- Research article
- Open Access
In silico polymorphism analysis for the development of simple sequence repeat and transposon markers and construction of linkage map in cultivated peanut
- Kenta Shirasawa1Email author,
- Padmalatha Koilkonda1,
- Koh Aoki1, 6,
- Hideki Hirakawa1,
- Satoshi Tabata1,
- Manabu Watanabe2,
- Makoto Hasegawa2,
- Hiroyuki Kiyoshima2,
- Shigeru Suzuki2,
- Chikara Kuwata2,
- Yoshiki Naito3,
- Tsutomu Kuboyama4,
- Akihiro Nakaya5,
- Shigemi Sasamoto1,
- Akiko Watanabe1,
- Midori Kato1,
- Kumiko Kawashima1,
- Yoshie Kishida1,
- Mitsuyo Kohara1,
- Atsushi Kurabayashi1,
- Chika Takahashi1,
- Hisano Tsuruoka1,
- Tsuyuko Wada1 and
- Sachiko Isobe1
https://doi.org/10.1186/1471-2229-12-80
© Shirasawa et al.; licensee BioMed Central Ltd. 2012
- Received: 15 February 2012
- Accepted: 6 June 2012
- Published: 6 June 2012
Abstract
Background
Peanut (Arachis hypogaea) is an autogamous allotetraploid legume (2n = 4x = 40) that is widely cultivated as a food and oil crop. More than 6,000 DNA markers have been developed in Arachis spp., but high-density linkage maps useful for genetics, genomics, and breeding have not been constructed due to extremely low genetic diversity. Polymorphic marker loci are useful for the construction of such high-density linkage maps. The present study used in silico analysis to develop simple sequence repeat-based and transposon-based markers.
Results
The use of in silico analysis increased the efficiency of polymorphic marker development by more than 3-fold. In total, 926 (34.2%) of 2,702 markers showed polymorphisms between parental lines of the mapping population. Linkage analysis of the 926 markers along with 253 polymorphic markers selected from 4,449 published markers generated 21 linkage groups covering 2,166.4 cM with 1,114 loci. Based on the map thus produced, 23 quantitative trait loci (QTLs) for 15 agronomical traits were detected. Another linkage map with 326 loci was also constructed and revealed a relationship between the genotypes of the FAD2 genes and the ratio of oleic/linoleic acid in peanut seed.
Conclusions
In silico analysis of polymorphisms increased the efficiency of polymorphic marker development, and contributed to the construction of high-density linkage maps in cultivated peanut. The resultant maps were applicable to QTL analysis. Marker subsets and linkage maps developed in this study should be useful for genetics, genomics, and breeding in Arachis. The data are available at the Kazusa DNA Marker Database (http://marker.kazusa.or.jp).
Keywords
- DNA marker
- Genetic linkage map
- Peanut (Arachis hypogaea)
- QTL analysis
- Ratio of oleic/linoleic acid (O/L ratio)
Background
Peanut (Arachis hypogaea) is an autogamous allotetraploid legume (2n = 4x = 40) composed of A and B genomes that are derived from two diploids, most likely A. duranensis (A genome) and A. ipaënsis (B genome). On the basis of branching habit, the presence/absence of flowers on the main stem, alternate vs. sequential branching, fruit and seed traits, and maturity, A. hypogaea has been categorized into two subspecies: hypogaea and fastigiata; six botanical varieties: hypogaea hirsuta fastigiata vulgaris aequatoriana, and peruviana; and four agronomic types: Virginia, Spanish, Valencia, and Southeast-runner [1, 2]. As the nuclear DNA content in peanut is calculated to be 5.914 pg/2 C [3], the genome size is estimated to be approximately 2.8 Gb based on an assumption that 1 pg of DNA is equivalent to 980 Mb [4]. Because of its allotetraploidy and large genome size, genomic study in the peanut has lagged far behind that of other legumes, such as Lotus japonicus[5], Glycine max[6], Medicago truncatula[7], and Cajanus cajan[8]. In addition, low genetic diversity within the species has inhibited the advance of genetic linkage map construction. In autogamous species, the genetic diversity of polyploids is generally more narrow than that of their diploid progenitors due to bottleneck effects, and this results in few alleles having been transferred from diploid progenitors to their polyploid descendents [9]. Moreover, genetic diversity is affected by the history of polyploidization. Tetraploid peanut is thought to have arisen approximately 3,500 years ago [10], and its short history has been considered a source of lower levels of polymorphism compared with diploid Arachis species [11].
At present, more than 6,000 DNA markers have been developed in Arachis spp. [12], e.g., restriction fragment length polymorphism [13, 14] and simple sequence repeats (SSRs) [11, 15–21]. Linkage maps were constructed in wild diploids [14, 19, 22, 23], cultivated species [21, 24–26], and artificial amphidiploids derived from wild diploids [13, 27]. Integrated maps based on cultivated peanuts were also constructed [28–31]. However, the peanut linkage map has not yet covered all of the chromosomes of the haploid genome (n = 20). Although extremely low genetic diversity has inhibited the construction of high-density genetic linkage maps in peanut, increasing the number of polymorphic markers is crucial for the saturation of linkage maps in peanut.
In general, SSR markers have been developed from randomly collected sequence data of complementary DNAs (cDNAs) [11, 15], SSR-enriched genomic DNA libraries [16–20], and BAC-ends [21]. Primers were designed based on flanking regions of identified SSRs of the obtained sequences, and then polymorphism of the targeted SSRs was investigated using gel or capillary electrophoresis of DNA amplified by PCR. However, since the degree of polymorphism of the markers depends on the genetic diversity of the germplasm, experimental analysis requires considerable cost, time, and labor to develop a large number of polymorphic markers in species having low genetic diversity. Therefore, this strategy is not effective for the large-scale development of polymorphic DNA markers in closely-related lines. For the development of single nucleotide polymorphism (SNP) markers, on the other hand, in silico polymorphism analysis, i.e., comparison of genomic or cDNA sequences derived from more than two lines, is often performed before synthesizing primers of DNA markers for lab validation. To our knowledge, this approach has been limited, e.g., polySSR [32] and SSRpoly [33]. However, we consider that in silico polymorphism analysis prior to primer synthesis is also effective for SSR and other types of DNA marker development.
Recently, we have developed a total of 504 AhMITE1 transposon markers in peanut [34]. The percentage of the transposon markers that were polymorphic between the two peanut lines was 22.0%, which was higher than that of the SSR markers [11, 35]. This result suggested that transposon markers, like SSR markers, represent potent, co-dominant, and PCR-based markers.
Peanut is widely cultivated in Asia, Africa, America, and Australia as a food and oil crop. Peanut breeding has achieved a rise in productivity by increasing the size and number of seeds, and by enhancing resistance to biotic and abiotic stresses [36]. Breeding has been mostly performed by conventional methods, e.g., a combination of crossing, phenotypic selections, and homogenization. In conventional breeding, large sizes of breeding populations are required, especially for the selection of recessive traits, because single gene mutations often do not confer phenotypic variation in peanut due to functional complementation by homoeologous genes. Thus, molecular breeding with marker-assisted selection has great promise and may lead to remarkable advances in peanut breeding. While quantitative trait locus (QTL) analysis is an effective method for identification of DNA markers linked to agronomically important traits, few QTL studies have been conducted due to the lack of high-density linkage maps in peanut [25, 28, 30, 31].
Because peanut is used as an oil crop, seed quality is also an important breeding objective. The major components of peanut oil are linoleic acid and oleic acid, consisting of 36-67% and 15-43% of total oil, respectively, in normal cultivars [37]. Whereas normal cultivars have a ratio of oleic to linoleic acid (O/L ratio) of about 1:4, the ratio can reach as high as 1:30 or 1:40 in high-O/L ratio cultivars [38]. Oleic acid is a monounsaturated fatty acid, whereas linoleic acid is a polyunsaturated fatty acid. Therefore oleic acid is less oxidized than linoleic acid, and it is considered that oleic acid is better for health and storage quality [39]. In high-O/L ratio plants, oleic acid is synthesized from stearic acid and is converted into linoleic acid by two fatty acid desaturases encoded by SAD and FAD2[40]. The selection of mutated alleles of FAD2, which is associated with oleic-acid content in seeds [41–43], is a straightforward strategy to efficiently generate high-oleic acid crops. In peanut, ahFAD2A and ahFAD2B have been identified on the A and B genomes, respectively, and the mutant alleles are reported to confer a high O/L ratio [44–49].
