In contrast with wild species, cultivated crop genomes consist of reshuffled recombination blocks, which occurred by crossing and selection processes. Accordingly, recombination block-based genomics analysis can be an effective approach for the screening of target loci for agricultural traits.
We propose the variation block method, which is a three-step process for recombination block detection and comparison. The first step is to detect variations by comparing the short-read DNA sequences of the cultivar to the reference genome of the target crop. Next, sequence blocks with variation patterns are examined and defined. The boundaries between the variation-containing sequence blocks are regarded as recombination sites. All the assumed recombination sites in the cultivar set are used to split the genomes, and the resulting sequence regions are termed variation blocks. Finally, the genomes are compared using the variation blocks. The variation block method identified recurring recombination blocks accurately and successfully represented block-level diversities in the publicly available genomes of 31 soybean and 23 rice accessions. The practicality of this approach was demonstrated by the identification of a putative locus determining soybean hilum color.
We suggest that the variation block method is an efficient genomics method for the recombination block-level comparison of crop genomes. We expect that this method will facilitate the development of crop genomics by bringing genomics technologies to the field of crop breeding.
Crop cultivars have low levels of genetic diversity but high frequencies of recombination
[1, 2]. Cultivars contain specific sequence blocks in their chromosomes, which may be associated with artificially selected phenotypic variations from many generations of breeding. In contrast with wild species (Figure
1A), cultivar genomes consist of genetically reshuffled recombination blocks that arose from breeding ancestors (Figure
[3–5]. For example, because the history of soybean breeding is too short for mutation accumulations (~70 years), the recombination blocks from common ancestors are usually identical in different cultivars, except for a small number of variations that are adjacent to the recombination points
. Therefore, as large-scale genome sequence data become available, the recombination block-based analysis is emerging as an efficient approach for comparing bred cultivar genomes with enough precision to detect molecular breeding targets.
Traditionally, recombination block identification has focused primarily on detecting linkage disequilibrium (LD) blocks, which can be determined by calculating the correlations of neighboring alleles. Combinations of alleles that are observed in the expected correlations are said to be in linkage equilibrium. In contrast, LD is higher-than-expected correlations between alleles at two loci that originate from single, ancestral chromosomes
. Based on the LD block calculation, it is possible to discover the haplotype block structure of a whole-genome
. Whole haplotype maps of a few model organisms have been created using LD blocks
[9–11]. To generate a reliable whole-genome haplotype map, it is needed to calculate the pairwise linkage disequilibrium between the single nucleotide variations (SNVs) in many samples
. For this reason, maps have been generated from only a few model plants, including Arabidopsis and maize
Recently, large-scale whole-genome sequencing by next-generation sequencing technology has been employed for recombinant inbred line (RIL) genotyping and even for linkage analyses to search for recombination breakpoints. However, the resulting data are prone to high error rates due to the relatively low levels of sequencing coverage that are typically attained. To overcome this drawback, a “bin” concept was introduced for the rice genome. Specifically, the sliding-window approach
 and the hidden Markov model
 were used to construct “bin maps” using the low-coverage sequence data. The bin map was successfully employed to reveal quantitative trait loci (QTL) that contained genes that are related to rice grain width
. However, there are some limitations to the usefulness of the bin-based method. Namely, almost all of the RIL individuals have to be sequenced to detect useful QTLs. Furthermore, a bin map of a RIL group cannot be reused for other RIL groups. However, if generally applicable comparative analysis methods are developed to identify bins, the effort and expense required to search for genes that are related to the target traits will be reduced.
Genomic analyses of various crops using whole-genome sequencing data have been previously reported. These include the analysis of 31 cultivated and wild soybean genomes using ~5× sequencing
, genome-wide association studies of 950 rice varieties using ~1× sequencing
, the identification of candidate regions that were selected during the domestication of 50 rice accessions using ~15× sequencing
, and a breeding-associated genetic regulation analysis of 90 chickpea genomes using ~9.5× sequencing
. Integrating such large-scale genomic data will accelerate the screening of loci that are related to the valuable target traits.
Here, we propose a variation block (VB) method using next-generation sequencing data for the detection and analysis of recombination patterns in the genomes of crop species. The rationale behind the VB method is the existence of reshuffled sequence blocks within crop varieties that originated from a limited number of ancestral contributors and were introduced relatively recently over the course of the past several decades
. We suggest that such sequence blocks can be detected by identifying the SNV density profiles and that the resulting sequence blocks represent recombination blocks. We demonstrate the general applicability of the VB method by applying it to the publicly accessible genomes of 31 soybean and 23 rice accessions. Finally, by using a small number of insertion/deletion (indel) markers, each of which is specific to a recombination block, we identified a putative locus for soybean hilum color with minimal screening. With the increasing availability of genome sequences, the VB method shows promise as a useful genomic selection technology for crop improvement.
Whole-genome sequencing of cultivated soybeans
Five soybean (Glycine max (L.) Merr.) cultivar genomes were sequenced. Two were parental cultivars (Baekun and Sinpaldal2), and two represented their crossed descendants (Daepoong and Shingi) (Additional file
1: Figure S1) and were used to detect inherited genome-wide recombination events. One of the descendants, Daepoong, has the highest productivity among Korean soybean cultivars (3.2 ton/ha) with excellent yield stability. We also used another elite line, Hwangkeum, which is not a member of this family but is popular for its attractive color and bean size. We produced paired-end DNA reads of 40–60-fold depths (Additional file
2: Table S1) for the five cultivar genomes and mapped them to the Williams 82 reference (PI 518671)
. The sequencing qualities of all the samples were high; 94%–99% of the cultivar sample reads were mapped to the reference, and 97%–99% of the reference genome was covered. A Williams 82 genome was also sequenced under the same conditions at a ~60-fold depth to reduce the base-calling noise. Additionally, G. soja, which is an undomesticated ancestor of G. max, was used as a control and analyzed with the same method.
