Mapping of quantitative trait loci affecting resistanceto Phytophthora nicotianae in tobacco (Nicotiana tabacum L.)line Beinhart-1000

Vijay Vontimitta • Ramsey S. Lewis

Received: 18 August 2010 / Accepted: 20 October 2010 / Published online: 4 November 2010

� Springer Science+Business Media B.V. 2010

Abstract Black shank, caused by Phytophthora

nicotianae, is one of the most important diseases

affecting tobacco (Nicotiana tabacum L.) production

worldwide. While monogenic resistance to Race 0 of

this pathogen has been transferred via interspecific

introgression, quantitative resistance is currently

needed to provide adequate resistance to all races.

The objective of this research was to gain increased

insight into the inheritance of resistance in the highly-

resistant cigar tobacco line, Beinhart-1000, and to

identify genomic regions contributing to this resistance.

A doubled haploid population was generated from a

cross between Beinhart-1000 and the susceptible

cultivar, Hicks. The population was evaluated for field

resistance in three environments and genotyped with

206 polymorphic microsatellite markers. No doubled

haploid line exhibited as high a level of resistance

as Beinhart-1000. Heritability was high and multiple

interval mapping suggested that at least six quantitative

trait loci (QTL) may contribute to the high level of

resistance in this line. The two largest QTL explained

25.4 and 20.4% of the observed phenotypic variation

for end percent survival. A model including all six

significant QTL explained 64.3% of the phenotypic

variation and 73.1% of the genetic variation. The two

major identified QTL and their associated markers may

be of use for employing Beinhart-1000 as a source of

black shank resistance in tobacco breeding. The major

QTL on linkage group four was found to cosegregate

with Abl, a gene involved in accumulation of the

trichome exudate cis-abienol. The question of pleiot-

ropy versus linkage needs to be investigated with

respect to partial resistance against P. nicotianae.

Keywords Black shank � Phytophthora nicotianae �Tobacco � Quantitative trait locus � cis-abienol

Introduction

The oomycete genus Phytophthora contains species

that are amongst the most destructive pathogens on a

very large range of economically important plant

species around the world. Phytophthora nicotianae is

the causal agent of black shank, the most important

disease affecting tobacco (Nicotiana tabacum L.)

production in the United States. This soil-borne

organism infects roots and stalks, resulting in stunting

and plant death. Growers can use combinations of

crop rotation, chemical inputs, and resistant varieties

to reduce loss from this pathogen. Genetic resistance

is the most attractive, however, because of the

potential for cost-effective protection.

Monogenic resistance to Race 0 provided by

the genes Php and Phl has been introgressed into

N. tabacum from the diploid relatives, N. plumbagin-

ifolia and N. longiflora, respectively (Valleau et al.

V. Vontimitta � R. S. Lewis (&)

Department of Crop Science, North Carolina State

University, Campus Box 7620, Raleigh, NC 27695, USA

e-mail: ramsey_lewis@ncsu.edu

123

Mol Breeding (2012) 29:89–98

DOI 10.1007/s11032-010-9528-8

1960; Apple 1962; Chaplin 1962; Johnson et al.

2002a). This resistance mechanism provides no

resistance to alternative races of the black shank

pathogen, however (Carlson et al. 1997). Recent

wide-scale planting of cultivars possessing the Php

gene has resulted in race shifts in many growing areas,

and alternative races which are able to overcome this

type of resistance are now prevalent (Sullivan et al.

2005). The other type of resistance that has been

utilized in tobacco breeding is polygenic in nature and

is probably mostly derived from the cigar tobacco

cultivar, Florida 301 (Tisdale 1931). This is a classic

example of quantitative disease resistance in plants in

that it is controlled by multiple genes, is partial (not

complete), and is believed to be race non-specific. In

flue-cured tobacco, this type of resistance appears to

be negatively correlated with yield.

Additional sources of resistance may be of value for

extending the range and level of resistance to black

shank in flue-cured and burley tobacco (the major

market types), and with less of a negative influence

on yield. In addition, DNA markers associated with

genomic regions contributing to resistance might

increase the efficiency of developing resistant culti-

vars. The cigar tobacco line, Beinhart-1000, has the

highest reported level of quantitative resistance to

P. nicotianae (Heggestad and Lautz 1957; Silber and

Heggestad 1963; Chaplin 1966; Wills 1971; Nielsen

1992). Resistance in this line is believed to be effec-

tive against all races and its inheritance is likely

complex in nature (Chaplin 1966). Past efforts to

incorporate resistance from this line into burley and

flue-cured tobacco cultivars have been unsuccessful

due to unfavorable associations between resistance and

undesirable cigar-type characteristics (Nielsen 1992).