In this study, we investigated whether in silico polymorphism analysis could increase the efficiency of development of polymorphic SSR and transposon markers. First, genomic sequences covering SSR and transposon-inserted regions derived from two peanut lines were compared in silico to identify candidate polymorphic regions. Then, the candidate polymorphic regions were subjected to lab validation. High-density genetic linkage maps were constructed using the developed polymorphic markers along with published markers. The developed linkage maps demonstrated their applicability to molecular breeding through QTL mapping for agronomical traits and FAD2 gene mapping for the development of varieties with high O/L ratios.
Methods
Plant materials and DNA extraction
Two F2 mapping populations, i.e., SKF2 and NYF2, were used for the construction of linkage maps. The SKF2 (n = 94) was generated from a cross between two lines belonging to different agronomic and morphological types, i.e., a Virginia type, ‘Satonoka’, and a Spanish type, ‘Kintoki’. The SKF2 population is expected to generate a larger number of polymorphic markers for construction of a high-density linkage map. It has also been used for the identification of 15 agronomic trait loci. The NYF2 (n = 186) is a breeding population derived from a cross between a Virginia type, ‘YI-0311’, which is also considered as a Southeast-runner type, and another Virginia type, ‘Nakateyutaka’. The former is a breeding line showing a high O/L ratio in seeds, and the latter is a leading cultivar in Japan with a normal O/L ratio. This mapping population was used for the identification of linkages between genotypes of FAD2 genes and O/L ratio in seeds. Genomic DNA from each line was extracted using the DNeasy Plant Mini kit (Qiagen, Germany).
Development of SSR markers by in silicopolymorphism analysis
To develop SSR markers, two SSR-enriched genomic libraries were constructed, as described by Nunome et al.[50]. While the first library was generated from a single line, ‘Satonoka’, the second library was developed using two lines, ‘Satonoka’ and ‘Kintoki’, for in silico polymorphism analysis. Both libraries were constructed with biotinylated oligo probes of (AC)12 and (CT)12. Sequencing analysis of all libraries and primer design for the first library were performed as described by Sraphet et al.[51]. For the second library, primers were designed based only on flanking sequences of polymorphic SSRs between ‘Satonoka’ and ‘Kintoki’, identified by in silico analysis as described below. The SSR motifs in the sequences were identified by the Fuzznuc tool from EMBOSS, version 6.1.0 [52], and the sequences with SSR motifs were assembled by the CAP3 program with parameters set to require 95% identify to be considered overlapping (−p 95) [53]. Of the obtained assemblies, contigs comprising sequences with different lengths of the same SSR motifs (different numbers of the same repeated sequence) between two lines were selected as polymorphic SSR candidates for primer design. All of the designed genomic SSR markers were designated as AHGS (rachis ypogaea genomic SSR) markers.
Development of transposon markers through in silicopolymorphism analysis
Shirasawa et al.[34] reported that insertion sites in the peanut genome for the transposon AhMITE1 differ among cultivars. They also demonstrated that the insertional polymorphisms of transposon elements can be used as DNA markers by developing 504 polymorphic markers derived from transposon-enriched genomic libraries. To develop additional transposon markers, transposon-enriched genomic libraries were constructed from ‘Satonoka’ and ‘Kintoki’, and sequences were obtained as previously described [34]. The individual sequences were assembled using the CAP3 program with default parameters [53]. Contigs and singlets having AhMITE1 sequences only on one cultivar (either ‘Satonoka’ or ‘Kintoki’, but not both) were selected as candidate polymorphic sequences. Using the PRIMER3 program [54], primers were designed on both flanking sequences of AhMITE1, or on one of the flanking sequences and an internal sequence of AhMITE1 in cases lacking either flanking sequence.
Additionally, the flanking sequences of the AhMITE1 transposons were cloned using inverse PCR. Genomic DNA fragments digested by MboI, MseI, or XspI were self-ligated using T4 DNA ligase (Promega, USA) and used as PCR templates. Ten microliters of PCR mixture, composed of 0.04 ng/μl template DNA, 0.5 pmol/μl primer pairs (Additional file 1), 1X PCR buffer (Bioline, UK), 0.2 mM dNTPs, 5 mM MgCl2, and 0.25 U BioTaq DNA polymerase (Bioline), was used. The thermal cycling conditions were as follows: a 1 min initial denaturation at 94 °C; 35 cycles of 30 s denaturation at 94 °C, 30 s of annealing at 60 °C, and a 90 s extension at 72 °C; and a 3 min final extension at 72 °C. The amplified DNAs were ligated into pGEM-T® Easy plasmids (Promega). Plasmids were introduced into Escherichia coli ElectroTen-blue (Stratagene, USA) by electroporation. Following the amplification of DNA inserts with the Illustra TempliPhi DNA Amplification Kit (GE Life Sciences, USA), nucleotide sequences were determined using the BigDye Terminator Kit (Applied Biosystems, USA) and an ABI 3730xl DNA sequencer (Applied Biosystems).
The transposon markers were designated as AhTE (rachis ypogaea transposable element), as described in Shirasawa et al.[34].
SSR markers derived from BAC-end sequences
BAC libraries for A. duranensis (AA) and A. ipaënsis (BB) have been constructed by Guimarães et al.[55]. Dr. Bertioli, University of Brasilia, Brazil, and his colleagues determined the end sequences of the BAC clones and designed primers on flanking regions of identified SSRs (Bertioli, personal communication). These BAC-end derived SSR markers kindly provided by Dr. Bertioli were also subjected to polymorphism analysis (Additional file 2).
Polymorphism analysis of the DNA markers
In addition to the AHGS and AhTE markers developed in this study, a total of 4,449 previously published markers [11, 15–20, 34] were used for the polymorphism analysis (Additional file 2). PCR reactions were performed using 0.5 ng genomic DNA in each 5 μl reaction. In addition to template DNA, PCR reaction mixtures contained 1X PCR buffer (Bioline), 3 mM MgCl2, 0.04 U BIOTAQTM DNA polymerase (Bioline), 0.2 mM dNTPs, and 0.8 μM of each primer. The thermal cycling conditions were as follows: 1 min denaturation at 94 °C; 35 cycles of 30 s denaturation at 94 °C, 30 s of annealing at 60 °C, and a 1 min extension at 72 °C; and a final 3 min extension at 72 °C. The PCR products were separated by 10% polyacrylamide gel electrophoresis in 1X TBE buffer according to the standard protocol, or with a fluorescent fragment analyzer, ABI 3730xl (Applied Biosystems, USA). In the latter case, the data were analyzed using GeneMapper software (Applied Biosystems).
The previously reported SNP in ahFAD2A and transposon insertional polymorphism in ahFAD2B were also investigated to check the existence of polymorphisms in the NYF2 population [49, 56, 57]. The SNP was genotyped by the TaqMan assay with primer pairs (5’-CCCTTCACTCTTGTCTATTAGTTCCTTAT-3’ and 5’-TGATACCTTTGATTTTGGTTTTGG-3’) and TaqMan probes (FAM-labeled 5’-CCTCGACCGCAACG-3’ for mutant allele and VIC-labeled 5’-CCTCGACCGCGACG-3’ for the wild-type allele) on the 7900HT Fast Real-Time PCR System (Applied Biosystems). The TaqMan assay was performed according to the protocol of the TaqMan Genotyping Master Mix (Applied Biosystems). Transposon insertional polymorphisms were detected on 2% agarose gel as a difference in mobility of the DNA fragments that had been amplified by PCR with primers bF19: 5’-CAGAACCATTAGCTTTG-3’ and R1: 5’-CTCTGACTATGCATCAG-3’ [49]. PCR and electrophoresis were performed as described above.
Construction of linkage maps
Linkage analysis was performed on segregated genotypic data from the two mapping populations using JoinMap® version 4 [58]. The marker loci were roughly classified using the JoinMap® grouping module with logarithm of odds (LOD) scores of 4.0–10.0. Marker order and genetic distance were calculated using a regression mapping algorithm with the following parameters: Haldane’s mapping function, recombination frequency ≤ 0.30, and LOD score ≥ 2.0. The graphical linkage maps were drawn with the MapChart program [59].