Comparative analysis procedure for cultivated soybean genomes using VB
The main objective of the VB method is to compare the reshuffled genome sequences of bred cultivars. A three-step process was applied to determine and compare the recombination blocks in the five soybean genomes (Figure
Step 1. SNV detection: SNVs and indels were detected by comparing bred soybean genomes with a reference genome (step 1 of Figure
1C). There were a total of 2,546,207 non-redundant SNVs (1,163,371–1,788,424 SNVs per cultivar) and a total of 486,010 small indels (225,815–348,642 indels per cultivar) in the five Korean soybean cultivars (Additional file
3: Table S2). Of these SNVs, 1,404,301 were novel (Additional file
4: Figure S2). The SNVs were highly clustered in certain chromosomal regions, whereas the regions that were genomically identical to the Williams 82 reference showed few or no SNVs.
Step 2. Determination of VBs: Two types of blocks were determined: the sparse variation blocks (sVBs), which are identical or nearly identical to the reference sequence, and the dense variation blocks (dVBs), which contain many variations (step 2 of Figure
1C, Methods). The boundaries of the dVBs and sVBs were regarded as recombination sites. The VBs are thus defined as the sequence fragments that are split by all of the assumed recombination sites. In the five soybean genomes, 30–47% of the regions were dVBs (Figure
2A). As expected, in the resequenced Williams 82 genome, only a fraction of the regions (3%) were dVBs, which appeared probably due to individual differences between the two Williams 82 cultivars. By contrast, most of the regions in the G. soja genome (95%) were dVBs, indicating that the genetic pool of G. soja has been rarely used to breed Williams 82 and the five cultivars that were sequenced in this study.
Most of the SNVs (96%) were located in dVBs, even though the dVBs occupied less than half of the genome (Figure
2B). A total of 4,332 sVBs and dVBs were identified in the five genomes, along with 4,132 boundary sites that demarcated the VBs. Figure
3 shows an overview of the block structures of all of the chromosomes (Figure
3A) and the detailed structure of chromosome 1 (Figure
3B). After eliminating redundancy, a total of 2,254 recombination sites and a set of 2,274 VBs covered the entire genomic region (Additional file
5: Table S3).
Recombination occurs more frequently in gene-rich regions than in gene-sparse regions
[24–27]. Consistent with this, we confirmed a strong positive correlation (r = 0.85) between the gene density and VB density in the soybean genome. As a result, short VBs (<100 kb) were found mainly in regions with the highest gene densities (Figure
2C), whereas most of the very long VBs were located in heterochromatic regions (Figure
3B). This observation is consistent with previous studies that reported the suppression of recombination in the heterochromatin of various crops, such as sorghum and tomato
Interestingly, there were identical variation patterns consistently appearing in the same regions of the examined genomes, indicating that these regions were inherited from common ancestors. The existence of boundaries that demarcate the dVBs and sVBs indicates that recombination has occurred at least once during breeding.• Step 3. Block comparison: The identity of each VB at specific locations was compared to all of the other VBs within the aligned column among the cultivar genomes (step 3 of Figure
1C). To compare the VBs among the genomes, two VBs were considered to be of an identical type and thus to have originated from a common parental genome when they had ≥99.8% sequence identity as well as ≥0.8 SNV concordance (Figure
4A). SNV concordance refers to the extent to which all the SNVs that are present in a VB are identical between genomes. Applying these thresholds, 98% of the VB types in the descendants (Daepoong and Shingi) were present in the parents (Baekun and Sinpaldal2). Figure
4B shows a comparison of VB types between Shingi and its parents. The resulting information was used to analyze the reshuffling patterns of the parental genomes in the descendants (Figure
4C).Almost all of the descendant chromosomal regions were present in the corresponding parental lines. However, some of the remaining (~1%) regions, such as an 8.3-Mb block in chromosome 1 of Shingi (shown in red in Figure
3B), were not observed similarly in the parental genomes, likely because the two individual parental plants that were used in this analysis are not the direct ancestors of the descendant cultivars.
Robustness of VB detection with respect to sequencing depth
We evaluated the performance of the VB detection at various sequencing depths and found that the VB method remained accurate even with 5-fold depths of mapping data (red curves in Figure
5). The performance of the VB method depends on that of the SNV calling, which, in turn, depends on the sequencing read depths. In our analysis, the sensitivity of the homologous SNV detection decreased dramatically at depths of <10-fold, amounting to 79% at a 6-fold depth and 73% at a 5-fold depth (black dotted curve in Figure
5). Nevertheless, even with the deterioration of the SNV-calling sensitivity, the sensitivity of the VB method remained greater than 90%, even at a 6-fold depth. At an 8-fold depth, the sensitivity and precision of VB detection reached approximately 95% of the highest values. Therefore, the VB method is very robust with respect to sequencing depths.
VB-based analysis of publicly available soybean and rice genomes
To assess its general applicability, we applied the VB method to two sets of publicly available genomes of 31 soybean lines and 23 rice accessions
The 31 publicly available soybean genomes consisted of 14 cultivated and 17 wild soybeans
. All of them were Chinese except for three cultivars from Brazil, Taiwan, and the USA. Nevertheless, for simplicity, all the 31 soybean types will be referred to collectively as “Chinese soybeans”. On average, the sequencing depth was 5-fold, which should thus allow for the attainment of at least 89% sensitivity and 88% precision according to depth-performance calibration (Figure
5). The VB method was successfully employed to analyze all but one cultivar genome that had distinctly fewer SNVs than did the others. As in the five Korean soybean cultivars, each of the 13 Chinese cultivar genomes also contained SNVs that were clustered in certain chromosomal regions with distinct SNV density profiles (Additional file
6: Figure S3). There were a total of 6,604 recombination sites, which was 2.9-fold higher than those that were observed in the five Korean soybean genomes, likely due to the higher number of analyzed genomes. More than half (62%, 1,390 of 2,254) of the recombination sites in the five Korean soybean genomes coincided with those in the 13 Chinese cultivar genomes. The remaining 38% may reflect differences in the genetic pools between the Chinese and Korean soybeans.
