Quantitative Trait Loci Alleles Research Articles

QTL analysis of Fusarium head blight resistance using a high-density linkage map in barley

Fusarium head blight (FHB) resistance was evaluated in a set of recombinant inbred (RI) lines from a cross between Russia 6 (resistant) and H.E.S. 4 (susceptible), which had one of the widest differences of FHB resistance reactions among ca. 5,000 barley germplasm accessions in Okayama University. Field-grown spikes were sampled and inoculated by the 'cut-spike test'. Resistance reactions on the parents and RI lines were scored by eleven grades, from resistant (0) to susceptible (10). Quantitative trait loci (QTL) analysis detected three QTL: two located on the long arm of chromosome 2H, and another on the short arm of chromosome 5H. A QTL located on chromosome 2H was coincident with the vrs1 locus, which governs inflorescence row type. The other QTL on chromosome 2H was positioned in the vicinity of cleistogamy locus (cly1 or Cly2) that determines inflorescence opening/closing. Resistant gene analog (RGA) and expressed sequence tag (EST) markers with homology for disease resistance genes were integrated into the high-density linkage map. Most of these markers were not localized near the identified resistance QTL, except for one RGA marker (FXLRRfor_XLRRrev170) localized in the vicinity of the cly1/Cly2 locus. Five AFLP markers localized in the vicinity of the identified QTL were sequenced to convert them into sequence tagged site (STS) markers. Genotyping of each RI line using two AFLP-STS markers and the vrs1 locus indicated that the RI lines with three Russia 6 QTL alleles exhibited the same level of high FHB resistance reactions as Russia 6. In contrast, RI lines with three susceptible alleles showed reactions close to H.E.S. 4. Therefore, the markers closely linked to the QTL can be efficiently used for the selection of resistance.

Effect of population size on the estimation of QTL: a test using resistance to barley stripe rust

The limited population sizes used in many quantitative trait locus (QTL) detection experiments can lead to underestimation of QTL number, overestimation of QTL effects, and failure to quantify QTL interactions. We used the barley/barley stripe rust pathosystem to evaluate the effect of population size on the estimation of QTL parameters. We generated a large (n = 409) population of doubled haploid lines derived from the cross of two inbred lines, BCD47 and Baronesse. This population was evaluated for barley stripe rust severity in the Toluca Valley, Mexico, and in Washington State, USA, under field conditions. BCD47 was the principal donor of resistance QTL alleles, but the susceptible parent also contributed some resistance alleles. The major QTL, located on the long arm of chromosome 4H, close to the Mlo gene, accounted for up to 34% of the phenotypic variance. Subpopulations of different sizes were generated using three methods-resampling, selective genotyping, and selective phenotyping-to evaluate the effect of population size on the estimation of QTL parameters. In all cases, the number of QTL detected increased with population size. QTL with large effects were detected even in small populations, but QTL with small effects were detected only by increasing population size. Selective genotyping and/or selective phenotyping approaches could be effective strategies for reducing the costs associated with conducting QTL analysis in large populations. The method of choice will depend on the relative costs of genotyping versus phenotyping.

Mapping of a major QTL for pre-harvest sprouting tolerance on chromosome 3A in bread wheat

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 inter-varietal 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.

QTL analysis of resistance to Fusarium head blight in wheat using a 'Wangshuibai'-derived population

Fusarium head blight (FHB) is a devastating disease that reduces the yield, quality and economic value of wheat. For quantitative trait loci (QTL) analysis of resistance to FHB, F 3 plants and F 3:5 lines, derived from a 'Wangshuibai' (resistant)/'Seri82' (susceptible) cross, were spray inoculated during 2001 and 2002, respectively. Artificial inoculation was carried out under field conditions. Of 420 markers, 258 amplified fragment length polymorphism and 39 simple sequence repeat (SSR) markers were mapped and yielded 44 linkage groups covering a total genetic distance of 2554 cM. QTL analysis was based on the constructed linkage map and area under the disease progress curve. The analyses revealed a QTL in the map interval Xgwm533-Xs18/m12 on chromosome 3BS accounting for up to 17% of the phenotypic variation. In addition, a QTL was detected in the map interval Xgwm539-Xs15/m24 on chromosome 2DL explaining up to 11% of the phenotypic variation. The QTL alleles originated from 'Wangshuibai' and were lagged with SSR markers. Using these SSR markers would facilitate marker-assisted selection to improve FHB resistance in wheat.