DNA markers closely associated with major genes

affecting resistance might increase the possibility of

transferring this resistance to commercial US tobacco

cultivars while selecting against the undesired cigar-

type characteristics of Beinhart-1000. The value of

DNA markers in breeding for black shank resistance in

tobacco has previously been demonstrated in connec-

tion with the Php gene derived from N. plumbaginifolia

(Johnson et al. 2002b).

To date, the vast majority of marker–trait associ-

ation studies in N. tabacum have focused on traits

introgressed from other Nicotiana species (Yi et al.

1998; Johnson et al. 2002b; Lewis 2005; Milla et al.

2005; Moon and Nicholson 2007). This is mostly due

to the limited amounts of polymorphisms revealed

amongst lines of N. tabacum using marker systems

such as restriction fragment length polymorphism

(RFLP), randomly amplified polymorphic DNA

(RAPD), or amplified fragment length polymorphism

(AFLP) (Bai et al. 1995; Ren and Timko 2001). Only

a few quantitative trait loci (QTL) mapping experi-

ments have been published to date for N. tabacum

(Nishi et al. 2003; Julio et al. 2006). Opportunities

for genome-wide mapping experiments have been

greatly enhanced with the development of microsat-

ellite markers for this species which exhibit an

increased degree of polymorphism and reproducibility

(Bindler et al. 2007; Moon et al. 2009a, b). The first

microsatellite marker-based linkage map for N. tabacum

was recently published (Bindler et al. 2007). To date,

microsatellite markers have been used to identify a

N. tabacum genomic region derived from Beinhart-

1000 carrying genes affecting leaf surface chemistry

(Vontimitta et al. 2010).

The overall purpose of the current investigation

was to gain an increased understanding of the inher-

itance of the high level of black shank resistance

exhibited by the cigar tobacco line, Beinhart-1000, and

to identify microsatellite markers associated with

genomic regions contributing to this resistance. The

first objective was to generate a doubled haploid

mapping population derived from a cross between

Beinhart-1000 and the black shank-susceptible flue-

cured tobacco cultivar Hicks. The second objective

was to genotype this population with several hundred

microsatellite markers and to subsequently perform

marker–trait association tests. Results for two genomic

regions identified to be associated with resistance may

have implications for breeding for resistance to this

disease, and for gaining increased insight into the

mechanisms underlying quantitative disease resistance

to Phytophthora in plants.

Materials and methods

Genetic materials

The cigar tobacco line, Beinhart-1000 (highly resis-

tant to black shank), was hybridized with the black

shank-susceptible flue-cured tobacco cultivar, Hicks.

The F1 was then crossed as a female with N. africana

to produce gynogenic haploids from seed according to

90 Mol Breeding (2012) 29:89–98

123

Burk et al. (1979). Random haploid plants were

chromosome doubled using the midvein culture

method of Kasperbaur and Collins (1972).

Field evaluation

The doubled haploid mapping population consisting

of 118 lines, along with the parental lines, were

evaluated in black shank disease nurseries at two

locations during 2007 (Cunningham Research Sta-

tion, Kinston, NC; and Upper Coastal Plain Research

Station, Rocky Mount, NC) and at one location

during 2008 (Cunningham Research Station, Kinston,

NC). The experimental design at each location was a

randomized complete block design with three repli-

cations. Plots consisted of single 12-plant rows. Inter-

row spacing was 1.20 m, and within-row spacing was

0.56 m at both locations. A cultivar with a low level

of partial black shank resistance, K 326, was planted

in every third row to permit testing for uniformity of

disease pressure in each of the three field environ-

ments. Beginning 30 days after transplanting, plots

were evaluated at 15-day intervals for number of

plants killed by black shank. At approximately

120 days after transplanting, end percent survival

was calculated for each plot.

