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a International Crops Research Inst. for the Semi-Arid Tropics (ICRISAT), Patancheru P.O., Andhra Pradesh 502 324, India
b Inst. of Grassland and Environmental Research, Aberystwyth, SY23 3EB, UK
* Corresponding author (t.nepolean{at}cgiar.org).
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ABSTRACT |
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 TOPNOTES ABSTRACT INTRODUCTIONMATERIALS AND METHODSRESULTS AND DISCUSSIONCONCLUSIONSREFERENCES |
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Abbreviations: BLUP, best linear unbiased predictor • GRMA, individual grain mass • GRNO, grain number • GRYLD, grain yield • HI, harvest index • LG, linkage group • LOD, logarithm of odds • MAS, marker-assisted selection • PNHI, panicle harvest index • QTL, quantitative trait locus • RFLP, restriction fragment length polymorphism • SSR, simple sequence repeat.
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INTRODUCTION |
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TOPNOTESABSTRACTINTRODUCTIONMATERIALS AND METHODSRESULTS AND DISCUSSIONCONCLUSIONSREFERENCES |
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Selection for drought tolerance per se is limited by the lack of proven selection criteria. Screening techniques have been proposed for various traits thought to be related to drought tolerance in pearl millet (Yadav and Weltzien-Rattunde, 1999), but few attempts to actively select for tolerance have been reported. Research on genotype yield differences under terminal (unrelieved postflowering) stress has suggested possible criteria for identifying tolerance to this type of stress (Bidinger et al., 1987b; Fussell et al., 1991). An extensive evaluation of one such criterion—maintenance of panicle harvest index under stress—for improved tolerance of terminal stress indicated that yield gains of 5% per initial cycle(s) under terminal stress are possible with this criterion (Bidinger et al., 2000). The feasibility of using more basic physiological parameters as selection criteria is limited in pearl millet by the available information on such traits as selection criteria.
Our own recent efforts to improve drought tolerance in pearl millet have focused on the identification of quantitative trait loci (QTLs) for grain yield or closely related traits under terminal stress conditions (Yadav et al., 2002, 2004). Several QTLs have been identified and the process of evaluating their effectiveness is underway (Bidinger et al., 2005; Serraj et al., 2005). The focus on drought tolerance in this work is linked to the relative ease in pearl millet of marker-assisted backcross (MABC) transfer of specific QTLs to improve specific aspects of widely used hybrid parental lines (Bidinger and Hash, 2004). Using MABC to enhance the drought tolerance of proven parental lines allows the breeder to concentrate on this trait, with the knowledge that the recurrent parents are otherwise fully acceptable to the seed industry (Witcombe and Hash, 2000). This should be an effective, short-term approach, provided the drought tolerance QTLs have sufficient expression in the hybrids of the recipient parental lines to justify the expense, with no negative effects on other desirable traits.
Apart from this specific application, the general breeding requirement remains not simply drought tolerance, but improved adaptation (as measured by grain yield) to the full range of expected moisture conditions during grain filling. Quantitative trait loci that enhance traits specifically linked to grain yield across the full range of grain-filling moisture environments would be more useful to a general breeding program, as they could be deliberately retained in segregating generations by marker-assisted selection (MAS), while phenotypic selection was practiced under stress-free conditions for desired agronomic characteristics. This approach would thus enhance broad adaptation to the full range of grain-filling moisture environments, simultaneously with selection for overall worth. As in the case of MABC for drought tolerance, however, the feasibility of this application of MAS depends on the identification of QTLs for yield or for strongly linked traits that are effective across the full range of expected moisture conditions during grain filling.
The objectives of this study were (i) to identify QTLs with favorable effects on grain yield or on closely linked traits that would be effective across a broad range of grain-filling moisture environments, and (ii) to estimate the probable effects of these when used as additional selection criteria (using MAS) during the segregating generations of a conventional millet breeding program.