The 17 wild soybeans had much fewer sVBs than did the cultivated soybeans. An average of 31% of the genomic regions of the wild soybeans contained sVBs, and there were a total of 5,895 recombination sites, 43% (2,525) of which coincided with those of the 13 cultivars. These observations suggest that the 17 wild soybeans might have had opportunities to outcross with the ancestors of the cultivated soybeans. To assess the value of the wild soybean genomes as genetic resources, we determined the number of VBs that are shared between the cultivated and wild soybean genomes. Many (60–75%) VBs from the cultivar genomes were present in at least one of the wild soybean genomes. By contrast, far fewer (17–71%) VBs from the wild soybean genomes were present in the cultivar genomes (Additional file
7: Figure S4), suggesting that wild soybeans have a much more diverse genetic pool than do the cultivars.
We next demonstrated that the VB method can be applied to monocot crops, such as rice. The procedures that were used in the soybean genome analysis were directly applied to rice genomes (Methods). We selected a total of 23 Oryza sativa spp. japonica genomes from 50 rice accessions that were reported in a previous study
. The following three varieties were included: seven temperate japonica (TEJ), ten tropical japonica (TRJ), and six aromatic (ARO) varieties. The average mapping depth of the sequencing data was approximately 14-fold, which is sufficient for accurate VB-based analysis (Figure
5). A total of 4,361 recombination sites were found in the rice genomes. Only 6.6% (289) of the sites were present in all three groups (Additional file
8: Figure S5). The seven temperate and ten tropical japonica genomes shared more than twice as many recombination sites as those that were shared with the six aromatic genomes. The resulting VB patterns indicated that the three groups are characterized by visually distinct SNV density profiles (Figure
6). The six aromatic varieties had the most distinct patterns. These findings are consistent with the conclusions of the original report, which were based on sequence homology
Quantification of genome diversity of crop population in terms of VBs
VBs were used to quantitatively estimate the genome diversities of crop populations. Whereas a sequence-based comparison infers the homology of genomes that have diverged via natural evolution (Additional file
9: Figure S6A), a block-based comparison determines whether two blocks from two genomes originated from the same parental genome (Additional file
9: Figure S6B).
We examined the genome diversity by calculating the “VB diversity score”, which is defined as the number of unique VB types per the number of all VB sites in a genome. The steps for the calculation of the VB diversity score are illustrated in Additional file
10: Figure S7A–C. The resulting graphs revealed differences in diversity among the four groups of crops (Figure
7). The 13 Chinese soybean cultivar genomes produced the most smoothly increasing curve of VB diversity scores that eventually leveled off, indicating limited genome diversity in the cultivars. In comparison, wild soybeans produced a steeply increasing irregular curve that did not level off, indicating diverse VB compositions. The genetic diversity of the five Korean soybeans can also be seen (Figure
7). The two parental genomes (the first two red dots) had a score of 1.50, which was similar to the score of the first two genomes in the other soybean groups.
The three subgroups of rice genomes showed distinguishable patterns representing subgroups of unique recombination sites (Figure
7). The curve for temperate japonica was the most irregular among the three subgroups. The VB diversity scores of the rice genomes were much lower than those of the 13 cultivated soybean genomes.
VBs as recombination blocks
Experimental validation supported that VBs are genuine recombination blocks. We determined the recombination frequencies in the 7.4–9.2 Mb region of chromosome 8 in 614 F4 progenies of RILs, which were the progenies of the cross that was made between Hwangkeum and Daepoong (Figure
8A). In this 1.8-Mb region, Hwangkeum had three dVBs that were 140 kb, 300 kb, and 190 kb in length alternating with three sVBs, whereas Daepoong had only one long sVB. The three sVB regions were of identical types between Daepoong and Hwangkeum.The analysis of the recombination frequencies in the 614 RILs showed that at least 6.6 times more recombination events occurred in the sVBs than in the dVBs. A total of nine and 113 recombination events occurred in 630 kb of dVBs and 1,190 kb of sVBs, respectively. This indicates that the more similar two sequences are, the more frequently recombination events occur. This relationship is consistent with a previous study that reported reduced recombination rates in regions surrounding non-alignable flanking sequences, such as SNVs and indels
The genetic linkage distances and VB lengths were also compared. VB-specific indel markers were used to determine the recombination status of chromosomes 6 and 8 for the F4 RILs that are described above. Genetic and physical maps were constructed using markers that could distinguish between the dVB types of two cultivars, as described in the Methods section. We found very large VBs within very short genetic distances. Five VBs that were larger than 2 Mb were located within 7.4 cM of chromosome 6 (120.2–127.6 cM) (Figure
8B), and two large VBs of 12 Mb and 3.5 Mb were found within 13.9 cM of chromosome 8 (131.7–145.6 cM) (Additional file
11: Figure S8). These results indicate that VBs are recombination units that rarely split.