Phenotyping and QTL analysis
A total of 15 morphological traits of the SKF2 population were investigated in the Peanut Plant Breeding Field of the Chiba Prefectural Agriculture and Forestry Research Center, Japan (35°37’54”N, 140°19’02”E). The seeds were sown in May, 2005 with 66 cm and 20 cm inter- and intra-row spacing, respectively. The flowering date for each plant was determined based on the opening of the first flower. Numbers and angles of branches, lengths of main stems and the longest branches, and fresh weights of the whole plant were measured at the harvesting stage. After drying of harvested pods under natural conditions for two weeks, the length, thickness, width, and weight of the matured pods were measured. In addition, constrictions on the pods were scored from 1 (deep) to 5 (shallow), and the shapes of the tips of the pods were also scored from 1 (round) to 5 (sharp). After that, numbers of seeds per plant and mean weights of single seeds were investigated. Colors of seed coats were classified as orange–yellow (2.5Y 8/6) or brown–red (2.5R 4/10), based on the Munsell color system.
To investigate the fatty acid content of the seeds, the 32 F1 parents of the NYF2 mapping population were planted in the Peanut Plant Breeding Field of the Chiba Prefectural Agriculture and Forestry Research Center in May, 2008 with 66 cm and 30 cm inter- and intra-row spacing, respectively. The F2 seeds were harvested in October and dried for one month in an open-air condition in their pods. One quarter of each of the dried seeds was cut off, and then 25 mg of the seeds was homogenized using TissueLyzer (Qiagen) with 400 μl of 100% (v/v) methanol. The homogenate was incubated at 70 °C for 15 min with 1,100 μl of 91% (v/v) methanol and then centrifuged at 15,000 rpm for 5 min. The supernatant was transferred to a glass tube. The pellet was resuspended in 750 μl of chloroform with 2 mg/ml nonadecanoic acid methyl ester (Sigma-Aldrich, USA), and the chloroform (resuspended pellet) layer was mixed with the supernatant layer. After washing the mixture with water, 900 μl chloroform and 1 ml of 3% (v/v) sulfuric acid in methanol were added to the chloroform layer, followed by washing with water. Then, 10 μl of the chloroform layer was dried with N2 gas, 70 μl n-heptane, 10 μl pyridine, and 10 μl N-methyl-N-(trimethylsilyl)trifluoroacetamide (Sigma-Aldrich). Fatty acid quantification was performed using gas chromatography time-of-flight mass spectrometry (GC-TOF-MS) with a 6890 N Network GC System (Agilent Technologies, USA) equipped with a column DB-17MS (length, 30 m; ID, 0.25 mm; film, 0.25 μm) (J&W Scientific, USA), coupled to Pegasus3 (Leco®, USA) with the following settings: inlet temperature, 250 °C; oven temperature, 70 °C for 5 min, increasing by 15 °C/min, and holding at 310 °C for 5 min; transfer tube temperature, 200 °C; and ion source temperature, 250 °C. Acquisition and analysis of mass spectral data were performed using the ChromaTOFTM version 2.32 optimized for Pegasus (Leco®). Concentrations of oleic acid, linoleic acid, palmitic acid, and stearic acid were estimated from calibration curves created using pure samples of each compound.
The phenotypic data regarding morphological traits of the SKF2 population were subjected to composite interval mapping by the Windows QTL Cartographer program [60]. The thresholds of LOD for each QTL were determined by 1,000 permutation tests. Since two genes, ahFAD2A and ahFAD2B, were reported to control the O/L ratio in seeds with epistatic interactions, QTL analysis of the NYF2 population was conducted with Genotype Matrix Mapping (GMM) software with the following parameters: Max Length of Locus Combination = 2; Min Number of Corresponding Samples = 1; Search Range = auto [61].
Results
Design of polymorphic genomic SSR markers
In the first library, a total of 11,673 genomic clones were sequenced. After removing redundant sequences, 2,661 primer pairs were designed to amplify the flanking regions of SSRs [DNA Data Bank of Japan (DDBJ): DH961577-DH964237] and designated as AHGS markers [62] (Additional file 3). Out of the 2,661 AHGS markers, 334 were screened for polymorphism compared with the four parental lines of the mapping populations using a fluorescent fragment analyzer. According to the results, 42 (12.6%) and 6 (1.8%) markers showed polymorphism between ‘Satonoka’ and ‘Kintoki’ and between ‘Nakateyutaka’ and ‘YI-0311’, respectively.
Polymorphic ratio of the investigated markers
Marker types | No. of tested markers | No. of polymorphic markers between 'Satonoka' and 'Kintoki' | % | No. of polymorphic markers between 'Nakateyutaka' and 'YI-0311' | % | References | |
|---|---|---|---|---|---|---|---|
DNA markers developed in this study | |||||||
Genomic SSRs | 2167 | 624 | 28.8 | 189 | 8.7 | This study | |
Transposons | 535 | 302 | 56.4 | 67 | 12.5 | This study | |
DNA markers derived from other studies | |||||||
Genomic SSRs | 418 | 87 | 20.8 | 18 | 4.3 | ||
EST-SSRs | 3375 | 50 | 1.5 | 9 | 0.3 | ||
BAC-end SSRs | 152 | 25 | 16.4 | 6 | 3.9 | Bertioli, pers. comm. | |
Transposons | 504 | 91 | 18.1 | 49 | 9.7 | [34] | |
Total | 7151 | 1179 | 16.5 | 338 | 4.7 | ||
A total of 6,680 AHGS markers were designed from the first and second SSR-enriched genomic libraries. Out of these markers, the poly (CT)n motif was the most abundant (2,390: 35.8%), followed by poly (AC)n (1,496: 22.4%), due to the usage of biotinylated oligo probes of poly (AC)12 and poly (CT)12 in the construction of the libraries (Additional file 4). The frequencies of the other di-, tri-, and tetra-nucleotide repeat motifs were 13.1%, 5.6%, and 23.1%, respectively. The distributions of the SSR motifs found in the two libraries were not different. Between ‘Satonoka’ and ‘Kintoki’, the polymorphic ratio of the poly (CT)n motif, 40.4% (498/1,232), was higher than that of the poly (AC)n motifs, 14.2% (88/620) (Additional file 4).
Design and polymorphism analysis of transposon markers
As with the AHGS marker development, transposon markers named AhTE were developed via in silico polymorphism analysis. A total of 13,248 and 12,351 clones derived from AhMITE1-enriched genomic libraries of ‘Satonoka’ and ‘Kintoki’, respectively, were sequenced. Of the total 25,599 sequences, 16,639 were identified as including AhMITE1 sequences in the fragments. These were then assembled into 1,198 contigs. Cultivar-specific transposon insertions were identified in 511 out of the 1,198 contigs. In addition, 24 additional insertion sites were found from the libraries derived from the inverse PCR analysis. In total, 535 primer pairs were designed based on the flanking regions of identified AhMITE1s [63; DDBJ: DH968257-DH968767] (Additional file 5). When polymorphism analysis was performed using these 535 primer pairs with the four parental lines of the mapping population, a total of 302 (56.4%) and 67 (12.5%) markers exhibited polymorphism between ‘Satonoka’ and ‘Kintoki’, and between ‘Nakateyutaka’ and ‘YI-0311’, respectively (Table 1).
Polymorphism analysis of the previously published marker loci
A total of 4,449 markers derived from other studies, including 418 genomic SSRs [16–20], 3,375 EST-SSRs [11, 15], 152 BAC-end SSRs (Bertioli, personal communication), and 504 transposon markers [34], were used for polymorphism analysis between the four mapping parents (Additional file 2). Out of the 4,449 markers, 253 (5.7%), including 87 genomic SSRs (20.8%), 50 EST-SSRs (1.5%), 25 BAC-end SSRs (16.4%), and 91 transposon markers (18.1%), were selected as polymorphic markers between ‘Satonoka’ and ‘Kintoki’ (Table 1). Between ‘Nakateyutaka’ and ‘YI-0311’, 18 genomic SSRs (4.3%), 9 EST-SSRs (0.3%), 6 BAC-end SSRs (3.9%), and 49 transposon markers (9.7%) showed polymorphism (Table 1).