Genotyping

DNA was isolated from the doubled haploid lines and

the parents according to Johnson et al. (1995), except

that a BIO 101 FastPrep machine (BIO 101, Holbrook,

NY, USA) was used for leaf tissue grinding. A total of

438 microsatellite primer pairs were screened to

identify those that amplified polymorphic bands

between the parental lines. Most primer sequences

were previously described by Bindler et al. (2007).

Additional primer sequences were provided by Altria

Client Services Inc. (Richmond, VA, USA). Detection

of PCR products was conducted using the labeling

method of Schuelke (2000). PCR reactions were

performed in 15 ll volumes containing 25 ng of

template DNA, 1 9 PCR buffer (10 mM KCl,

20 mM Tris–HCl pH 8.8, 2 mM MgSO4), 0.015 lM

forward primer, 0.06 lM reverse primer, 0.06 lM

M-13 primer, 0.2 mM dNTPs, and 1 U Taq DNA

polymerase (New England Biolabs, Ipswich, MA).

PCR reaction conditions consisted of a denaturation

step at 94�C for 5 min; 29 cycles of 94�C for 30 s, 55�C

for 45 s, 72�C for 45 s; followed by 7 cycles of 94�C for

30 s, 52�C for 45 s, 72�C for 45 s; and a final extension

step at 72�C for 5 min. Amplification products were

separated using 8% polyacrylamide gels and a LI-COR

4300 DNA Analysis System (LI-COR Biosciences,

Lincoln, NE, USA) under the following conditions:

1,500 V, 40 mA, 40 W, and 45�C for 2.5 h. IRDye

700- or 800-labeled molecular weight standards

(50–700 bp) were loaded on each gel to facilitate allele

sizing using AFLP Quantar 1.0 software (KeyGene

Products B.V., Wageningen, The Netherlands).

Statistical analysis

An arcsine transformation of the end percent survival

data was performed in order to approximate homoge-

neity of residual variance. Arcsine-transformed data

was used for analysis of variance (ANOVA) using

PROC GLM of SAS 9.1 software (SAS Institute,

Cary, NC, USA), and composite interval mapping and

multiple interval mapping analyses were performed as

described below. Various correlation coefficients

were calculated using PROC CORR of SAS 9.1.

Heritability on a plot basis and entry mean basis was

calculated using a univariate mixed model analysis as

described by Holland et al. (2003).

Linkage map construction

Linkage analysis and genetic map construction were

carried out using MapMaker/EXP 3.0 (Lander et al.

1987). Linkage was established on the basis of a

minimum logarithm of odds (LOD) score of 3.0 and

maximum map distance of 40 centimorgans (cM).

Marker order was established by first selecting a

subset of eight markers and then identifying the best

order by executing the ‘Compare’ command. Remain-

ing markers were added using the ‘Try’ command.

The final marker order was confirmed by executing

the ‘Ripple’ command. Map distances in cM were

estimated from recombination fractions using the

Haldane mapping function (Haldane 1919). Graphical

representations of linkage groups were created using

MAPCHART software (Voorrips 2002).

QTL detection

Composite interval mapping (Zeng 1994) and multi-

ple interval mapping (Kao and Zeng 1997; Kao et al.

Mol Breeding (2012) 29:89–98 91

123

1999) were performed using Windows QTL Cartog-

rapher V2.5 (Wang et al. 2007). In composite interval

mapping, log-likelihood values for statistically sig-

nificant associations between genotype and arcsine-

transformed end percent survival were generated by

calculating LOD scores at 0.5 cM intervals along

linkage groups. LOD threshold significance levels

were determined using 1,000 permutations of the

method of Churchill and Doerge (1994). Composite

interval mapping was executed only to provide an

initial model for further analysis using multiple

interval mapping. QTL peaks from composite interval

mapping with a LOD threshold value greater than 2.5

were used in the initial model for multiple interval

mapping. Models were created and tested in an

iterative, stepwise fashion, searching for new QTL,

and testing the significance of the QTL after each

search cycle. New models were accepted only when

they had a decreasing effect on the value of the

Bayesian Information Criterion (BIC) (Piepho and

Gauch 2001). After QTL were added according to the

BIC criterion, all possible interactions between QTL

were tested for their significance and were retained in

the model if they decreased the BIC. While develop-

ing the model using multiple interval mapping, there

is always a possibility of over-fitting the model, and to

avoid this, it was made sure that the proportion of total

phenotypic variation explained by QTL did not

exceed the trait heritability. The model with minimum

BIC was chosen, and the QTL effects were simulta-

neously estimated using the ‘summary’ option of QTL

Cartographer. Genetic variability explained by the

identified QTL was calculated as the total phenotypic

variation explained by QTL divided by the entry mean

heritability of the trait. Confidence intervals for the

identified QTL were calculated according to Lander

and Botstein (1989) using 1 LOD score as the support

interval.