Time-related mapping of quantitative trait loci controlling grain-filling in rice (Oryza sativa L.)

Grain-filling is a crucial process that determines final grain yield in rice (Oryza sativa L.). To understand the genetic basis of dynamics of grain-filling, quantitative trait locus (QTL) analysis was conducted using time-related phenotypic data on grain-filling collected from a population of 155 recombinant inbred lines (F12), derived from a cross between Milyang 23 and Akihikari. Two QTLs detected on chromosomes 8 and 12 were strongly associated with increased filling percentage per panicle. These QTLs were not linked with those controlling spikelet numbers per panicle. This result confers the possibility of improving grain-filling together with an enlargement of sink size. The QTL for filling percentage per panicle on chromosome 8 exactly overlapped that for non-structural carbohydrate (NSC) content in the culm and leaf sheaths during grain-filling, and the Milyang 23 allele associated with increased grain-filling percentage per panicle was associated with decreased NSC content. Therefore, this QTL may be directly involved in NSC translocation from the culm and leaf sheaths to panicle. In addition, the Milyang 23 alleles of QTLs associated with greater spikelet number per panicle on chromosomes 1 and 6 were also related with a reduction in NSC content in the culm and leaf sheaths during grain-filling. These results indicate that NSC dynamics during grain-filling is partly dependent on sink size. NSC accumulation in the culm and leaf sheaths at the heading stage was mainly controlled by different genetic regulations from NSC dynamics during grain-filling. Nitrogen dynamics during grain-filling may also be involved in carbohydrate dynamics.

Molecular mapping of Fusarium head blight resistance in the winter wheat population Dream/Lynx

Fusarium head blight (FHB), mainly caused by Fusarium graminearum and F. culmorum, can significantly reduce the grain quality of wheat (Triticum aestivum L.) due to mycotoxin contamination. The objective of this study was to identify quantitative trait loci (QTLs) for FHB resistance in a winter wheat population developed by crossing the resistant German cultivar Dream with the susceptible British cultivar Lynx. A total of 145 recombinant inbred lines (RILs) were evaluated following spray inoculation with a F. culmorum suspension in field trials in 2002 in four environments across Germany. Based on amplified fragment length polymorphism and simple sequence repeat marker data, a 1,734 cM linkage map was established assuming that the majority of the polymorphic parts of the genome were covered. The area under disease progress curve (AUDPC) was calculated based on the visually scored FHB symptoms. The population segregated quantitatively for FHB severity. Composite interval mapping analysis for means across the environments identified four FHB resistance QTLs on chromosomes 6AL, 1B, 2BL and 7BS. Individually the QTLs explained 19%, 12%, 11% and 21% of the phenotypic variance, respectively, and together accounted for 41%. The QTL alleles conferring resistance on 6AL, 2BL and 7BS originated from cv. Dream. The resistance QTL on chromosome 6AL partly overlapped with a QTL for plant height. The FHB resistance QTL on 7BS coincided with a QTL for heading date, but the additive effect on heading date was of minor importance. The resistance QTL on chromosome 1B was associated with the T1BL.1RS wheat-rye translocation of Lynx.

Experimental designs for reliable detection of linkage disequilibrium in unstructured random population association studies.

A method is given for design of experiments to detect associations (linkage disequilibrium) in a random population between a marker and a quantitative trait locus (QTL), or gene, with a given strength of evidence, as defined by the Bayes factor. Using a version of the Bayes factor that can be linked to the value of an F-statistic with an existing deterministic power calculation makes it possible to rapidly evaluate a comprehensive range of scenarios, demonstrating the feasibility, or otherwise, of detecting genes of small effect. The Bayes factor is advocated for use in determining optimal strategies for selecting candidate genes for further testing or applications. The prospects for fine-scale mapping of QTL are reevaluated in this framework. We show that large sample sizes are needed to detect small-effect genes with a respectable-sized Bayes factor, and to have good power to detect a QTL allele at low frequency it is necessary to have a marker with similar allele frequency near the gene.