Construction of the SKF2 genetic linkage map
Length, number of mapped marker loci, and segregation distortion of the linkage maps
Linkage group | SKF2 | NYF2 | ||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Length | Number of marker loci | Marker density | Segregation distortion ratioa) | Length | Number of marker loci | Marker density | Segregation distortion ratioa) | |||||||
(cM) | Total | AHGS | AhTEb) | Publishedc) | (cM/loci) | (%) | (cM) | Total | AHGS | AhTEb) | Publishedc) | (cM/loci) | (%) | |
LG01.1 | 126.9 | 59 | 29 | 23 | 7 | 2.2 | 28.8 | 74.7 | 46 | 28 | 13 | 5 | 1.7 | 4.3 |
LG01.2 | 60.9 | 61 | 34 | 19 | 8 | 1.0 | 19.7 | 56.9 | 13 | 10 | 3 | 0 | 4.7 | 15.4 |
LG02.1 | 101.3 | 69 | 36 | 20 | 13 | 1.5 | 18.8 | 66.6 | 30 | 22 | 7 | 1 | 2.3 | 3.3 |
LG02.2 | 94.7 | 66 | 34 | 18 | 14 | 1.5 | 28.8 | 4.9 | 2 | 2 | 0 | 0 | 4.9 | 50.0 |
LG02.3 | 44.1 | 24 | 14 | 3 | 7 | 1.9 | 16.7 | 21.1 | 5 | 3 | 1 | 1 | 5.3 | 0.0 |
LG03.1 | 117.0 | 78 | 42 | 26 | 10 | 1.5 | 39.7 | 97.2 | 31 | 19 | 9 | 3 | 3.2 | 0.0 |
LG03.2 | 101.7 | 55 | 32 | 14 | 9 | 1.9 | 16.4 | 76.0 | 9 | 6 | 1 | 2 | 9.5 | 0.0 |
LG04.1 | 125.6 | 87 | 34 | 37 | 16 | 1.5 | 31.0 | 104.3 | 13 | 6 | 7 | 0 | 8.7 | 23.1 |
LG04.2 | 103.5 | 40 | 29 | 7 | 4 | 2.7 | 32.5 | 106.6 | 15 | 7 | 7 | 1 | 7.6 | 20.0 |
LG05.1 | 112.2 | 43 | 21 | 18 | 4 | 2.7 | 9.3 | 62.4 | 6 | 4 | 2 | 0 | 12.5 | 33.3 |
LG05.2 | 98.3 | 60 | 37 | 16 | 7 | 1.7 | 23.3 | 84.2 | 8 | 1 | 7 | 0 | 12.0 | 0.0 |
LG06.1 | 114.0 | 42 | 27 | 12 | 3 | 2.8 | 38.1 | - | - | - | - | - | - | - |
LG06.2 | 91.7 | 56 | 25 | 24 | 7 | 1.7 | 19.6 | 146.5 | 44 | 21 | 17 | 6 | 3.4 | 9.1 |
LG07.1 | 160.9 | 55 | 38 | 9 | 8 | 3.0 | 29.1 | 167.3 | 44 | 29 | 10 | 5 | 3.9 | 6.8 |
LG07.2 | 146.3 | 62 | 26 | 21 | 15 | 2.4 | 43.5 | 2.9 | 2 | 1 | 1 | 0 | 2.9 | 0.0 |
LG08.1 | 199.8 | 38 | 17 | 15 | 6 | 5.4 | 15.8 | 98.5 | 24 | 8 | 12 | 4 | 4.3 | 4.2 |
LG08.2 | 91.8 | 68 | 38 | 20 | 10 | 1.4 | 57.4 | 49.4 | 15 | 10 | 4 | 1 | 3.5 | 13.3 |
LG09.1 | 112.6 | 81 | 48 | 27 | 6 | 1.4 | 19.8 | 72.8 | 7 | 2 | 4 | 1 | 12.1 | 14.3 |
LG09.2 | 83.1 | 50 | 25 | 15 | 10 | 1.7 | 22.0 | 16.7 | 10 | 5 | 2 | 3 | 1.9 | 0.0 |
LG10.1(t) | 79.9 | 8 | 5 | 2 | 1 | 11.4 | 37.5 | - | - | - | - | - | - | - |
LG10.2(t) | 59.3 | 12 | 7 | 5 | 0 | 5.4 | 8.3 | - | - | - | - | - | - | - |
LGX | - | - | - | - | - | - | - | 23.9 | 2 | 2 | 0 | 0 | 23.9 | 0.0 |
Total | 2166.4 | 1114 | 598 | 351 | 165 | 1.9 | 27.7 | 1332.9 | 326 | 186 | 107 | 33 | 4.3 | 7.7 |
The SKF2 linkage map and positions of QTLs for agronomical traits. Scale bars on the left side describe map distance in centimorgans. Genomic SSR, transposon, EST-SSR, and BAC-end SSR markers are shown as black, red, green, and blue lines, respectively. Anchor markers to previously reported maps are underlined. QTLs with positive additive effects in Satonoka and Kintoki alleles are shown by hashed and black boxes, respectively, to the right of each linkage group. The vertical bars on the boxes show the regions over which significant LOD values were calculated by a permutations test (n = 1,000).
The LGs were named according to the consensus numbers of the previously reported maps by comparison of the mapping position of the 122 commonly mapped markers [19, 22, 23, 25, 27, 28, 30] (Figure 1, Additional file 7). Eight homoeologous pairs of linkage groups (HGs) were identified, i.e., 1, 3, 4, 5, 6, 7, 8, and 9. Those pairs were suffixed ‘.1’ and ‘.2’; for example, LG01.1 and LG01.2 (Table 2, Figure 1, Additional file 6). In HG2, three linkage groups were identified and designated as LG02.1, LG02.2, and LG02.3. Because two remaining linkage groups were generated with some common markers from previously reported maps, these linkage groups were tentatively named LG10.1(t) and LG10.2(t).
The average marker density of the SKF2 maps was 1.9 cM in total, ranging from 1.1 cM for LG01.2 to 11.4 cM for LG10.1(t) in the 21 linkage groups. The largest interval between two loci was 25.6 cM, observed between AHGS1270 and AHGS1947 on LG08.1. In the SKF2 map, 28.3% of the total marker loci showed segregation distortions, ranging from 42.2% in LG07.2 to 8.3% in LG10.2(t) (Table 2, Additional file 6).
Construction of the NYF2 linkage map
A total of 338 of the 7,151 (4.7%) tested markers showed polymorphism between ‘Nakateyutaka’ and ‘YI-0311’. While four markers identified double polymorphic loci, the other 334 markers generated single polymorphic loci. Consequently, 342 polymorphic loci were generated from the 338 markers. Because the SNP for the D150N change in ahFAD2A[56, 57] and the transposon insertion polymorphism at the 665th base in ahFAD2B[49] segregated in the NYF2 population, the ahFAD2A and ahFAD2B genes were subjected to genotyping of the F2 population together with the 342 polymorphic loci. A total of 326 segregating loci (94.8%) formed 19 linkage groups (LGs) (Table 2, Additional file 6, Additional file 8). Of the 19 LGs, 18 were numbered corresponding to the SKF2 maps and the previously reported maps with 18 anchor markers [19, 22, 23, 25, 27, 28, 30] (Additional file 7, Additional file 8). The remaining linkage group was tentatively named LGX because this LG had the possibility of corresponding to either LG06.1, LG10.1(t), or LG10.2(t) of the SKF2. The ahFAD2A and ahFAD2B genes were mapped onto LG9.2 and LG9.1, respectively, as reported by Qin et al.[30]. The total length of the map was 1,332.9 cM, with individual LGs ranging from 2.9 cM (LG07.2) to 167.3 cM (LG07.1). The average marker density was 4.3 cM, ranging from 1.7 cM in the LG01.1 to 23.9 cM in the LGX. In the NYF2 map, 7.7% of the marker loci showed significant segregation distortions. The highest segregation distortion was observed in LG2.2 (50%), whereas seven LGs had no distorted loci (Table 2, Additional file 6). Of the 326 loci, 293 were generated from markers developed in this study and in Shirasawa et al.[34].
