652 efforts are being made to develop large-scale SSR markers after mining the end sequences of bacterial artificial chromosome (BAC) clones (Bohra et al. 2011). In addition to non-availability of appropriate genomic resources, occurrence of a very narrow genetic diversity pose another serious constraint for developing the genetic map and quantitative trait loci (QTL) analysis for traits of interest to breeders in pigeonpea (Odeny et al. 2007; Saxena et al. 2010a; Bohra et al. 2011; Yang et al. 2011). It is, therefore, evident that there is a need to develop large-scale genomic resources in pigeonpea that can be used not only for enhancing the basic genome research but also in crop improvement program. Single feature polymorphisms (SFPs) in the context of oligonucleotide arrays, including single nucleotide polymor- phism (SNPs) and insertions and deletions (INDELs), are particularly amenable to microarray-based genotyping (Shiu and Borevitz 2008). In case of food legume crops, micro- arrays (Affymetrix Genome Arrays) have been designed only in soybean (Glycine max). Because of occurrence of orthologous genes in closely related species that share high sequence similarity, microarrays developed for one species can be used in the other closely related species (Bar-or et al. 2006; Nuzhdin et al. 2004). In terms of phylogenetic relationships among legumes, pigeonpea, cowpea (Vigna ungiculata), and common bean (Phaseolous vulgaris) are grouped together with soybean under the Phaseoloid clade. This indicates that Affymetrix soybean genome arrays can be used to identify SFPs in genotypes of interest in aforemen- tioned closely related Phaseoloid species (Das et al. 2008). With an objective of extending repertoire of genomic resources in pigeonpea, the present study has deployed soybean genome arrays to identify the SFPs between the parental genotypes of three mapping populations of pigeon- pea segregating for agronomic traits like drought tolerance and resistance to Helicoverpa armigiera, the pod borer insect. Subsequently, a subset of genes detecting SFPs was used for Sanger sequencing on the parental lines used in the study to measure the precision of SFP prediction. Further- more, a set of candidate genes for drought tolerance has been identified by gene ontology analysis of the pigeonpea genes, homologues to the soybean genes predicting SFPs in the parental genotypes of mapping populations segregating for drought tolerance. Materials and methods Plant materials and soybean genome array A total of six accessions namely ICP 28, ICPL 8755, ICPL 151, ICPL 87, and ICPL 227 from the cultivated genepool (C. cajan) and ICPW 94 a wild relative of pigeonpea Funct Integr Genomics (2011) 11:651–657 (Cajanus scarabaeoides) were used in this study. These six genotypes are the parents of three different mapping populations segregating for important agronomic traits such as drought tolerance and pod borer resistance. The Affymetrix Soybean Genome Arrays used in this study contained 37,500 probe sets derived from soybean unigenes. This represents 61% of the total probe sets on the chip, with the remainder targeting two pathogens important for soybean genetics research, of which 15,800 (26%) probe sets target Phytophthora sojae (a water mold) and 7,500 (12%) probe sets target Heterodera glycines (soybean cyst nematode). The genome array used probe sets composed of 11 probe pairs to measure the expression of each gene. Each probe pair consists of a perfect match (PM) probe and a mismatch (MM) probe. RNA isolation and microarray hybridization Root tissue samples were collected from all the six pigeonpea genotyped mentioned above after 15 days of sowing. RNA was isolated following the protocol of Schmitt et al. (1990). RNA quality was assessed using formamide gel electrophoresis and Agilent 2100 Bioanna- lyzer (Agilent technologies, Palo Alto, CA, USA). Expression data were generated by hybridizing cRNA of pigeonpea genoypes to the soybean genome arrays. R version 2.10.1 and packages “Affy” and “gcrma” within BioConduc- tor were used for data pre-processing. For correction of background and non-specific binding, GeneChip-Robust Multichip Average (GC-RMA; Wu et al. 2004) was used. Quantile normalization was used for probe-level normaliza- tion (Irizarry et al. 2003). To eliminate probe sets with absent transcripts when pigeonpea cRNAs were hybridized to the soybean genome array, we adopted the filtering procedure suggested by Schuster et al. (2007). Briefly, the MM probe values were replaced with the mean PM value (after GC- RMA transformation) of probe sets that were very likely to have absent target transcripts. By using the Micro array Suite version 5.0 (MAS 5.0) of Affyemetrix Inc., the present/ absent calls were calculated based on the transformed PM and MM probe intensities. The probe sets that were “present” in both conditions under comparison were used for SFP detection (Das et al. 2008). SFP prediction We used robustified projection pursuit (RPP) method for SFP detection (Cui et al. 2005), for which only the GC- RMA adjusted PM probe values from “present” probe sets were utilized. A probe set was called “present” if it had present calls in all biological replicates of the two genotypes under comparison. Separate pair-wise compar- isons were made among ICP 28, ICPW 94, ICPL 151,