters as described above and were included for further selection.
Procedure and Iteration Algorithm for the Selection of a Core Set of SNPs
SNPs were assigned a priority group of A, B, C or D as follows: A. SNPs were called from at least one genotype with read depth greater than or equal to 1, and the two SNP alleles from the reference and non-reference sequences were consistent with the IUB code called from mixed DNA or the SNPs were already present in dbSNP at NCBI and had previously been successfully assayed in multiple genotypes. B. SNPs were identified in at least two genotypes. C. SNPs were detected in only one genotype but the read depth was greater than 3. D. SNPs were called from only one genotype with read depth less than three, and the non-reference SNP allele was also present in the mixed DNA. A priority score (Ri) was then calculated for each SNP as the ratio of verified to examined SNPs in each priority group among the 767 SNP-containing loci in the verification of SNP calls using ABI 3730 Sanger sequence analysis. In addition, the Illumina Infinium design score provided by Illumina, which is a predictor of assay success, was assigned for each SNP using Illumina’s Assay Design Tool for Infinium (Illumina, Inc., San Diego). SNPs with a design score less than 0.4 were eliminated. For each SNP, a SNP selection index score (Ii) was calculated by multiplying the priority score (Ri) with the Illumina design score (Di), i.e. Ii = Ri×Di.
Based on the SNP selection index scores, an iteration algorithm was developed to select the desired number of SNPs with desired physical distance between adjacent SNPs in euchromatic and heterochromatic regions and with elevated index scores. The total number of SNPs to be selected from the euchromatic and the heterochromatic regions was determined based upon the ratio of genetic map distance to physical distance in bp in the euchromatic versus the heterochromatic regions. The definition of the physical lengths of the euchromatic and heterochromatic regions was as follows: The physical positions of mapped SNPs and simple sequence repeats (SSRs) , , , ,  in the soybean genome were determined by BLAST analysis of the SNP and SSR-containing source sequence to the soybean whole genome sequence using the standalone Megablast software as previously described . The cumulative genetic distances (cM)  were plotted against their cumulative physical distance (Mbp) to determine the base pair/centiMorgan relationship via the common SSR and SNP loci positions on the genetic linkage map and their genome sequence along each chromosome. The region between the two inflection points of the cumulative genetic distance against cumulative physical distance on the plot was defined as the heterochromatic region. The reliability of defining heterochromatic regions using this method was examined in rice, and showed that the heterochromatic region defined by suppressed recombination rate was in agreement with that identified by the conventional DAPI staining method . Since the ratio is about 5∶1, the density of selected SNPs was determined to be 5 times greater in the euchromatic vs. the heterochromatic regions. The desired number of SNPs to be selected from euchromatic and heterochromatic regions of each chromosome was calculated as Lij/Tj×Wj, where Lij is the sequence length (bp) of the euchromatic (j = 1) or heterochromatic region (j = 2) for the ith chromosome and Tj and Wj were the total sequence length (bp) and total number of SNPs to be selected in the euchromatic or heterochromatic regions across the 20 soybean chromosomes, respectively. For each iteration of the algorithm, gaps (sequence length) between adjacent SNPs were calculated along each chromosome. Subsequently, the smallest gap along each chromosome was identified and the SNP selection index scores of the SNPs flanking the gap were calculated. The SNP with the smallest SNP selection index score was removed. If the SNP selection index scores of the two SNPs were identical, the SNP with the smallest gap to another adjacent SNP was eliminated. If two or more gaps in a single iteration were identical, the SNP with the smallest SNP selection index score among the SNPs flanking the gaps was eliminated. The iteration continued until the desired number of SNPs in euchromatic and heterochromatic regions was reached. In addition, the whole genome sequence of Williams 82 (Glyma1.01) contains 236 unanchored scaffolds with lengths from 10 kb to 100 kb and 51 unanchored scaffolds with lengths greater than 100 kb. SNPs identified in these scaffolds were also included in order to facilitate anchoring of these scaffolds for the next genome assembly.
