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Published online 22 January 2007
Published in Crop Sci 47:200-206 (2007)
© 2007 Crop Science Society of America
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GENOMICS, MOLECULAR GENETICS & BIOTECHNOLOGY

Validating the Fhb1 QTL for Fusarium Head Blight Resistance in Near-Isogenic Wheat Lines Developed from Breeding Populations

Michael O. Pumphrey, Rex Bernardo and James A. Anderson*

Dep. of Agronomy and Plant Genetics, Univ. of Minnesota, 411 Borlaug Hall, 1991 Buford Cir., St. Paul, MN 55108. M.O. Pumphrey, current address: Dep. of Plant Pathology, Kansas State Univ., Manhattan, KS 66506

* Corresponding author (ander319{at}umn.edu)


   ABSTRACT
TOP
 ABSTRACT
INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 REFERENCES
 
Quantitative trait loci (QTLs) have been identified for numerous species since the 1990s using populations developed from biparental crosses. The most common methods of validating QTLs are to quantify their effects in additional mapping populations or test near-isogenic lines (NILs) developed from the original mapping population. These approaches to QTL validation fail to adequately examine the effectiveness of a QTL in breeders' populations. We have developed an alternative QTL validation method in which NILs are developed from existing breeding populations segregating for the QTL. Our objective was to validate this method using Fhb1, a major Fusarium head blight [FHB; causal agent Fusarium graminearum (Schwabe)] resistance QTL in wheat (Triticum aestivum L.). Microsatellite markers flanking the QTL region were used to develop 19 QTL-NIL pairs by sampling F3:4 families from 13 different populations. Each pair was tested in a greenhouse point-inoculation experiment and four field FHB resistance screening nurseries. Near-isogenic lines with the Fhb1 resistance allele had significant (P < 0.001) average reductions of 23% for disease severity ratings and 27% for infected kernels in harvested grain. Disease spread for 9 out of 19 total pairs was significantly (P < 0.05) lower in greenhouse point-inoculation experiments when the Fhb1 resistance allele was present. The QTL validation methodology employed in this study should be broadly applicable to other quantitative traits and plant species.

Abbreviations: NILs, near-isogenic lines • QTLs, quantitative trait loci


   INTRODUCTION
TOP
 ABSTRACT
 INTRODUCTION
MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 REFERENCES
 
THE DISCOVERY of promising QTLs for a trait of interest is an important, but preliminary step in developing a marker-assisted selection program for genetic improvement. Ideally, the identified QTLs should be validated in additional genetic backgrounds and environments and should not have undesirable effects on other important traits. The most common means of QTL validation is the analysis of additional segregating populations to confirm QTL position and phenotypic contribution to the trait of interest. However, the time required to develop, genotype, and adequately phenotype the validation populations represents a substantial investment and slows the application of marker information to cultivar enhancement. Estimates of phenotypic effects attributed to QTLs in a mapping or validation population are specific to the populations in which they were studied and may be significantly overestimated (Beavis, 1998).

Approaches utilizing NILs to accelerate QTL discovery, validation, and germplasm improvement have been proposed (Paterson et al., 1990; Kaeppler et al., 1993; Tuinstra et al., 1997). Near-isogenic lines are particularly effective genetic stocks for studying phenotypic effects attributable to a QTL since the genetic background, including morphological and phenological characters that commonly influence phenotypic assessments of quantitative traits, is standardized. Resulting germplasm may be very useful for assessing phenotypic effects of specific donor loci and potentially serve as genetic material for QTL localization. By essentially fixing the genetic background, NILs are ideal for construction of high-resolution mapping populations, gene expression profiling, and more direct hypothesis-driven biological experimentation. Backcross-based methods for using NILs to merge QTL discovery, validation, and germplasm improvement also have been reported (Tanksley and Nelson, 1996; Stuber et al., 1999).