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MATERIALS AND METHODS |
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TOPNOTESABSTRACTINTRODUCTIONMATERIALS AND METHODSRESULTS AND DISCUSSIONCONCLUSIONSREFERENCES |
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Genotyping
The genotyping of the F2 plants from the ICMB 841 x 863B mapping population and the construction of the linkage map was done and described by Yadav et al. (2004). In this study, collinear markers were removed from the analysis before the map was constructed using Mapmaker/Exp 3.0 (Lander et al., 1987). The map obtained spans a total length of 551 cM and comprises 79 loci including 50 restriction fragment length polymorphism (RFLP) loci (Liu et al., 1994) and 29 simple sequence repeat (SSR) markers (Allouis et al., 2001; Qi et al., 2001, 2004) distributed across the seven linkage groups (LGs) as named by Liu et al. (1994). Minor changes in the positions of closely linked markers (
1.0 cM) were obtained but otherwise all markers mapped to the same positions as the previously published map of this cross.
Phenotyping
Test-crossed F4 progenies were evaluated in similarly managed replicated trials in the dry-season drought nursery at ICRISAT, Patancheru, India, during the years 1998, 1999, 2000, and 2001. Phenotyping environments each year included a fully irrigated, stress-free environment and two (early- and late-onset) postflowering stress environments. Irrigation in the early-onset treatment was terminated approximately 1 wk before flowering of the main shoot, to initiate the stress about mid-flowering to affect both seed number and seed filling. Irrigation in the late-onset treatment was terminated 7 to 10 d later to initiate the stress in early to mid-grain filling to affect primarily seed filling.
All trials were planted in alpha (incomplete block) designs with nine plots per block, replicated thrice. Individual plots were two rows by 4.0 m long by 0.6 m apart; net (harvested) plot area was two rows by 3.0 by 0.6 m. Trials were uniformly managed to maximize growth and grain yield within the limits of the moisture treatments, as described in Yadav et al. (2004). Data were recorded on a harvested-area basis for oven-dry biomass and grain yields (GRYLD) and converted to a square-meter basis. Individual grain mass (GRMA) was determined from the weight of triplicate samples of 100 oven dry grains, and grain number per square meter (GRNO) was estimated from grain yield and individual grain mass. Harvest index (HI) was calculated as the ratio of grain to biomass yield, and panicle harvest index (PNHI) from the ratio of grain to total panicle weights. The former was considered an index of the ability to convert biomass to grain across moisture environments and the latter as a measure of the ability to set and fill grains across moisture environments (Bidinger, 2002).
Results of the 1998 and 1999 evaluations done in the terminal stress treatments only (four of the total of 12 phenotyping environments reported here) were used by Yadav et al. (2004) to assess the effects of year, stress intensity, and tester on identification of QTLs for terminal drought tolerance. This study used data from the full set of phenotyping environments—4 yr and three postflowering moisture environments—to identify and assess QTLs for grain yield, its two major components (GRNO and GRMA), and the two indices of partitioning efficiency (HI and PNHI), across the full range of postflowering moisture environments.
Data Analysis
Plot-level data on each trait were subjected to a linear mixed model analysis using ReML in GENSTAT (GENSTAT 6 Committee, 2002). The data across the 12 environments were analyzed based on the following linear mixed effects model:
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ijkl represents the residual for the ijklth plot. Each effect in Eq. [1], except for µ and Ei, was treated as a normally distributed random variable with an expected value of zero and a constant variance. Since our major interest here was to assess the consistency of detected QTLs across the three moisture environments, the environment term Ei in Eq. [1] was factorially partitioned as Ei = Euv = Yu + Mv + (YM)uv, with Eq. [1] accordingly expanded to extract the progeny x moisture-environment interaction effects (GM)kv. Here Yu is the effect of year u, Mv is the effect of moisture environment v, and (YM)uv represents their interaction, with moisture-environment effects considered fixed. Plot-level data from each of the 12 individual environments were analyzed using the following linear mixed effects model
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Multi-environment QTL mapping was done using the best linear unbiased predictors (BLUPs) corresponding to the progeny term Gk as obtained from Eq. [1–2]
, following the method of composite interval mapping as outlined in Utz et al. (2000) and implemented in PLABQTL (Utz and Melchinger, 1996). The presence of a putative QTL in any interval was tested using a logarithm of odds (LOD) threshold of 2.5.