Results

Phenotypic analyses

Averaged over all three environments, Beinhart-1000

exhibited the greatest resistance (93.5% average end

percent survival) (Fig. 1). The black shank-susceptible

parent, Hicks, exhibited 0% survival. The doubled

haploid population of 118 lines displayed a wide range

of variation for end percent survival ranging from 0 to

91.7% (Fig. 1). No doubled haploid line exhibited an

end percent survival that was superior to that for

Beinhart-1000. Arcsine-transformed end percent sur-

vival data for the doubled haploid lines was subjected

to ANOVA which revealed significant differ-

ences amongst lines (P \ 0.0001), and significant

genotype 9 environment interaction (P \ 0.0001)

(Table 1). Despite the significant genotype 9 envi-

ronment interaction that was observed, the correlations

between end percent survival means from any two

environments was always greater than r = 0.81.

Means calculated over environments were therefore

used for QTL analyses.

As mentioned previously, a cultivar with a low

level of partial black shank resistance, K 326, was

planted in every third row to permit testing for

uniformity of disease pressure in each of the three

field environments. From a separate ANOVA on only

0

5

10

15

20

25

30

35

5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95 100

End percent survival

Beinhart-1000

Hicks

Num

ber

of L

ines

Fig. 1 Frequency distribution for doubled haploid lines

exhibiting indicated levels of end percent survival (averaged

over three North Carolina environments). End percent survival

for parental lines is indicated by arrows

Table 1 Analysis of variance for arcsine-transformed end

percent survival for doubled haploid population derived from

the cross Beinhart-1000 9 Hicks

Source df Mean

squares

F-value P-value

Environment 2 0.4456 9.5 \0.0001

Block (Environment) 6 0.5208 11.1 \0.0001

Genotype 117 1.3877 29.6 \0.0001

Genotype 9 Environment 234 0.0879 1.9 \0.0001

Pooled error 702 0.0469 – –

df degrees of freedom

92 Mol Breeding (2012) 29:89–98

123

PT605570.0

PT5153911.1

PT5226015.5

PT5101329.0

PT3035142.5PT2039142.9PT2018943.3

PT3048550.2

1

PT303270.0PT201633.9PT518785.2PT507369.5PT5263312.3PT5369713.6PT3024215.7PT3011416.7PT5162417.6PT30183PT3006721.4

PT5054322.7PT6158324.5PT60715PT5370124.9

2

PT400210.0

PT203724.7PT30197PT124210.1

PT2028736.1

PT3036840.5

PT3001163.2

3

PT302720.0PT302242.9PT302233.3

PT3035427.7PT30209PT2031531.6

PT52061PT6136232.9

PT5131135.7PT6014637.0PT5509137.9PT3012440.1PT6137340.7PT5116443.8

PT5096256.4

PT2034372.3PT3034673.2

4

PT202750.0

PT2028917.7PT3031618.1PT3033718.5PT2019620.3PT3030221.2

5

PT302960.0PT20234PT300874.7

PT3024515.9

PT3018422.6

PT3046841.1

PT1199PT3016948.9

PT3047752.8

6

PT303930.0PT301383.8PT303944.9PT302026.1

PT3021516.1

PT3029221.7PT3041923.3

PT3045244.0

7

PT607990.0PT614724.4PT3017412.4PT6151220.1PT5000123.2PT5140527.3PT5275330.1PT5351830.5PT3014732.8PT5379533.7PT52986PT5133134.1