Heterogeneous inbred family analysis was proposed as a method to quickly develop NILs for an identified QTL in inbred lines (Tuinstra et al., 1997). In this method, heterogeneous recombinant inbred lines from a single QTL mapping population are screened with trait-associated DNA markers to obtain homozygous NILs for QTLs of interest. Although NILs developed from heterogeneous lines in a QTL mapping population provide a means to test phenotypic effects of a QTL per se, the results are still limited to the specific mapping population the NILs were developed from. If population size is limited and inbreeding has been performed to advanced generations (F6 and later), which is often the case for populations used in QTL mapping studies, the number of lines for which QTL-NILs can be extracted may be limited. Because genetic background may influence the penetrance and expressivity of a QTL, a larger number of NILs may be beneficial to identify materials suitable for further experimentation.

A major QTL, Fhb1 (syn. Qfhs.ndsu-3BS), for FHB resistance in wheat has been identified from the cultivar ‘Sumai 3’. Microsatellite markers linked to this QTL on the short arm of chromosome 3B account for 20–60% of the phenotypic variation in resistance across populations (Waldron et al., 1999; Anderson et al., 2001; Buerstmayr et al., 2002, 2003; Zhou et al., 2002). The consistency with which this QTL is detected and the magnitude of phenotypic effects in each mapping population imply that it should be useful for marker-assisted selection. Additional validation of Fhb1 was conducted by characterizing four validation populations where a cultivar carrying the resistance allele of Fhb1 was crossed with a susceptible cultivar (Yang et al., 2003; Zhou et al., 2003). Recent high-resolution mapping has confirmed the position of Fhb1 in an approximately 5-cM interval (Liu et al., 2006) between XBARC133 (Song et al., 2005) and Xgwm493 (Röder et al., 1998). The extensive validation of Fhb1 in segregating populations made it well suited for our objective, which was to test a QTL validation methodology in which NILs are developed from existing breeding populations segregating for the QTL.


   MATERIALS AND METHODS
TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
RESULTS
 DISCUSSION
 REFERENCES
 
Near-Isogenic Line Development
Many populations from University of Minnesota wheat breeding nurseries were amenable to QTL-NIL development because lines derived from Sumai 3, which carries a resistance allele at Fhb1, had been extensively used in crosses in the 1990s in an effort to develop FHB-resistant cultivars for production in the Upper Midwest of the United States. From summer breeding nurseries at St. Paul, MN, in 2000 and 2001, F3:4 head-rows representing 13 different cross combinations were genotyped with codominant microsatellite markers gwm493, gwm533 (Röder et al., 1998), and barc133 to identify families segregating for Fhb1. Each of the 13 selected populations had an FHB-resistant parent with Sumai 3 in its pedigree and alleles that are unique at the Fhb1 locus as represented by the three SSR markers that bracket this QTL (Liu and Anderson, 2003).

Head-row selection was first conducted based on acceptable plant height, maturity, and leaf rust (caused by Puccinia triticina Eriks.) and stem rust (caused by P. graminis Pers.:Pers. f. sp. tritici Eriks. & E. Henn.) resistance. After phenotypic selection, a bulk of leaf tissue from five random plants per head-row from each of approximately 120 head-rows with Sumai 3 parentage was collected and genotyped to identify segregating F3:4 families. Sampling five plants per head-row provided a greater than 99% probability of identifying segregating families. Approximately 25% of these F3–derived lines were expected to be segregating assuming 1:2:1 segregation in the F2 generation and no selection on this locus. Five random plants per segregating family were then harvested, which gave a 97% probability of obtaining single F4 heterozygous plants. The progeny of each plant were then genotyped as a bulk of five seeds to identify single F4 heterozygous plants. Once single heterozygous plants were identified, screening five or more seeds from that plant with the same markers identified homozygous NIL pairs (F4:5 sib lines) with and without the targeted QTL region (Fig. 1 ). Individually genotyping 10 F5 seeds from the F4 heterozygous plant gives a 94% probability of identifying both homozygous classes. Heterozygous F5s were also identified at this stage, which allowed further inbreeding to provide a more uniform genetic background for the NIL testing in some pairs.