Survival Analysis of Life Span Quantitative Trait Loci in Drosophila melanogaster

We used quantitative trait loci (QTL) mapping to evaluate the age specificity of naturally segregating alleles affecting life span. Estimates of age-specific mortality rates were obtained from observing 51,778 mated males and females from a panel of 144 recombinant inbred lines (RILs). Twenty-five QTL were found, having 80 significant effects on life span and weekly mortality rates. Generation of RILs from heterozygous parents enabled us to contrast effects of QTL alleles with the means of RIL populations. Most of the low-frequency alleles increased mortality, especially at younger ages. Two QTL had negatively correlated effects on mortality at different ages, while the remainder were positively correlated. Chromosomal positions of QTL were roughly concordant with estimates from other mapping populations. Our findings are broadly consistent with a mix of transient deleterious mutations and a few polymorphisms maintained by balancing selection, which together contribute to standing genetic variation in life span.

Combined line-cross and half-sib QTL analysis of crosses between outbred lines

Data from an F 2 cross between breeds of livestock are typically analysed by least squares line-cross or half-sib models to detect quantitative trait loci (QTL) that differ between or segregate within breeds. These models can also be combined to increase power to detect QTL, while maintaining the computational efficiency of least squares. Tests between models allow QTL to be characterized into those that are fixed (LC QTL), or segregating at similar (HS QTL) or different (CB QTL) frequencies in parental breeds. To evaluate power of the combined model, data wih various differences in QTL allele frequencies (FD) between parental breeds were simulated. Use of all models increased power to detect QTL. The line-cross model was the most powerful model to detect QTL for FD>0.6. The combined and half-sib models had similar power for FD<0.4. The proportion of detected QTL declared as LC QTL decreased with FD. The opposite was observed for HS QTL. The proportion of CB QTL decreased as FD deviated from 0.5. Accuracy of map position tended to be greatest for CB QTL. Models were applied to a cross of Berkshire and Yorkshire pig breeds and revealed 160 (40) QTL at the 5% chromosome (genome)-wise level for the 39 growth, carcass composition and quality traits, of which 72, 54, and 34 were declared as LC, HS and CB QTL. Fourteen CB QTL were detected only by the combined model. Thus, the combined model can increase power to detect QTL and mapping accuracy and enable characterization of QTL that segregate within breeds.

A comparison of regression interval mapping and multiple interval mapping for linked QTL

Regression interval mapping and multiple interval mapping are compared with regard to mapping linked quantitative trait loci (QTL) in inbred-line cross experiments. For that purpose, a simulation study was performed using genetic models with two linked QTL. Data were simulated for F(2) populations of different sizes and with all QTL and marker alleles fixed for alternative alleles in the parental lines. The criteria for comparison are power of QTL identification and the accuracy of the QTL position and effect estimates. Further, the estimates of the relative QTL variance are assessed. There are distinct differences in the QTL position estimates between the two methods. Multiple interval mapping tends to be more powerful as compared to regression interval mapping. Multiple interval mapping further leads to more accurate QTL position and QTL effect estimates. The superiority increased with wider marker intervals and larger population sizes. If QTL are in repulsion, the differences between the two methods are very pronounced. For both methods, the reduction of the marker interval size from 10 to 5 cM increases power and greatly improves QTL parameter estimates. This contrasts with findings in the literature for single QTL scenarios, where a marker density of 10 cM is generally considered as sufficient. The use of standard (asymptotic) statistical theory for the computation of the standard errors of the QTL position and effect estimates proves to give much too optimistic standard errors for regression interval mapping as well as for multiple interval mapping.