Identification of the locus determining soybean hilum color
Finally, we showed the practicability of the VB method for map-based screening by identifying a putative locus that determines soybean hilum color. Although hilum color is thought to be related to the I locus on chromosome 8
[30–32], the exact locus has not yet been identified. Using the VB method, we attempted to detect the hilum color-determining locus. As a result, the entire screening process for narrowing down the target region required only the following three steps in the 614 F4 progenies of RILs that were selected from the cross of Hwangkeum (yellow hilum) and Daepoong (brown hilum) (Figure
We began by selecting 32,000 indel markers that were longer than four base pairs, which allowed for the discrimination of the VBs from Hwangkeum from those from Daepoong (Methods). Among these indel markers, the first screening was performed with 464 markers in all 20 chromosomes, each of which represented a single VB that was identified from the Hwangkeum and Daepoong genomes. The HK096 marker on chromosome 8 had the highest correlation (r = 0.993) with the hilum color phenotype (Additional file
12: Figure S9 and Figure
9B, 1st pass, indicated by the upward red arrow). The second step was performed with five markers that represented one of the five dVBs near HK096. Because recombination events within the dVBs occurred rarely, as described in the previous section, one marker was sufficient to represent one dVB. We found that the target locus was in a 300-kb dVB spanning the 8.28–8.58 Mb region (Figure
9B, 2nd pass, indicated by the upward red arrow), which showed the highest correlation with the hilum color phenotype. The final step was performed with seven markers (Figure
9B, 3rd pass) to locate the exact hilum color-determining locus, which was located in a very large block of approximately 197 kb.
To identify the locus that determines hilum color, a comparative analysis was conducted. Soybean seed coats and hilum color are regulated by chalcone synthase (CHS) genes that control the anthocyanin and proanthocyanidin pigments via a posttranscriptional mode of gene silencing
[33, 34]. Therefore, we compared the sequence differences for Daepoong and Hwangkeum of CHSs in the I locus, which is located close to the HD8.481 marker in the 197-kb block. There were no significant sequence differences except for one non-synonymous SNV in CHS9 of the I locus (Figure
9C, indicated by the upward black arrow), which cannot affect the regulation of gene silencing mechanisms between two sequences in this region. Instead, we found that two highly similar (98.0%) genes, CHS3 and CHS5, were present as inverted repeats near the HD8.386 marker downstream of the I locus (Figure
9C). Except for these CHSs, there were no genes that are involved in the anthocyanin metabolic pathway in this 197-kb block. We concluded that an inverted repeat structure of CHS3 and CHS5 is a candidate locus for hilum color determination.
We compared the sequence differences among the six soybean cultivars in the 197-kb block and found that the only notable difference was a 5-bp deletion in the CHS3 promoter regions of every genome except for Hwangkeum (Figure
9C, indicated by the upward red arrow and multiple sequence alignment). If the expression of CHS3 and CHS5 is regulated by a gene silencing mechanism that is similar to that of the ii allele on the I locus, then these two genes may be silenced in Hwangkeum, which has an intact CHS3 promoter. However, if the 5-bp deletion inhibits promoter activity, CHS5 would not be silenced due to the absence of interfering RNA. Therefore, the 5-bp deletion may produce dark-colored hila in all of the soybeans except for Hwangkeum. We also examined 86 other soybean cultivars and found that the 5-bp deletion perfectly correlated with colored hila (Additional file
13: Table S4). We named this putative locus HC, in which the Ih and ih alleles control the yellow and brown hilum colors, respectively. These results suggest that the seed color phenotype of Hwangkeum is determined by two linked loci, consisting of the ii allele (yellow seed coat) of the I locus and the Ih allele (yellow hilum color) of the HC locus.
We proposed an efficient recombination block detection method that is based on genomic variation patterns. This method provides key information for the map-based screening of bred cultivars. The VB method can be applied to various crop species that have available reference genomes. In this study, representative monocot and dicot model crops, rice and soybeans, were successfully analyzed using the VB method.
The VB-based comparative genomics method has several advantages over other methods. The first advantage is that the samples can be compared directly at the block level. Therefore, an agricultural trait-associated locus or gene can be identified with reduced screening efforts by using a small number of markers that represent the VBs. We demonstrated this advantage by identifying a putative locus determining hilum color in soybeans. There is no need to use markers on the VBs of the same type that are present in two genomes, such as the rear half of Gm02, the middle of Gm14, and the front half of Gm20 in the 1st pass (Additional file
12: Figure S9). The VBs were also useful in the 2nd pass of the screening. Since VBs are the units that rarely split by recombination, only one marker can represent one dVB. In this report, only five markers were used to screen five dVBs in the 2 Mb target region (Figure
9B). These results indicate that the VB method enables the minimal use of molecular markers and efficient screening via an accurate recombination map. Map-based cloning in soybeans has thus far identified few genes
, and this method represents a significant advancement.
The second advantage is that the VB method does not depend on the number of samples. In contrast to other statistical methods, such as the LD analysis, the VB method can identify recombination blocks using only one genome if a reference genome sequence is available. This feature is especially useful in genomic screening against RIL populations. Even with the availability of only two parental genome sequences, researchers can still define the VB blocks, compare the two genomes at the recombination block level, and predict the possible recombination sites that are likely to occur in the RIL population.
The third advantage is that the VB method can accurately detect recombination blocks even with low-depth sequencing data. The VB method detected recombination blocks with more than 90% sensitivity and precision even with 6-fold depth data.There were many chromosomal regions that have the same types of VBs across multiple genomes, reflecting the limited genetic diversity resulting from artificial selection during the breeding history. As shown in Figure
8A, the identical regions showed 6.6 times higher recombination rates than those of the other regions. In contrast, recombination occurs randomly in wild-type genomes, which rarely share identical regions with each other. These results imply that low genetic diversity can lead to non-random recombination, followed by the conservation of VBs.
