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 , Table , Additional file 6
). Genetic linkage maps in Arachis
spp. have been constructed using mapping populations derived from crosses between interspecific diploids [14
] or synthetic tetraploids [13
], as well as cultivated tetraploids [21
]. 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
]. 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
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
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.
] 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 AhMITE1
s originated from the B genome [34
] but are currently distributed throughout the whole peanut genome (Figure , Table , Additional file 6
, Additional file 8
). This indicates that the AhMITE1
s 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
], 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
] and L. japonicus
]; both of these genomes have been sequenced [5
]. 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.