Stability of QTLs for rice grain dimension and endosperm chalkiness characteristics across eight environments

Rice appearance quality, including traits specifying grain dimension and endosperm chalkiness, represents a major problem in many rice-producing areas of the world. In this study, the genetic basis of six appearance quality traits of milled rice was dissected into quantitative trait loci (QTL) main effects, and the stability of these QTLs was assessed in a population of 66 chromosome segment substitution lines (CSSLs) across eight environments. The CSSLs showed transgressive segregation for many of the traits, and significant correlations were detected among most of the traits. Twenty-two QTLs were identified on eight chromosomes, and numerous QTLs affecting related traits were mapped in the same regions, probably reflecting pleiotropic effects. Nine QTLs, namely qGL-1,qGL-3, qGW-5,qLWR-3, qLWR-5,qPGWC-8, qPGWC-9, qACE-8, and qDEC-8, were consistently detected across the eight environments. The additive main effect and multiplicative interaction (AMMI) analysis showed that genotype (G) x environment (E) interaction was significant for all six traits, with the first three iPCA terms accounting for over 80% of the G x E variance. Both D(I) values and the iPCA1-iPCA2 biplots showed that the CSSLs harboring the nine QTL alleles were more stable than those carrying any of the additional 13 QTL alleles, thereby confirming their environmental stability and pointing to their appropriateness as targets for marker-assisted selection for high-quality rice varieties.

Regulatory Variation at Glypican-3 Underlies a Major Growth QTL in Mice

The genetic basis of variation in complex traits remains poorly understood, and few genes underlying variation have been identified. Previous work identified a quantitative trait locus (QTL) responsible for much of the response to selection on growth in mice, effecting a change in body mass of approximately 20%. By fine-mapping, we have resolved the location of this QTL to a 660-kb region containing only two genes of known function, Gpc3 and Gpc4, and two other putative genes of unknown function. There are no non-synonymous polymorphisms in any of these genes, indicating that the QTL affects gene regulation. Mice carrying the high-growth QTL allele have approximately 15% lower Gpc3 mRNA expression in kidney and liver, whereas expression differences at Gpc4 are non-significant. Expression profiles of the two other genes within the region are inconsistent with a factor responsible for a general effect on growth. Polymorphisms in the 3′ untranslated region of Gpc3 are strong candidates for the causal sequence variation. Gpc3 loss-of-function mutations in humans and mice cause overgrowth and developmental abnormalities. However, no deleterious side-effects were detected in our mice, indicating that genes involved in Mendelian diseases also contribute to complex trait variation. Furthermore, these findings show that small changes in gene expression can have substantial phenotypic effects.

A quantitative trait locus for aortic smooth muscle cell number acting independently of blood pressure: implicating the angiotensin receptor AT1B gene as a candidate

Vascular hyperplasia may be involved in the remodeling of vasculature. It was unknown whether there were genetic determinants for aortic smooth muscle cell number (SMCN) and, if so, whether they acted independently of those for blood pressure (BP). To unravel this issue, we utilized congenic strains previously constructed for BP studies. These strains were made by replacing various chromosome 2 segments of the Dahl salt-sensitive (S) rat with those of the Milan normotensive rat (MNS). We measured and compared SMCN in aortic cross-sectional areas and BPs of these strains. Consequently, a quantitative trait locus (QTL) for SMCN was localized to a chromosome region not containing a BP QTL, but harboring the locus for the angiotensin II receptor AT1B (Agtr1b). Agtr1b became a candidate for the SMCN QTL because 1) two significant mutations were found in the coding region between S and all congenic strains possessing the MNS alleles, and 2) contractile responses to angiotensin II were significantly and selectively reduced in congenic rats harboring the MNS alleles of the SMCN QTL compared with S rats. The current investigation presents the first line of evidence that a QTL for aortic SMCN exists, and it acts independently of QTLs for BP. The relevant congenic strains developed therein potentially provide novel mammalian models for the studies of vascular remodeling disorders.