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RESULTS AND DISCUSSION |
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TOPNOTESABSTRACTINTRODUCTIONMATERIALS AND METHODSRESULTS AND DISCUSSIONCONCLUSIONSREFERENCES |
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Progeny and Progeny x Moisture Environment Interactions
Progeny differences were significant for all variables reported (Table 1), and the ranges in the progeny BLUPs across all three moisture environments varied widely (Table 2). Thus, despite the relatively small number of mapped progenies used, plus the fact that 50% of the genome of each test-crossed progeny was similar, the variation among test-crossed progenies for yield and yield components across a range of grain-filling moisture environments was substantial. Differences among progenies under terminal stress were expected because of the differential tolerance of such stress between the parents of the mapping population, but the range in progeny values was generally as large in the favorable environment as it was in the drought-stressed ones (Table 2).
Progeny x moisture environment interactions were, somewhat surprisingly, significant only for HI and PNHI (Table 1). Differences among related, elite genetic materials in HI and PNHI are often small in the absence of stress, but become greater as stress affects seed set, seed filling, and assimilate supply to the grain differentially in different genotypes; so interactions with moisture environment are expected in these two variables. The lack of significant progeny x moisture environment interactions for GRYLD, GRNO, or GRMA is surprising, and suggests that the primary differences among progenies for these variables were constitutive ones, which were little affected by moisture environment during grain filling. This conclusion was supported by a strong similarity in ranking of individual progeny test crosses across the three grain-filling moisture environments: rank correlation coefficients for GRYLD were 
0.88 and for GRNO and GRMA 0.97 (data not presented). This is a positive finding in the context of this research, i.e., if potential yield differences are more important in determining realized yield in the stress environments than is stress tolerance, the chance of finding effective across-environment QTLs are greater. However, QTLs for traits related to stress tolerance are still of interest, especially when these are significantly correlated to grain yield in severe stress environments (Table 3).
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Relationships of Yield and Component Variables
The questions of the relationship of grain yield with both the basic yield components and the indicators of stress response (plus the effects of moisture environment on the relationships) were examined by correlation of grain yield and the component and indicator variables within and across moisture environments. The two basic yield components (GRNO and GRMA) were significantly, but only very modestly, related to grain yield across environments, with variation in both components explaining approximately 20% of the variation in GRYLD (Table 3). As grain-filling stress increased (i.e., earlier stress onset), the importance of GRNO to GRYLD declined slightly, and the importance of GRMA increased slightly (Table 3), but in neither case were the changes striking. In contrast, the across-environment relationships of GRYLD with both HI and PNHI were considerably stronger, with variation in HI explaining 44% of the variation in GRYLD and that in PNHI explaining 52% (Table 3). In both cases, the strength of the relationship of GRYLD and the stress response indicator variable increased with increasing stress severity; correlation coefficients increased from 0.51 to 0.76 in the case of HI and from 0.57 to 0.79 in the case of PNHI (Table 3). This suggests that genotype differences in HI and especially PNHI are useful indicators of genotype differences in adaptation to stress, as measured by genotype grain yield. It is not clear therefore why progeny x moisture environment interactions in HI and PNHI were not reflected in parallel progeny x moisture environment interactions in GRYLD. On the strength of the behavior of the correlations of GRYLD with both HI and PNHI, however, we mapped QTLs for these variables, along with GRYLD, GRNO, and GRMA, both within and across grain-filling moisture environments.