Validation of the SoySNP50K iSelect SNP Beadchip
A group of 96 diverse landraces, 96 elite cultivars and 96 G. soja accessions were used to determine the allele frequency of selected SNPs on the SoySNP50K iSelect SNP BeadChip. The 96 landraces  were from China, Japan and Korea and represent a wide range of geographic origin and maturity groups. The 96 elite cultivars were from North America and represent a diversity of publically developed cultivars released from 1990 to 2000 , . The 96 G. soja accessions were from China, Korea, Japan and Russia and were selected based upon the wide ranges of latitude and longitude from where they were collected (Table S3). SNP genotyping was conducted on the Illumina platform by following the Infinium® HD Assay Ultra Protocol (Illumina, Inc. San Diego, CA). The Infinium II assay protocol includes the procedures to make, incubate and fragment amplified DNA, prepare the bead assay, hybridize samples to the bead assay, extend and stain samples, and image the bead assay. The SNP alleles were called using the GenomeStudio Genotyping Module v1.8.4 (Illumina, Inc. San Diego, CA). A stringent standard to define SNPs with successful calls was adopted so that only those SNPs with two or three discrete clusters and both alleles with high signal intensity on the SNP Graph Alt based on the assessment populations were counted and included for the calculation of the success rate of genotyping even though some of the exclusions were likely real SNPs.
A Pearson’s correlation coefficient was used to investigate the correlation between the SNP selection index score, the Illumina Infinium SNP design score and the rate of SNPs with successful allele calls.
Genetic distance among pairs of genotypes within the 96 landraces, within the 96 elite cultivars and within the 96 wild accessions was calculated as the ratio of the number of polymorphic SNPs vs. total number of SNPs for which allele calls were made for the pair.
The fixation index (Fst) is a measure of population differentiation. Fst values range from 0 to 1, with zero representing no allele differentiation and 1 representing complete allele differentiation between the two populations. The Fst between G. soja and the landraces and between the landraces and elite cultivars were calculated for each locus using the software Arlequin v3.1 and the significance of the Fst was tested using a non-parametric permutation approach . Subsequently, an average Fst of SNP loci in 100 kb windows along each chromosome was calculated.
The physical position of SSR markers flanking QTL controlling seed size, protein and oil content, seed hardness, seed color, and other traits that had been previously reported were obtained from BARCSOYSSR_1.0 .
SNP type, chromosome and SNP position, alternative alleles, SNP flanking sequences, priority group, scores, polymorphism, position in gene (if applicable), MAF in elite, landrace and wild soybean populations.
Average Fst G. soja vs. Landraces and Fst Elite cultivars vs. Landraces in 100 kbp windows along each chromosome.
We thank Rob Parry, Chris Pooley, Steven Schroder for their technical support in assembling the necessary hardware and software required for the Illumina sequence analysis.
Designed the Illumina Infinium BeadChip: QS DLH PC. Conceived and designed the experiments: DLH PC. Performed the experiments: CQ EF GJ. Analyzed the data: QS DLH PC. Contributed reagents/materials/analysis tools: RLN. Wrote the paper: QS PC RLN.
- 1. Brookes AJ (1999) The essence of SNPs. Gene 234(2): 177–186.
- 2. Lin CH, Yeakley JM, McDaniel TK, Shen R (2009) Medium- to high-throughput SNP genotyping using VeraCode microbeads. Methods Mol Biol 496: 129–142.
- 3. Ding C, Jin S (2009) High-throughput methods for SNP genotyping. Methods Mol Biol 578: 245–254.
- 4. Close TJ, Bhat PR, Lonardi S, Wu Y, Rostoks N, Ramsay L (2009) Development and implementation of high-throughput SNP genotyping in barley. BMC Genomics 10: 582.
- 5. Barreiro LB, Henriques R, Mhlanga MM (2009) High-throughput SNP genotyping: combining tag SNPs and molecular beacons. Methods Mol Biol 578: 255–276.
- 6. Yoon MS, Song QJ, Choi IY, Specht JE, Hyten DL, et al. (2007) BARCSoySNP23: a panel of 23 selected SNPs for soybean cultivar identification. Theor Appl Genet 114: 885–899.
- 7. Nasu S, Suzuki J, Ohta R, Hasegawa K, Yui R, et al. (2002) Search for and analysis of single nucleotide polymorphisms (SNPs) in rice (Oryza sativa, Oryza rufipogon) and establishment of SNP markers. DNA Res 9: 163–171.
- 8. Somers DJ, Kirkpatrick R, Moniwa M, Walsh A (2003) Mining single-nucleotide polymorphisms from hexaploid wheat ESTs. Genome 46: 431–437.
- 9. Zhu YL, Song QJ, Hyten DL, Van Tassell CP, Matukumalli LK, et al. (2003) Single-nucleotide polymorphisms in soybean. Genetics 163: 1123–1134.
- 10. Schmutz J, Cannon SB, Schlueter J, Ma J, Mitros T, et al. (2010) Genome sequence of the palaeopolyploid soybean. Nature 463: 178–183.