In conclusion, we propose the VB method for the identification and comparison of the reshuffled genome sequences of bred cultivars. We demonstrated the usefulness and generality of the VB method by applying it to the publicly available genomes of 31 soybeans and 23 rice accessions. The VB-representing indel markers accurately identified a putative locus that determines the yellow hilum color in soybean. Thus, the VB method is applicable in the cloning of agronomically important genes in a simple and fast manner.
DNA extraction and massively parallel sequencing
The total genomic DNA was extracted from the leaf tissues of the soybean cultivars using the cetyltrimethyl ammonium bromide method
. DNA libraries that were constructed from Daepoong and Hwangkeum were sequenced using an Illumina GAIIx sequencer (Illumina Inc., San Diego, CA, USA). Four libraries of three Korean cultivars (Baekun, Shingi, and Sinpaldal2) and Williams 82 were sequenced using an Illumina HiSeq 2000 sequencer. Each sequenced sample was prepared using Illumina protocols. Paired-end 101-bp or 104-bp reads were generated.
Reads alignment and variation detection
The sequence reads of five Korean soybean cultivars and twenty-three rice accessions were aligned to the Gmax109 soybean reference genome
 and the IRGSP Build 4 rice reference genome
, respectively, using the BWA algorithm
 ver. 0.5.9. Two mismatches were permitted in the 45-bp seed sequence. To remove the PCR duplicates of the sequence reads, which can be generated during the library construction process, the rmdup command of SAMtools
 was used. The aligned reads were realigned at indel positions with the GATK
 IndelRealigner algorithm to enhance the mapping quality. The GATK TableRecalibration algorithm was used to recalibrate the base quality scores. The 23 rice genome sequence datasets were downloaded from the NCBI Short Read Archive (http://www.ncbi.nlm.nih.gov/Traces/sra/sra.cgi?study=SRP003189). The SNVs of 31 Chinese soybeans were downloaded from the BGI ftp site (ftp://public.genomics.org.cn/BGI/soybean_resequencing/).
Analysis of SNVs and small indels
The SNVs were called and filtered using the UnifiedGenotyper and VariantFiltration commands in GATK, respectively. The options that were used for SNP calling were a minimum of 5- to a maximum of 200-read mapping depths with a consensus quality of 20 and a prior likelihood of heterozygosity value of 0.01 for the soybean genomes. For the rice genomes, the same options as in the soybean genomes were used except for the read mapping depths, in which a minimum of 3 to a maximum of 150 were used.
Recombination block detection
The homozygous SNV densities for 10-kb bins were calculated for six cultivar genomes (Williams 82, Baekun, Shingi, Sinpaldal2, Hwangkeum, and Daepoong). The SNVs that were detected in Williams 82 were regarded as false positives and excluded from the other cultivars. Bins with < 4 SNVs were defined as members of similar-to-standard recombination blocks (sRBs), and those with ≥ 4 SNVs were defined as different-to-standard recombination blocks (dRBs). Neighboring dRBs that were ≥ 90 kb were merged into one dVB. Similarly, sRBs that were ≥ 30 kb were merged into one sVB. The distances of 90 kb and 30 kb were determined heuristically. VBs were defined as the sequence fragments that were split by all of the observed recombination sites, which were the boundaries of the dVBs and sVBs.
Determination of thresholds for VB identity comparison
We employed two types of filters to determine whether two VBs from different genomes were of identical type. The first filter was sequence identity. When inherited, most of the VBs in the descendant genomes had ≥ 99.8% identity to those of the parents (red dots of top panel in Figure
4A). The second filter was the concordance of the SNV sets in the VBs. When inherited, most of the SNV concordances were ≥ 0.80 (bottom panel of Figure
4A). If two VBs had ≥ 99.8% sequence identity and ≥ 0.80 SNV concordance, they were considered to be of the same type, which originated from a common ancestor.
Performance test of SNV and VB detection
The sequence reads of the cultivar soybean Baekun were divided into small read sets, each of which was able to cover a 2-fold depth. With the incremental addition of each small read set, a series of sequence read sets was generated, resulting in mapping depths of 2.0×, 4.0×, 5.0×, 6.0×, 7.9×, 9.8×, 11.8×, 15.6×, 22.0×, 26.7×, 31.4×, and 36.1×. These read sets were aligned to the Gm109 soybean reference genome, and the SNVs were called as described above, except for the minimum two-read mapping depth. By using the resulting SNV sets, the VB blocks were detected and compared as described above. The SNVs and VBs that were detected from the largest read set (36.1×) were used as standards for the performance assessment. Only the homozygous SNVs were used in the performance assessment and VB detection.
Indel marker analysis
The PCR analysis was performed using 10-μl reaction mixtures containing 20 ng of total genomic DNA, 0.4 μM of primer, and 5 μl of GoTaq Green Master Mix (Promega, Madison, WI, USA) using a Biometra T1 Thermal Cycler (Biometra, Goettingen, Germany). The PCR conditions were as follows: initial denaturation for 5 min at 95°C; 34 cycles of 30 sec at 95°C, 30 sec at 48°C, and 30 sec at 72°C; and a final extension for 7 min at 72°C. The PCR products were separated by 3% agarose gel electrophoresis.
Construction of genetic and physical maps
A genetic map of the Hwangkeum/Daepoong population was constructed using JoinMap ver. 4.0 (http://www.kyazma.nl/index.php/mc.JoinMap). Prior to the map construction, all of the segregated markers were subjected to the chi-square test using the locus genotype frequencies feature of JoinMap. The linkage groups of chromosome 6 and 8 were separated using an independence LOD (logarithm of the odds) score of 3.0. The marker orders within the linkage groups were established using the regression-mapping algorithm. The recombination values were converted to genetic distances (cM) using the Kosambi mapping function
Availability of supporting data
The raw reads for this project have been deposited in the Sequence Read Archive (SRA) project under the accession number SRA052312.