Detection of QTLs for agronomical traits
Positions, effects, and phenotypic variation explained by QTLs for 15 agronomic traits detected in the SKF2 population
Trait | QTL name | Linkage group | Marker interval | LOD | Additive effecta) | Dominant effecta) | Phenotypic variation explained (%) |
|---|---|---|---|---|---|---|---|
Flowering date (day) | qFD02.1 | LG02.1 | AHGS2736-AHGS1251 | 5.0 | 1.7 | −2.4 | 19.5 |
Angle of branch (1: erect to 5: spread-out) | qAB05.1 | LG05.1 | AHGS2534-AHGS2622 | 5.7 | 0.5 | −0.9 | 11.9 |
qAB07.2 | LG07.2 | AHGS1215-AhTE0615 | 4.6 | −0.6 | 0.2 | 23.2 | |
Length of main stem (cm) | qLMS06.2 | LG06.2 | AhTE0589-Ah1TC3H7 | 7.9 | 2.8 | 7.2 | 4.8 |
qLMS04.2 | LG04.2 | AHGS2155-AHGS3725 | 6.8 | 4.5 | 2.3 | 19.2 | |
qLMS05.2 | LG05.2 | AHGS2020-AHGS2450 | 4.6 | −4.4 | 2.0 | 15.7 | |
Length of the longest branch (cm) | qLLB06.2 | LG06.2 | AhTE0697-Ah1TC3H7 | 5.7 | 7.6 | 9.9 | 21.1 |
qLLB01.2 | LG01.2 | AHGS1813b-AhTE1016 | 4.7 | 4.9 | −7.0 | 14.2 | |
Number of branches (branch) | qNB06.2 | LG06.2 | AhTE0967-AhTE0074 | 5.1 | 5.2 | 3.8 | 15.6 |
Weight of plant (g) | qWP06.2 | LG06.2 | AhTE0697-Ah1TC3H7 | 5.4 | 7.8 | 12.9 | 11.8 |
Weight of mature pod per a plant (g) | qWMP09.2 | LG09.2 | AHGS0422-AHGS2635 | 7.2 | 5.4 | 2.2 | 28.1 |
Length of pod (mm) | qPL05.1 | LG05.1 | AhTE0601-AHGS1413 | 6.7 | −2.4 | 2.2 | 28.2 |
qPL09.2 | LG09.2 | AHS1684-AhTE0707 | 5.4 | −1.5 | −1.6 | 8.4 | |
qPL06.2 | LG06.2 | AhTE0745-AhTE0826 | 4.9 | −2.1 | 1.1 | 20.5 | |
Thickness of pod (mm) | qPT07.1 | LG07.1 | AHGS1803a-AhTE0025 | 11.9 | 1.0 | 0.4 | 21.7 |
Width of pod (mm) | qPW07.1 | LG07.1 | AhTE0025-pPGPSeq2E6b | 11.4 | 0.6 | 0.4 | 15.2 |
qPW08.2 | LG08.2 | AHGS1286-AHGS2249 | 9.5 | −0.7 | 0.4 | 25.5 | |
Constriction of pod (1: deep to 5: shallow) | qCP09.2 | LG09.2 | AHGS0362-AhTE0726 | 8.1 | −0.6 | −0.5 | 18.1 |
qCP09.1 | LG09.1 | AHGS1673-AhTE0222 | 6.2 | 0.3 | 0.6 | 6.9 | |
Shape of tip of pods (1: round to 5: sharp) | qSTP03.2 | LG03.2 | AhTE0570-AHGS1744 | 5.9 | −0.3 | 0.7 | 9.9 |
Weight of a seed (g) | qWS08.2 | LG08.2 | AhTE0846-AhTE0974 | 4.9 | −0.1 | 0.1 | 19.1 |
Number of seeds per a plant (grain) | qNS08.2 | LG08.2 | AHGS1286-AHGS2249 | 5.2 | 3.0 | 28.3 | 6.8 |
Color of seed coat (2: orange–yellow to 5: brown–red) | qCSC03.2 | LG03.2 | PM3a-AHGS1792 | 67.4 | 0.6 | 1.4 | 9.7 |
Relationship between mutations of ahFAD2genes and the O/L ratio
Ratio of oleic-acid to linoleic-acid content (O/L ratio) in peanut seeds of different genotypes of ahFAD2A and ahFAD2B genes in 185 NYF2 plants. Dominant and recessive alleles of FAD2 genes were shown by capital (FAD2A, FAD2B) and lowercase (fad2a, fad2b) letters, respectively. Numeric descriptions are average values for the O/L ratio in peanut seeds, with standard errors.
Discussion
In this study, we developed a high-density genetic linkage map, SKF2, of a total length of 2,166.4 cM consisting of 1,114 marker loci (Figure 1, Table 2, Additional file 6). Genetic linkage maps in Arachis spp. have been constructed using mapping populations derived from crosses between interspecific diploids [14, 19, 22, 23] or synthetic tetraploids [13, 27], as well as cultivated tetraploids [21, 24–26]. In addition, the integration of more than two maps by connecting common markers as anchors has been conducted to produce a higher number of marker loci than that on single maps [28–31]. While it is true that map integration is an effective way to increase marker loci on a single map, the development of new markers is still required to saturate linkage maps in peanut. As far as we know, the SKF2 map covering 2,166.4 cM with 1,114 loci is the highest-density genetic linkage map in Arachis, and probably covers a large portion of the peanut genome because the total length of the map is almost equal to those of maps for tetraploids (2,210 cM with 370 loci [13], 1,844 cM with 298 loci [27], and 1,785 cM with 191 loci [26]), and double those of maps for wild diploids (1,063 cM with 117 loci [14], 1,231 cM with 170 loci [19], and 1,294 cM with 149 loci [23]).
Our results suggested that in silico polymorphism analysis worked effectively for the development of polymorphic SSR and transposon markers. This was the first time in silico polymorphism analysis has been used in peanut. The polymorphic ratios in SKF2 increased from 15.9% (=133/838) to 54.4% (=331/609) in total, i.e., 12.6% (=42/334) to 39.2% (=29/74) for genomic SSR markers and 18.1% (=91/504) to 56.4% (=302/535) for transposon markers, by employing in silico polymorphism analysis. In this study, we performed empirical analysis for 1,833 of 4,019 primer pairs generated via in silico polymorphism analysis. If 32% of SSR markers derived from a second library show polymorphisms in the SKF2 population, an additional 700 [=0.32 × (4019–1833)] markers would map to the SKF2 map.
Though in silico polymorphism analysis was performed for parental lines of SKF2, the analysis increased polymorphic ratios in the NYF2 population as well. This result suggested that in silico polymorphism analysis between two lines enhances the efficiency of polymorphic marker development in this species. Koilkonda et al.[11] investigated genetic distances for 16 Arachis spp. accessions, including the four parental lines used in this study. According to their results, greater genetic diversity was observed among cultivated peanut lines than among our four parental lines. Thus, we considered that marker subsets developed in this study could be useful sources for obtaining polymorphic markers in other mapping populations. However, in parallel, the generation of an insufficient number of polymorphic markers in NYF2 suggested that additional in silico polymorphism analysis is required to develop polymorphic markers that can differentiate between closely-related lines such as the parents of NYF2.
Meanwhile, of the candidate polymorphic sequences, 60.8% of the SSRs and 43.6% of the transposon markers did not show polymorphisms. Two reasons might account for such identification of false positives in in silico polymorphism analysis. The first is related to the presence of the A and B genomes in tetraploid peanut. For example, in the case of sequences of one parent being derived from only the A genome and those of another parent being derived from only the B genome, there is a high possibility that homoeologous polymorphisms could be identified but not allelic polymorphisms. Another possible reason is sequencing errors introduced through the use of the Sanger method. New robust sequencing technologies, e.g., pyrosequencing and sequencing by synthesis or ligation, which have been used in massive parallel sequencers, may overcome these two possible causes because the principles underlying the sequencing reaction are different from those of the Sanger method, and duplication ratios of sequences per target region can be increased because of the ability to conduct high-throughput data generation.
The number of linkage groups of the SKF2 was one more than the number of haploid chromosomes of A. hypogaea, and the diversified density of DNA markers on each linkage group ranged from 1.1 to 11.4 cM/marker-locus. This indicates that genetic diversity is different between chromosomes of cultivated peanut. The polymorphism analysis in diploid Arachis species suggested that genetic diversity between B genome species was considerably lower than that between A genome species [23]. Though we cannot draw any conclusions from this study, it was predicted that similar differences might occur in the tetraploid genome. It has been suggested that the AhMITE1s originated from the B genome [34] but are currently distributed throughout the whole peanut genome (Figure 1, Table 2, Additional file 6, Additional file 8). This indicates that the AhMITE1s transposed from the B genome to the combined A and B genome without any bias in insertional position.