- 11. Hyten DL, Cannon SB, Song QJ, Weeks N, Fickus EW, et al. (2010) High-throughput SNP discovery through deep resequencing of a reduced representation library to anchor and orient scaffolds in the soybean whole genome sequence. BMC Genomics 11: 38.
- 12. Kim MY, Lee S, Van K, Kim TH, Jeong SC, et al. (2010) Whole-genome sequencing and intensive analysis of the undomesticated soybean (Glycine soja Sieb. and Zucc.) genome. Proc Natl Acad Sci 107: 22032–22037.
- 13. Lam HM, Xu X, Liu X, Chen W, Yang G (2010) Resequencing of 31 wild and cultivated soybean genomes identifies patterns of genetic diversity and selection. Nat Genet 42: 1053–1059.
- 14. Song QJ, Marek LF, Shoemaker RC, Lark KG, Concibido VC, et al. (2004) A new integrated genetic linkage map of the soybean. Theor Appl Genet 109: 122–128.
- 15. Song QJ, Jia G, Zhu Y, Grant D, Nelson RT, et al. (2010) Abundance of SSR motifs and development of candidate polymorphic SSR markers (BARCSOYSSR_1.0) in soybean. Crop Sci 50: 1950–1960.
- 16. Concibido VC, La Vallee B, McLaird P, Pineda N, Meyer J, et al. (2003) Introgression of a quantitative trait locus for yield from Glycine soja into commercial soybean cultivars. Theor Appl Genet 106: 575–582.
- 17. Qi ZM, Han X, Sun YN, Wu Q, Shan DP, et al. (2011) An integrated quantitative trait locus map of oil content in soybean, Glycine max (L.) Merr., generated using a meta-analysis method for mining genes. Agricultural Science in China 10: 1681–1692.
- 18. Hoeck JA, Fehr WR, Shoemaker RC, Welke GA, Johnson SL, et al. (2003) Molecular marker analysis of seed size in soybean. Crop Sci 43: 68–74.
- 19. Li D, Pfeiffer TW, Cornelius PL (2008) Soybean QTL for yield and yield components associated with Glycine soja alleles. Crop Sci 48: 571–581.
- 20. Specht JE, Chase K, Macaulay M, Graef GL, Chung J, et al. (2001) Soybean response to water: a QTL analysis of drought tolerance. Crop Sci 41: 493–509.
- 21. Chen H, Shan Z, Sha A, Wu B, Yang Z, et al. (2011) Quantitative trait loci analysis of stem strength and related traits in soybean. Euphytica 179: 485–497.
- 22. Yue P, Arelli PR, Sleper DA (2001) Molecular characterization of resistance to Heterodera glycines in soybean PI 438489B. Theor Appl Genet 102: 921–928.
- 23. Vuong TD, Sleper DA, Shannon JG, Nguyen HT (2011) Confirmation of quantitative trait loci for resistance to multiple-HG types of soybean cyst nematode (Heterodera glycines Ichinohe). Euphytica 181: 101–113.
- 24. Orf JH, Chase K, Jarvik T, Mansur LM, Cregan PB, et al. (1999) Genetics of soybean agronomic traits: I. comparison of three related recombinant inbred populations. Crop Sci 39: 1642–1651.
- 25. Zhang WK, Wang YJ, Luo GZ, Zhang CY, et al. (2004) QTL mapping of ten agronomic traits on the soybean (Glycine max L. Merr.) genetic map and their association with EST markers. Theor Appl Genet 108: 1131–1139.
- 26. Rostoks N, Ramsay L, MacKenzie K, Cardle L, Bhat PR, et al. (2006) Recent history of artificial outcrossing facilitates whole-genome association mapping in elite inbred crop varieties. Proc Natl Acad Sci 103: 18656–18661.
- 27. Hao Z, Li X, Xie C, Weng J, Li M, et al. (2011) Identification of functional genetic variations underlying drought tolerance in maize using SNP markers. J Integr Plant Biol 53: 641–652.
- 28. Luo MC, Xu K, Ma Y, Deal KR, Nicolet CM, et al. (2009) A high-throughput strategy for screening of bacterial artificial chromosome libraries and anchoring of clones on a genetic map constructed with single nucleotide polymorphisms. BMC Genomics 10: 28.
- 29. Hyten DL, Song QJ, Fickus EW, Quigley CV, Lim JS, et al. (2010) High-throughput SNP discovery and assay development in common bean. BMC Genomics 11: 475.