PT6131134.5PT30218PT3039535.8

PT5430540.2

8

PT202910.0

PT3047027.2PT4001527.6

PT114042.3PT139946.1

PT3010758.7

PT2023594.0

PT30416102.4PT30140PT30213103.5

PT30421105.3PT20149110.3

9

PT300770.0PT302501.8PT304085.4

PT3013212.5

PT30372PT2044524.7

PT2019225.1

10

PT300310.0PT301700.5PT534933.8PT518598.7PT300089.6

PT3001819.6

PT127923.9

PT2002136.6

11

PT300960.0PT300993.1

PT2024227.9

PT6049434.4PT2021334.8PT5053937.1

PT5518861.5

12

PT304800.0PT304172.6

PT3004611.6

PT5280421.0

PT3042026.9

PT134833.5PT2038337.1

13PT303140.0PT301735.1PT553925.5PT6101112.1PT6133712.5PT5441914.3PT54081PT5328716.1

PT3046218.4PT3040322.5

14

PT1154PT605100.0

PT204595.5

PT3046320.1

PT6011424.6

PT107845.4

15

PT528100.0

PT502468.0PT300529.8PT3006313.1

PT6005324.5

PT5357637.7

16

PT553600.0

PT300536.5

PT3015617.7PT2037621.7PT3027423.2

PT6149930.2PT3033931.5

17

PT301110.0PT11930.4PT302850.8PT600502.7PT527529.3

PT5510317.8

PT3009437.5

18

PT300050.0

PT302484.1

PT4000912.8

PT3016519.0

19

PT302890.0

PT30043PT300449.1

PT2012714.0PT5070015.9

PT3026536.2

PT2040048.8

20

PT302350.0PT301100.5PT604862.8PT11945.1PT503925.5

PT5472920.5

21

PT601830.0

PT5342410.7

PT3002830.3PT6114334.8PT131341.2PT30095PT1194-2PT30084

43.0

PT30034PT3036444.8

PT3017247.6

22

PT202020.0PT303551.1PT302575.8

PT3042412.9

23

PT301710.0

PT400056.1PT13059.1PT3009711.4

PT5231827.1

24

Unlinked markersPT20165 PT20286 PT61153 PT55416 PT61019 PT20291-2

*

*

*

**

**

*

****

*********

****

*

*

**

***

*

*

*

*

Fig. 2 Linkage map for 206 microsatellite markers generated using 118 doubled haploid lines derived from the cross Beinhart-

1000 9 Hicks. Asterisks (*) indicate those markers exhibiting segregation distortion at the P \ 0.05 level

Mol Breeding (2012) 29:89–98 93

123

these plots, no significant differences in disease

pressure were observed among plots within blocks

for any of the three environments. The heritability for

end percent survival was high. On a plot basis,

heritability was found to be h2 = 0.70, while herita-

bility on an entry mean basis was calculated to be

h2 = 0.88.

Genetic linkage map

From a total of 438 microsatellite primer pairs that

were made available to us, 204 were found to produce

clear bands that were polymorphic between Beinhart-

1000 and Hicks. Nearly all primer pairs amplified

single PCR products. Two primer pairs, PT1194 and

PT20291, generated two polymorphic products. As a

result, a total of 206 markers were subjected to linkage

analysis using MapMaker/EXP 3.0 (Lander et al.

1987). Two hundred markers were placed into 24

linkage groups, while six markers remained unlinked

(Fig. 2). Linkage groups consisted of 4–17 markers

with genetic lengths of each group ranging from 12.9

to 110.3 cM. The total genetic length of the linkage

map was estimated to be 990.8 cM. Significant levels

of segregation distortion (P \ 0.05) were observed

for 36 markers (Fig. 2). For the majority of these

markers (28 of 36), Beinhart-1000 alleles were

predominantly observed.