In the present study, agronomically important traits for flowering date, plant architecture, pod and seed characters, and seed quality were identified. Whereas several QTLs for drought stress tolerance and resistance to rust and foliar diseases have been reported in peanut [24–26, 28, 31], QTL analyses focused on morphological and physiological traits considered important for breeding have not been conducted. On the other hand, genomic and genetic studies of such traits have progressed in soybean [63–66] and L. japonicus[67]; both of these genomes have been sequenced [5, 6]. If the genetic knowledge gained through comparative genomics using models is to be applied to crop legumes, the in silico polymorphism analysis must be effective for EST-SSR markers. Because nucleotide sequences of ESTs generally show higher levels of similarity across different species, genera, and families than sequences from intergenic regions, generating and mapping additional EST-SSR markers might help the progress of comparative analysis with other Arachis spp. and model legumes. Further genetic analysis will provide helpful information that will allow the identification of QTLs in peanut corresponding to the genome sequences of model legumes.
Candidate gene approaches, as well as comparative maps, will greatly help to develop DNA markers tightly linked to important traits that have been gained through the study of model legumes. Direct selection of two recessive alleles of the FAD2 genes will facilitate high oleic-acid peanut breeding. Furthermore, introgression breeding of the high-oleic acid trait into elite cultivars can be easily performed with recurrent backcrossings and marker-assisted selection for the sake of monitoring both alleles of the FAD2 genes along with genetic background nature. Similarly, flowering date, pod and seed characters, and plant architecture, as well as stress tolerance and disease resistance, can be efficiently altered by molecular breeding with marker-assisted selection.
Conclusion
The efficiency of polymorphic marker development was improved remarkably by using in silico polymorphism analysis, in comparison with the previous method, in which primers were simply designed based on the flanking sequences of SSR motifs. The resultant linkage maps possess the highest number of marker loci in cultivated peanut as well as Arachis spp. Moreover, the developed linkage maps are applicable to the identification of QTLs and genes for agronomical traits, including seed quality. These data should be useful for genetics, genomics, and breeding in Arachis spp. This type of in silico polymorphism analysis should also be applicable to other crop species.
Abbreviations
- BAC:
-
Bacterial artificial chromosome
- cDNA:
-
Complementary DNA
- EST:
-
Expressed sequence tag
- GC-TOF-MS:
-
Gas chromatography time-of-flight mass spectrometry
- GMM:
-
Genotype Matrix Mapping
- HG:
-
Homoeologous linkage groups
- LG:
-
Linkage group
- LOD:
-
Logarithm of odds
- PCR:
-
Polymerase chain reaction
- QTL:
-
Quantitative trait locus
- SSR:
-
Simple sequence repeat
- SNP:
-
Single nucleotide polymorphism.
Declarations
Acknowledgments
We are grateful to Dr. Bertioli, University of Brasilia, Brazil, for providing information for SSR markers derived from BAC-end sequences. This work was supported by the foundation of the Kazusa DNA Research Institute.
Authors’ Affiliations
References
- Krapovickas A, Gregory W: Taxonomia del genero Arachis (Leguminosae). Bonplandia. 1994, 8: 1-186.Google Scholar
- Krapovickas A, Gregory W: Taxonomy of the genus Arachis (Leguminosae). Bonplandia. 2007, 16: 1-205.Google Scholar
- Temsch EM, Greilhuber J: Genome size variation in Arachis hypogaea and A. monticola re-evaluated. Genome. 2000, 43: 449-451.PubMedView ArticleGoogle Scholar
- Bennett MD, Bhandol P, Leitch IJ: Nuclear DNA amounts in angirosperms and their modern uses - 807 new estimates. Ann Bot. 2000, 86: 859-909. 10.1006/anbo.2000.1253.View ArticleGoogle Scholar
- Sato S, Nakamura Y, Kaneko T, Asamizu E, Kato T, Nakao M, Sasamoto S, Watanabe A, Ono A, Kawashima K, Fujishiro T, Katoh M, Kohara M, Kishida Y, Minami C, Nakayama S, Nakazaki N, Shimizu Y, Shinpo S, Takahashi C, Wada T, Yamada M, Ohmido N, Hayashi M, Fukui K, Baba T, Nakamichi T, Mori H, Tabata S: Genome structure of the legume, Lotus japonicus. DNA Res. 2008, 15: 227-239. 10.1093/dnares/dsn008.PubMedPubMed CentralView 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: 178-183. 10.1038/nature08670.PubMedView ArticleGoogle Scholar
- Young ND, Debellé F, Oldroyd GE, Geurts R, Cannon SB, Udvardi MK, Benedito VA, Mayer KF, Gouzy J, Schoof H, Van de Peer Y, Proost S, Cook DR, Meyers BC, Spannagl M, Cheung F, De Mita S, Krishnakumar V, Gundlach H, Zhou S, Mudge J, Bharti AK, Murray JD, Naoumkina MA, Rosen B, Silverstein KA, Tang H, Rombauts S, Zhao PX, Zhou P, et al: The Medicago genome provides insight into the evolution of rhizobial symbioses. Nature. 2011, 480: 520-524.PubMedPubMed CentralView ArticleGoogle Scholar
- Varshney RK, Chen W, Li Y, Bharti AK, Saxena RK, Schlueter JA, Donoghue MT, Azam S, Fan G, Whaley AM, Farmer AD, Sheridan J, Iwata A, Tuteja R, Penmetsa RV, Wu W, Upadhyaya HD, Yang SP, Shah T, Saxena KB, Michael T, McCombie WR, Yang B, Zhang G, Yang H, Wang J, Spillane C, Cook DR, May GD, Xu X, et al: Draft genome sequence of pigeonpea (Cajanus cajan), an orphan legume crop of resource-poor farmers. Nat Biotechnol. 2012, 30: 83-89.View ArticleGoogle Scholar
- Udall JA, Wendel JF: Polyploidy and crop improvement. Crop Sci. 2006, 46: S3-S14.View ArticleGoogle Scholar
- Hammons RO: The Groundnut Crop: A scientific basis for improvement. The origin and history of the groundnut. Edited by: Smartt J. 1994, Chapman and Hall, London, 24-42.Google Scholar
- Koilkonda P, Sato S, Tabata S, Shirasawa K, Hirakawa H, Sakai H, Sasamoto S, Watanabe A, Wada T, Kishida Y, Tsuruoka H, Fujishiro T, Yamada M, Kohara M, Suzuki S, Hasegawa M, Kiyoshima H, Isobe S: Large-scale development of expressed sequence tag-derived simple sequence repeat markers and diversity analysis in Arachis spp. Mol Breed. 2011, 10.1007/s11032-011-9604-8.Google Scholar
- Pandey MK, Monyo E, Ozias-Akins P, Liang X, Guimarães P, Nigam SN, Upadhyaya HD, Janila P, Zhang X, Guo B, Cook DR, Bertioli DJ, Michelmore R, Varshney RK: Advances in Arachis genomics for peanut improvement. Biotechnol Adv. 2011, 30: 639-651.PubMedView ArticleGoogle Scholar