Figure 1
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  		Fig. 1. Development of quantitative trait locus–near-isogenic lines (QTL-NILs) for testing QTL effects in diverse genetic backgrounds. In Step 1, heterogeneous F4 lines are identified after genotyping a bulk harvest of F3:4 plants with DNA markers flanking the QTL of interest. In Steps 2 through 5, progeny screening of heterozygous plants produces F4–derived QTL-NILs that are then increased. The + and – symbols indicate the allelic status of the markers for the QTL, with + representing the marker for the allele under selection and – representing the allele not under selection.

 
Genotyping
DNA extraction was performed as described by Liu et al. (2006) with modifications. Five hundred microliters of extraction buffer (Riede and Anderson, 1996) was added to ground tissue and samples were placed into a 65°C water bath for 20 min. Then, 500 µL chloroform/isoamyl (24:1, v/v) solution was added and tubes were mixed vigorously before centrifugation (10 000 x g) for 10 min, and 500 µL of the resulting aqueous phase was transferred to a new 1.5 mL tube and precipitated with 1 mL 95% ethanol. Tubes were then centrifuged (10 000 x g) for 10 min to pellet the DNA, which was then rinsed with 1 mL 70% ethanol and dried before adding 150 µL TE buffer. All DNA samples were diluted 1:10 (v/v) into sterile deionized water for PCR. PCR and gel electrophoresis were performed as described by Liu and Anderson (2003).

Field Resistance Screening
QTL-NIL pairs and check cultivars were evaluated in a split-plot design at the St. Paul, MN, and Crookston, MN, misted FHB-screening nurseries in 2002 and 2003 with two replications per location. Main plots were NIL pairs and subplots were NILs with or without the flanking markers associated with Fhb1. Ten grams of seed from each entry were planted in 2-m rows with 0.3 m between rows. Check cultivars were ‘BacUp’ (moderately resistant) (Busch et al., 1998), ‘Roblin’ (susceptible) (CN 43847), ‘Alsen’ (moderately resistant) (PI 615543), and ‘Wheaton’ (susceptible) (Busch et al., 1984).

At St. Paul, a suspension of macroconidia (1 x 105 macroconidia mL–1) from multiple field isolates of F. graminearum was applied to individual rows at anthesis at a rate of 30 mL m–1. A second application was made 3 d later. Macroconidia inoculum was prepared according to the method of Dill-Macky (2003). The Small Grains Pathology research group at the University of Minnesota provided all inoculum. On inoculation dates, macroconidia were mixed with tap water and Tween 20 followed by application via CO2–pressurized (2.76 x 105 Pa) backpack sprayers equipped with flat fan nozzles (Dill-Macky, 2003). A mist irrigation system was used to maintain moisture from anthesis of earliest maturity germplasm through mid grain-fill of germplasm with the slowest rate of maturity on a schedule of 9 min h–1 from 1730 to 0830 h. Each misting event produced 0.02 mm of water, with a total application of 0.3 mm per night.

At Crookston, maize (Zea mays L.) grain colonized by a mixture of isolates of F. graminearum was used for inoculum, which was produced by staff at the Northwest Research and Outreach Center, Crookston, MN, according to the methods of Dill-Macky (2003). Colonized grain was broadcast evenly by hand throughout the field at a rate of 112 kg ha–1 approximately 2 wk before heading of the earliest material. The mist irrigation system was started before heading and operated on a schedule of 10 min misting every 80 min from 1700 to 0800 h.

At both locations, the number of symptomatic spikelets on 20 arbitrarily selected spikes per row was assessed 18 to 21 d post-inoculation for calculating disease severity (Dill-Macky, 2003). Disease severity was calculated by dividing the number of infected spikelets on a single spike by the total number of spikelets. Thirty spikes were harvested from each row and were carefully threshed to retain diseased kernels. Visually scabby kernel estimates (referred to as scabby kernels in this paper) (Jones and Mirocha, 1999) and 30-spike kernel weight measurements were recorded.

