QTL interval mapping for grain protein content (GPC) in bread wheat was conducted for the first time, using a framework map based on a mapping population, which was available in the form of 100 ...recombinant inbred lines (RILs). The data on GPC for QTL mapping was recorded by growing the RILs in five different environments representing three wheat growing locations from Northern India; one of these locations was repeated for 3 years. Distribution of GPC values followed normal distributions in all the environments, which could be explained by significant g x e interactions observed through analyses of variances, which also gave significant effects due to genotypes and environments. Thirteen (13) QTLs were identified in individual environments following three methods (single-marker analysis or SMA, simple interval mapping or SIM and composite interval mapping or CIM) and using LOD scores that ranged from 2.5 to 6.5. Threshold LOD scores (ranging from 3.05 to 3.57), worked out and used in each case, however, detected only seven of the above 13 QTLs. Only four (QGpc.ccsu-2B.1; QGpc.ccsu-2D.1; QGpc.ccsu-3D.1 and QGpc.ccsu-7A.1) of these QTLs were identified either in more than one location or following one more method other than CIM; another QTL (QGpc.ccsu-3D.2), which was identified using means for all the environments, was also considered to be important. These five QTLs have been recommended for marker-assisted selection (MAS). The QTLs identified as above were also validated using ten NILs derived from three crosses. Five of the ten NILs possessed 38 introgressed segments from 16 chromosomes and carried 42 of the 173 markers that were mapped. All the seven QTLs were associated with one or more of the markers carried by the above introgressed segments, thus validating the corresponding markers. More markers associated with many more QTLs to be identified should become available in the future by effective MAS for GPC improvement.
In bread wheat, single-locus and two-locus QTL analyses were conducted for seven yield and yield contributing traits using two different mapping populations (P I and P II). Single-locus QTL analyses ...involved composite interval mapping (CIM) for individual traits and multiple-trait composite interval mapping (MCIM) for correlated yield traits to detect the pleiotropic QTLs. Two-locus analyses were conducted to detect main effect QTLs (M-QTLs), epistatic QTLs (E-QTLs) and QTL x environment interactions (QE and QQE). Only a solitary QTL for spikelets per spike was common between the above two populations. HomoeoQTLs were also detected, suggesting the presence of triplicate QTLs in bread wheat. Relatively fewer QTLs were detected in P I than in P II. This may be partly due to low density of marker loci on P I framework map (173) than in P II (521) and partly due to more divergent parents used for developing P II. Six QTLs were important which were pleiotropic/coincident involving more than one trait and were also consistent over environments. These QTLs could be utilized efficiently for marker assisted selection (MAS).
During the last two decades, DNA-based molecular markers have been extensively utilized for a variety of studies in both plant and animal systems. One of the major uses of these markers is the ...construction of genome-wide molecular maps and the genetic analysis of simple and complex traits. However, these studies are generally based on linkage analysis in mapping populations, thus placing serious limitations in using molecular markers for genetic analysis in a variety of plant systems. Therefore, alternative approaches have been suggested, and one of these approaches makes use of linkage disequilibrium (LD)-based association analysis. Although this approach of association analysis has already been used for studies on genetics of complex traits (including different diseases) in humans, its use in plants has just started. In the present review, we first define and distinguish between LD and association mapping, and then briefly describe various measures of LD and the two methods of its depiction. We then give a list of different factors that affect LD without discussing them, and also discuss the current issues of LD research in plants. Later, we also describe the various uses of LD in plant genomics research and summarize the present status of LD research in different plant genomes. In the end, we discuss briefly the future prospects of LD research in plants, and give a list of softwares that are useful in LD research, which is available as electronic supplementary material (ESM).
Quantitative trait loci (QTL) analysis was conducted for pre-harvest sprouting tolerance (PHST) in bread wheat for a solitary chromosome 3A, which was shown to be important for this trait in earlier ...studies. An intervarietal mapping population in the form of recombinant inbred lines (RILs) developed from a cross between SPR8198 (a PHS tolerant genotype) and HD2329 (a PHS susceptible cultivar) was used for this purpose. The parents and the RIL population were grown in six different environments and the data on PHS were collected in each case. A framework linkage map of chromosome 3A with 13 markers was prepared and used for QTL analysis. A major QTL (QPhs.ccsu-3A.1) was detected on 3AL at a genetic distance of approximately 183 cM from centromere, the length of the map being 279.1 cM. The QTL explained 24.68% to 35.21% variation in individual environments and 78.03% of the variation across the environments (pooled data). The results of the present study are significant on two counts. Firstly, the detected QTL is a major QTL, explaining up to 78.03% of the variation and, secondly, the QTL showed up in all the six environments and also with the pooled data, which is rather rare in QTL analysis. The positive additive effects in the present study suggest that a superior allele of the QTL is available in the superior parent (SPR8198), which can be used for marker-aided selection for the transfer of this QTL allele to obtain PHS-tolerant progeny. It has also been shown that the red-coloured grain of PHS tolerant parent is not associated with the QTL for PHST identified during the present study, suggesting that PHS tolerant white-grained cultivars can be developed.