Quantitative trait loci controlling root growth and architecture in Arabidopsis thaliana confirmed by heterogeneous inbred family

Arabidopsis thaliana provides a scientifically attractive and simple model for studying root growth and architecture and, subsequently, for discovering new genes involved in the control of these characters in plants. We have used the natural variation available in Arabidopsis accessions and mapped quantitative trait loci (QTLs) for primary root length (PRL), lateral root number (LRN) and density (LRD) and for total length of the lateral root system (LRL) in the Bay-0 x Shahdara population. Total phenotypic variation was very large, and despite the importance of the environmental component we were able to map 13 QTLs and one epistatic interaction between QTLs. Our results highlight the biological relevance and genetic control of lateral root density in this material. We were also able to show that variation in the extent of the lateral root system depends mainly on the growth of the existing lateral roots rather than in a change in LRN. Factors controlling lateral root growth seemed to have no major effect on primary root growth. Moreover, Shahdara QTL alleles always increased the length of the lateral roots, which may be taken as an adaptation to its very dry natural environment in Tadjikistan. A QTL for PRL was confirmed using a type of near-isogenic line called a heterogeneous inbred family (HIF), and this QTL is a candidate for further fine-mapping and cloning.

A biplot approach for investigating QTL-by-environment patterns

Due to the universal presence of genotype by environment interactions, understanding the pattern of quantitative trait loci (QTL)-by-environment interactions is a prerequisite for effective marker-assisted selection. In this report, we describe a biplot approach for investigating QTL-by-environment patterns. This approach involves two steps. It starts with a two-way table containing effects of individual QTLs in individual environments for the trait under investigation. This table is decomposed into principal components via singular value decomposition, and the first two principal components are plotted for both QTLs and environments to form a biplot. The resulting ‘QQE biplot’ contains information on QTL main effects (Q) and QTL-by-environment interactions (QE). A QQE biplot displays the QTL-by-environment patterns and allows visualization of: (1) the magnitude of the effect of a QTL, (2) the average effect of a QTL and its stability across environments, (3) the effects of a QTL in individual environments, (4) the similarity/dissimilarity among QTLs in effect and response to the environments, (5) the similarity/dissimilarity among environments in modulating QTL effects, (6) any differentiation of mega-environments, and (7) the combination of QTL alleles for maximum/minimum expression of the trait for each environment or mega-environment. A case study is provided using the QTL-by-environment two-way table for barley yield.

The use of linkage disequilibrium to map quantitative trait loci

This paper reviews the causes of linkage disequilibrium and its use in mapping quantitative trait loci. The many causes of linkage disequilibrium can be understood as due to similarity in the coalescence tree of different loci. Consideration of the way this comes about allows us to divide linkage disequilibrium into 2 types: linkage disequilibrium between any 2 loci, even if they are unlinked, caused by variation in the relatedness of pairs of animals; and linkage disequilibrium due to the inheritance of chromosome segments that are identical by descent from a common ancestor. The extent of linkage disequilibrium due to the latter cause can be logically measured by the chromosome segment homozygosity which is the probability that chromosome segments taken at random from the population are identical by descent. This latter cause of linkage disequilibrium allows us to map quantitative trait loci to chromosome regions. The former cause of linkage disequilibrium can cause artefactual quantitative trait loci at any position in the genome. These artefacts can be avoided by fitting the relatedness of animals in the statistical model used to map quantitative trait loci. In the future it may be convenient to estimate this degree of relatedness between individuals from markers covering the whole genome. The statistical model for mapping quantitative trait loci also requires us to estimate the probability that 2 animals share quantitative trait loci alleles at a particular position because they have inherited a chromosome segment containing the quantitative trait loci identical by descent. Current methods to do this all involve approximations. Methods based on concepts of coalescence and chromosome segment homozygosity are useful, but improvements are needed for practical analysis of large datasets. Once these probabilities are estimated they can be used in flexible linear models that conveniently combine linkage and linkage disequilibrium information.