Point-Inoculation Resistance Screening
Point-inoculation assays for resistance to disease spread were conducted in fall 2001 (greenhouse), spring 2002 (greenhouse), and summer 2002 (growth chamber). In each experiment, five replications (pots) with three plants per pot were screened (~15 plants total per entry). Temperature was maintained at 20°C (range 16–27°C) and lighting systems were set for 16 h days. Single florets were inoculated to measure resistance to disease spread in NIL pairs. At anthesis, 10 µL macroconidia (1 x 105 mL–1) (isolate Butte86ADA-11, Evans et al., 2000) were placed into a single central floret. A single spike per plant was inoculated. Plants were incubated in a dew chamber (100% relative humidity, 20°C) for 72 h after inoculation. The number of symptomatic spikelets and total spikelet number were counted at 21 d post-inoculation to calculate disease severity (Dill-Macky, 2003).

Data Analysis
Field experiments were analyzed as a split-plot design using PROC MIXED, SAS v.8.1 (SAS Institute, Cary, NC). The homogeneity of error variances in individual environments was determined by Levene's test (P > 0.05) in the SAS program. QTL allele was specified as a fixed effect, whereas environments, replications within environments, NIL pair, NIL pair x replications within environments, and NIL pair x QTL were specified as random. Type III F-tests were used to test the significance (P = 0.05) of QTL effects against the NIL pair x QTL interaction. Significant QTL effects identified by analyses of variance were further analyzed by separating least square means with t tests. Variance components were estimated using the restricted maximum likelihood (REML) method (Littell et al., 1996). The statistical significance of differences between NILs in greenhouse point-inoculation evaluations was tested by t tests assuming unequal variance between near-isolines. The percentage reduction in disease due to Fhb1 (QTL effect) within an NIL pair was calculated for field and greenhouse experiments by subtracting the Fhb1 near-isoline mean from the non-Fhb1 near-isoline mean and dividing by the non-Fhb1 near-isoline, expressed as a percentage.


   RESULTS
TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
DISCUSSION
 REFERENCES
 
Near-Isogenic Line Development
Since the effects of Fhb1 had to be assessed in multiple genetic backgrounds to provide a robust test for our QTL validation method, the development and results of 19 pairs of NILs, representing the 13 populations, are reported herein (Table 1).


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  		Table 1. Quantitative trait locus–near-isogenic line (QTL-NIL) pairs contrasting for Fhb1, derived generations, SSR marker alleles at Fhb1, Fusarium head blight (FHB) resistance trait means, and QTL effects.

 
Field Resistance Screening
The error variances of the four environments for scabby kernel and kernel weight measurements were homogeneous; however, error variances were not homogeneous for disease severity observations (data not shown). After further analysis, error variances for disease severity were homogeneous between locations and differences were primarily due to changes in magnitude in different environments, as each of the three phenotypes were correlated measures of FHB resistance in our experiments (r = –0.73 between disease severity and kernel weight; r = 0.69 between disease severity and scabby kernels; r = –0.70 between kernel weight and scabby kernels). Therefore, all traits were analyzed across environments.

The Sumai 3 allele of the Fhb1 QTL significantly reduced disease severity (P < 0.001) and scabby kernels (P < 0.001), and increased kernel weight (P < 0.01) across the 19 NIL pairs in the four field screening nurseries (Table 2). The average QTL effect on disease severity due to Fhb1 was 23% across all environments, where near-isolines with the resistance allele averaged 21% disease severity and those without averaged 28%. The average QTL effect on scabby kernels was 27%, with isolines with the resistance allele averaging 13% and those without 18%. The average kernel weight from 30 spikes of NILs containing the resistance allele was 21.1 g compared to 19.5 g in lines with an alternative allele. NIL pairs (different genetic backgrounds) accounted for 30% of the variation in disease severity, 18% in scabby kernels, and 17% in kernel weights (Table 2). The interaction between Fhb1 and NIL pair accounted for 2–7% of the total variation in disease measures (Table 2).


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  		Table 2. Type III tests of Fhb1 effects in near-isogenic line pairs, least square means (mean), and standard error of differences (SE of diff.), and covariance parameter estimates for disease severity, scabby kernels, and kernel weight of 30 spikes in inoculated field nurseries in St. Paul and Crookston, MN, in 2002 and 2003.

