Variation block-based genomics method for crop plants
- Yul Ho Kim†1Email author,
- Hyang Mi Park†1,
- Tae-Young Hwang†1,
- Seuk Ki Lee†1,
- Man Soo Choi1,
- Sungwoong Jho2,
- Seungwoo Hwang3,
- Hak-Min Kim2,
- Dongwoo Lee2,
- Byoung-Chul Kim2,
- Chang Pyo Hong4,
- Yun Sung Cho2,
- Hyunmin Kim4,
- Kwang Ho Jeong1,
- Min Jung Seo1,
- Hong Tai Yun1,
- Sun Lim Kim1,
- Young-Up Kwon1,
- Wook Han Kim1,
- Hye Kyung Chun1,
- Sang Jong Lim1,
- Young-Ah Shin2,
- Ik-Young Choi5,
- Young Sun Kim6,
- Ho-Sung Yoon6,
- Suk-Ha Lee7 and
- Sunghoon Lee2, 4Email author
© Kim et al.; licensee BioMed Central Ltd. 2014
Received: 19 February 2014
Accepted: 3 June 2014
Published: 15 June 2014
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.
KeywordsComparative genomics Recombination Whole-genome sequencing Soybean Crop plants
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 [13, 14].
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 1C, Methods).
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.
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 [26, 28].
Robustness of VB detection with respect to sequencing depth
VB-based analysis of publicly available soybean and rice genomes
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.
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).
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
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
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 i i 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 I h and i h 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 i i allele (yellow seed coat) of the I locus and the I h 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.
- 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.0604379103.PubMed CentralPubMedView ArticleGoogle Scholar
- 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/cropsci2005.0016.View ArticleGoogle Scholar
- 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.107.074245.PubMed CentralPubMedView ArticleGoogle Scholar
- 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, 737-740. 2009/08/08Google Scholar
- 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.0032982.PubMed CentralPubMedView ArticleGoogle Scholar
- 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.110.166736.PubMed CentralPubMedView ArticleGoogle Scholar
- 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. 10.1038/35075590.PubMedView ArticleGoogle Scholar
- 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/bth457.PubMedView ArticleGoogle Scholar
- 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.0130101100.PubMed CentralPubMedView ArticleGoogle Scholar
- Consortium TIH: A haplotype map of the human genome. Nature. 2005, 437 (7063): 1299-1320. 10.1038/nature04226.View ArticleGoogle Scholar
- 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.124.PubMedView ArticleGoogle Scholar
- 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: 200-10.1186/1471-2105-8-200.PubMed CentralPubMedView ArticleGoogle Scholar
- 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.1138632.PubMedView ArticleGoogle Scholar
- 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.1177837.PubMedView ArticleGoogle Scholar
- 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.089516.108.PubMed CentralPubMedView ArticleGoogle Scholar
- 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.1005931107.PubMed CentralPubMedView ArticleGoogle Scholar
- 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.715.PubMedView ArticleGoogle Scholar
- 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 ArticleGoogle Scholar
- 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 ArticleGoogle Scholar
- 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.2491.PubMedView ArticleGoogle Scholar
- 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/cropsci1995.0011183X003500050012x.View ArticleGoogle Scholar
- 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/nature08670.PubMedView ArticleGoogle Scholar
- 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.1009526107.PubMed CentralPubMedView ArticleGoogle Scholar
- 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.286.5449.2468.PubMedView ArticleGoogle Scholar
- 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.808603.PubMed CentralPubMedView ArticleGoogle Scholar
- 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.105.048215.PubMed CentralPubMedView ArticleGoogle Scholar
- 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/nrg1970.PubMedView ArticleGoogle Scholar
- 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.PubMed CentralPubMedView ArticleGoogle Scholar
- 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.105.044834.PubMed CentralPubMedView ArticleGoogle Scholar
- 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.8.4.687.PubMed CentralPubMedView ArticleGoogle Scholar
- 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/g04-049.PubMedView ArticleGoogle Scholar
- 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/s11103-007-9169-4.PubMedView ArticleGoogle Scholar
- 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/esq078.PubMedView ArticleGoogle Scholar
- 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: 155-10.1186/1471-2229-11-155.PubMed CentralPubMedView ArticleGoogle Scholar
- 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.108.098772.PubMed CentralPubMedView ArticleGoogle Scholar
- Rogers SO, Bendich AJ: Extraction of total cellular DNA from plants, algae and fungi. Plant Molecular Biology Manual 2nd ed. 1994, D1: 1-8.Google Scholar
- 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.1068275.PubMedView ArticleGoogle Scholar
- Li H, Durbin R: Fast and accurate short read alignment with Burrows-Wheeler transform. Bioinformatics. 2009, 25 (14): 1754-1760. 10.1093/bioinformatics/btp324.PubMed CentralPubMedView ArticleGoogle Scholar
- 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/btp352.PubMed CentralPubMedView ArticleGoogle Scholar
- 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.107524.110.PubMed CentralPubMedView ArticleGoogle Scholar
- Kosambi DD: The estimation of map distance from recombination values. Ann Eugen. 1943, 12: 172-175. 10.1111/j.1469-1809.1943.tb02321.x.View ArticleGoogle Scholar
This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.