Quantitative Trait Loci for Grain Yield and Component Traits
Grain Yield
Quantitative trait loci for mean GRYLD across moisture environments were mapped on LG 2, LG 3, and LG 4 (Table 4). The GRYLD QTLs on LG 2 also mapped in each of the three individual moisture environments. Those on LG 3 and LG 4 mapped in just the late-onset stress environment (Table 4), which suggests that they are primarily associated with yield differences under terminal stress. Neither had a significant QTL x environment (Q x E) interaction, however, so their benefit under terminal stress is not likely to be at the cost of GRYLD in the absence of stress, even if the effect is greater in stress environments. The LG 2 QTL (linked to markers Xpsm458–Xpsmp2059, Genomic Region 2, Fig. 1) was the most interesting, as it had substantial LOD scores in all three moisture environments (6.3–6.9) as well as across environments (7.9) and accounted for a significant proportion of the phenotypic variance for GRYLD in both the stress (27–38%) and the stress-free (28%) environments, as well as in the means across environments (25%, Table 4). The favorable allele at this locus was from 863B. The LG 3 and LG 4 QTLs had lower LOD scores and accounted for much smaller fractions of the phenotypic variance for GRYLD, both in the late-onset stress environment and across environments, than did the LG 2 QTL. The favorable alleles at both of these loci were from ICMB 841.
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0.05) interaction with environment (Table 4). Presumably this was a consequence of differences in the magnitude of effects in different moisture environments, as no crossover interaction was observed. Noncrossover Q x E interaction is commonly due to either inconsistency in detection of QTLs in different environments or differential levels of expression of QTLs in different environments (Veldboom and Lee, 1996; Austin and Lee, 1998; Li et al., 2003). As potential GRNO is largely determined before actual flowering, when the stress environment differences began, the significant across-environment effect of a GRNO QTL was expected. This QTL co-mapped with a GRYLD QTL for both favorable and moderate stress environments, and with a GRMA QTL in all three individual moisture environments (Table 4). The favorable allele in the case of both the GRYLD and GRMA co-mapped QTLs was contributed by 863B, however, not ICMB 841 as was the case for the GRNO QTL. Thus the ICMB 841 allele at this QTL may be linked to a smaller grain size, as GRNO and GRMA are commonly negatively correlated in the crop (Bidinger et al., 2001). Because the importance of GRMA to GRYLD increases as stress becomes more severe (Table 3), it is the 863B allele at this QTL, linked to larger GRMA, which is likely to be the more favorable for maintaining GRYLD under severe stress. Thus if this QTL were considered for use in yield improvements across a range of moisture environments, it would be the 863B allele, with its favorable effect on GRMA under stress, not the ICMB 841 allele with its favorable effect on GRNO, that would be the choice for selection.
Individual Grain Mass
There were two QTLs for GRMA detected across moisture environments, one on LG 1 and one on LG 3, one of which (LG 3) had significant Q x E interaction (Table 4). The QTL on LG 1 (Genomic Region 1, Fig. 1) explained a very significant proportion (40%) of the phenotypic variance of the mean GRMA across environments, with significant effects in both the stress-free and mild-stress environments, where it explained 40% of the phenotypic variance for GRMA, and a very strong effect in the severe-stress environments, where it explained 57% of the phenotypic variance for this trait (Table 4). In all cases, the favorable allele was contributed by the drought-tolerant parent 863B. The LG 3 QTL accounted for a much smaller proportion of the mean variance in GRMA across environments (16%, Table 4). Its effect was similar in the favorable and severe-stress conditions (LOD scores of 3.0–4.3, with additive effects of the ICMB 841 allele of 0.2 mg grain–1) but it was not detected in the moderate-stress environments. There was also a moderately strong GRMA QTL in LG 2 detected in the severe-stress environments, but it was not detected in the other environments and not detected across environments (Table 4).
All three of these GRMA QTLs co-mapped with QTLs for GRYLD, but only the co-mapped GRYLD QTLs on LG 2 and LG 3 were detected across environments (Table 4). These two QTLs each accounted for a useful proportion of the phenotypic variance for mean GRYLD across all environments (25% by the LG 2 QTL and 13% by the LG 3 QTL). The strongest effects of the LG 2 GRMA QTL on GRYLD was in the early-onset stress environment, where it accounted for 35 and 38% of the phenotypic variance for GRMA and GRYLD, respectively (Table 4), confirming the effectiveness of the LG 2 QTL under severe stress. The strong LG 1 GRMA QTL had a smaller effect on grain yield, despite its stronger effect on GRMA (Table 4).