Pigeonpea (Cajanus cajan), an important food legume crop in the semi-arid regions of the world and the second most important pulse crop in India, has an average crop productivity of 780 kg/ha. The ...relatively low crop yields may be attributed to non-availability of improved cultivars, poor crop husbandry and exposure to a number of biotic and abiotic stresses in pigeonpea growing regions. Narrow genetic diversity in cultivated germplasm has further hampered the effective utilization of conventional breeding as well as development and utilization of genomic tools, resulting in pigeonpea being often referred to as an ‘orphan crop legume'. To enable genomics-assisted breeding in this crop, the pigeonpea genomics initiative (PGI) was initiated in late 2006 with funding from Indian Council of Agricultural Research under the umbrella of Indo-US agricultural knowledge initiative, which was further expanded with financial support from the US National Science Foundation's Plant Genome Research Program and the Generation Challenge Program. As a result of the PGI, the last 3 years have witnessed significant progress in development of both genetic as well as genomic resources in this crop through effective collaborations and coordination of genomics activities across several institutes and countries. For instance, 25 mapping populations segregating for a number of biotic and abiotic stresses have been developed or are under development. An 11X-genome coverage bacterial artificial chromosome (BAC) library comprising of 69,120 clones have been developed of which 50,000 clones were end sequenced to generate 87,590 BAC-end sequences (BESs). About 10,000 expressed sequence tags (ESTs) from Sanger sequencing and ca. 2 million short ESTs by 454/FLX sequencing have been generated. A variety of molecular markers have been developed from BESs, microsatellite or simple sequence repeat (SSR)-enriched libraries and mining of ESTs and genomic amplicon sequencing. Of about 21,000 SSRs identified, 6,698 SSRs are under analysis along with 670 orthologous genes using a GoldenGate SNP (single nucleotide polymorphism) genotyping platform, with large scale SNP discovery using Solexa, a next generation sequencing technology, is in progress. Similarly a diversity array technology array comprising of ca. 15,000 features has been developed. In addition, >600 unique nucleotide binding site (NBS) domain containing members of the NBS-leucine rich repeat disease resistance homologs were cloned in pigeonpea; 960 BACs containing these sequences were identified by filter hybridization, BES physical maps developed using high information content fingerprinting. To enrich the genomic resources further, sequenced soybean genome is being analyzed to establish the anchor points between pigeonpea and soybean genomes. In addition, Solexa sequencing is being used to explore the feasibility of generating whole genome sequence. In summary, the collaborative efforts of several research groups under the umbrella of PGI are making significant progress in improving molecular tools in pigeonpea and should significantly benefit pigeonpea genetics and breeding. As these efforts come to fruition, and expanded (depending on funding), pigeonpea would move from an ‘orphan legume crop' to one where genomics-assisted breeding approaches for a sustainable crop improvement are routine.
Chickpea (Cicer arietinum L.) is the second most important cool season food legume cultivated in arid and semiarid regions of the world. The objective of the present study was to study variation for ...protein content in chickpea germplasm, and to find markers associated with it. A set of 187 genotypes comprising both international and exotic collections, and representing both desi and kabuli types with protein content ranging from 13.25% to 26.77% was used. Twenty-three SSR markers representing all eight linkage groups (LG) amplifying 153 loci were used for the analysis. Population structure analysis identified three subpopulations, and corresponding Q values of principal components were used to take care of population structure in the analysis which was performed using general linear and mixed linear models. Marker-trait association (MTA) analysis identified nine significant associations representing four QTLs in the entire population. Subpopulation analyses identified ten significant MTAs representing five QTLs, four of which were common with that of the entire population. Two most significant QTLs linked with markers TR26.205 and CaM1068.195 were present on LG3 and LG5. Gene ontology search identified 29 candidate genes in the region of significant MTAs on LG3. The present study will be helpful in concentrating on LG3 and LG5 for identification of closely linked markers for protein content in chickpea and for their use in molecular breeding programme for nutritional quality improvement.
