Molecular Breeding 14: 91–104, 2004. © 2004 Kluwer Academic Publishers. Printed in the Netherlands.
91
Validation of quantitative trait loci for Fusarium head blight and kernel discoloration in barley Paulo C. Canci1, Lexingtons M. Nduulu1, Gary J. Muehlbauer1, Ruth Dill-Macky2, Donald C. Rasmusson1 and Kevin P. Smith1,* 1Dept.
of Agronomy and Plant Genetics, University of Minnesota, St. Paul, MN 55108; 2Dept. of Plant Pathology, University of Minnesota, Saint Paul, MN 55108; *Author for correspondence (e-mail:
[email protected].; phone: 612 624-1211; fax: 612 625-1268) Received 15 May 2003; accepted in revised form 17 December 2003
Key words: Fusarium graminearum, Bipolaris sorokiniana, FHB, QTL, Disease resistance, Deoxynivalenol, breeding, Marker assisted selection
Abstract Validation of quantitative trait loci 共QTLs兲 is a prerequisite to marker assisted selection 共MAS兲, however, only a fraction of QTLs identified for important plant traits have been independently tested for validation. Resistance to the diseases kernel discoloration 共KD兲 and Fusarium head blight 共FHB兲 in barley is complex and technically difficult to assess, and therefore QTLs for these traits are suitable targets for MAS. We selected two lines, from a QTL mapping population created using the resistant variety Chevron, and crossed them to susceptible parents to generate two validation populations. Genetic maps of both populations were developed for five chromosomes covering 15 selected regions containing QTLs for FHB severity, KD score and concentration of deoxynivalenol 共DON兲, a mycotoxin produced by the FHB pathogen. QTL analyses using these validation populations confirmed that five of the possible 15 QTL regions were associated with at least one of the three traits. While some QTL were detected inconsistently across environments, QTL that could be subjected to validation in both populations were confirmed in both populations in seven out of eight instances. A QTL for KD score on chromosome 6共6H兲 was confirmed in both validation populations in eight of nine environments and was also associated with FHB in three of six environments. A QTL for FHB on chromosome 2共2H兲 was confirmed and was also associated with KD and heading date. Marker assisted selection at these two QTLs should enhance disease resistance, however, the QTL on chromosome 2共2H兲 will also delay heading date. Abbreviations: KD – kernel discoloration; FHB – Fusarium head blight; DON – deoxynivalenol; QTL – quantitative trait locus; MAS – marker assisted selection; SSR – simple sequence repeat; RIL – recombinant inbred line; HD – heading date
Introduction Kernel diseases are a major limiting factor in barley 共Hordeum vulgare L.兲 production in the Midwestern United States. Fusarium head blight, caused predominantly by Fusarium graminearum Schwabe, is one of the most serious crop diseases of the last century
共McMullen et al. 1997兲. Losses from FHB are due primarily to grain contaminated with the mycotoxin DON, which reduces its acceptability for malt and feed use. While not as serious as FHB, KD, a disease complex caused by several organisms including Bipolaris sorokiniana Sacc. Shoemaker and F. graminearum 共Mathre 1997兲, results in price reduc-
92 tion and even rejection of grain by the malting barley industry. Progress in exploiting genetic resistance to FHB, KD and DON accumulation has been slow due to the technical difficulties and expense in disease screening, complex nature of resistance, the preponderance of genotype x environment interaction, and coincidence of QTLs for FHB with other QTLs associated with plant development and spike morphology. Screening methods are very time consuming and laborious since they require the evaluation of adult plants in replicated field trials using specially inoculated and irrigated nurseries 共Steffenson 2002兲. Quantitative trait loci for FHB resistance which exert relatively small effects have been mapped in three different sources of resistance and are distributed throughout the genome 共de la Peña et al. 1999; Ma et al. 2000; Mesfin et al. 2003; Zhu et al 1999兲. Mesfin et al. 共2003兲 identified a QTL for FHB resistance near the Vrs1 locus which controls two-rowed/six-rowed spike type. Zhu et al. 共1999兲 found associations between FHB resistance and lateral kernel size. Several studies have observed coincident QTLs for heading date 共HD兲 and FHB resistance 共de la Peña et al. 1999; Ma et al. 2000; Mesfin et al. 2003; Zhu et al 1999兲. In addition, Mesfin et al. 共2003兲 observed that the method of screening FHB influenced the ability to detect QTLs. Ironically, the same reasons that make these kernel diseases difficult to study make them good candidate traits for MAS. Genetic mapping using a Chevron/M69 共CM兲 population identified 10, 11, and four QTLs associated with FHB severity, KD score, and DON concentration, respectively 共de la Peña et al. 1999兲. Three of the ten FHB severity QTLs were also associated with KD score and three with DON concentration. A number of these FHB QTLs were also associated with plant height and HD. An independent mapping study of FHB resistance also using Chevron as a resistant parent identified FHB and DON QTLs in five chromosomes 共Ma et al. 2000兲. Five of these QTLs are in the same general chromosome regions found to be associated with FHB severity and DON concentration in the CM population 共de la Peña et al. 1999兲. However, a lack of common markers between mapping populations made comparison of the QTLs detected in these two studies difficult. Thus, independent validation of these QTLs is necessary to determine whether they can be exploited by MAS. Numerous QTLs for important traits have been identified in barley 共cf. Hayes et al. 1993, Tinker et
al. 1996兲. However, only a limited number of studies to validate QTLs have been conducted. Spaner et al. 共1999兲 used a set of unevaluated lines from the original mapping population 共Harrington/TR306兲 to validate QTL for grain yield, plant height, maturity and lodging severity on chromosome 7共5H兲. Similarly, yield and malting quality QTLs were validated using two sets of unmapped lines from the Steptoe/Morex mapping population 共Han et al. 1997; Romagosa et al. 1999兲. Larson et al. 共1996兲 validated a yield QTL using a set of near-isogenic lines. Castrol et al. 共2003兲 validated two stripe rust QTLs while simultaneously pyramiding the two resistance alleles in a common background. To fully exploit the information derived from mapping studies for MAS, it is advantageous to conduct mapping within breeding program germplasm. The CM population has been simultaneously subjected to mapping and phenotypic selection for resistance to FHB, KD and DON accumulation to identify resistant lines to be used in breeding. Our objective was to test QTLs for validation that were previously identified for FHB severity, KD score and DON concentration using two breeding populations whose resistant parents trace back to the CM population.
