Pushpendra K. Gupta

Linkage disequilibrium and association studies in higher plants: Present status and future prospects

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)

Genetic mapping of 66 new microsatellite (SSR) loci in bread wheat.

Abstract.In hexaploid bread wheat (Triticum aestivum L. em. Thell), ten members of the IWMMN (International Wheat Microsatellites Mapping Network) collaborated in extending the microsatellite (SSR = simple sequence repeat) genetic map. Among a much larger number of microsatellite primer pairs developed as a part of the WMC (Wheat Microsatellite Consortium), 58 out of 176 primer pairs tested were found to be polymorphic between the parents of the ITMI (International Triticeae Mapping Initiative) mapping population W7984 × Opata 85 (ITMIpop). This population was used earlier for the construction of RFLP (Restriction Fragment Length Polymorphism) maps in bread wheat (ITMImap). Using the ITMIpop and a framework map (having 266 anchor markers) prepared for this purpose, a total of 66 microsatellite loci were mapped, which were distributed on 20 of the 21 chromosomes (no marker on chromosome 6D). These 66 mapped microsatellite (SSR) loci add to the existing 384 microsatellite loci earlier mapped in bread wheat.

The use of microsatellites for detecting DNA polymorphism, genotype identification and genetic diversity in wheat

Abstract A set of 20 wheat microsatellite markers was used with 55 elite wheat genotypes to examine their utility (1) in detecting DNA polymorphism, (2)in the identifying genotypes and (3) in estimating genetic diversity among wheat genotypes. The 55 elite genotypes of wheat used in this study originated in 29 countries representing six continents. A total of 155 alleles were detected at 21 loci using the above microsatellite primer pairs (only 1 primer amplified 2 loci; all other primers amplified 1 locus each). Of the 20 primers amplifying 21 loci, 17 primers and their corresponding 18 loci were assigned to 13 different chromosomes (6 chromosomes of the A genome, 5 chromosomes of the B genome and 2 chromosomes of the D genome). The number of alleles per locus ranged from 1 to 13, with an average of 7.4 alleles per locus. The values of average polymorphic information content (PIC) and the marker index (MI) for these markers were estimated to be 0.71 and 0.70, respectively. The (GT)n microsatellites were found to be the most polymorphic. The genetic similarity (GS) coefficient for all possible 1485 pairs of genotypes ranged from 0.05 to 0.88 with an average of 0.23. The dendrogram, prepared on the basis of similarity matrix using the UPGMA algorithm, delineated the above genotypes into two major clusters (I and II), each with two subclusters (Ia, Ib and IIa, IIb). One of these subclusters (Ib) consisted of a solitary genotype (E3111) from Portugal, so that it was unique and diverse with respect to all other genotypes belonging to cluster I and placed in subcluster Ia. Using a set of only 12 primer pairs, we were able to distinguish a maximum of 48 of the above 55 wheat genotypes. The results demonstrate the utility of microsatellite markers for detecting polymorphism leading to genotype identification and for estimating genetic diversity.

Array-based high-throughput DNA markers for crop improvement

The last two decades have witnessed a remarkable activity in the development and use of molecular markers both in animal and plant systems. This activity started with low-throughput restriction fragment length polymorphisms and culminated in recent years with single nucleotide polymorphisms (SNPs), which are abundant and uniformly distributed. Although the latter became the markers of choice for many, their discovery needed previous sequence information. However, with the availability of microarrays, SNP platforms have been developed, which allow genotyping of thousands of markers in parallel. Besides SNPs, some other novel marker systems, including single feature polymorphisms, diversity array technology and restriction site-associated DNA markers, have also been developed, where array-based assays have been utilized to provide for the desired ultra-high throughput and low cost. These microarray-based markers are the markers of choice for the future and are already being used for construction of high-density maps, quantitative trait loci (QTL) mapping (including expression QTLs) and genetic diversity analysis with a limited expense in terms of time and money. In this study, we briefly describe the characteristics of these array-based marker systems and review the work that has already been done involving development and use of these markers, not only in simple eukaryotes like yeast, but also in a variety of seed plants with simple or complex genomes.

Single-molecule DNA sequencing technologies for future genomics research

During the current genomics revolution, the genomes of a large number of living organisms have been fully sequenced. However, with the advent of new sequencing technologies, genomics research is now at the threshold of a second revolution. Several second-generation sequencing platforms became available in 2007, but a further revolution in DNA resequencing technologies is being witnessed in 2008, with the launch of the first single-molecule DNA sequencer (Helicos Biosciences), which has already been used to resequence the genome of the M13 virus. This review discusses several single-molecule sequencing technologies that are expected to become available during the next few years and explains how they might impact on genomics research.

