The prime objective of the present study was to identify novel chromosomal regions influencing yield and yield components in a RIL population developed from cross Pusa1266, a new plant type genotype, derived from indica X japonica cross and Jaya, a popular indica rice variety in India. As per gramene website, a total of 2,234 QTLs were identified for yield and yield related traits through more than 50 QTL mapping studies in rice. Many researchers have used wild species like O.nivara, O.rufipogon, O.glaberrima, and O.glumaepatula and land races to uncover the hidden genetic variation. However, linkgae drag associated with alien gene transfer is a major bottleneck in utilizing wild species in breeding programme. In the present study, which is one of the firsts of its kind, an effort was made to identify QTLs for yield and its components from sub-species japonica into indica background using inter sub-specific variation generated by crossing a NPT line Pusa 1266 with Jaya, a widely grown indica rice variety. Therefore, the present investigation is a novel attempt to utilize a new plant type based recombinant inbred mapping population to identify QTLs for yield and yield related traits.
For declaring the significant association of chromosomal region with trait, thresholds levels for each trait at each location were separately computed by conducting permutation test with 1000 permutations and used for composite interval mapping. The experimental threshold LOD mean was 2.96 at 0.05 and LOD 3.69 at 0.01 level of significance. A total of 112 QTLs were detected including 11 QTLs expressing across three environments, 23 QTLs across two locations and 78 location specific QTLs. Out of 112 QTLs, 34 stable QTLs expressing across locations, were identified including at least one for each trait except yield. The phenotypic variance explained by these QTLs ranged from 5% for filled grains per panicle to 53% for flag leaf width at 0.01 level of significance showing the reliability and robustness of the QTLs identified.
The extent of complex nature of the traits was evident from the observation on number of significant QTLs per trait which ranged from two QTLs for number of per cent sterility to 14 for flag leaf length. The per cent contribution of individual QTLs to total phenotypic variation for respective traits ranged from 3 to 53% suggesting complex inheritance pattern of the traits under study. Twenty significant QTLs contributing >15% variation and expressing at two or more locations were detected. The new plant type parent Pusa1266 contributed 53 QTLs (47.3%) including most of the loci for spikelets per panicle, flag leaf width, per cent sterility and spikelet setting density whereas, Jaya contributed 59 QTLs (52.7%) for plant height, panicle length, panicles per plant, thousand grain weight and yield per plant. This establishes the importance of variation in parental allele distribution as the critical component of QTL detection and estimation of the concerned traits. The indica X japonica diversity would have played a significant role in accumulation of number of QTL alleles in the parent Pusa1266. Distribution of yield enhancing loci among these diverse cultivated lines would also be helpful in pyramiding of QTLs with relatively higher contribution to the value of trait related to yield.
Lack of consistency of QTLs across environments is the major hurdle for utilization of the information generated through QTL mapping experiments. In the present study twenty three QTLs expressing across two locations and eleven QTLs expressing across all three locations were identified. Such robust QTLs are important from breeding point of view.
In the present study, 68 out of 112 QTLs identified were reported earlier (9–33). To understand whether genomic regions from where genes for yield related traits were cloned, are influencing yield and its components in the present study, primers were designed for 15 genes including sd1, Ebisud2, Ghd7, Hd1, Hd3, Hd6, PLA1, D10, TB1, HTD1, MOC1, Lax1, GS3, GW2 and CKX2 and used for polymorphism survey. Of these, one marker from GS3 region was polymorphic between Pusa1266 and Jaya. The marker from Ghd7 region did not show amplification. Despite the presence of sufficient variation for the traits between the two parents, most of the gene based markers did not show polymorphism indicating the possibility of novel QTLs. An elaborate genotyping of RIL population with additional polymorphic markers particularly from the genomic region with poor coverage would be necessary so that other additional QTLs could be tapped. Alternatively, the failure to detect polymorphism may be due to insufficient resolution offered by the metaphor agarose. A sequence analysis of these regions may help in developing markers, which can detect QTLs in this region. The monomorphism for sd1 based markers indicated the presence of dwarfing allele in both parents as both parents were semi dwarf in height. For days to 50% flowering, panicles per plant, spikelets per panicle and thousand grain weight one QTL each was identified from the genomic region where Hd1, Hd3, Htd1, CKX1 and GS3 genes were cloned. At the genomic location of gene GS3, five QTLs i.e., qFLL3-4, qSPP3-2, qFGP3-2, qPSTE3-1 and qTGW3-3 were identified of which a one major robust QTL influencing per cent sterility explained 18% of phenotypic variation across three locations can be used to understand about sterility in indica/ japonica crosses.