Materials and methods Parents and populations Two lines from the CM population, MNS93 and M92-299, that are partially resistant to FHB and KD, were crossed to elite susceptible lines Stander and M81, respectively. Stander is a released cultivar 共Rasmusson et al. 1993兲 and M81 is an elite breeding line from the University of Minnesota that traces to a cross that included Stander as one of its parents. The Stander/MNS93 共SMN兲 and M92-299/M81 共MM兲 mapping populations consisted of 93 F4- and 121 F5derived families, respectively. The CM mapping population was evaluated for disease resistance in seven nurseries between 1995 and 1997 by de la Peña et al. 共1999兲. The SMN population was grown in a total of six nurseries located at Crookston and St. Paul in 1997 and 1998. In Crookston in each year, the nursery was inoculated with F. graminearum. In St. Paul in each year, one nursery was inoculated with F. graminearum and one with B. sorokiniana. Kernel discoloration and HD were evaluated in all six envi-
93 ronments. Fusarium head blight severity was evaluated in the four Crookston and St. Paul nurseries inoculated with F. graminearum. The grain from the two Crookston nurseries was evaluated for DON concentration. The MM population was evaluated in three nurseries located at St. Paul and Crookston in 1998. At St. Paul, one nursery was inoculated with F. graminearum and another with B. sorokiniana. The Crookston nursery was inoculated with F. graminearum. Kernel discoloration and HD were evaluated in the three nurseries. Fusarium head blight severity was assessed at Crookston and in the F. graminearum nursery at St. Paul. DON concentration was determined on grain harvested at Crookston. In all populations, individual lines were seeded in one-row plots, 1.8-2.4 m long, spaced 30 cm apart in a randomized complete block design with two replicates at each location except for the SMN population at the St. Paul F. graminearum nursery in 1997, which had only one replicate. Phenotypic Data Two methods of inoculation were used in the FHB nurseries as described by Mesfin et al. 共2003兲. A spray inoculum comprised of macroconidia was used in St. Paul and a grain-spawn inoculum at Crookston. Mist irrigation was used to promote spore dispersal in the grain-spawn inoculated nurseries and disease development in all nurseries. At Crookston, mist irrigation was initiated 2 weeks before anthesis and continued for a period of one month. On days without rain, plots were misted at night for a 10-h period. In 1997, plots were misted for 15 min h–1 and in 1998 for 8 min h–1. At St. Paul, misting was initiated after the first spray inoculation and continued for 20-d for 30 of every 120 min during a 24h period. The percent FHB severity was assessed visually, approximately two weeks after inoculation by counting the number of diseased kernels in 10 randomly chosen spikes. Deoxynivalenol concentration was determined on harvested grain following the methodology described by Mirocha et al. 共1998兲. Bipolaris sorokiniana-inoculated nurseries were established at St. Paul in 1997 and 1998 as described by Canci et al 共2003兲. KD was scored as described by Miles et al. 共1987兲. Date of heading was recorded when 50% of the heads were at least half way emerged from the boot and was assessed in all nine nurseries.
DNA markers The DNA was isolated from samples of at least 10 plants per line as described by de la Peña et al. 共1996兲. Barley genomic DNA and cDNA probes previously mapped in the CM population were screened for RFLPs between Stander, MNS93, M92-299 and M81 using the restriction endonucleases EcoRI, EcoRV, HindIII, and DraI. The DNA gel blot analyses were performed as described by de la Peña et al. 共1996兲. Barley simple sequence repeat 共SSR兲 markers 共Liu et al. 1996, Ramsey et al. 2000兲 were screened on parents and populations following published protocols. Products from PCR amplification with SSR primers were resolved on either silver stained gels 共Bassam et al. 1991兲 or on an infrared detection instrument 共Global IR2 System, LI-COR Biosciences, Lincoln, NE兲. Data Analysis Genetic maps were constructed with RFLP and SSR marker segregation data in the F4:6 and F5:7 generation for the SMN and MM populations, respectively. A revised version of the CM map with an additional 45 SSR markers was used 共Canci et al. 2003兲. Markers in regions of the genome where QTLs were detected in the CM population 共de la Peña et al. 1999兲 were used to screen the parents for the SMN and MM populations. In these targeted regions, linkage analysis was performed using GMendel 3.0 共Holloway and Knapp 1994兲 by evaluating the SMN and MM populations as F4 and F5 generation, respectively, using a LOD score of 4.0. The linkage groups and marker order were assigned according to published maps 共Kleinhofs et al. 1993; Qi et al. 1996; Liu et al. 1996; Ramsay et al. 2000兲. Composite interval mapping 共CIM兲, using the software PLABQTL 共Utz and Melchinger 1996兲, was employed to identify QTL. Markers to be used as cofactors were selected by a stepwise regression procedure with the default selection parameters. A LOD score of 3.3 was used for detection of QTLs corresponding to experiment-wise and comparison-wise error of P⫽0.05 and P⫽0.005, respectively, as calculated by the Bonferroni Chi-square approximation suggested by Zeng 共1994兲. The proportion of phenotypic variance explained by each QTL was determined by using the coefficient of determination 共R2兲, which is based on partial correlation of putative QTL with the trait adjusted for cofactors in the multi-locus