- 30. Akhunov E, Nicolet C, Dvorak J (2009) Single nucleotide polymorphism genotyping in polyploid wheat with the Illumina GoldenGate assay. Theor Appl Genet 119: 507–517.
- 31. Hyten D, Choi IK, Song QJ, Specht JE, Carter TE, et al. (2010) A high density integrated genetic linkage map of soybean and the development of a 1536 universal soy linkage panel for quantitative trait locus mapping. Crop Sci 50: 960–968.
- 32. Ganal MW, Durstewitz G, Polley A, Bérard A, Buckler ES, et al. (2011) A large maize (Zea mays L.) SNP genotyping array: Development and germplasm genotyping, and genetic mapping to compare with the B73 reference genome. PLoS ONE 6: e28334.
- 33. Bachlava E, Taylor CA, Tang S, Bowers JE, Mandel JR, et al. (2012) SNP discovery and development of a high-density genotyping array for sunflower. PLoS ONE 7: e29814.
- 34. Chagné D, Crowhurst RN, Troggio M, Davey MW, Gilmore B, et al. (2012) Genome-wide SNP detection, validation, and development of an 8K SNP array for apple. PLoS ONE 7: e31745.
- 35. Sim SC, Durstewitz G, Plieske J, Wieseke R, Ganal MW, et al. (2012) Development of a large SNP genotyping array and generation of high-density genetic maps in tomato. PLoS ONE 7: e40563.
- 36. Klein RJ (2007) Power analysis for genome-wide association studies. BMC Genet 8: 58.
- 37. Matukumalli LK, Lawley CT, Schnabel RD, Taylor JF, Allan MF, et al. (2009) Development and characterization of a high density SNP genotyping assay for cattle. PLoS One 4: e5350.
- 38. Ricard A, Legarra A, Danvy S, Guyon C, Meriaux J, et al. (2011) Can we predict the quality of an equine breeding for the CSO from genomics? 37th Journée de la Recherche Équine, Le Pin au Haras, France, 24 February 227–230.
- 39. Fan B, Du ZQ, M GD, Rothschild MF (2010) Development and application of high-density SNP arrays in genomic studies of domestic animals. Asian-Aust J Anim Sci 23: 833–847.
- 40. Ramos AM, Crooijmans RP, Affara NA, Amaral AJ, Archibald AL, et al. (2009) Design of a high density SNP genotyping assay in the pig using SNPs identified and characterized by next generation sequencing technology. PLoS ONE 4: e6524.
- 41. Gizlice Z, Carter TE, Burton JW (1994) Genetic base of North American public soybean cultivars released between 1947 and 1988. Crop Sci 34: 1143–1151.
- 42. Li H, Ruan J, Durbin R (2008) Mapping short DNA sequencing reads and calling variants using mapping quality scores. Genome Res 18: 1851–1858.
- 43. Schuler GD (1997) Sequence mapping by electronic PCR. Genome Res 7: 541–550.
- 44. Choi IY, Hyten DL, Matukumalli LK, Song QJ, Chaky JM, et al. (2007) A soybean transcript map: gene distribution, haplotype and single-nucleotide polymorphism analysis. Genetics 176: 685–696.
- 45. Marth GT, Korf I, Yandell MD, Yeh RT, Gu Z, et al. (1999) A general approach to single-nucleotide polymorphism discovery. Nat Genet 23: 452–456.
- 46. Matukumalli LK, Grefenstette JJ, Hyten DL, Choi IY, Cregan PB, et al. (2006) Application of machine learning in SNP discovery. BMC Bioinformatics 7: 4.
- 47. Cregan PB, Jarvik T, Bush AL, Shoemaker RC, Lark KG, et al. (1999) An integrated genetic linkage map of the soybean. Crop Sci 39: 1464–1490.
- 48. Zhao Q, Zhang Y, Cheng Z, Chen M, Wang S (2002) A fine physical map of the rice chromosome 4. Genome Res 12: 817–823.
- 49. Hyten DL, Song QJ, Choi IY, Yoon MS, Specht JE, et al. (2008) High-throughput genotyping with the GoldenGate assay in the complex genome of soybean. Theor Appl Genet 116: 945–952.
- 50. Hyten DL, Song QJ, Zhu Y, Choi IY, Nelson RL, et al. (2006) Impacts of genetic bottlenecks on soybean genome diversity. Proc Natl Acad Sci 103: 16666–16671.
- 51. Excoffier LGL, Schneider S (2005) Arlequin Ver. 3.0: An integrated software package for population genetics data analysis. Evolutionary Bioinformatics Online 1: 47–50.