QTL identification

Using composite interval mapping, four genomic

regions on linkage groups 4, 8, 11 and 14 were found

to be significantly associated with end percent

survival (LOD scores greater than the threshold level

of 2.5). These four QTL were included in the initial

model for multiple interval mapping, and two addi-

tional QTL were added using the BIC criterion. The

final multiple interval mapping model included six

QTL located on linkage groups 2, 4, 8, 9, 11, and 14

(Table 2; Fig. 3). No significant QTL 9 QTL inter-

action effects were found, and the final model

therefore contained only six QTL main effects

(Table 2). High LOD score values of 11.53 and

13.20 were observed for the QTL identified on linkage

groups 4 and 8, respectively. These QTL explained

20.4 and 25.4% of the observed phenotypic variation,

respectively. The major QTL on linkage group 4 was

relatively well resolved with a 1 LOD confidence

interval of 2.8 cM, while that on linkage group 8 had a

confidence interval of 5.0 cM. The major objective of

this paper is to report these two major QTL and their

associated markers. The remaining four QTL were

minor and explained much lower percentages of the

observed phenotypic variation (\6.0%), and had

larger confidence intervals (Fig. 3). We are reporting

this information because of its statistical significance,

but the practical value of these additional marker–trait

associations is probably low. Beinhart-1000 contrib-

uted the favorable allele at each of the six genomic

regions. The final model composed of six QTL

explained 64.3% of the phenotypic variation and

73.1% of the genetic variation for end percent

survival. As the phenotypic variation explained by

the final model was less than the calculated heritabil-

ity on a plot basis (h2 = 0.70), over-fitting of the

model was determined not to be a problem.

Discussion

Previous researchers have concluded that the inheri-

tance of black shank resistance in N. tabacum is

complex. Incomplete penetrance typically compli-

cates studies designed to investigate this ques-

tion. Resistance derived from Florida 301 has been

Table 2 Estimates of QTL

positions, effects, and

explained phenotypic

variation from multiple

interval analysis

a Effect is presented as

back-transformed value to

approximate the

corresponding end percent

survival

QTL Linkage

group

Left

marker

Right

marker

Position

(cM)

LOD Effecta % Of phenotypic

variance explained

1 8 PT61472 PT30174 11.90 13.20 14.94 25.4

2 4 PT61373 PT51164 40.80 11.53 14.17 20.4

3 11 PT53493 PT51859 5.80 4.69 9.64 6.2

4 14 PT30314 PT30173 4.00 3.36 6.38 5.5

5 9 PT20291 PT30470 7.00 1.39 4.70 3.5

6 2 PT30067 PT50543 21.50 2.04 6.53 3.3

94 Mol Breeding (2012) 29:89–98

123

reported to be from simple recessive (Clayton 1958),

to partially dominant and affected by modifying

factors (Moore and Powell 1959), to polygenic and

additive (Smith and Clayton 1948; Crews et al. 1964;

Chaplin 1966). In studies of F1 hybrids involving

Beinhart-1000 and more susceptible lines, Silber and

Heggestad (1963) found resistance to be intermediate.

Chaplin (1966) concluded that resistance of Beinhart-

1000 was complex and highly influenced by the

environment. The current investigation provides fur-

ther evidence for the conclusion that the inheritance of

resistance in Beinhart-1000 is complex. This is

supported by the frequency distribution in Fig. 1

where none of the 118 doubled haploid lines exhibited

as high a level of resistance as Beinhart-1000, and also

by the fact that six significant QTL were identified.

The range in their effects was great, with two of the

six QTL exhibiting very large effects. The level of

dominance of the identified regions cannot be deter-

mined from the current data, but past research has

suggested at least some degree of additivity (Silber

and Heggestad 1963; Chaplin 1966).

PT607990.0

PT614724.4

PT3017412.4

PT6151220.1

PT5000123.2

PT5140527.3PT5275330.1PT5351830.5PT3014732.8PT5379533.7PT52986PT5133134.1

PT6131134.5PT30218PT3039535.8

PT5430540.2

QT

L-1

0 5

10 PT302720.0PT302242.9PT302233.3

PT3035427.7PT30209PT2031531.6

PT52061PT6136232.9

PT5131135.7PT6014637.0PT5509137.9PT3012440.1PT6137340.7PT5116443.8

PT5096256.4

PT2034372.3PT3034673.2

QT

L-2

0 5

10 PT300310.0PT301700.5

PT534933.8

PT518598.7PT300089.6

PT3001819.6

PT127923.9

PT2002136.6

QT

L-3

0 5

10

PT303140.0

PT301735.1PT553925.5

PT6101112.1PT6133712.5

PT5441914.3

PT54081PT5328716.1

PT3046218.4

PT3040322.5

QT

L-4

0 5

10

PT202910.0

PT3047027.2PT4001527.6

PT114042.3PT139946.1

PT3010758.7

PT2023594.0

PT30416102.4PT30140PT30213103.5

PT30421105.3PT20149110.3

QT

L-5

0 5

10

PT303270.0

PT201633.9

PT518785.2

PT507369.5

PT5263312.3

PT5369713.6

PT3024215.7PT3011416.7PT5162417.6

PT30183PT3006721.4

PT5054322.7

PT6158324.5PT60715PT5370124.9

QT

L-6

0 5

10

Linkage Group 8 Linkage Group 4 Linkage Group 11

Linkage Group 14 Linkage Group 9 Linkage Group 2

Fig. 3 LOD score peaks generated from multiple interval

analysis of arcsine-transformed end percent survival data for

Beinhart-1000 9 Hicks doubled haploid mapping population.