Harvest Index
Harvest index in this experiment was used as a measure of genotype adaptation to various moisture environments during grain filling, expressed as the ability to maximize partitioning of total biomass to grain yield, despite varying levels of current photosynthesis for grain filling. Four QTLs were detected for across-environment HI, one each on four of the seven pearl millet LGs (Table 4). Percentage of the phenotypic variance for across-environment HI accounted for by the individual QTLs ranged from 15 to 30%. Except for the QTL on LG 3, the favorable alleles for HI QTLs were contributed by 863B. Three of the four HI QTLs were subject to significant Q x E interaction, consistent with the significant genotype x environment interaction found for the trait itself (Table 1). Of these three, the first (on LG 2) appeared to be primarily a drought-tolerance QTL, as the proportion of variance in HI it explained (14–38%) increased as moisture stress increased (Table 4). This QTL co-mapped with QTLs for both GRYLD and GRMA that showed similar patterns of effects across the three moisture environments. The second HI QTL (on LG 5) was detected only in one moisture environment (early-onset stress) and across environments, and did not co-map with across-environment QTLs for GRYLD, GRNO, or GRMA (Table 4), and thus appears to be of secondary interest. The third HI QTL (on LG 7) accounted for a slightly greater proportion of the phenotypic variance for HI in the stress-free environments (35%) than in the stress environments or the mean across environments (approximately 30%). It also did not co-map with QTL for across-environment GRYLD or its components, however, and is thus also of secondary interest, despite its strong effect on HI. The one HI QTL that was not subject to Q x E interaction, on LG 3, accounted for a similar proportion of the phenotypic variance (17–23%) in all three moisture environments, and 29% of the variance in the mean across all environments (Table 4). This HI QTL also co-mapped with QTLs for GRMA and GRYLD, which also showed a similarly consistent effects in all the environments in which the HI QTL was mapped.
The two most useful of the HI QTLs thus appear to be those on LG 2 and LG 3, both of which appear to affect grain filling (GRMA), and both of which co-map with GRYLD QTLs but have different environmental expressions. The LG 2 HI QTL appears to be primarily a drought-tolerance QTL, with a significant Q x E interaction, but still with a strong effect on the mean across environments, and the favorable allele from 863B. The LG 3 HI QTL has less environment-specific effects (no Q x E interaction), with moderate effects in the individual moisture environments and a strong effect across environments, and with the favorable allele contributed by ICMB 841.
Panicle Harvest Index
Panicle harvest index in this experiment was used specifically as a measure of tolerance to terminal drought, expressed as the ability to set and fill grains under limited moisture. Four across-moisture-environment PNHI QTLs were identified, of which two, on LG 1 and LG 2, each accounted for nearly half of the phenotypic variance in PNHI across environments (Table 4). Both of these appeared to be primarily stress-tolerance QTLs, accounting for a higher proportion of phenotypic variance in the stress environments than in the stress-free ones, and both showing significant Q x E interactions. The favorable allele at both QTLs was from 863B. The LG 1 PNHI QTL co-mapped with an across-environment GRMA QTL, which also expressed more strongly in the severe-stress environments, but which did not show a significant Q x E interaction (Table 4). The LG 2 PNHI QTL co-mapped with across-environment QTLs for GRYLD and HI, which similarly expressed more strongly in the stress environments. The co-mapping of PNHI QTL with both GRMA QTL and HI QTL is not unexpected under grain-filling moisture stress.