94 Table 1. Parental and M92-299/M81 共MM兲 population means, population ranges, and P-values for Fusarium head blight 共FHB兲, kernel discoloration 共KD兲, heading date 共HD兲 and deoxynivalenol 共DON兲 concentration. Trait FHB
KD
Environments c
d
DON e d HD f
a
H98 Cr98 SPFg98 SPBs98 SPFg98 Cr98 Cr98 SPBs98 SPFg98 Cr98
M92-299
M81
Population
Population range
P-valueb
7.8 5.6 12.0 1.5 1.5 1.0 5.6 63.0 63.5 61.0
32.0 24.2 32.0 4.0 4.0 4.0 34.9 58.5 58.0 55.0
16.5 16.2 15.6 2.6 2.8 2.7 17.6 61.3 61.6 58.8
7.8-30.5 1.6-45.2 3.3-43.3 1.0-4.0 1.0-5.0 1.0-5.0 5.3-33.9 57.0-66.0 56.5-66.0 54.5-61.5
⬍ 0.001 ⬍ 0.001 ⬍ 0.001 ⬍ 0.001 ⬍ 0.001 ⬍ 0.001 ⬍ 0.001 ⬍ 0.001 ⬍ 0.001 ⬍ 0.001
H⫽Hangzhou, China; Cr ⫽ Crookston, MN; SP ⫽ St. Paul, MN; Fg ⫽ F. graminearum nursery; Bs ⫽ Bipolaris sorokiniana nursery; 97 ⫽ 1997; 98⫽1998; bF test for significant variation among F5 families; cFHB severity 共% of infected kernels兲; dKD score on a 1-5 scale 共1⫽no discoloration, 5⫽heavily discolored兲; ePPM of deoxynivalenol; fDays to heading. a
Table 2. Parental and Stander/MNS93 共SMN兲 population means, population ranges, and P-values for Fusarium head blight 共FHB兲, kernel discoloration 共KD兲, heading date 共HD兲, and deoxynivalenol 共DON兲 concentration. Trait FHB
KD
d
DON HD
Environments c
e
f
Cr97 SPFg97 Cr98 SPFg98 SPBs97 Cr97 SPFg97 SPBs98 SPFg98 Cr98 Cr97 Cr98 SPBs97 SPFg97 Cr97 SPBs98 SPFg98 Cr98
a
MNS93
Stander
PopulationMean
Populationrange
P-valueb
12.7 9.2 3.8 24.5 1.5 2.0 2.0 1.5 2.0 2.0 37.5 8.1 60.0 55.5 56.5 60.5 61.0 60.5
71.9 12.7 34.5 45.7 4.0 5.0 4.0 3.5 4.0 4.0 82.5 30.9 56.5 50.0 50.0 55.0 55.5 55.0
39.8 11.7 16.1 30.5 2.8 3.0 2.9 2.6 2.8 2.7 47.4 23.1 58.3 53.2 53.7 59.1 58.9 55.3
2.5-77.0 2.3-31.4 2.9-42.3 7.3-46.5 1.5-4.0 1.5-4.5 1.0-5.0 1.0-4.5 1.0-4.5 1.0-4.5 23.3-73.4 9.2-43.3 55.0-61.0 49.0-62.0 50.5-57.5 55.0-63.1 54.5-63.5 50.0-69.5
⬍ 0.001 ndg ⬍ 0.001 ⬍ 0.001 ⬍ 0.001 0.14 nd ⬍ 0.001 ⬍ 0.001 ⬍ 0.001 ⬍ 0.32 ⬍ 0.001 ⬍ 0.001 nd ⬍ 0.001 ⬍ 0.001 ⬍ 0.001 ⬍ 0.001
Cr ⫽ Crookston, MN; SP ⫽ St. Paul, MN; Fg ⫽ F. graminearum nursery; Bs ⫽ Bipolaris sorokiniana nursery; 97 ⫽ 1997; 98⫽1998; bF test for significant variation among F4 families; cFHB severity 共% of infected kernels兲; dKD score on a 1-5 scale 共1⫽no discoloration, 5⫽heavily discolored兲; ePPM of deoxynivalenol; fDays to heading; gnd not determined since trial has only one replication. a
model. In addition, we estimated the additive effect of the Chevron allele 共alpha兲 by using the regression coefficient from the multi-locus model.
Results The M92-299 and MNS93 parents exhibited more resistance to FHB, KD and DON accumulation and were about five days later in HD than Stander and M81 in all environments tested 共Tables 1 and 2兲.
Population means for FHB, KD, DON, and HD were usually near the mid-parent value, with a few exceptions where the means were closer to the resistant parents. The skewness was most prevalent for KD resistance and HD in both populations 共Tables 1 and 2兲. Significant differences among lines in the MM population were observed for all traits evaluated 共Table 1兲. Significant differences were observed for the SMN population lines for most of the traits evaluated except for KD and DON at Crookston in 1997 共Table 2兲.
95 Revised CM map and QTL analysis The original CM mapping study identified 16 different regions associated with FHB severity, KD score or DON concentration using the criteria that a unique region is one in which the markers flanking the QTL are not shared with any other markers flanking other QTL 共de la Peña et al. 1999兲. Using the revised map we observed several differences from the original analysis. In the revised analysis, 15 regions were associated with at least one of the three traits. Nine of these 15 were among the 16 QTL regions identified in the original study. The other 6 regions were newly detected regions and corresponded to regions of the genome where new markers were added to the map. Seven of the 16 QTL regions detected in the original study were not detected using the revised map. Two of these undetected QTLs had LOD scores near the level of detection in the original mapping study. Two other undetected QTL were in regions where new markers were added, and one was in a marker interval 共CDO400-CDO59b兲 that was removed in the revised map since we could not confirm that it belonged to chromosome 5H. The other two remaining undetected QTL were likely not detected because of the substantial changes in the revised map and its effect on the selection of co-factors for the CIM analysis. Five QTLs associated with FHB severity were detected in the revised analysis 共Table 3兲. Only three of the ten original FHB QTLs were detected all of which were on chromosome 2共2H兲. Two additional FHB QTLs, which were not previously detected, were identified on chromosomes 3共3H兲 and 6共6H兲 in regions where the addition of SSR markers enhanced map coverage. In the revised analysis, five QTLs associated with DON concentration were identified 共Table 4兲. Four of these were detected in the original study. The QTL on chromosome 7共5H兲 that was not detected in the revised analysis had a LOD score near the level of detection in the original study. In addition, one new QTL for DON concentration was detected on chromosome 6共6H兲. This new QTL was identified in a region where additional SSR markers were mapped. A total of eight QTLs associated with KD score were detected using the revised map 共Table 5, Canci et al. 2003兲. Six of these eight were detected in the original QTL study. The other five QTL detected in the original study were not detected using the revised map. Three of these five QTL had LOD scores near the level of detection. The other two QTL were in re-