Interval Mapping of Quantitative Trait Loci for Resistance to Late Blight [Phytophthora infestans (Mont.) de Bary], Height and Maturity in a Tetraploid Population of Potato (Solanum tuberosum subsp. tuberosum)

Interval mapping of quantitative trait loci (QTL) for resistance to late blight, height, and maturity was performed on a tetraploid full-sib family of potato comprising 227 clones from a cross between a susceptible parent, 12601ab1, and a resistant cultivar, Stirling, which were of similar height and main crop maturity. Thirty-eight AFLP primer combinations provided 585 informative markers, and 23 SSRs proved useful for identifying linkage groups (LGs). A simplex QTL allele was found on LGV of Stirling close to marker STM3179, which was associated with early maturity, short plants, and susceptibility to blight and explained 54.7, 26.5, 26.3, and 17.5% of the variation for maturity, height, tuber blight, and foliage blight. When the residuals from the regressions of foliage and tuber blight on maturity were analyzed, there was no significant effect of a QTL on LGV, but a duplex QTL allele for resistance was found on LGIV of Stirling, which explained 30.7 and 13.6% of the variation for foliage and tuber blight on an additive model. Partial dominance for resistance explained even more of the variation, up to 37.2% for foliage blight. A major gene for blight resistance in Stirling was also mapped to LGXI.

Genotype-assisted optimum contribution selection to maximize selection response over a specified time period

Genotype-assisted selection (GAS), i.e. selection for an identified quantitative trait locus (QTL) and polygenic background genes, has been shown to increase short-term genetic gain but may reduce long-term genetic gains. In order to avoid this reduction of long-term gain, multi-generation optimization of truncation selection schemes is needed. This paper presents a multi-generation optimization of optimum contribution (OC) selection with selection on an identified QTL. This genotype-assisted optimum contribution (GAOC) selection method assumes that the optimum selection differential at the QTL is constant over the time horizon, and achieves this by controlling the increase of the frequency of the positive QTL allele. Implementation was straightforward by an additional linear restriction in the OC algorithm. GAOC achieved 35.2%, 2.3% and 1.1%, respectively, more cumulative genetic gain than OC selection (ignoring the QTL) using time horizons of 5, 10 and 15 generations. When one-generation optimization of GAS was used instead of multi-generation optimization, these figures were 2.8%, 3.1% and 3.2%, respectively. Simulated annealing was used to optimize the increases of the frequency of the positive QTL allele in order to test the optimality of GAOC. This latter resulted in genetic gains that were always within 0.4% of those of GAOC. In practice, short-term genetic gains are also important, which makes one-generation optimization of genetic gain closer to optimal.

An analytical formula to estimate confidence interval of QTL location with a saturated genetic map as a function of experimental design.

Analytical formulae are derived for the confidence interval for location of a quantitative trait locus (QTL) using a saturated genetic map, as a function of the experimental design, the QTL allele substitution effect, and the number of individuals genotyped and phenotyped. The formulae are derived assuming evenly spaced recombination events, rather than the actual unevenly spaced distribution. The formulae are useful for determining desired sample size when designing a wide variety of QTL mapping experiments, and for evaluating a priori the potential of a given mapping population for defining the location of a QTL. The formulae do not take into account the finite number of recombination events in a given sample.

Mapping As You Go

The advent of high throughput molecular technologies has led to an expectation that breeding programs will use marker–trait associations to conduct marker‐assisted selection (MAS) for traits. Many challenges exist with this molecular breeding approach for so‐called complex traits. A major restriction to date has been the limited ability to detect and quantify marker–trait relationships, especially for traits influenced by the effects of gene‐by‐gene and gene‐by‐environment interactions. A further complication has been that estimates of quantitative trait loci (QTL) effects are biased by the necessity of working with a limited set of genotypes in a limited set of environments, and hence the applications of these estimates are not as effective as expected when used more broadly within a breeding program. The approach considered in this paper, referred to as the Mapping As You Go (MAYG) approach, continually revises estimates of QTL allele effects by remapping new elite germplasm generated over cycles of selection, thus ensuring that QTL estimates remain relevant to the current set of germplasm in the breeding program. Mapping As You Go is a mapping‐MAS strategy that explicitly recognizes that alleles of QTL for complex traits can have different values as the current breeding material changes with time. Simulation was used to investigate the effectiveness of the MAYG approach applied to complex traits. The results indicated that greater levels of response were achieved and these responses were less variable when estimates were revised frequently compared with situations where estimates were revised infrequently or not at all.