Study of inheritance and identification of molecular markers for seed protein content in pigeonpea (Cajanus cajan (L.) Millsp.)

Abstract

Pigeonpea is an important source of protein to the vegetarian and poor families around the globe, however, very little is known about the genetic control of seed protein content (SPC) and how it relates with other traits of agronomic importance in the crop. Availability of genomic resources such as a reference genome and whole genome resequencing data of germplasm lines in pigeonpea coupled with recent advances in next generation sequencing technologies provide opportunity to dissect the genetic architecture of SPC in the crop. The objectives of this study were to: (i) determine variation of SPC and its relationship with agronomic traits of importance in a set of breeding lines and landraces, (ii) study the inheritance of SPC and its relationship with seed weight and seed yield, (iii) identify quantitative trait loci (QTLs) conditioning SPC, and (iv) identify candidate genes involved in the accumulation of SPC using whole genome sequencing approach. To determine variation in SPC and its relationship with some agronomic traits in pigeonpea, 23 pigeonpea genotypes were used. The genotypes are parents of different mapping populations presently being developed at the International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), Patancheru, India. The 23 genotypes were evaluated under field conditions at ICRISAT in 2014-2015 growing season. The experiment was carried out in RCB design with two replications. Data were recorded on SPC, number of days to first flowering (DTF), plant height (PH) at maturity, number of pods per plant (NPP), number of seeds per pod (NSP), 100- seed weight (SW) and seed yield per plant (SY). Seed protein content ranged from 19.3 to 25.5%, DTF (48 to 156 days), PH (67.5 to 230 cm), NPP (31.7 to 582 pods), NSP (2.9 to 4.6 seeds/pod), SW (6.2 to 20.8 g) and SY (7.9 to 333.4 g). There were significant differences among genotypes for all traits. Broad-sense heritability was 0.693 for SPC and ranged from 0.517 to 0.999 among the agronomic traits. Genetic advance (GA) was 2.4 % for SPC but ranged from 1.2 % to 141. % among the agronomic traits. Genetic gain, which is GA expressed as a percentage of the trait’s grand mean, was 11.0 % for SPC but ranged from 56.4 to 713.4 % among the agronomic traits. Simple correlation indicated that SPC is generally negatively associated with all measured traits but only significantly with SW. However, path coefficient analysis revealed that, in addition to SW, NPP also had a strong negative direct influence on SPC, whereas SY had strong positive direct effect on SPC. Indirect effects of the agronomic ii traits on SPC were also noticeable with NPP and SW having strong negative and positive effects, respectively on SPC via SY. To investigate inheritance pattern of SPC in pigeonpea, four elite germplasm lines of varying SPC were used to develop three crosses. Six generations (P1, P2, F1, F2, BC1P1 and BC1P2) were generated. Generation mean analysis (GMA) revealed the importance of dominance and epistatic effects for SPC. Duplicate and negative additive × additive epistasis were predominant. Transgressive segregation for SPC was conspicuous. Additive genetic variance component was higher than the environmental and dominance components. Broad-sense heritability ranged from 0.52 to 0.60. Predicted genetic gain after one cycle of selection was highest at 5% selection intensity. Seed weight and yield were positively and negatively correlated with SPC, respectively. The results suggests that careful selection of parents, and recurrent selection procedure targeting transgressive segregants should be effective for improving SPC in pigeonpea. For the identification of QTLs associated with SPC and its relationship with some agronomic traits, five F2 mapping populations segregating for SPC were developed, genotyped using genotyping-by-sequencing and phenotyped for SPC, 100-seed weight (SW), seed yield (SY), days to first flower (DTF) and growth habit (GH) under field conditions. The average inter marker distance in the population-specific maps varied from 1.6 cM to 3.5 cM. On the basis of the population-specific and consensus linkage maps, a total of 196 main effect QTLs (M QTLs) across all traits were detected that explained 0.7 to 91.3% of the phenotypic variation for the five traits across the five F2 mapping populations. In the case of SPC as the core trait in the present study, a total of 48 main effect QTLs (M-QTLs) with phenotypic variance explained (PVE) ranging from 0.7 to 23.5% were detected across five populations of which 15 M-QTLs were major (PVE≥10). Twenty seven of the M-QTLs from the five F2 mapping populations could be projected into six consensus M-QTL regions. Out of 573 epistatic QTLs (E-QTLs) detected with PVE ranging from 6.3 to 99.4% across traits and populations, 34 involved SPC with PVE ranging from 6.3 to 69.8%. Several co-localization of M-QTLs and E-QTLs affecting SPC and the agronomic traits were also detected and could explain the genetic basis of the significant (P < 0.05) correlations of SPC with SW (r2 = 0.22 to 0.30), SY iii (r2 = -0.18 to -0.28), DTF (r2 = -0.17 to -0.31) and GH (r2 = 0.18 to 0.34). The quantitative nature of genetic control of SPC and its relationship with agronomic traits suggest that marker assisted recurrent selection or genomic selection would be effective for the simultaneous improvement of SPC and other important traits. To identify candidate variants and genes associated with SPC, whole genome resequencing (WGRS) data with an average of 12× coverage per genotype when compared to the Asha (ICPL 87119) reference genome was used. By combining a common variant (CV) filtering strategy with knowledge of gene functions in relation to SPC, 108 sequence variants whose presence lead to protein change were selected. The variants were found in 57 genes spread over all chromosomes except CcLG05. Identified genes were assigned to 19 categories based on gene ontology molecular function with fifty six percent of the identified genes belonging to only two functional categories. Sanger sequencing confirmed the presence of 52 (75.4%) sequence variants in 37 genes between low and high SPC genotypes. Fifty nine variants were converted into CAPS/dCAPS markers and assayed for polymorphism. Highest level of polymorphism was in low by high SPC parental pairs, while the lowest was in high by high parental pairs. Assay of 16 polymorphic CAPS/dCAPS markers on an F2 segregating population of the cross ICP 5529 × ICP 11605 (high × low), resulted in 11 of the markers being incorporated into a GBS-derived SNPs genetic map. Single marker analysis (SMA) indicated four of the 16 CAPS/dCAPS markers to be significantly correlated with SPC. Three out of the four markers were positioned at <10.0 cM distance away from main effect SPC QTLs all on CcLG02. All the three markers found in close proximity to SPC QTL positions and those with significant association to SPC were derived from mutations in the same genes including NADH-GOGAT, copper transporter and BLISTER all on CcLG02. Results from this study provide a foundation for future basic research and marker-assisted breeding of pigeonpea for increased SPC. In general, the complex nature of the genetic architecture of SPC as revealed by classical quantitative genetic analysis, QTL analysis and candidate gene analysis suggests that breeding approaches that target genome wide variations for crop improvement would be more appropriate in achieving larger genetic gains for SPC in shorter periods than using conventional phenotype-based selection