gions where additional markers were added to the map. Two new QTL were detected on chromosomes 2共2H兲 and 1共7H兲. Construction of validation maps for selected QTL regions We screened 80 RFLP and 45 SSR markers from the revised CM map for polymorphism between Chevron, MNS93, M92-299, M69, Stander, and M81. These markers spanned the barley genome and covered all of the QTL regions identified by de la Peña, et. al 共1999兲. Using the marker screening results, we determined the regions of the MNS93 and M92-299 genomes which carried Chevron alleles. Of the fifteen possible QTL regions identified in the revised CM map, MNS93 and/or M92-299 carried the Chevron allele in nine of them. Therefore, these regions were subjected to investigation in the SMN and MM populations. The six QTL regions 共#8, #9, #11, #12, #14, #15兲 in which there was no polymorphism among the parents could not be tested for validation. Four QTL regions 共#2, #3, #4, #10兲 could be tested using both populations 共Tables 3, 4, and 5兲. Genetic maps covering nine selected QTL regions from the CM map were constructed for the SMN and MM populations. Figures 1 and 2 show seven of these regions that mapped to 2共2H兲 and 6共6H兲, respectively. These maps contained most but not all markers used in the CM map for a particular chromosome region and in some cases they included new SSR markers. Despite the differences in the marker sets used, the genetic maps in the two validation populations covered the important regions of the genome that were previously associated with FHB severity, DON concentration, and KD score. Since there was a tendency for QTLs for the three traits to cluster near each other, we examined each region and report whether any of the three traits could be validated regardless of whether they were detected in that region with the CM population. We defined homologous QTL regions in the three populations based on common markers that comprised a QTL region. Therefore, detection of a QTL in the validation population共s兲 linked to the same marker as in the CM population or to markers that map to the same region was considered validation of the QTL. In some cases, QTLs could be identified in one or both populations and for each population in a number of environments. In this sense, the degree to which a QTL is validated
a
b
2H MWG557-MWG887 MWG887-ABG459 MWG557-Ebmac0521c 2H ABC306-BCD1087b Bmac0093-GMS03 Bmac0093-GMS03 2H KSUF15-ABG497a 3H HVM09-Bmag0138 6H Bmag0173-Bmag0870 cMWG652a-Bmag0807 GMS06-ABG458 ABG458-HVM65
Chr Marker interval 6-7 5-6 7-8 8 8 8 13 6 6-7 3-6 6 6
BIN
共cM兲 17.5 8.0 2.3 SMN, 6.7 MM 4.1 6.7 6.7 7.8 CM, 9.3 SMN 3.8 3.0 22.1 6.8 20.0
Region
Length
Cr97 Cr97 SPFg97
Cr97
H97 Cr97
e
4.7 22.2 3.34 5.9 26.8 3.57 6.0 24.6 ⫺ 0.92
6.5 29.0 ⫺ 5.25
3.9 16.1 ⫺ 2.97 5.5 25.1 ⫺ 4.29
Lod R2d Alpha
⫺ 4.69 ⫺ 2.95 4.3 20.1 3.5 16.5
SPFg98 Cr98
⫺ 4.68
Alpha
13.0 48.1 ⫺ 13.12 6.7 29.1 ⫺ 5.83 3.1 14.8 ⫺ 2.26
3.3 15.5
Lod R2
Cr97 Cr98 SPFg97
Cr98
Env
SMN 共4 environments兲
CM 共4 environments兲 Envc
Validation populations
Original Mapping Population
SPFg98
f
Cr98
Env
Alpha
3.6 12.9 ⫺ 2.55
22.9 58.2 ⫺ 9.00
Lod R2
MM 共2 environments兲
Asterisk indicates a QTL for FHB that was not identified at that position in the original study by de la Peña et. al, 1999; bMarkers in the QTL region that flank the peak of the LOD scan; H⫽Hangzhou, China; Cr ⫽ Crookston, MN; SP ⫽ St. Paul, MN; Fg ⫽ F. graminearum nursery; Bs ⫽ Bipolaris sorokiniana nursery; 97 ⫽ 1997; 98⫽1998; dPercentage phenotypic variance explained by QTLs.; eEffect of “Chevron, M92-299, or MNS93” alleles on FHB severity expressed as regression coefficient; fShaded rows indicate where a QTL region could not be tested with the corresponding population.
c
a
QTL#5 QTL#6 * QTL#10 *
QTL#4
QTL#3
Region
QTL
Interval
Table 3. Quantitative trait loci associated with Fusarium head blight 共FHB兲 severity for the Chevron/M69 共CM兲, Stander/MNS93共SMN兲, and M92-299/M81 共MM兲 populations.
96
Asterisk indicates a QTL for DON that was not identified at that position in the original study by de la Peña et. al, 1999; bMarkers in the QTL region that flank the peak of the LOD scan; See Table 3 for description of environments and F⫽Fargo; 95⫽1995. dPercentage phenotypic variance explained by QTLs. e Effect of “Chevron, M92-299, or MNS93” alleles on FHB severity expressed as regression coefficient. fShaded rows indicate where a QTL region could not be tested with the corresponding population.
⫺ 4.06 1.22 ⫺ 1.53 16.6 14.3 28.8 F97 3.8 H97 3.4 Cr95 4.7 3H 6H 7H QTL#7 QTL#12 * QTL#14
c
f
⫺ 4.16 21.2 Cr98 4.6 2H QTL#4
a
⫺ 3.53 Cr98 3.6 ⫺ 3.05 28.4 Cr98 6.5 ⫺ 1.37 36.5 Cr95 6.3
⫺ 2.29 14.8 3.4 F97
2-3 2 8 8 8 6-8 11-13 3-5 ABC311-ABG703b ABG703b-HVM36 Bmac0093-EBmac0521c EBmac0521c-EBmac0558 Ebmaco557-Bmac0093 Bmag0225-MWG2227c Bmac0040-MWG2196 MWG564-MWG836 QTL#1
2H
15.5 19.5 3.0 1.5 9.3 12.7 19.1 20.1
Alpha R2 d BIN 共cM兲
Envc Lod
Region Interval Length
Marker intervalb Chr Regiona
QTL
12.9
Alphad R2 Lod
MM 共1 environment兲
Env Alpha R2 Lod
SMN 共2 environments兲
Env
CM 共3 environments兲
e
Validation populations Original Mapping Population
Table 4. Quantitative trait loci associated with deoxynivalenol 共DON兲 concentration for the Chevron/M69 共CM兲, Stander/MNS93 共SMN兲, and M92-299/M81 共MM兲 populations.