The other two across-environment PNHI QTLs (LG 3 and LG 6) did not have significant Q x E interactions, but accounted for a lower proportion (25 and 18% respectively) of the mean phenotypic variance in PNHI across environments than did the QTLs on LG 1 and LG 2 (Table 4). The PNHI QTL on LG 3, for which the favorable allele was from ICMB 841, was detected in all three moisture environments as well as across environments. It co-mapped with across-environment QTLs for GRYLD, HI, and GRMA, but only the HI QTL also had a nonsignificant Q x E interaction (Table 4). The PNHI QTL on LG 6, with the favorable allele from 863B, was detected only in the severe-stress environment and across environments. It co-mapped with a QTL for GRMA, which also expressed only under severe-stress conditions.
Relationships to Previously Identified Quantitative Trait Loci
Using the same mapping population and genotyping data, with only 2 yr of stress environment phenotyping data but two testers, Yadav et al. (2004) mapped QTLs for grain yield and yield-related traits under terminal stress (indicated as stress-tolerance QTLs). The QTLs for GRYLD on LG 1 and LG 2, for HI on LG 2 and LG 3, and for PNHI on LG 1, LG 2, and LG 3 were identified in both studies, with similar positions on these LGs, despite the different QTL mapping programs used in this study (PLABQTL 1.1) and the study of Yadav et al. (2004) (Mapmaker/QTL 1.1). The two studies differed in the nature of Q x E interactions found, however. In the earlier study (Yadav et al., 2004), QTLs were mapped for individual years of testing, which identified some Q x E interactions that were of the crossover type. In this study, all Q x E interactions were due to differences in the magnitude of QTL effects in different environments, despite the fact that this study included a wider range of moisture environments than did the earlier study. The QTLs with non-crossover Q x E interactions are preferable in a marker-based breeding program, as the target allele has a similar effect in different environments (although these may differ in magnitude). With crossover Q x E interactions, different alleles condition favorable performance in different environments, so that a gain in one environment may be offset by a loss in a contrasting environment.
This study also extends the information on the common QTLs identified in the two studies. The major LG 2 GRYLD QTL (863B allele) identified in both studies is clearly not simply a drought-tolerance QTL, as earlier reported, as it has a highly significant effect on GRYLD across all three moisture environments, including the stress-free environments (Table 4). This QTL co-maps with a strong QTL for HI across environments, suggesting that its major effect is a general increase in partitioning of biomass to grain. The general effect of this QTL on partitioning is also expressed under terminal stress in terms of a highly significant effect on PNHI (explaining 40–50% of the phenotypic variance), which underlines the particular value of better partitioning to grain under conditions of limited assimilate availability.
This study has also clarified the nature of and the reasons for the limited utility of the QTL on LG 1 (Table 4). The ICMB 841 allele at this QTL has a significant and consistent effect on GRNO both in the individual environments and across all environments (accounting for 34–41% of the variation for this trait). The increase in grain number is linked to a smaller grain size, however, which is expressed equally strongly in all three moisture environments and across environments (accounting for 33–57% of the variation for this trait)—hence the failure of this QTL to express as a GRYLD QTL across environments. In the severe-stress environment, the 863B allele at this QTL accounted for a substantial portion (20%) of the phenotypic variance for GRYLD through its very strong effect on GRMA. Across environments, however, this QTL has limited value (although the 863B allele at least had no negative effects on GRYLD in any of the three moisture environments).
The LG 3 HI and PNHI QTL detected in this study was also detected in the study of Yadav et al. (2004), but only across environments, and only for these two traits. This study strengthens the value of this QTL considerably, as it is now shown to have a significant effect on both traits in all three moisture environments, as well across environments, plus a significant (if modest) effect on both GRMA and GRYLD across environments. Finally, this study has identified two additional across-environment QTLs (LG 4 and LG 6), one of which may be of value in MAS. The LG 4 GRYLD QTL, with the favorable allele from ICMB 841, accounts for very useful 19% of the variation in this trait across environments (Table 4). This QTL does not co-map with any other observed trait across environments, so it is not clear how it achieves its effect on GRYLD. Despite its significant Q x E interaction, however, the favorable allele from widely accepted ICMB 841 is of interest for MAS. The LG 6 PNHI and GRMA QTL, with the favorable allele from 863B, expresses mainly in the severe-stress environment and thus appears to be a secondary drought-tolerance QTL (Table 4).
