Semi-dwarf plant type and synchronous early maturity are two very important agronomic traits for enhancing crop productivity per unit area and time by harnessing high harvest index and cropping efficiency. Grain legumes, particularly pigeonpea has lagged behind in the development of high yielding cultivars due to lack of basic information on the genomic regions and genes associated with these traits. In the present study, a dense molecular linkage map of pigeonpea was constructed using genic-SNP and genic-SSR markers to identify the QTLs or genomic regions associated with the plant type and early maturity traits. The genic-SNP markers showed significantly higher level of polymorphism (11.58%) than the genic-SSR markers (4.7%). Although this may be partly due to higher resolving power of the SNP assays, it will be prudent to use SNP markers for high density genetic mapping in crops like pigeonpea with narrow genetic base. This is due to high abundance of the SNP markers and high success rate of the GoldenGate SNP genotyping assays. In contrast, small allelic size differences of the SSR markers are not easily resolved by common agarose gel electrophoresis, although capillary electrophoresis can resolve these small size differences and produce higher polymorphism success rates
. The genic markers used in this study were developed from a comprehensive transcriptome assembly dataset; therefore this linkage map represents a random gene set of the pigeonpea genome
. Earlier a BES-SSR based integrated inter-specific genetic map of C. cajan × C. scarabaeoides has been reported that has 239 loci with a genome map length of 930.9 cM
. The inter–specific mapping population comprised of 79 F2 plants and a large proportion of the markers (63.5%) showed distorted segregation as compared to the present intra-specific population of 186 F2 plants with only 3.4% markers showing distorted segregation. The present map is the first high density intra-specific molecular linkage map of pigeonpea based on genic-SNP and genic-SSR markers and covers a much higher genome map length of 1520.22 cM. This is due to a larger population size and better pairing and crossing over between the chromosomes of two varieties of the same species. Furthermore, the present map represents expressed regions of the pigeonpea genome; therefore it will be highly useful for comparative genomics and synteny studies. However, genic markers represent mostly the euchromatic regions of the genome; hence heterochromatin and other repeat regions may be underrepresented, leading to large physical gaps between genic markers spanning these regions. There were three gaps of larger than 30 cM in the present genetic map, one each on LG_Cc4, LG_Cc5 and LG_Cc7, Presence of large gaps may lead to failure in detection of QTLs in the gap regions. Interestingly, large gaps were also observed in the recently developed genomic-SSR based consensus genetic map on LG8, LG10 and LG11
. Although a direct comparison of the two maps was not possible due to lack of common markers, a non-uniform distribution of markers was apparent in both the maps.
This is the first report in pigeonpea on the mapping of QTLs for plant architecture and maturity time related traits which are very important for the development of superior varieties. Thirteen QTLs were identified, including ten QTLs having major effects with PVE of higher than 10%. The number of QTLs identified in a bi-parental mapping population depends on the number of trait controlling loci having contrasting alleles between the two parents
. The high PVE exhibited by the QTLs for many of the traits indicates involvement of segregating alleles of only a few critical genes leading to a large change in the plant architecture and maturity time of the two parents, particularly for PH and FL (Figure
1). However, a possibility of the overestimation of QTL effects due to small population size, sometimes referred to as the Beavis effect, could not be ruled out in this preliminary investigation
[24–26]. For further investigation of possible Beavis effects and validation of QTLs under different environments and genetic backgrounds, RIL mapping populations are being developed.
Plant height is an important agronomic and yield contributing trait for which two major QTLs were identified. Pusa Dwarf allele at the locus qPH4.1 increased plant height by 21.8 cm, whereas HDM04-1 allele at the locus qPH5.1 increased plant height by 20.1 cm. The two dwarfing genes will be very useful for the modification of plant type in pigeonpea. A high number of primary and secondary branches and number of pods per plant are also important yield contributing traits for which QTLs were collocated in the same marker interval. The QTLs qPB4.1 and qPB5.1 both showed positive additive effects with Pusa Dwarf alleles contributing to higher number of primary branches. The qSB5.1 also showed positive additive effect with Pusa Dwarf allele contributing to a high number of secondary branches. The number of pods per plant directly contributes to higher grain yield and two major additive effect QTLs, qPD4.1 and qPD5.1 were identified, both showing positive additive effect with Pusa Dwarf alleles enhancing 17.75 pods and 15.86 pods per plant, respectively. A minor QTL on linkage group LG_Cc3, qPD3.1 also showed positive additive effect with Pusa Dwarf allele increasing 7.81 pods per plant. Thus, Pusa Dwarf has several positive yield contributing traits that can be utilized in the pigeonpea improvement using marker-assisted breeding. It will be useful to combine the tallness allele of Pusa Dwarf at the qPH4.1 locus with the dwarfing allele from Pusa Dwarf at the qPH5.1 locus to obtain a semi-dwarf plant type with high number of primary branches, determinate growth habit and large number of pods per plant.
Pigeonpea has a large variation in the flowering and maturity time; therefore genetic mapping of these traits has direct implications for the development of short duration high yielding pigeonpea varieties. Two additive effect QTLs were identified for days to flowering (qFL4.1 and qFL5.1) and alleles from the early flowering genotype HDM04-1 at these loci decreased the time of flowering by 14.4 days and 5.54 days, respectively. Three QTLs were identified for days to maturity, the two major loci qMT4.1 and qMT5.1 showed positive additive effects with alleles from Pusa Dwarf increasing the maturity time by 11.72 and 13.09 days, respectively. In addition, HDM04-1 allele at a minor locus qMT10.1 increased the maturity time by 4.13 days. Hence, combining HDM04-1 alleles at the qMT5.1 locus with Pusa Dwarf allele at the qMT10.1 locus will reduce the days to maturity by about 17 days. Due to pleiotropic effects of the two QTL loci on height and maturity traits, it will be necessary to have the Pusa Dwarf allele at the qMT.4.1 locus which is also associated with large number of primary branches and number of pods per plant. However, these are our preliminary observations, further validation of the QTLs at multiple environments and in different genetic background are needed for molecular breeding applications.
Epistatic interaction between qPD3.1 and qPD5.1 loci for number of pods was significant, accounting for 6.79% of the phenotypic variance. It illustrates the need for considering both additive and epistatic effects for devising effective molecular breeding strategy. Ten of the thirteen QTLs identified in this study were co-localized in just two genomic regions on linkage groups LG_Cc4 and LG_Cc5. The clustering of QTLs can be explained either by the presence of different tightly linked genes or by pleiotropic effects of a single regulatory gene
. Clustering of QTLs for different agronomic traits has been reported in many crops including soybean, common bean and rice
[28–31]. Statistical analysis using Qgene software
 devised for this purpose revealed that co-location of QTLs was due to significant pleiotropic effects at each of the two loci. The pleiotropy is possibly due to involvement of genes for the synthesis of phytohormones regulating common signaling pathways of the traits during plant development. Plant height was positively correlated with the number of primary branches, number of pods, flowering time and maturity time. As the flowering and maturity times increase, the plant gets more time for increased plant height and vegetative growth due to indeterminate growth habit.
Unfortunately, large genetic intervals were observed for some QTL regions, leading to poor resolution of closely linked QTLs and preventing accurate estimation of PVE. Although many of the QTLs identified in this study were mapped to small genomic regions of 5-15 cM (qPH5.1, qPB4.1, qFL5.1, qMT5.1) and were closely flanked by other polymorphic markers, their utilization in MAS is possible after validation in different genetic backgrounds and environments. Therefore, QTL validation and fine mapping is the next step towards successful application of these findings in MAS.