97 is related to the number of validation population-environments in which it is detected. Validation of QTL for FHB severity, KD score, and DON concentration Of the nine QTL regions identified in the CM population that could be tested, five were validated on chromosomes 2共2H兲 and 6共6H兲. We were unable to validate disease related QTLs in region #2 on chromosome 2共2H兲 共Table 5兲, regions #6 and #7 on chromosome 3共3H兲 共Tables 3 and 4兲, or region #13 on chromosome 1共7H兲 共Table 5兲. Chromosome 2(2H) Four of the five QTL regions detected on chromosome 2共2H兲 were validated. A QTL associated with DON concentration in QTL region #1 was detected in the SMN population in an adjacent marker interval 共ABG703b-HVM36兲 in a single environment 共Figure 1, Table 4兲. The KD score QTL in region #2 was not validated 共Table 5兲. The FHB severity QTL in region #3 was validated in the SMN and MM populations 共Table 3兲. In the MM map of this region, FHB severity, KD score, DON concentration and HD QTLs are all coincident, but it is not possible to distinguish between regions #3 and #4 共Figure 1兲. In the CM population the flanking markers for QTL regions #3 and #4 are 28 cM apart, while in the SMN map they are 11 cM apart and in the MM population map together. The order of these markers in the SMN and MM maps is consistent with the BIN maps 共http://barleygenomics.wsu.edu/兲. Thus, it appears that regions #3 and #4 represent a single region that is coincident for all three disease traits and a major QTL for HD 共Table 6兲. This HD QTL on chromosome 2共2H兲 was detected in all of the environments in which the SMN and MM populations were evaluated and explained 17-86% of the variation in individual environments. In this region, the Chevron allele conditioned later heading date and lower FHB severity, KD score, and DON concentration. The relationship between HD and disease resistance observed in the validation populations is consistent with the original mapping population. The FHB QTL in region #5 was detected in the SMN population in a single environment 共Table 3兲. Chromosome 6(6H) Only one of the three QTLs on chromosome 6共6H兲 detected using the CM population could be tested in the validation populations 共Figure 2兲. This region
Chr
2H 2H
4H 5H
6H
6H 7H 7H
QTL#2 QTL#4*
QTL#8 QTL#9
QTL#10
QTL#11 QTL#13 QTL#15*
Bmag0500-MWG916 MWG916-Bmag0807 Bmag0807-Bmag0173 Bmag003c-Bmag0613 cMWG652a-Bmag0807 GMS06-ABG458 GMS06-ABG458 GMS06-ABG458 ABG458-HVM65 GMS06-ABG458 Amy1-Bmag001 Bmag0206-HVM04 ABG497b-ABG461b
ABG8-MWG858 Ebmac0558-HVM23 MWG557-EBmac0521c MWG557-EBmac0521c EBmac0521c-EBmac0558 MWG557-EBmac0521a CDO20-ABG397 MWG503b-ABC717
Marker interval
b
9.6 17.0 3.9 5.9 22.1 6.8 6.8 6.8 20.0 6.8 17.9 12.4 10.2
15.2 2.7 6.7 6.7 1.5 10.8 9.1 15.7
共cM兲
5 5-6 6-7 6-8 3-6 6 6 6 6 6 9 1-2 11-12
3-4 8 7-8 7-8 8 7-8 10-11 9
BIN Lod
Mo95 Cr95 Cr95
SPFg97 SPFg97 SPFg95 SPFg97 Cr95
8.7 3.4 3.8
3.6 4.4 3.4 4.3 4.3
SPFg97 4.4 Cr95 5.1
Env
c
30.2 6.4 10.1
10.6 8.4 10.8 19.7 21.5
15.3 7.5
R
2d
Length Region CM 共4 environments兲 e
⫺ 0.48 ⫺ 0.20 ⫺ 0.26
⫺ 0.20 0.20 0.28 ⫺ 0.30 ⫺ 0.50
⫺ 0.28 ⫺ 0.24
Alpha
Original Mapping Population
SPBs97 SPFg97 SPBs98 SPFg98 Cr98
f
Cr98
Env
15.0 6.2 12.3 5.1 16.1
4.3
Lod
53.1 27.0 46.4 22.8 56.6
20.1
R
2
SMN 共6 environments兲
Validation populations
⫺ 0.46 ⫺ 0.45 ⫺ 0.51 ⫺ 0.39 ⫺ 0.58
⫺ 0.50
Alpha
Lod
SPFg98 14.3 SPBs98 3.9 Cr98 13.1
SPFg98 11.0 SPBs98 6.9 Cr98 18.3
Env
42.0 13.8 39.5
34.1 23.1 50.1
R2
MM 共3 environments兲
⫺ 0.43 ⫺ 0.23 ⫺ 0.43
⫺ 0.37 ⫺ 0.31 ⫺ 0.53
Alpha
Asterisk indicates a QTL for KD that was not identified at that position in the original study by de la Peña et. al, 1999; bMarkers in the QTL region that flank the peak of the LOD scan; See Table 3 and F⫽Fargo, ND; Mo⫽Morris, MN; 95⫽1995; dPercentage phenotypic variance explained by QTLs; eEffect of “Chevron, M92-299, or MNS93” alleles on FHB severity expressed as regression coefficient. fShaded rows indicate where a QTL region could not be tested with the corresponding population.
c
a
Region
a
QTL
Interval
Table 5. Quantitative trait loci associated with kernel discoloration 共KD兲 score for the Chevron/M69 共CM兲, Stander/MNS93 共SMN兲, and M92-299/M81 共MM兲 populations.
98
99
Figure 1. Genetic maps and QTL regions on chromosome 2共2H兲 for the Chevron/M69, Stander/MNS93 共SMN兲 and M92-299/M81 共MM兲 populations. Marker designations are given on the left side of each chromosome. Centimorgan distances were obtained from Kosambi mapping function and are given on the right side of each chromosome. The total length for region of chromosome 2共2H兲 mapped for the CM, SMN, and MM populations is 217 cM, 216 cM, and 109 cM, respectively. Markers included in the QTL regions are bracketed on the left side of the CM map. Vertical bars are adjacent to the marker interval that includes the QTL peak共s兲.
共#10兲 was associated with FHB and KD in the CM population and in the SMN and MM populations. The effect of this region on FHB was detected in both validation populations 共Table 3兲. This region was associated with HD in the CM and MM populations in one environment for each population. However, the effect of the Chevron allele on HD was negative in the CM population and positive in the MM population 共Table 6兲. The effect of this region on KD was also detected in both the SMN and MM populations in five and three environments, respectively 共Table 5兲. These coincident QTLs are located in BIN 6. This same region is important for KD resistance in the variety MNBrite, a cultivar derived from Chevron 共Canci et al. 2003兲.