 
The resistance allele produced a statistically significant reduction in at least one FHB parameter for 15 of the 19 NIL pairs. For the remaining four pairs, at least three of the four FHB parameters were reduced by the presence of the resistant allele, although none of these responses was statistically significant. A significant QTL effect opposite of expected was observed for only one out of 76 pair-trait combinations measured (Table 1, pair 17 scabby kernels). This is in contrast to 30 significant differences out of 76 with the expected QTL effect.

Point-Inoculation Resistance Screens
In greenhouse assays, 9 out of the 19 Fhb1 NIL pairs had significant reductions (P < 0.05) in disease severity when the resistance allele was present (Table 1). In nonsignificant pairs, the general trend was toward more resistant genotypes with the resistance allele, with an additional six of the 19 NIL pairs having QTL effects ranging from 15 to 29%. The average QTL effect across all 19 pairs was 31%. In no case did the NIL containing an alternative allele have significantly less disease severity, and only pair 8 had a negative, yet nonsignificant QTL effect. Susceptible checks Roblin and Wheaton had greater than 95% disease severity in each point-inoculation experiment, whereas Sumai 3 showed stable resistance with disease severity means from 16 to 28%.


   DISCUSSION
TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
REFERENCES
 
The method we present for developing NILs for a QTL of interest was efficient and effective for validating Fhb1 in experimental breeding materials. The presence of the resistance allele of Fhb1 in NILs consistently reduced symptoms of infection by F. graminearum compared to NILs with alternative alleles, and the reduction in FHB disease parameters when analyzed across 19 NIL pairs in the four field screening nurseries was significant, thus indicating that this is an effective method of QTL validation.

The large effect of NIL pairs observed in field screening nurseries may be expected when sampling NILs from different populations. Similarly, the modest interaction detected between Fhb1 and NIL pairs may be in part explained by the differences in base levels of resistance among NIL pairs. A line with moderate resistance due to the presence of other resistance QTL may not benefit as much by the addition of another resistance locus as a line without other resistance genes. This is evidenced by the field screening results of individual NIL pairs (Table 1), in which there was no significant difference within the two NIL pairs (1 and 18) that had the lowest disease severity and scabby kernel levels when compared to all other entries. Near-isogenic line pairs 1 and 18 likely have additional genes conferring high levels of resistance to initial infection (Type I resistance [Schroeder and Christensen, 1963]), because even the genotype with the non-Fhb1 allele within each of these pairs had very high levels of resistance when compared to all other entries. Alternatively, other NIL pairs could lack effective genes for Type I resistance. When screened under field conditions, where the ultimate resistance of a line is a combination of resistance to initial infection and spread, a low level of Type I resistance in an NIL pair (higher initial infection rate = higher incidence) may mask resistance to disease spread. Although field resistance screening is confounded by the presence of loci that influence initial infection, and therefore may not be the best direct test for the effect of Fhb1, field screening is critical to validate and document that Fhb1 should enhance resistance under field conditions and is a worthwhile molecular breeding investment. Epistatic interactions also may contribute to QTL x NIL pair interaction. The negative QTL effect on kernel traits in pair 17 is likely due to the segregation of other loci influencing these disease parameters or experimental error.

It is problematic to directly compare validation results between this study and those performed on segregating populations. In each of the other mapping studies that identified Fhb1, resistance was measured by directly inoculating a single floret per spike to measure resistance to disease spread, or Type II resistance (Schroeder and Christensen, 1963). Differences in inoculation techniques may isolate different genetic factors that condition resistance. A major QTL on chromosome 5A has been consistently detected in Sumai 3–derived material when screened under conditions measuring resistance to initial infection (Type I), but not disease spread (Buerstmayr et al., 2003). The QTL Fhb1 was detected in the ‘CM-82036’/‘Remus’ QTL mapping population when screened in field conditions by a spray-inoculation technique, although the proportion of phenotypic variation explained by Fhb1 was lower (R2 = 29%) than for the same population screened by point-inoculation methods (R2 = 40–60%) (Buerstmayr et al., 2002, 2003). The amount of disease pressure in screening nurseries also is expected to significantly influence the amount of variation accounted for by the QTL.