To identify the genomic regions influencing yield component traits across different populations, the QTLs positions in the present study were compared with earlier reports and found that, 68 out of 112 QTLs identified were reported earlier [9
]. The major stable QTLs like qDFF8-1qPPP4-2qFLW4-2qSPP4-1qFGP4-2
that mapped to the same genomic regions consistent across Oryza sp. can be directly used for marker assisted transfer of them in to widely adopted high yielding varieties.
In the present study, 44 novel QTLs were reported for the first time of which, 19 were contributed by new plant type parent Pusa 1266. All the QTLs influencing days to 50% flowering and panicles per plant shared same chromosomal regions from earlier reports indicating their possible evolutionary association in rice varieties [9
]. Five novel QTLs were identified for plant height and all were contributed by Jaya. A novel QTL i.e., qPHT4-2
stable at New Delhi and Karnal explaining 5-6% phenotypic variation was identified. Apparently, this locus would provide the needed diversity for the trait in rice breeding programs. Remaining eight QTLs for plant height identified in genomic regions reported earlier [17
Four novel QTLs were found for panicle length in the present study and all were contributed by Jaya. Two QTLs, qPL2-2
expressing over two locations explaining 5-12% phenotypic variation would be much useful for improvement of panicle length of japonica lines. Eight out of twelve QTLs identified for panicle length were reported in earlier studies [5
]. Nine novel QTLs were identified for flag leaf length of which five were contributed by Pusa1266. One novel QTL qFLL6-1
contributed by Pusa1266 expressed at New Delhi and Karnal explaining up to 12% phenotypic variation and can be utilized for improvement of the trait at both locations. Remaining all QTLs for flag leaf length and width are in the same chromosomal regions as reported earlier [25
]. Three novel QTLs were identified for spikelets per panicle and one QTL qSPP6-
1 contributed by the Pusa1266 allele explaining 30% of phenotypic variation was identified at New Delhi location. Nine QTLs for spikelets per panicle and all QTLs for filled grains per panicle are reported earlier [10
]. In the present study, QTLs for spikelet sterility were identified on chromosome 3 and 11 of which, qPSTE3-1
is interesting as it is located near a gene influencing grain size GS3. In a recent study a QTL has been identified for percent seed set under cold water conditions at reproductive stage on chromosome 3 and used for identification of lines with high spikelet fertility under cold stress which may be different from the QTL identified in this study (34, 35). Five novel QTLs were identified for thousand grain weight of which, two QTLs qTGW6-1
were stable across three locations explaining 7-25% phenotypic variation and remaining QTLs were reported in earlier studies [19
]. Thousand grain weight and spikelets per panicle were two component traits of grain yield. Negative correlation between these traits has been noted by rice breeders. This could be due to increase in the one of the trait and decrease in the other trait by QTLs in the same chromosomal region.
The yield QTLs in the present study were found to be specific to either NewDelhi or Aduthurai locations, explaining up to 16% of total phenotypic variation. At Aduthurai qYLD4-1
and at New Delhi qYLD9-1
explained 16% and 11% of phenotypic variation, respectively may be useful in further enhancing yield of cultivars areas adapted in these regions. Of the five yield QTLs identified in the present study, three QTLs qYLD3-1qYLD4-1
shared same chromosomal regions as yield QTLs reported earlier [10
]. Five novel QTLs stable over two or more locations explaining 8-25% of phenotypic variation were identified for spikelet setting density. Out of 44 novel QTLs reported in the present study, twelve are stable over locations. These novel stable QTL regions are potential candidates for fine mapping and map based cloning.