Spine gourd (Momordica dioica Roxb.) is a highly nutritious vegetable crop with dioecious reproductive nature. Forty-eight spine gourd genotypes including 32 female and 16 male genotypes were ...assessed for molecular divergence to establish phenotypic relationships using ISSR markers. Twenty-two out of a total of 25 ISSR primers studied yielded a total of 88 bands of which 80 bands were polymorphic, with three of them being unique in their profile. Each primer thus produced a mean of 4.0 bands per marker, with 3.64 mean polymorphic bands per marker. Fifteen primers showed 100 percent polymorphism. In the dendrogram, genotypes were distinguished from each other with a similarity range of 0.465 to 0.959. A wider range of molecular diversity detected by ISSR markers reflected the presence of a high level of genetic variation forming different 5 broad groups of clusters. The clustering pattern based on molecular variation during this investigation revealed five clusters; of which cluster three had twenty-eight (all 16 malealong with 12 female genotypes) genotypes; while cluster 4 and 5 were mono-genotypic.
Quantitative trait loci (QTL) analysis for pre-harvest sprouting tolerance (PHST) in bread wheat was conducted following single-locus and two-locus analyses, using data on a set of 110 recombinant ...inbred lines (RILs) of the International Triticeae Mapping Initiative population grown in four different environments. Single-locus analysis following composite interval mapping (CIM) resolved a total of five QTLs with one to four QTLs in each of the four individual environments. Four of these five QTLs were also detected following two-locus analysis, which resolved a total of 14 QTLs including 8 main effect QTLs (M-QTLs), 8 epistatic QTLs (E-QTLs) and 5 QTLs involved in QTL x environment (QE) or QTL x QTL x environment (QQE) interactions, some of these QTLs being common. The analysis revealed that a major fraction (76.68%) of the total phenotypic variation explained for PHST is due to M-QTLs (47.95%) and E-QTLs (28.73%), and that only a very small fraction of variation (3.24%) is due to QE and QQE interactions. Thus, more than three-quarters of the genetic variation for PHST is fixable and would contribute directly to gains under selection. Two QTLs that were detected in more than one environment and at LOD scores above the threshold values were located on 3BL and 3DL presumably in the vicinity of the dormancy gene TaVp1. Another QTL was found to be located on 3B, perhaps in close proximity to the R gene for red grain colour. However, these associations of QTLs for PHST with genes for dormancy and grain colour are only suggestive. The results obtained in the present study suggest that PHST is a complex trait controlled by large number of QTLs, some of them interacting among themselves or with the environment. These QTLs can be brought together through marker-aided selection, leading to enhanced PHST.
In bread wheat, QTL interval mapping for four growth characters (early growth habit, days to heading, days to maturity and plant height), and association studies for two leaf characters (leaf colour ...and leaf waxiness) were conducted utilising the International Triticeae Mapping Initiative reference population (ITMI
pop) that was used in a number of earlier studies on molecular mapping in this crop. Using QTL Cartographer, composite interval mapping (CIM) for all the four growth characters and multitrait composite interval mapping (MCIM) for three correlated traits (excluding plant height) were conducted. For growth characters, CIM suggested the presence of 16 QTL (LOD=2.0–12.7), of which only six were common with those among the 18 QTL identified by MCIM. This suggested possible presence of some false positives among QTL identified by CIM. Fourteen (14) molecular markers that were closest to the 14 QTL identified by CIM were also tested for marker–trait association using regression and
t-tests. Five markers showed significant association, and therefore, are recommended for marker-assisted selection (MAS). Incidentally, the QTL associated with these five markers were identified by both CIM and MCIM thus placing higher level of confidence in these markers. Some of the QTL identified by CIM and joint MCIM also affected more than one trait each, suggesting that the observed correlation may be either due to tight linkage or due to pleiotropy. During CIM for individual traits, effects of all QTL (phenotypic variations explained or PVE) that were identified at LOD score of 2.0 or above, together accounted for approximately 17–91% of the phenotypic variation. However QTL effects, when measured irrespective of LOD score, exhibited characteristic L-shaped distribution, suggesting that there are many minor QTL, which should be taken into account during MAS. The two leaf characters exhibited 100% correlation. Consequently, the 14 markers that were identified showed significant marker–trait association with both the traits. Some of these markers are the same, which also exhibited association with some growth and yield traits, studied by us earlier, thus adding to their utility in wheat breeding through MAS.
An F2 population was developed from a cross between rice (Oryza sativa L.) genotypes, EK 70 (highly susceptible to blast) and RDN 98-2-3-5-14 (resistant to blast), to study the inheritance of blast ...resistance and to identify the marker associated with resistance.
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