Our validation results also confirm that selecting for Fhb1 with molecular markers increases the chances of enhancing FHB resistance in diverse breeding populations, which should increase grain yield under field conditions. We are currently investigating these QTL-NIL pairs for agronomic, grain quality, and other disease reactions to assess the effect of this QTL region on other traits. The QTL-NIL pairs developed during the course of this research, particularly those with large and consistent phenotypic differences, are useful genetic stocks to further investigate FHB resistance. For example, near-isoline pair number 19 (Table 1) was advanced to the F7 generation by selecting for heterozygosity at this QTL region in each generation of selfing. Multiple heterozygous F8 plants were selfed to produce a segregating population of more than 3000 individuals that are currently being used for map-based cloning of this QTL region (Liu et al., 2006).

Depending on the quantitative trait being assessed, the NILs developed with this method may be immediately suitable for phenotyping and validation studies in greenhouse conditions. For FHB resistance screening, where seed production may be severely reduced due to disease, it was necessary to self-pollinate homozygous plants for future replicated testing and seed increase. Therefore, the heterozygous F4 plants identified in one growing season produced NILs ready for replicated testing at multiple locations by the next growing season, with one greenhouse cycle in the fall for identification of homozygotes and one greenhouse cycle in the spring for seed increase. This timeline may require modification depending on cycle time, availability of suitable growth environments, and required seed production. The development of F4–derived NIL materials may not be suitable for all QTL validation studies, especially for QTLs that account for a relatively low amount of variation and/or for traits where numerous QTL are present and segregating in breeding populations. However, the relative ease of identifying heterozygous plants and convenient location of our F4 breeding nursery made selecting at this stage practical for our program. As demonstrated for 10 out of the 19 NIL pairs, additional inbreeding generations (F5–derived and higher) may be readily developed if desired. Data from three FHB nurseries are sufficient to determine a QTL effect (Fuentes-Granados et al., 2005), so the targeted QTL could be validated within 1 yr. Alternatively, validation of a QTL using a second mapping population of F4–derived recombinant inbred lines would require approximately six growing cycles (2 yr) to produce the population and increase seed for testing, followed by multilocation evaluation the following growing season. This effort would be separate from regular breeding activities, would require much greater time and personnel resources, and may not adequately represent the effect of the QTL in breeding-relevant germplasm.

The methodology we present for QTL validation should be broadly applicable to other traits and plant species, the only requirements being: (i) DNA markers that allow for efficient discrimination between breeding parents at a QTL of interest, and (ii) the QTL donor parent or derived lines have been used as parental lines in the breeding program. These criteria are likely to be met in most breeding programs. Advantages of this validation approach are that it does not require additional crosses, space, or time for population development, and only one generation is needed for increasing seed from NIL pairs identified in regular breeding materials. As a result, a larger and more representative sample of breeding populations may be evaluated. Perhaps the greatest advantage of this strategy is that QTL validation and germplasm improvement activities are combined, and QTL effects can be assessed in elite genetic backgrounds that are relevant to breeders.


   ACKNOWLEDGMENTS
 
We are grateful for technical expertise and assistance provided by Dr. Ruth Dill-Macky, Dr. Kent Evans, Amar Elakkad, Karen Wennburg, Galen Thompson, and Gary Linkert in conducting disease-screening experiments. Dr. Sixin Liu provided invaluable information on parental genotypes and DNA markers. Dr. George Milliken provided helpful discussion on statistical analyses. This work was supported by the U.S. Department of Agriculture, under Agreement No. 59-0790-9-025. This is a cooperative project with the U.S. Wheat & Barley Scab Initiative. Any opinions, findings, conclusions, or recommendations expressed in this publication are those of the authors and do not necessarily reflect the view of the U.S. Department of Agriculture.

Received for publication March 31, 2006.


   REFERENCES
TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 REFERENCES
 




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J. A. Anderson, S. Chao, and S. Liu
Molecular Breeding Using a Major QTL for Fusarium Head Blight Resistance in Wheat
Crop Sci., December 18, 2007; 47(Supplement_3): S-112 - S-119.
[Abstract] [Full Text] [PDF]

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