This work was supported by grants from the Next-Generation BioGreen 21 Program (Plant Molecular Breeding Center No. PJ008060012012 and PJ00911001 and SSAC No. PJ009614) of the Rural Development Administration, Republic of Korea. SH was supported by the KRIBB Research Initiative Program. The “Bioinformatics platform development for next generation bioinformation analysis” study was funded by the Ministry of Knowledge Economy (MKE, Korea). We thank Maryana Bhak for editing.
National Institute of Crop Science, Rural Development Administration
Personal Genomics Institute, Genome Research Foundation
Korean Bioinformation Center, Korea Research Institute of Bioscience and Biotechnology
Theragen Bio Institute, TheragenEtex
National Instrumentation Center for Environmental Management, College of Agriculture and Life Science, Seoul National University
Department of Biology, Kyungpook National University
Department of Plant Science and Research Institute for Agriculture and Life Sciences, Seoul National University
Hyten DL, Song Q, Zhu Y, Choi IY, Nelson RL, Costa JM, Specht JE, Shoemaker RC, Cregan PB: Impacts of genetic bottlenecks on soybean genome diversity.Proceedings of the National Academy of Sciences of the United States of America 2006,103(45):16666–16671. 10.1073/pnas.0604379103View ArticlePubMed CentralPubMed
Stefaniak TR, Hyten DL, Pantalone VR, Klarer A, Pfeiffer TW: Soybean Cultivars Resulted from More Recombination Events Than Unselected Lines in the Same Population.Crop Science 2006,46(1):43. 10.2135/cropsci2005.0016View Article
Yu J, Holland JB, McMullen MD, Buckler ES: Genetic design and statistical power of nested association mapping in maize.Genetics 2008,178(1):539–551. 10.1534/genetics.107.074245View ArticlePubMed CentralPubMed
McMullen MD, Kresovich S, Villeda HS, Bradbury P, Li H, Sun Q, Flint-Garcia S, Thornsberry J, Acharya C, Bottoms C, Brown P, Browne C, Eller M, Guill K, Harjes C, Kroon D, Lepak N, Mitchell SE, Peterson B, Pressoir G, Romero S, Oropeza Rosas M, Salvo S, Yates H, Hanson M, Jones E, Smith S, Glaubitz JC, Goodman M, Ware D, et al.: Genetic properties of the maize nested association mapping population.Science, Volume 325 2009/08/08 edition. 2009, 737–740.
Yonemaru J, Yamamoto T, Ebana K, Yamamoto E, Nagasaki H, Shibaya T, Yano M: Genome-wide haplotype changes produced by artificial selection during modern rice breeding in Japan.PloS one 2012,7(3):e32982. 10.1371/journal.pone.0032982View ArticlePubMed CentralPubMed
Haun WJ, Hyten DL, Xu WW, Gerhardt DJ, Albert TJ, Richmond T, Jeddeloh JA, Jia G, Springer NM, Vance CP, Stupar RM: The composition and origins of genomic variation among individuals of the soybean reference cultivar Williams 82.Plant Physiol 2011,155(2):645–655. 10.1104/pp.110.166736View ArticlePubMed CentralPubMed
Reich DE, Cargill M, Bolk S, Ireland J, Sabeti PC, Richter DJ, Lavery T, Kouyoumjian R, Farhadian SF, Ward R, Lander ES: Linkage disequilibrium in the human genome.Nature 2001,411(6834):199–204. 10.1038/35075590View ArticlePubMed
Barrett JC, Fry B, Maller J, Daly MJ: Haploview: analysis and visualization of LD and haplotype maps.Bioinformatics 2005,21(2):263–265. 10.1093/bioinformatics/bth457View ArticlePubMed
Wiltshire T, Pletcher MT, Batalov S, Barnes SW, Tarantino LM, Cooke MP, Wu H, Smylie K, Santrosyan A, Copeland NG, Jenkins NA, Kalush F, Mural RJ, Glynne RJ, Kay SA, Adams MD, Fletcher CF: Genome-wide single-nucleotide polymorphism analysis defines haplotype patterns in mouse.Proc Natl Acad Sci U S A 2003,100(6):3380–3385. 10.1073/pnas.0130101100View ArticlePubMed CentralPubMed
Consortium TIH: A haplotype map of the human genome.Nature 2005,437(7063):1299–1320. 10.1038/nature04226View Article
Saar K, Beck A, Bihoreau MT, Birney E, Brocklebank D, Chen Y, Cuppen E, Demonchy S, Dopazo J, Flicek P, Foglio M, Fujiyama A, Gut IG, Gauguier D, Guigo R, Guryev V, Heinig M, Hummel O, Jahn N, Klages S, Kren V, Kube M, Kuhl H, Kuramoto T, Kuroki Y, Lechner D, Lee YA, Lopez-Bigas N, Lathrop GM, Mashimo T: SNP and haplotype mapping for genetic analysis in the rat.Nat Genet 2008,40(5):560–566. 10.1038/ng.124View ArticlePubMed
Osabe D, Tanahashi T, Nomura K, Shinohara S, Nakamura N, Yoshikawa T, Shiota H, Keshavarz P, Yamaguchi Y, Kunika K, Moritani M, Inoue H, Itakura M: Evaluation of sample size effect on the identification of haplotype blocks.BMC Bioinformatics 2007, 8:200. 10.1186/1471-2105-8-200View ArticlePubMed CentralPubMed