We identified fifteen QTL hotspots (clusters) on eight chromosomes 1–4, 6–8 and 12 harboring QTLs traits such as plant height, panicle length, flag leaf, spikelets per panicle, filled grains per panicle and spikelet setting density. It is very interesting to examine co-locating QTLs in biological and breeding perspective while considering phenotypic and genetic correlations. One of the central concepts in genetical genomics is the existence of QTL hotspots, where a single polymorphism leads to widespread downstream changes in the expression of distant genes, which are all mapping to the same genomic locus [37
]. In this study multiple QTL clusters affecting many traits were identified of which, some are either genetically correlated or allometrically related. It is very difficult to speculate the causative mechanism between all these traits in a hotspot as correlations do not suggest link between them. It is possible that these clusters represent more than one gene but the present mapping population resolution is not sufficient to differentiate whether it is due to either linkage or pleiotropy. It is observed that some hotspots contain QTLs that are not allometrically linked. It may possible that these loci represent trans
acting QTL (most likely transcription factors) where the effect of alterations in regulation or structural characteristics would be expected to have smaller effects on many traits [38
]. It can be concluded that each QTL within a QTL hotspot might only contribute a small positive effect, but co-location of multiple traits indicate that selection for beneficial allele at these loci will result in a cumulative increase in yield due to the integrative positive effect of various QTLs.
In the present study, 6 epistatic QTLs were identified for five traits with phenotypic variation ranging from 6.1% for panicle length to 21.6% for spikelet setting density. However, only limited number of epistatic interactions between QTLs were identified firstly because analysis of primary populations like RILs does not provide much information about the real nature of epistatic interactions because of the confounding effect of background loci or other QTLs with larger effect interfere with detection of interactions [39
]. Secondly, in a RIL population only those QTLs showing additive x additive type of interaction could be identified, because identification of additive X dominance and dominance X dominance type epistatis is not possible owing to lack of heterozygosity.
The yield and its component traits showed a range of sensitivity response to changing environment. QTLs of traits such as plant height, flag leaf length, grain yield per plant with high environment main effect could be detected only in one or two environments. Furthermore, consistent with this observation, those genomic regions of quantitative traits with the least G X E interaction and high genotypic main effects expressed comparable phenotypes across different environments. In the present study, 78 QTLs expressing at a specific location were identified, of which 21 were major QTLs explaining phenotypic variance ranging from 10-30% with higher LOD values (3–11). These QTLs will be useful for location specific breeding like qSPP6-1
22) for New Delhi, Karnal and Aduthurai, respectively. Number of QTLs (57) with minor effect were identified in the present study will be useful based on the fact that quantitative traits were governed by many genes of small effect. These QTLs will help in improvement of yield in smaller way but in cumulative manner.
In the present study, many major QTLs expressing across environments were identified. These QTLs would be highly valuable for breeding the lines with wide adaptation over different locations and crop conditions throughout India. Among robust QTLs expressing over locations, a genomic location at marker interval RM3276-RM5709 on chromosome 4 influencing 4 traits namely flag leaf width (33-53%), spikelets per panicle (9-16%), filled grains per panicle (12-22%) and spikelet setting density (14-19%) and two QTLs i.e., qPPP4-2
will be fine mapped to identify candidate genes in future. Most of the major QTLs expressing over locations are flanked by markers at genetic distance of less than 10
cM will be directly be used for marker assisted transfer into the varieties for enhancement of yield through improving the component traits. Pyramiding of the stable QTLs identified in our study with earlier known QTLs of respective traits will be undertaken which will help in raising genetic ceiling to yield.