- Burow MD, Simpson CE, Starr JL, Paterson AH: Transmission genetics of chromatin from a synthetic amphidiploid to cultivated peanut (Arachis hypogaea L.): broadening the gene pool of a monophyletic polyploid species. Genetics. 2001, 159: 823-837.PubMedPubMed CentralGoogle Scholar
- Halward T, Stalker HT, Kochert G: Development of an RFLP linkaeg map in diploid peanut species. Theor Appl Genet. 1993, 87: 379-384. 10.1007/BF01184927.PubMedView ArticleGoogle Scholar
- Proite K, Leal-Bertioli SC, Bertioli DJ, Moretzsohn MC, da Silva FR, Martins NF, Guimarães PM: ESTs from a wild Arachis species for gene discovery and marker development. BMC Plant Biol. 2007, 7: 7-10.1186/1471-2229-7-7.PubMedPubMed CentralView ArticleGoogle Scholar
- Ferguson ME, Burow MD, Schulze SR, Bramel PJ, Paterson AH, Kresovich S, Mitchell S: Microsatellite identification and characterization in peanut (A. hypogaea L.). Theor Appl Genet. 2004, 108: 1064-1070. 10.1007/s00122-003-1535-2.PubMedView ArticleGoogle Scholar
- He G, Meng R, Newman M, Gao G, Pittman RN, Prakash CS: Microsatellites as DNA markers in cultivated peanut (Arachis hypogaea L.). BMC Plant Biol. 2003, 3: 3-10.1186/1471-2229-3-3.PubMedPubMed CentralView ArticleGoogle Scholar
- Moretzsohn MC, Hopkins MS, Mitchell SE, Kresovich S, Valls JF, Ferreira ME: Genetic diversity of peanut (Arachis hypogaea L.) and its wild relatives based on the analysis of hypervariable regions of the genome. BMC Plant Biol. 2004, 4: 11-10.1186/1471-2229-4-11.PubMed CentralView ArticleGoogle Scholar
- Moretzsohn MC, Leoi L, Proite K, Guimaraes PM, Leal-Bertioli SC, Gimenes MA, Martins WS, Valls JF, Grattapaglia D, Bertioli DJ: A microsatellite-based, gene-rich linkage map for the AA genome of Arachis (Fabaceae). Theor Appl Genet. 2005, 111: 1060-1071. 10.1007/s00122-005-0028-x.PubMedView ArticleGoogle Scholar
- Naito Y, Suzuki S, Iwata Y, Kuboyama T: Genetic diversity and relationship analysis of peanut germplasm using SSR markers. Breed Sci. 2008, 58: 293-300. 10.1270/jsbbs.58.293.View ArticleGoogle Scholar
- Wang H, Penmetsa RV, Yuan M, Gong L, Zhao Y, Guo B, Farmer AD, Rosen BD, Gao J, Isobe S, Bertioli DJ, Varshney RK, Cook DR, He G: Development and characterization of BAC-end sequence derived SSRs, and their incorporation into a new higher density genetic map for cultivated peanut (Arachis hypogaea L.). BMC Plant Biol. 2012, 12: 10-10.1186/1471-2229-12-10.PubMedPubMed CentralView ArticleGoogle Scholar
- Leal-Bertioli SC, Jose AC, Alves-Freitas DM, Moretzsohn MC, Guimaraes PM, Nielen S, Vidigal BS, Pereira RW, Pike J, Favero AP, Parniske M, Varshney RK, Bertioli DJ: Identification of candidate genome regions controlling disease resistance in Arachis. BMC Plant Biol. 2009, 9: 112-10.1186/1471-2229-9-112.PubMedPubMed CentralView ArticleGoogle Scholar
- Moretzsohn MC, Barbosa AV, Alves-Freitas DM, Teixeira C, Leal-Bertioli SC, Guimarães PM, Pereira RW, Lopes CR, Cavallari MM, Valls JF, Bertioli DJ, Gimenes MA: A linkage map for the B-genome of Arachis (Fabaceae) and its synteny to the A-genome. BMC Plant Biol. 2009, 9: 40-10.1186/1471-2229-9-40.PubMedPubMed CentralView ArticleGoogle Scholar
- Khedikar YP, Gowda MV, Sarvamangala C, Patgar KV, Upadhyaya HD, Varshney RK: A QTL study on late leaf spot and rust revealed one major QTL for molecular breeding for rust resistance in groundnut (Arachis hypogaea L.). Theor Appl Genet. 2010, 121: 971-984. 10.1007/s00122-010-1366-x.PubMedPubMed CentralView ArticleGoogle Scholar
- Varshney RK, Bertioli DJ, Moretzsohn MC, Vadez V, Krishnamurthy L, Aruna R, Nigam SN, Moss BJ, Seetha K, Ravi K, He G, Knapp SJ, Hoisington DA: The first SSR-based genetic linkage map for cultivated groundnut (Arachis hypogaea L). Theor Appl Genet. 2009, 118: 729-739. 10.1007/s00122-008-0933-x.PubMedView ArticleGoogle Scholar
- Ravi K, Vadez V, Isobe S, Mir RR, Guo Y, Nigam SN, Gowda MV, Radhakrishnan T, Bertioli DJ, Knapp SJ: Varshney RK:. Identification of several small main-effect QTLs and a large number of epistatic QTLs for drought tolerance related traits in groundnut (Arachis hypogaea L.). Theor Appl Genet. 2011, 122: 1119-1132. 10.1007/s00122-010-1517-0.PubMedPubMed CentralView ArticleGoogle Scholar
- Foncéka D, Hodo-Abalo T, Rivallan R, Faye I, Sall MN, Ndoye O, Fávero AP, Bertioli DJ, Glaszmann JC, Courtois B, Rami JF: Genetic mapping of wild introgressions into cultivated peanut: a way toward enlarging the genetic basis of a recent allotetraploid. BMC Plant Biol. 2009, 9: 103-10.1186/1471-2229-9-103.PubMedPubMed CentralView ArticleGoogle Scholar
- Gautami B, Pandey MK, Vadez V, Nigam SN, Ratnakumar P, Krishnamurthy L, Radhakrishnan T, Gowda MVC, Narasu ML, Hoisington DA, Knapp SJ, Varshney RK: Quantitative trait locus analysis and construction of consensus genetic map for drought tolerance traits based on three recombinant inbred line populations in cultivated groundnut (Arachis hypogaea L.). Mol Breed. 2011, 10.1007/s11032-011-9660-0.Google Scholar
- Hong Y, Chen X, Liang X, Liu H, Zhou G, Li S, Wen S, Holbrook CC, Guo B: A SSR-based composite genetic linkage map for the cultivated peanut (Arachis hypogaea L.) genome. BMC Plant Biol. 2010, 10: 17-10.1186/1471-2229-10-17.PubMedPubMed CentralView ArticleGoogle Scholar
- Qin H, Feng S, Chen C, Guo Y, Knapp S, Culbreath A, He G, Wang ML, Zhang X, Holbrook CC, Ozias-Akins P, Guo B: An integrated genetic linkage map of cultivated peanut (Arachis hypogaea L.) constructed from two RIL populations. Theor Appl Genet. 2011, 124: 653-664.PubMedView ArticleGoogle Scholar
- Sujay V, Gowda MVC, Pandey MK, Bhat RS, Khedikar YP, Nadaf HL, Gautami B, Sarvamangala C, Lingaraju S, Radhakrishan T, Knapp SJ, Varshney RK: Quantitative trait locus analysis and construction of consensus genetic map for foliar disease resistance based on two recombinant inbred line populations in cultivated groundnut (Arachis hypogaea L.). Mol Breed. 2011, 10.1007/s11032-011-9661-z.Google Scholar
- Tang J, Baldwin SJ, Jacobs JM, Linden CG, Voorrips RE, Leunissen JA, van Eck H, Vosman B: Large-scale identification of polymorphic microsatellites using an in silico approach. BMC Bioinformatics. 2008, 9: 374-10.1186/1471-2105-9-374.PubMedPubMed CentralView ArticleGoogle Scholar
- SSRPoly: an efficient tool for polymorphic Simple Sequence Repeat identification. [http://acpfg.imb.uq.edu.au/ssrpoly.php].