Clark RM, Schweikert G, Toomajian C, Ossowski S, Zeller G, Shinn P, Warthmann N, Hu TT, Fu G, Hinds DA, Chen H, Frazer KA, Huson DH, Scholkopf B, Nordborg M, Ratsch G, Ecker JR, Weigel D: Common sequence polymorphisms shaping genetic diversity in Arabidopsis thaliana.Science 2007,317(5836):338–342. 10.1126/science.1138632View ArticlePubMed
Gore MA, Chia JM, Elshire RJ, Sun Q, Ersoz ES, Hurwitz BL, Peiffer JA, McMullen MD, Grills GS, Ross-Ibarra J, Ware DH, Buckler ES: A first-generation haplotype map of maize.Science 2009,326(5956):1115–1117. 10.1126/science.1177837View ArticlePubMed
Huang X, Feng Q, Qian Q, Zhao Q, Wang L, Wang A, Guan J, Fan D, Weng Q, Huang T, Dong G, Sang T, Han B: High-throughput genotyping by whole-genome resequencing.Genome Res 2009,19(6):1068–1076. 10.1101/gr.089516.108View ArticlePubMed CentralPubMed
Xie W, Feng Q, Yu H, Huang X, Zhao Q, Xing Y, Yu S, Han B, Zhang Q: Parent-independent genotyping for constructing an ultrahigh-density linkage map based on population sequencing.Proc Natl Acad Sci U S A 2010,107(23):10578–10583. 10.1073/pnas.1005931107View ArticlePubMed CentralPubMed
Lam HM, Xu X, Liu X, Chen W, Yang G, Wong FL, Li MW, He W, Qin N, Wang B, Li J, Jian M, Wang J, Shao G, Sun SS, Zhang G: Resequencing of 31 wild and cultivated soybean genomes identifies patterns of genetic diversity and selection.Nat Genet 2010,42(12):1053–1059. 10.1038/ng.715View ArticlePubMed
Huang X, Zhao Y, Wei X, Li C, Wang A, Zhao Q, Li W, Guo Y, Deng L, Zhu C, Fan D, Lu Y, Weng Q, Liu K, Zhou T, Jing Y, Si L, Dong G, Huang T, Lu T, Feng Q, Qian Q, Li J, Han B: Genome-wide association study of flowering time and grain yield traits in a worldwide collection of rice germplasm.Nat Genet 2012,44(1):32–39.View Article
Xu X, Liu X, Ge S, Jensen JD, Hu F, Li X, Dong Y, Gutenkunst RN, Fang L, Huang L, Li J, He W, Zhang G, Zheng X, Zhang F, Li Y, Yu C, Kristiansen K, Zhang X, Wang J, Wright M, McCouch S, Nielsen R, Wang W: Resequencing 50 accessions of cultivated and wild rice yields markers for identifying agronomically important genes.Nat Biotechnol 2012,30(1):105–111.View Article
Varshney RK, Song C, Saxena RK, Azam S, Yu S, Sharpe AG, Cannon S, Baek J, Rosen BD, Tar'an B, Millan T, Zhang X, Ramsay LD, Iwata A, Wang Y, Nelson W, Farmer AD, Gaur PM, Soderlund C, Penmetsa RV, Xu C, Bharti AK, He W, Winter P, Zhao S, Hane JK, Carrasquilla-Garcia N, Condie JA, Upadhyaya HD, Luo MC, et al.: Draft genome sequence of chickpea (Cicer arietinum) provides a resource for trait improvement.Nat Biotechnol 2013,31(3):240–246. 10.1038/nbt.2491View ArticlePubMed
Lorenzen LL, Boutin S, Young N, Specht JE, Shoemaker RC: Soybean Pedigree Analysis Using Map-Based Molecular Markers: I Tracking RFLP Markers in Cultivars.Crop Science 1995,35(5):1326–1336. 10.2135/cropsci1995.0011183X003500050012xView Article
Schmutz J, Cannon SB, Schlueter J, Ma J, Mitros T, Nelson W, Hyten DL, Song Q, Thelen JJ, Cheng J, Xu D, Hellsten U, May GD, Yu Y, Sakurai T, Umezawa T, Bhattacharyya MK, Sandhu D, Valliyodan B, Lindquist E, Peto M, Grant D, Shu S, Goodstein D, Barry K, Futrell-Griggs M, Abernathy B, Du J, Tian Z, Zhu L, et al.: Genome sequence of the palaeopolyploid soybean.Nature 2010,463(7278):178–183. 10.1038/nature08670View ArticlePubMed
Kim MY, Lee S, Van K, Kim TH, Jeong SC, Choi IY, Kim DS, Lee YS, Park D, Ma J, Kim WY, Kim BC, Park S, Lee KA, Kim DH, Kim KH, Shin JH, Jang YE, Kim KD, Liu WX, Chaisan T, Kang YJ, Lee YH, Moon JK, Schmutz J, Jackson SA, Bhak J, Lee SH: Whole-genome sequencing and intensive analysis of the undomesticated soybean (Glycine soja Sieb. and Zucc.) genome.Proc Natl Acad Sci U S A 2010,107(51):22032–22037. 10.1073/pnas.1009526107View ArticlePubMed CentralPubMed
Copenhaver GP, Nickel K, Kuromori T, Benito MI, Kaul S, Lin X, Bevan M, Murphy G, Harris B, Parnell LD, McCombie WR, Martienssen RA, Marra M, Preuss D: Genetic definition and sequence analysis of Arabidopsis centromeres.Science 1999,286(5449):2468–2474. 10.1126/science.286.5449.2468View ArticlePubMed
Akhunov ED, Goodyear AW, Geng S, Qi LL, Echalier B, Gill BS, Miftahudin, Gustafson JP, Lazo G, Chao S, Anderson OD, Linkiewicz AM, Dubcovsky J, La Rota M, Sorrells ME, Zhang D, Nguyen HT, Kalavacharla V, Hossain K, Kianian SF, Peng J, Lapitan NL, Gonzalez-Hernandez JL, Anderson JA, Choi DW, Close TJ, Dilbirligi M, Gill KS, Walker-Simmons MK, Steber C, et al.: The organization and rate of evolution of wheat genomes are correlated with recombination rates along chromosome arms.Genome Res 2003,13(5):753–763. 10.1101/gr.808603View ArticlePubMed CentralPubMed