Magnitude and reproducibility of validated QTL The QTLs identified in this study varied considerably in terms of reproducibility across environments. The FHB and DON QTL were the least consistent across environments. A total of five FHB QTL were detected in the original mapping population in either one or two of the four environments in which the population was tested 共Table 3兲. In terms of validation, four of the five FHB QTL were detected in at least one population in one environment. The FHB QTL in region #10 on chromosome 6共6H兲 was detected in three validation population-environments. For all FHB QTL, the effect of Chevron allele was in the same direction in the validation population and the original mapping population, except region #5. The amount of variation explained by the QTL was similar in the original and validation populations. One exception was QTL region #3 on chromosome 2共2H兲 that ex-
100
Figure 2. Genetic maps and QTL regions on chromosome 6共6H兲 for the Chevron/M69, Stander/MNS93 共SMN兲 and M92-299/M81 共MM兲 populations. Marker designations are given on the left side of each chromosome. Centimorgan distances were obtained from Kosambi mapping function and are given on the right side of each chromosome. The total length for region of chromosome 6共6H兲 mapped for the CM, SMN, and MM populations is 155 cM, 132 cM, and 95 cM, respectively. Markers included in the QTL regions are bracketed on the left side of the CM map. Vertical bars are adjacent to the marker interval that includes the QTL peak共s兲.
plained 16 ⫺ 25% of the variation in the original mapping population and 58% in the MM validation population. Deoxynivalenol concentration was evaluated in grain samples from only three environments from the two validation populations. The DON QTL in region #1 on chromosome 2共2H兲 was detected in one environment in the SMN population while the DON QTL in region #4 also on chromosome 2共2H兲 was detected in one environment for each of the validation populations 共Table 4兲. In the case of QTL region #1, the R2 was greater in the validation population, while in QTL region #4 the R2 was smaller in the validation populations. Kernel discoloration was the most consistent disease-related trait across environments. The QTL at region #4 was validated in four of the nine possible population-environments and the QTL at region #10 共chromosome 6H兲 was validated at eight of the nine population-environments 共Table 5兲. The direction of the effect of the Chevron allele at both loci was negative for the original and validation populations. In this case, the R2 values were generally higher in the validation population compared with the original mapping population.
The region associated with HD on chromosome 2共2H兲 identified in the CM population was also associated with HD in the validation populations. The QTL regions #3 and #4, which likely represent a single region, were associated with HD in all tested environments in the validation populations 共Table 6兲. This region was associated with both FHB and HD in the Frederickson/Stander mapping population 共Mesfin et al. 2003兲 and is likely at the same position as the maturity gene eam6 共Frankowiak and Konishi 2002兲.
Discussion The kernel disease traits we investigated are particularly challenging to study in terms of host genetics and yet it is the complexity of these diseases that make them ideal candidates for MAS. Therefore, validation is essential prior to using QTL for these traits in MAS. The use of validation populations created with parents selected from a mapping population is advantageous because it offers the possibility of conducting genetic mapping at the same time as progenies of these populations are advanced to the
6H
7H
7H
QTL#10 *
QTL#13
QTL#14
SPBs95 7.3 Cr95 10.7 SPFg96 6.8
Cr95 3.6 SPBs95 4.6
28.3 38.7 26.6
15.2 18.8
32.3 34.8 21.2
Cr95 8.6 SPFg96 9.4 Cr95 5.2
R2d
18.8
Lod
SPBs95 4.6
Envc Env
1.18 2.18 1.02
⫺ 1.10 1.13
18.0 3.6 14.6 10.6 18.9 37.7
Lod
SPBs98 4.7 SPFg97 3.7
1.13 SPFg97 Cr97 SPBs97 SPFg98 Cr98 SPBs98 1.58 1.23 ⫺ 1.19
e
Alpha
22.3 17.9
60.6 16.6 52.1 42.3 62.4 85.8
R2
SMN 共6 environments兲
CM 共4 environments兲
0.69 0.62
1.75 1.18 1.30 1.86 2.57 2.51
Alpha
Lod
f
SPBs98 3.4
SPFg98 9.7 Cr98 10.7 SPBs98 37.1
Env
12.3
30.8 33.4 75.6
R2
MM 共3 environments兲
0.50
2.05 2.29 1.99
Alpha
Asterisk indicates a QTL for HD that was not identified at that position in the original study by de la Peña et. al, 1999; bMarkers in the QTL region that flank the peak of the LOD scan; See Table 3 for description of environments and 95⫽1995; 96⫽1996; dPercentage phenotypic variance explained by QTLs; eEffect of “Chevron, M92-299, or MNS93” alleles on FHB severity expressed as regression coefficient; fShaded rows indicate where a QTL region could not be tested with the corresponding population.
c
a
2H
7-8 7-8 8 8 8 8 8 8 8 8 8 6-7 3-6 2 2-3 3 3 5-7 3-5 3-5
QTL#4
6.7 6.7 1.5 2.9 CM, 6.7 SMN 6.7 6.7 6.7 6.7 6.7 1.3 1.3 3.0 22.1 12.4 14.2 5.5 5.5 12.6 20.1 20.1
2H
QTL#3
MWG557-Ebmac0521c MWG557-Ebmac0521c EBmac0521c-EBmac0558 Bmac0093-GMS03 Bmac0093-GMS03 Bmac0093-GMS03 Bmac0093-GMS03 Bmac0093-GMS03 Bmac0093-GMS03 GMS03-HVM23 GMS03-HVM23 Bmag0807-Bmag0173 cMWG652a-Bmag0807 Bmag0206-HVM04 HVM04-ABC151a ABC151a – MWG564 ABC151a – MWG564 MWG836-ABG476 MWG564-MWG836 MWG564-MWG836
BIN
共cM兲
Chr
Regiona
Marker intervalb
Region
Interval Length
QTL
Validation populations
Original Mapping Population
Table 6. Quantitative trait loci associated with heading date 共HD兲 for the Chevron/M69 共CM兲, Stander/MNS93 共SMN兲, and M92-299/M81 共MM兲 populations.