- Shirasawa K, Hirakawa H, Tabata S, Hasegawa M, Kiyoshima H, Suzuki S, Sasamoto S, Watanabe A, Fujishiro T, Isobe S: Characterization of active miniature inverted-repeat transposable elements in the peanut genome. Theor Appl Genet. 2012, 124: 1429-1438. 10.1007/s00122-012-1798-6.PubMedPubMed CentralView ArticleGoogle Scholar
- Pandey MK, Gautami B, Jayakumar T, Sriswathi M, Upadhyaya HD, Gowda MVC, Radhakrishnan T, Bertioli DJ, Knapp SJ, Cook DR, Varshney RK: Highly informative genic and genomic SSR markers to facilitate molecular breeding in cultivated groundnut (Arachis hypogaea). Plant Breed. 2012, 131: 139-147. 10.1111/j.1439-0523.2011.01911.x.View ArticleGoogle Scholar
- Holbrook CC, Stalker HT: Plant Breeding Reviews. Volume 22. In Peanutbreeding and genetic resources. Edited by Janick J. New York: John Willey andSons; 2003:297–356.Google Scholar
- Moore KM, Knauft DA: The inheritance of high oleic acid in peanut. J Hered. 1989, 80: 252-253.Google Scholar
- Norden AJ, Gorbet DW, Knauft DA, Young CT: Variability in oil quality among peanut genotypes in the Florida breeding program. Peanut Sci. 1987, 14: 7-11. 10.3146/i0095-3679-14-1-3.View ArticleGoogle Scholar
- Grundy SM: Comparison of monounsaturated fatty acids and carbohydrates for lowering plasma cholesterol. N Engl J Med. 1986, 314: 745-748. 10.1056/NEJM198603203141204.PubMedView ArticleGoogle Scholar
- Clemente TE, Cahoon EB: Soybean oil: genetic approaches for modification of functionality and total content. Plant Physiol. 2009, 151: 1030-1040. 10.1104/pp.109.146282.PubMedPubMed CentralView ArticleGoogle Scholar
- Garces R, Mancha M: In vitro oleate desaturase in developing sunflower seeds. Phytochemistry. 1991, 30: 2127-2130. 10.1016/0031-9422(91)83599-G.View ArticleGoogle Scholar
- Lee MS, Guerra DJ: Biochemical characterization of temperature-induced changes in lipid metabolism in a high oleic acid mutant of Brassica rapa. Arch Biochem Biophys. 1994, 315: 203-211. 10.1006/abbi.1994.1491.PubMedView ArticleGoogle Scholar
- Martin BA, Rinne RW: A comparison of oleic acid metabolism in the soybean (Glycine max [L.] Merr.) genotypes Williams and A5, a mutant with decreased linoleic acid in the seed. Plant Physiol. 1986, 81: 41-44. 10.1104/pp.81.1.41.PubMedPubMed CentralView ArticleGoogle Scholar
- Barkley NA, Chenault KD, Chamberlin C, Wang ML, Pittman RN: Development of a real-time PCR genotyping assay to identify high oleic acid peanuts (Arachis hypogaea L.). Mol Breed. 2010, 25: 541-548. 10.1007/s11032-009-9338-z.View ArticleGoogle Scholar
- Bruner AC, Jung S, Abbott AG, Powell GL: The naturally occurring high oleate oil character in some peanut varieties results from reduced oleoyl-PC desaturase activity from mutation of aspartate 150 to asparagine. Crop Sci. 2001, 41: 522-526. 10.2135/cropsci2001.412522x.View ArticleGoogle Scholar
- Chu Y, Holbrook C, Ozias-Akins P: Two alleles of ahFAD2B control the high oleic acid trait in cultivated peanut. Crop Sci. 2009, 49: 2029-2036. 10.2135/cropsci2009.01.0021.View ArticleGoogle Scholar
- López Y, Nadaf HL, Smith OD, Connell JP, Reddy AS, Fritz AK: Isolation and characterization of the Δ12-fatty acid desaturase in peanut (Arachis hypogaea L.) and search for polymorphisms for the high oleate trait in Spanish market-type lines. Theor Appl Genet. 2000, 101: 1131-1138. 10.1007/s001220051589.View ArticleGoogle Scholar
- López Y, Nadaf HL, Smith OD, Simpson CE, Fritz AK: Expressed variants of Δ12-fatty acid desaturase for the high oleate trait in spanish market-type peanut lines. Mol Breed. 2002, 9: 183-190. 10.1023/A:1019767825486.View ArticleGoogle Scholar
- Patel M, Jung S, Moore K, Powell G, Ainsworth C, Abbott A: High-oleate peanut mutants result from a MITE insertion into the FAD2 gene. Theor Appl Genet. 2004, 108: 1492-1502. 10.1007/s00122-004-1590-3.PubMedView ArticleGoogle Scholar
- Nunome T, Negoro S, Miyatake K, Yamaguchi H, Fukuoka H: A protocol for the construction of microsatellite enriched genomic library. Plant Mol Biol Rep. 2006, 24: 305-312. 10.1007/BF02913457.View ArticleGoogle Scholar
- Sraphet S, Boonchanawiwat A, Thanyasiriwat T, Boonseng O, Tabata S, Sasamoto S, Shirasawa K, Isobe S, Lightfoot DA, Tangphatsornruang S, Triwitayakorn K: SSR and EST-SSR-based genetic linkage map of cassava (Manihot esculenta Crantz). Theor Appl Genet. 2011, 122: 1161-1170. 10.1007/s00122-010-1520-5.PubMedView ArticleGoogle Scholar
- Rice P, Longden I, Bleasby A: EMBOSS: the European Molecular Biology Open Software Suite. Trends Genet. 2000, 16: 276-277. 10.1016/S0168-9525(00)02024-2.PubMedView ArticleGoogle Scholar
- Huang X, Madan A: CAP3: a DNA sequence assembly program. Genome Res. 1999, 9: 868-877. 10.1101/gr.9.9.868.PubMedPubMed CentralView ArticleGoogle Scholar
- Rozen S, Skaletsky H: Primer3 on the WWW for general users and for biologist programmers. Methods Mol Biol. 2000, 132: 365-386.PubMedGoogle Scholar
- Guimarães PM, Garsmeur O, Proite K, Leal-Bertioli SC, Seijo G, Chaine C, Bertioli DJ, D'Hont A: BAC libraries construction from the ancestral diploid genomes of the allotetraploid cultivated peanut. BMC Plant Biol. 2008, 8: 14-10.1186/1471-2229-8-14.PubMedPubMed CentralView ArticleGoogle Scholar
- Jung S, Powell G, Moore K, Abbott A: The high oleate trait in the cultivated peanut [Arachis hypogaea L]. II. Molecular basis and genetics of the trait. Mol Gen Genet. 2000, 263: 806-811.PubMedGoogle Scholar
- Jung S, Swift D, Sengoku E, Patel M, Teule F, Powell G, Moore K, Abbott A: The high oleate trait in the cultivated peanut [Arachis hypogaea L.]. I. Isolation and characterization of two genes encoding microsomal oleoyl-PC desaturases. Mol Gen Genet. 2000, 263: 796-805. 10.1007/s004380000244.PubMedView ArticleGoogle Scholar
- Van Ooijen JW: JoinMapW4, software for the calculation of genetic linkagemaps in experimental populations. Wageningen, Netherlands: Kyazma BV;2006.Google Scholar
- Voorrips RE: MapChart: software for the graphical presentation of linkage maps and QTLs. J Hered. 2002, 93: 77-78. 10.1093/jhered/93.1.77.PubMedView ArticleGoogle Scholar
- Wang S, Basten CJ, Zeng ZB: Windows QTL cartographer 2.5. Raleigh, NC:Department of Sraristics, North Carolina State University:; 2011.Google Scholar
- Isobe S, Nakaya A, Tabata S: Genotype matrix mapping: searching for quantitative trait loci interactions in genetic variation in complex traits. DNA Res. 2007, 14: 217-225. 10.1093/dnares/dsm020.PubMedPubMed CentralView ArticleGoogle Scholar
- Kazusa DNA Marker Database.[http://marker.kazusa.or.jp].
- Liu B, Watanabe S, Uchiyama T, Kong F, Kanazawa A, Xia Z, Nagamatsu A, Arai M, Yamada T, Kitamura K, Masuta C, Harada K, Abe J: The soybean stem growth habit gene Dt1 is an ortholog of Arabidopsis TERMINAL FLOWER1. Plant Physiol. 2010, 153: 198-210. 10.1104/pp.109.150607.PubMedPubMed CentralView ArticleGoogle Scholar
- Suzuki M, Fujino K, Nakamoto Y, Ishimoto M, Funatsuki H: Fine mapping and development of DNA markers for the qPDH1 locus associated with pod dehiscence in soybean. Mol Breed. 2010, 25: 407-418. 10.1007/s11032-009-9340-5.View 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: 1251-1262. 10.1534/genetics.108.098772.PubMedPubMed CentralView ArticleGoogle Scholar
- Watanabe S, Xia Z, Hideshima R, Tsubokura Y, Sato S, Yamanaka N, Takahashi R, Anai T, Tabata S, Kitamura K, Harada K: A map-based cloning strategy employing a residual heterozygous line reveals that the GIGANTEA gene is involved in soybean maturity and flowering. Genetics. 2011, 188: 395-407. 10.1534/genetics.110.125062.PubMedPubMed CentralView ArticleGoogle Scholar
- Gondo T, Sato S, Okumura K, Tabata S, Akashi R, Isobe S: Quantitative trait locus analysis of multiple agronomic traits in the model legume Lotus japonicus. Genome. 2007, 50: 627-637. 10.1139/G07-040.PubMedView ArticleGoogle Scholar
Copyright
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/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
