H. S. Balyan

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.

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.

Identification of eight chromosomes and a microsatellite marker on 1AS associated with QTL for grain weight in bread wheat

Abstract The present study in bread wheat was undertaken, firstly, to identify chromosomes carrying QTLs controlling 1000 grain weight (GW) and, secondly, to develop molecular marker(s) linked with this trait. Using the genotype Rye Selection111 (RS111), we carried out a monosomic analysis that suggested that 8 chromosomes (1A, 1D, 2B, 4B, 5B, 6B, 7A and 7D) carried QTLs controlling GW, with only 3 of these (1A, 2B, 7A) carrying alleles for high GW. To tag the QTLs present on these chromosomes, we crossed the genotype RS111 with high GW (56.83 g) with the genotype Chinese Spring (CS) with low GW (23.74 g) and obtained 100 RILs. These RILs showed normal distribution for GW. The parental genotypes were analysed with as many as 346 STMS primer pairs for detection of polymorphism. Of these, 267 primer pairs gave scorable amplification products, 63 of which detected polymorphism between the parents. Using each of these 63 primer pairs, we carried out bulked segregant analysis on RILs representing two extremes of the distribution. One primer pair (WMC333) showed an association of the marker locus Xwmc333 with grain weight. This was confirmed through selective genotyping, and the co-segregation data on molecular marker locus Xwmc333 and GW were analysed following a single marker linear regression approach. Significant regression suggested linkage between Xwmc333 and a QTL for GW. The results showed that the above QTL accounted for 15.09% of the variation for GW between the parents. The marker has been located on chromosome arm 1AS, and QTL was designated QGw1.ccsu-1A.

QTL analysis for grain weight in common wheat

Quantitative trait loci (QTL) analysis for grain weight (GW = 1000 grain weight) in common wheat was conducted using a set of 100 recombinant inbred lines (RILs) derived from a cross ‘Rye Selection 111 (high GW) × Chinese Spring (low GW)’. The RILs and their two parental genotypes were evaluated for GW in six different environments (three locations × two years). Genotyping of RILs was carried out using 449 (30 SSRs, 299 AFLP and 120 SAMPL) polymorphic markers. Using the genotyping data of RILs, framework linkage maps were prepared for three chromosomes (1A, 2B, 7A), which were earlier identified by us to carry important/major genes for GW following monosomic analysis. QTL analysis for GW was conducted following genome-wide single marker regression analysis (SMA) and composite interval mapping (CIM) using molecular maps for the three chromosomes. Following SMA, 12 markers showed associations with GW, individual markers explaining 6.57% to 10.76% PV (phenotypic variation) for GW in individual environments. The high grain weight parent, Rye Selection111, which is an agronomically superior genotype, contributed favourable alleles for GW at six of the 12 marker loci identified through SMA. The CIM identified two stable and definitive QTLs, one each on chromosome arms 2BS and 7AS, which were also identified through SMA, and a third suggestive QTL on 1AS. These QTLs explained 9.06% to 19.85% PV for GW in different environments. The QTL for GW on 7AS is co-located with a QTL for heading date suggesting the occurrence of a QTL having a positive pleiotropic effect on the two traits. Some of the markers identified during the present study may prove useful for marker-assisted selection, while breeding for high GW in common wheat.

Gene networks in hexaploid wheat: interacting quantitative trait loci for grain protein content.

In hexaploid wheat, single-locus and two-locus quantitative trait loci (QTL) analyses for grain protein content (GPC) were conducted using two different mapping populations (PI and PII). Main effect QTLs (M-QTLs), epistatic QTLs (E-QTLs) and QTL × environment interactions (QE, QQE) were detected using two-locus analyses in both the populations. Only a few QTLs were common in both the analyses, and the QTLs and the interactions detected in the two populations differed, suggesting the superiority of two-locus analysis and the need for using several mapping populations for QTL analysis. A sizable proportion of genetic variation for GPC was due to interactions (28.59% and 54.03%), rather than to M-QTL effects (7.24% and 7.22%), which are the only genetic effects often detected in the majority of QTL studies. Even E-QTLs made a marginal contribution to genetic variation (2.68% and 6.04%), thus suggesting that the major part of genetic variation is due to changes in gene networks rather than the presence or absence of specific genes. This is in sharp contrast to the genetic dissection of pre-harvest sprouting tolerance conducted by us earlier, where interaction effects were not substantial, suggesting that the nature of genetic variation also depends on the nature of the trait.

Characterization of Microsatellites and Development of Chromosome Specific STMS Markers in Bread Wheat

Microsatellites, or simple sequence repeats (SSRs), have become the markers of choice for genetic studies with many crop species including wheat. Currently an international effort is underway to enrich the repertoire of available sequence tagged microsatellite site (STMS) markers in wheat. As a part of this effort, we have sequenced 43 clones obtained from a microsatellite-enriched wheat genomic library; 34 clones contained 41 different microsatellites. These microsatellites (mono-, di-, tri- nucleotide repeats) were classified as 19 simple perfect, 18 simple imperfect and 4 compound imperfect types. Dinucleotide repeats were the most abundant (70%). Primer pairs for only 16 microsatellites could be designed, since the flanking sequences of the others were either too short or were otherwise not suitable for designing the microsatellite specific primers. Microsatellite loci of the expected size and polymorphism were successfully amplified from 15 of these 16 primer pairs using three wheat varieties. 14 loci detected by 12 out of the 15 functional primer pairs were assigned to 11 specific chromosomes.

QTL mapping for growth and leaf characters in bread wheat

Abstract 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 (ITMIpop) 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.