101
102 next cycle of breeding. Likewise, the information obtained from mapping and validation experiments can be used to develop more efficient MAS strategies. Several of the QTLs identified by de la Peña et al. 共1999兲 were also identified by Ma et al. 共2000兲, who also used Chevron as a source of resistance. Steffenson 共2002兲 compared the QTL regions identified in these two studies and while very few markers were common to both studies, a few QTLs identified in the de la Peña et al. 共1999兲 study were consistent with those identified by Ma et al. 共2000兲. A dominant feature of the genetics of resistance to FHB, KD and DON concentration in barley is the lack of consistent effects of alleles at QTLs across environments 共de la Peña et al. 1999; Ma et al. 2000; Mesfin et al. 2003; Zhou et al. 1999兲. QTL analysis for FHB severity, KD score and DON concentration is difficult because of both the etiological and genetic complexity of the disease and the difficulty in obtaining accurate phenotype data. In this study, the four FHB severity QTL that were validated were detected in half or less of the environments tested. Several possibilities exist to explain the lack of consistent detection across environments including: QTL x E interaction, large error variance in disease severity reducing the power to detect QTL in each environment, and limited power to detect QTL due to population size 共Beavis 1998兲. In the case of FHB, all of these factors may play a role. Some of the inconsistency observed may simply be due to the lack of accurate phenotype data for FHB severity in some environments. Alternatively, differences in the environment itself may effect the expression of resistance at a particular QTL. Mesfin et al. 共2003兲 observed that three different QTLs associated with FHB severity on chromosome 2共2H兲 were detected differently depending on whether disease evaluations were done in the field or greenhouse and whether field nurseries used a spray or grain spawn inoculum. Lastly, small population size can affect marker order in a linkage group, which ultimately affects the final map used for QTL detection. In addition, several studies have shown that small populations sizes result in low power to detect QTL and upward biased estimates of QTL effects 共Melchinger et al. 1998; Utz et al. 2000兲. While detection of QTL across environments was inconsistent, detection of QTL across validation populations was consistent. There were eight instances where we could evaluate a QTL region for either FHB, KD, or DON in both validation populations. In five of these cases, the QTL was detected in
both validation populations. In one case, the QTL was not detected in both validation populations. In two of the eight cases, the QTL was detected in one validation population but not the other. One of these two cases was QTL#4 for FHB, which we later concluded was the same region as QTL#3 so, in fact, this region was detected in both populations. The other case was QTL#1 for DON which was detected in SMN but not MM. However, MM was evaluated in only one environment for DON, so there was less of an opportunity to detect that QTL. Therefore, in seven of the eight cases where a QTL could be evaluated in both validation populations its detection was consistent in both populations. Given the lack of strong candidate markers for MAS to improve FHB resistance identified in other studies, it is reasonable to consider the markers linked to FHB QTL validated in this study for MAS. The QTL region on chromosome 2共2H兲, QTL#3 – #4, is associated with DON, KD and HD, so selection for the Chevron allele at this locus will likely result in KD and FHB resistant lines with later HD. We estimated the effects of the Chevron allele at this QTL using data from environments where the QTL was detected in the validation populations. We estimated the value of the two genotypic classes 共homozygous Chevron vs homozygous susceptible parent兲 using the values of alpha from the QTL models and the population means, calculated the percent change between the two genotypic classes, and averaged this across all environments in which the QTL was detected. For the QTL#3 – #4 region, the Chevron allele reduced FHB, KD and DON by 42%, 73%, and 68%, respectively, and increased HD by 3.8 days. The association of FHB severity with HD is particularly interesting since it is possible that disease levels are lower in later heading genotypes because the host is exposed to less inoculum 共Steffenson, 2002兲. We are currently fine mapping this region to determine if these associations at this locus are due to linkage or pleiotropy. Another region worthy of consideration for MAS is region #10 on chromosome 6共6H兲. This region was validated in both SMN and MM populations and is also consistently associated with resistance to KD. For this locus, we estimate that introgressing the Chevron allele would decrease FHB severity and KD score by 56% and 73%, respectively, and increase HD by 0.5 days. Canci et al. 共2003兲 showed the cultivar MNBrite 共Rasmusson et al. 1999兲 derives resistance to KD, in part, from the Chevron allele at this QTL on chromosome 6共6H兲. They also showed that this
103 KD QTL was coincident with grain protein concentration, so selection for the Chevron allele at this locus could increase protein concentration. We are also fine mapping this region to determine whether the effects on FHB, KD and grain protein concentration are due to a single gene or linked genes.
Acknowledgements We thank Charlie Gustus and Ed Schiefelbein for technical assistance, Weiping Xie for analyzing DON in grain samples, and David Garvin and Rex Bernardo for suggestions on an earlier draft of this manuscript. This research was supported in part by the American Malting Barley Association, U.S. Wheat and Barley Scab Initiative, North American Barley Genome Project, and the Minnesota Small Grains Initiative.
