| dc.description.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
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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
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(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 | en_US |