Selection of Quantitative Trait Loci for Marker-Assisted Selection
Marker-assisted selection is clearly limited to a small number of target QTLs, as population sizes and costs for marker analyses increase significantly as target QTL numbers increase. Therefore it is important to select QTL targets for MAS on the basis of as much information as possible. The primary considerations in selection of target QTLs are (i) their likely direct (on the target trait) and indirect (on other traits) effects, and (ii) the expected stability of expression of the QTL across environments. The likely direct effects of selection for the favorable allele (in a homozygous condition) at a target QTL can be estimated as twice the additive effect of the allele, as determined from the analysis of the mapping population data. The QTL effect was estimated for both the main and co-mapped traits affected by each QTL, for the unstressed environment, the combined drought-stressed environments, and for all environments (Table 5). The expected stability across environments was assessed from the presence and nature of Q x E interactions in multi-environment QTL analyses (Table 4).
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The second target QTL for MAS would probably be the ICMB 841 allele at either of the GRYLD QTLs on LG 3 (linked to marker Xpsm108) or LG 4 (linked to marker Xpsm1003d), both of which have a significant effect on across-environment GRYLD (Table 5). Despite their being detected on the basis of across-environment effects, both appear to be primarily drought-tolerance QTLs, as the only individual environment in which they had a significant effect on GRYLD was the late-stress environment (Table 4). Selection for the ICMB 841 allele at LG 3 has a predicted effect of increasing GRYLD by 7.4 g m–2 across environments, as well as under moderate stress, and of increasing HI and PNHI by between 1.6 and 2% (in absolute terms) both across environments and in all individual environments (Table 5). Selection for the ICMB 841 allele at LG 4 had predicted effects of increasing GRYLD by 10 g m–2 across environments and by up to 9.2 g m–2 in the moderate-stress environment, but was subject to a significant Q x E interaction (Table 5). As both the LG 3 and LG 4 alleles have generally similar predicted effects on grain yield across environments, the choice comes down to the improvement in either individual environments, individual traits, or the expected Q x E interaction. All of these factors favor the LG 3 QTL. It has a predicted favorable effect on both HI and PNHI across environments, favorable effects on several traits in the stress-free environment, and nonsignificant Q x E interaction, none of which were true for the LG 4 QTL (Tables 4 and 5).
Secondary Quantitative Trait Loci
There were several additional strong QTLs for across-environment traits that did not have significant or direct effects on GRYLD in the test environments, despite the positive correlations of grain yield and all of the traits for which QTLs were identified (Table 3). The first of such QTLs is the GRMA and PNHI QTL on LG 1, with a favorable allele from 863B. Marker-assisted selection for this allele at this QTL predicted an increase in both traits across environments, as well as in most individual environments (Table 5); however, its effect on GRYLD was not consistent among individual environments. Its LOD score (2.4) for GRYLD was just below the minimum necessary to be considered as effective across environments, and its Q x E interaction would have been significant had it been identified across environments. (The reason is probably the offsetting effects of the alternate 863B and ICMB 841 alleles at this locus, the former of which had a beneficial effect on GRMA and latter a beneficial effect on GRNO). Therefore this QTL would be less useful for MAS than the primary QTLs above.
The next of the secondary QTLs is the strong across-environment HI QTL on LG 7 (linked to marker Xpsm717), with the favorable allele from 863B. The predicted effect of selection for this QTL ranged from a gain of 2.6 to 3.0% in HI (in absolute terms) in individual environments and 2.4% across moisture environments, and an increase GRYLD by 8 g m–2 in the severe-stress environment, where there was a strong correlation of HI and grain yield (Table 3). This makes this QTL of potential interest for strengthening adaptation to more serious stress environments (i.e., for improving drought tolerance). The third of the secondary QTL is the relatively strong PNHI QTL on LG 6 (linked to marker Xpsm588), with the favorable allele again from 863B. Because of the large predicted gain in PNHI from MAS for this QTL (Table 5), it may have value for the improvement of partitioning to grain under terminal stress, without a cost in stress-free environments; however, this QTL would be of less interest for improving GRYLD across a range of environments.