Kim JS, Islam-Faridi MN, Klein PE, Stelly DM, Price HJ, Klein RR, Mullet JE: Comprehensive molecular cytogenetic analysis of sorghum genome architecture: distribution of euchromatin, heterochromatin, genes and recombination in comparison to rice.Genetics 2005,171(4):1963–1976. 10.1534/genetics.105.048215View ArticlePubMed CentralPubMed
Gaut BS, Wright SI, Rizzon C, Dvorak J, Anderson LK: Recombination: an underappreciated factor in the evolution of plant genomes.Nat Rev Genet 2007,8(1):77–84. 10.1038/nrg1970View ArticlePubMed
Wang Y, Tang X, Cheng Z, Mueller L, Giovannoni J, Tanksley SD: Euchromatin and pericentromeric heterochromatin: comparative composition in the tomato genome.Genetics 2006,172(4):2529–2540.View ArticlePubMed CentralPubMed
Hammarlund M, Davis MW, Nguyen H, Dayton D, Jorgensen EM: Heterozygous insertions alter crossover distribution but allow crossover interference in Caenorhabditis elegans.Genetics 2005,171(3):1047–1056. 10.1534/genetics.105.044834View ArticlePubMed CentralPubMed
Todd JJ, Vodkin LO: Duplications That Suppress and Deletions That Restore Expression from a Chalcone Synthase Multigene Family.Plant Cell 1996,8(4):687–699. 10.1105/tpc.8.4.687View ArticlePubMed CentralPubMed
Clough SJ, Tuteja JH, Li M, Marek LF, Shoemaker RC, Vodkin LO: Features of a 103-kb gene-rich region in soybean include an inverted perfect repeat cluster of CHS genes comprising the I locus.Genome 2004,47(5):819–831. 10.1139/g04-049View ArticlePubMed
Kasai A, Kasai K, Yumoto S, Senda M: Structural features of GmIRCHS, candidate of the I gene inhibiting seed coat pigmentation in soybean: implications for inducing endogenous RNA silencing of chalcone synthase genes.Plant Mol Biol 2007,64(4):467–479. 10.1007/s11103-007-9169-4View ArticlePubMed
Yang K, Jeong N, Moon JK, Lee YH, Lee SH, Kim HM, Hwang CH, Back K, Palmer RG, Jeong SC: Genetic analysis of genes controlling natural variation of seed coat and flower colors in soybean.J Hered 2010,101(6):757–768. 10.1093/jhered/esq078View ArticlePubMed
Gillman JD, Tetlow A, Lee JD, Shannon JG, Bilyeu K: Loss-of-function mutations affecting a specific Glycine max R2R3 MYB transcription factor result in brown hilum and brown seed coats.BMC Plant Biol 2011, 11:155. 10.1186/1471-2229-11-155View ArticlePubMed CentralPubMed
Watanabe S, Hideshima R, Xia Z, Tsubokura Y, Sato S, Nakamoto Y, Yamanaka N, Takahashi R, Ishimoto M, Anai T, Tabata S, Harada K: Map-based cloning of the gene associated with the soybean maturity locus E3.Genetics 2009,182(4):1251–1262. 10.1534/genetics.108.098772View ArticlePubMed CentralPubMed
Rogers SO, Bendich AJ: Extraction of total cellular DNA from plants, algae and fungi.Plant Molecular Biology Manual 2nd ed 1994, D1:1–8.
Goff SA, Ricke D, Lan TH, Presting G, Wang R, Dunn M, Glazebrook J, Sessions A, Oeller P, Varma H, Hadley D, Hutchison D, Martin C, Katagiri F, Lange BM, Moughamer T, Xia Y, Budworth P, Zhong J, Miguel T, Paszkowski U, Zhang S, Colbert M, Sun WL, Chen L, Cooper B, Park S, Wood TC, Mao L, Quail P, et al.: A draft sequence of the rice genome (Oryza sativa L. ssp. japonica).Science 2002,296(5565):92–100. 10.1126/science.1068275View ArticlePubMed
Li H, Durbin R: Fast and accurate short read alignment with Burrows-Wheeler transform.Bioinformatics 2009,25(14):1754–1760. 10.1093/bioinformatics/btp324View ArticlePubMed CentralPubMed
Li H, Handsaker B, Wysoker A, Fennell T, Ruan J, Homer N, Marth G, Abecasis G, Durbin R: The Sequence Alignment/Map format and SAMtools.Bioinformatics 2009,25(16):2078–2079. 10.1093/bioinformatics/btp352View ArticlePubMed CentralPubMed
McKenna A, Hanna M, Banks E, Sivachenko A, Cibulskis K, Kernytsky A, Garimella K, Altshuler D, Gabriel S, Daly M, De Pristo MA: The genome analysis toolkit: a MapReduce framework for analyzing next-generation DNA sequencing data.Genome Res 2010,20(9):1297–1303. 10.1101/gr.107524.110View ArticlePubMed CentralPubMed
Kosambi DD: The estimation of map distance from recombination values.Ann Eugen 1943, 12:172–175. 10.1111/j.1469-1809.1943.tb02321.xView Article
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