Marker-assisted wheat breeding: present status and future possibilities

Wheat production and productivity in the past witnessed a remarkable growth. However, this growth rate could not be sustained during the last decade, causing concern among world wheat community. Marker-assisted selection (MAS), which is being practiced for improvement of a variety of traits in wheat around the world, may at least partly help in providing the desired solution. Marker-trait associations are now known for a number of simple, but difficult-to-score traits, so that MAS has been found useful for improvement of several of these important economic traits. Breeding strategies including marker-assisted backcrossing, forward breeding, MAS involving doubled haploid technology and F2 enrichment have been successfully utilized for this purpose. However, for improvement of complex polygenic traits, newer technologies based on high throughput genotyping associated with new marker systems (e.g., DArT and SNP), and new selection strategies such as AB-QTL, mapping-as-you-go, marker-assisted recurrent selection and genome-wide selection will have to be tried in future. The progress made in all these aspects of marker-assisted wheat breeding, and the limitations and future prospects of this emerging technology have been reviewed in this article.

Identification of a microsatellite on chromosomes 6B and a STS on 7D of bread wheat showing an association with preharvest sprouting tolerance

Abstract In bread wheat, the transfer of tolerance to preharvest sprouting (PHS) that is associated with genotypes having red kernel colour to genotypes with amber kernels is difficult using conventional methods of plant breeding. The study here was undertaken to identify DNA markers linked with tolerance to PHS as these would allow indirect marker-assisted selection of PHS-tolerant genotypes with amber kernels. For this purpose, a set of 100 recombinant inbred lines (RILs) was developed using a cross between a PHS-tolerant genotype, SPR8198, with red kernels and a PHS-susceptible cultivar, ‘HD2329’, with white kernels. The two parents were analysed with 232 STMS (sequence-tagged microsatellite site) and 138 STS (sequence-tagged site) primer pairs. A total of 300 (167 STMSs and 133 STSs) primer pairs proved functional by giving scorable PCR products. Of these, 57 (34%) STMS and 30 (23%) STS primer pairs detected reproducible polymorphism between the parent genotypes. Using these primer pairs, we carried out bulked segregant analysis on two bulked DNAs, one obtained by pooling DNA from 5 PHS-tolerant RILs and the other similarly derived by pooling DNA from 5 PHS-susceptible RILs. Two molecular markers, 1 STMS primer pair for the locus wmc104 anda STS primer pair for the locus MST101, showed apparent linkage with tolerance to PHS. This was confirmed following selective genotyping of individual RILs included in the bulks. Chi-square contingency tests for independence were conducted on the cosegregation data collected on 100 RILs involving each of the two molecular markers (wmc104 and MST101) and PHS. The tests revealed a strong association between each of the markers and tolerance to PHS. Using nullisomic-tetrasomic lines, we were able to assign wmc104 and MST101 to chromosomes 6B and 7D, respectively. The results also indicated that the tolerance to PHS in SPR8198 is perhaps governed by two genes (linked with two molecular markers) exhibiting complementary interaction.

A microsatellite marker associated with a QTL for grain protein content on chromosome arm 2DL of bread wheat

Abstract This study was undertaken with a view to tag gene(s) controlling grain protein content (GPC) using molecular markers in bread wheat. For this purpose, the genotype PH132 with high protein content (13.5%) was crossed with genotype WL711 with significantly lower protein content (9.7%), and 100 RILs were derived. These RILs showed normal distribution for protein content. The parental genotypes were analysed with 232 STMS primer pairs for detection of polymorphism. Of these, 167 primer pairs gave scorable amplification products, and 57 detected polymorphism between the parents. Using each of these 57 primer pairs, we carried out bulked segregant analysis on RILs representing the two extremes of the distribution. One primer pair for the locus wmc41 showed association with protein content. This was further confirmed through selective genotyping. The co-segregation data on the molecular marker (wmc41) and protein content on 100 RILs was analysed by means of a single-marker linear regression approach. Significant regression suggested linkage between wmc41 and a QTL (designated as QGpc.ccsu-2D.) for protein content. The results showed that this marker-linked QTL accounted for 18.73% of the variation for protein content between the parents. The marker has been located on chromosome arm 2DL using nulli-tetrasomic lines and two ditelocentric stocks for chromosome 2D.

QTL analysis for grain protein content using SSR markers and validation studies using NILs in bread wheat.

Abstract.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 × 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.

Genetic basis of pre-harvest sprouting tolerance using single-locus and two-locus QTL analyses in bread wheat

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 × environment (QE) or QTL × QTL × 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.