Two other criteria need to be considered in choosing among potential target QTLs for MAS: (i) the availability of easily scored marker polymorphism at loci flanking the QTL; and (ii) confidence in the QTL (related both to the complexity of genetic and environmental factors controlling the trait and to the power of the QTL detection experiment [mapping population size, marker numbers, and marker distribution]). Both of the primary QTLs (LG 2 and LG 3) are acceptable on both criteria. Genomic Region 2 (LG 2 for GRYLD, HI, and PNHI) has several closely linked markers (Xpsm322–Xpsmp2050, spanning the length of 14 cM) and a high level (as much as 0.92) of polymorphic information content (PIC) for the microsatellite markers (Qi et al., 2004), making this region very amenable to high-throughput genotyping. The LG 3 GRYLD, GRMA, HI, and PNHI QTL has linked marker Xpsm18, which is an RFLP, but it maps to almost the same position as SSR locus Xpsmp2070 (PIC of 0.90), so again this QTL also should be very amenable to MAS.
The pearl millet map is being constantly updated with newly developed SSR, expressed sequence tag SSR, single-strand conformational polymorphism–single nucleotide polymorphism, conserved-intron scanning primers, and target region amplified polymorphism based markers (C.T. Hash, personal communication, 2006). As a consequence, highly polymorphic polymerase chain reaction compatible markers will soon flank most of the RFLP markers that now anchor the pearl millet linkage map. Use of these newly added markers will reduce costs and time needed to exploit QTLs in targeted MAS programs for variable grain-filling moisture conditions.
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CONCLUSIONS |
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TOPNOTESABSTRACTINTRODUCTIONMATERIALS AND METHODSRESULTS AND DISCUSSIONCONCLUSIONSREFERENCES |
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Three QTLs (on LG 2, LG 3, and LG 4) were identified as primary candidates for MAS for improved grain yield across variable postflowering moisture environments. The most promising of the three, those on LG 2 (863B allele) and LG 3 (ICMB 841 allele) explained a useful proportion (25 and 13%, respectively) of phenotypic variance for GRYLD across environments (Table 5). They also co-mapped with QTLs for HI across environments, and with QTLs for GRMA, PNHI, and HI under severe terminal stress. Neither had a significant Q x E interaction, meaning that their predicted effects should occur across a broad range of available moisture environments. Finally, both are linked to SSR markers so they are amenable to efficient MAS. The remaining QTL (LG 4) is of secondary interest as it has a less consistent performance across individual moisture environments and a less clear effect on secondary traits. Responses to MAS predicted for each of the identified GRYLD QTLs ranged from 7 to 10 g m–2 (70–100 kg ha–1) across environments and as much 16 g m–2 in individual moisture environments. Finally, this study has clarified the action and utility of several of the QTLs identified in an initial analysis of a subset (four of the 12 environments) of this data set (Yadav et al., 2004).
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ACKNOWLEDGMENTS |
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NOTES |
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TOPNOTESABSTRACTINTRODUCTIONMATERIALS AND METHODSRESULTS AND DISCUSSIONCONCLUSIONSREFERENCES |
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Received for publication July 15, 2006.
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REFERENCES |
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TOPNOTESABSTRACTINTRODUCTIONMATERIALS AND METHODSRESULTS AND DISCUSSIONCONCLUSIONSREFERENCES |
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| HOME | HELP | FEEDBACK | SUBSCRIPTIONS | ARCHIVE | SEARCH | TABLE OF CONTENTS |
| The SCI Journals | Agronomy Journal | Vadose Zone Journal | |||
| Journal of Natural Resources and Life Sciences Education |
Soil Science Society of America Journal | ||||
| Journal of Plant Registrations | Journal of Environmental Quality |
The Plant Genome | |||