References Bassam B.J., Caetano-Anolles G. and Gresshoff. P.M. 1991. Fast and sensitive silver staining of DNA in polyacrylamide gels. Anal. Biochem. 196: 80–83. Beavis E.E. 1998. QTL Analysis: power, precision and accuracy. In: Paterson A.H. 共ed.兲, Molecular dissection of complex traits. CRC Press, Boca Raton, Florida, USA, p. 145–162. Canci P.C., Nduulu L., Dill-Macky R., Muehlbauer G.J., Rasmusson D.C. and Smith K.P. 2003. The genetic relationship between kernel discoloration and grain protein in barley. Crop Sci. 43: 1671–1679. Castrol A.J., Chen X., Hayes P.M. and M. Johnston. 2003. Pyramiding quantitative trait locus 共alleles兲 determining resistance to barley stripe rust: effects on resistance at the seedling stage. Crop Sci. 43: 651–659. Chen X.M., Line R.F. and H. Leung. 1998. Genome scanning for resistance gene analogs in rice, barley, and wheat by high resolution electrophoresis. Theor. Appl. Genet. 97: 345–355. de la Peña R.C., Murray T.D., Jones S.S. 1996. Linkage relationships among eyespot resistance gene Pch2, endopeptidase Ep-A1b, and RFLP marker Xpsr121 on chromosome 7A of wheat. Plant Breeding 115: 273–275. de la Peña R., Smith K.P., Capettini F., Muehlbauer G.J., GalloMeagher M., Dill-Macky R., Somers D. and Rasmusson D.C. 1999. Quantitative trait loci associated with resistance to Fusarium head blight and kernel discoloration in barley. Theor. Appl. Genet. 99: 561–569. Franckowiak J.D., Konishi T. 2002. BGS 98, early maturity 6, Eam6. Barley Genet. Newsl. 32: 88–89. Han F., Romagosa I., Ullrich S.E., Jones B.L., Hayes P.M. and Wesenberg D.M. 1997. Molecular marker-assisted selection for malting quality traits in barley. Mol. Breed. 3: 427–437. Hayes P.M., Liu B.H., Knapp S.J., Chen F., Jones B., Blake T., Franckowiak J., Rasmusson D., Sorrells M., Ullrich D. Wesen-
berg and A. Kleinhofs. 1993. Quantitative trait locus effects and environmental interaction in a sample of North American barley germplasm. Theor. Appl. Genet. 87: 392–401. Holloway J.L. and Knapp S.J., 1994. Gmendel 3.0 Users Guide, Department of Crop and Soil Science, Oregon State University, Corvallis, Oregon, USA. Available at:http://gnome.agrenv.mcgill.ca/info/gmendel.htm Kleinhofs A., Kilian A., Saghai Maroof M.A., Biyashev R.M., Hayes P., Chen F.Q., Lapitan N., Fenwick A., Blake T.K., Kanazin V., Ananiev E., Dahleen L., Kudrna D., Bollinger I., Knapp S.I., Liu B., Sorrells M, Heun M., Franckowiak J.D., Hoffmann D., Skadsen R. and Steffenson B.J. 1993. A molecular, isozyme and morphological map of barley 共Hordeum vulgare兲 genome. Theor. Appl. Genet. 86: 705–712. Larson S.R., Kadyrzhanova D., McDonald D., Sorrells M. and Blake T.K. 1996. Evaluation of barley chromosome-3 yield QTLs in a backcross F2 population using STS-PCR. Theor. Appl. Genet. 93: 618–625. Liu Z.W., Biyashev R.M. and Saghai Maroof M.A. 1996. Development of simple sequence repeat DNA markers and their integration into a barley genetic map. Theor. Appl. Genet. 93: 869– 876. Ma Z., Steffenson B.J., Prom L.K. and Lapitan N.L.V. 2000. Mapping quantitative trait loci for Fusarium head blight resistance in barley. Phytopathology 90: 1079–1088. Mathre D.E. 1997. Compendium of barley diseases, 2nd edn. American Phytopathological Society, St. Paul, Minnesota, USA. McMullen M.P., Jones R. and Gallenberg D. 1997. Scab of wheat and barley: A re-emerging disease of devastating impact. Plant Dis. 81: 1340–1348. Mesfin A., Smith K.P., Dill-Macky R., Evans C.K., Waugh R., Gustus C.D. and Muehlbauer G.J. 2003. Quantitative trait loci for Fusarium head blight resistance in barley detected in a tworowed by six-rowed population. Crop Sci. 43: 307–318. Miles M.R., Wilcoxson R.D., Rasmusson D.C., Wiersma J. and Warnes D. 1987. Influence of genotype and environment on kernel discoloration of Midwestern malting barley. Plant Dis. 71: 500–504. Mirocha C.J., Kolaczkowski E., Xie W., Yu H. and Jelen H. 1998. Analysis of deoxynivalenol and its derivatives 共batch and single kernel兲 using gas chromatography/mass spectrometry. J. Agric. Food Chem. 46: 1414–1418. Qi X., Stam P. and Lindhout P. 1996. Comparison and integration of four barley genetic maps. Genome 39: 379–394. Ramsay L., Macaulay M., degli Ivanissevich S., MacLean K., Cardle L., Fuller J., Edwards K.J., Tuvesson S., Morgante M., Massari A., Maestri E., Marmiroli N., Sjakste T., Ganal M., Powell W. and Waugh R. 2000. A simple sequence repeat-based linkage map of barley. Genetics 156: 1997–2005. Rasmusson D.C., Wilcoxson R.D., Dill-Macky R., Schiefelbein E.L. and Wiersma J.V. 1999. Registration of ‘MNBrite’ Barley. Crop Sci. 39: 290. Rasmusson D.C., Wilcoxson R.D. and Wiersma J.V. 1993. Registration of ‘Stander’ Barley. Crop Sci. 33: 1403. Romagosa I., Han F., Ullrich S.E., Hayes P.M. and Wesenberg D.M. 1999. Verification of yield QTL through realized molecular marker-assisted selection responses in a barley cross. Mol. Breed. 5: 143–152. Spaner D., Rossnagel B.G., Legge W.G., Scoles G.J., Eckstein P.E., G.A. Penner, N.A. Tinker, Briggs K.G., Flak D.E., Afele J.C., Hayes P.M. and Mather D.E. 1999. Verification of quantitative a
104 trait locus affecting agronomic traits in two-row barley. Crop Sci. 39: 248–252. Steffenson B.J. 2002. Fusarium Head Blight of Barley: Impact, Epidemics, Management, and Strategies for Identifying and Utilizing Genetic Resistance.. In: Leonard K.L. and Bushnell W.R. 共eds兲, Fusarium head blight of wheat and barley. The American Phytopathological Society, St. Paul, Minnesota, USA. Tinker N.A., Mather D.E., Rossnagel B.G., Kasha K.J., Kleinhofs A., Hayes P.M., Falk D.E., Ferguson T., Shugar L.P., Legge W.G., Irvine R.B., Choo T.M., Briggs K.G., Ulrich S.E., Franckowiak J.D., Blake T.K., Graf R.J., Dofing S.M., Saghai-Maroof M.A., Scoles G.J., Hoffman D., Dahleen L.S., Killian A., Chen F., Biyashev R.M., Kudrna D.A. and Steffenson B.J. 1996. Regions of the genome that affects agronomic performance in tworow barley. Crop Sci. 36: 1053–1062. Utz H. F., Melchinger A.E. and Schön C.C. 2000. Bias and sampling error of the estimated proportion of genotypic variance ex-
plained by quantitative trait loci determined from experimental data in maize using cross validation and validation with independent samples. Genetics 154: 1839–1849. Utz H.F. and Melchinger A.E.. 1996. PLABQTL: A program for composite interval mapping of QTL. J. Quant. Trait Loci. http:// probe.nalusda.gov:800/otherdocsa/jqtl Zeng Z.B. 1994. Precision mapping of quantitative trait loci. Genetics 136: 1457–1468. Zhu H., Gilchrist L., Hayes P., Kleinhofs A., Kudrna D., Liu Z., Prom L., Steffenson B., Toojinda T. and Vivar H. 1999. Does function follow form? Principal QTLs for Fusarium head blight are coincident with QTLs for inflorescence traits and plant height in a doubled-haploid population of barley. Theor. Appl. Genet. 99: 1221–1232.