Length of vertical solid black bars indicates confidence

interval (96.8%) calculated with a support interval of 1 LOD

score

Mol Breeding (2012) 29:89–98 95

123

Previous research efforts to map N. tabacum

genomic regions controlling quantitative traits have

been greatly limited relative to most major crops.

This is largely due to low levels of DNA polymor-

phism revealed by earlier molecular marker systems.

Opportunities for mapping quantitative traits in this

species have now been greatly improved with the

development of microsatellite markers (Bindler et al.

2007). The map presented here is only the second

published N. tabacum map based upon microsatellite

markers. The total genetic length of the map reported

here was only 990.8 cM, however, and likely covers

slightly less than 50% of the total genome. Never-

theless, we were successful in identifying genomic

regions that, together, explained 73.1% of the genetic

variation. As mentioned previously, the major objec-

tive of this publication is to report the two major QTL

found to be associated with black shank resistance on

linkage groups 4 and 8 (explaining 20.4 and 25.4% of

the phenotypic variation, respectively). The QTL on

linkage group 4 appeared to have been mapped fairly

precisely with a confidence interval of only 2.8 cM.

Although we have reported additional statistically

significant marker–trait associations, their potential

contribution to a breeding scheme is likely very

small. As additional microsatellite markers become

publicly available, further genotyping may permit

identification of more genomic regions contributing

to black shank resistance in Beinhart-1000, and

permit an increased marker density in regions of

interest identified here.

The overriding objective of this research is to

develop marker-based tools for utilizing a previously

unused source of black shank resistance in flue-cured

and burley tobacco. In the end, it may be possible to

increase the range and level of resistance to this

important disease. The objective would be to transfer

genomic regions positively affecting resistance to

these tobacco types while selecting against the

remainder of the Beinhart-1000 genome. This is the

case because cigar tobacco characteristics are gener-

ally undesirable for flue-cured and burley tobacco

cultivars. The two major QTL identified in this study

on linkage groups 4 and 8 might be candidates for

marker-assisted selection. Additional markers might

need to be added to better resolve the QTL on linkage

group 8, however. Further work might also be

conducted to determine if any of the markers identi-

fied to be associated with black shank resistance in

Beinhart-1000 are also associated with resistance

derived from Florida 301. If this were the case, such

markers might be immediately useful in flue-cured

and burley tobacco populations segregating for resis-

tance derived from this source. Figure 1 illustrates the

low frequency of lines exhibiting resistance levels

comparable to the resistant parent. Genotyping of

plants in initial segregating generations derived from

breeding crosses followed by intercrossing desirable

genotypes might increase the frequency of lines

exhibiting high levels of resistance extracted from

breeding populations.

Finally, an interesting finding of this research is

that the major black shank resistance QTL identified

on linkage group 4 co-locates with two genes, Abl and

BMVSE, influencing the accumulation of certain

trichome exudates on above-ground plant parts in

Oriental and some cigar tobaccos. The first gene, Abl,

is associated with the accumulation of cis-abienol, and

may encode for cis-abienol synthase (Wagner 1999).

The second gene, BMVSE, is involved in controlling

the biosynthesis of Groups III–VI sucrose esters

(Gwynn et al. 1985). These two genes were previously

mapped onto linkage group 4 using the exact same

population utilized in the current investigation

(Vontimitta et al. 2010). Microsatellite marker PT30124

exhibited complete cosegregation with Abl and also

resides within the small black shank resistance QTL

confidence interval on linkage group 4, which corre-

sponds to chromosome A of the N. tabacum genome

(Vontimitta et al. 2010). Microsatellite markers

PT30209 and PT20315 exhibited complete cosegre-

gation with BMVSE at a distance of 8.5 cM from Abl,

and were outside of the confidence interval for the

black shank resistance QTL identified on this chro-

mosome. The possibility that either of these trichome

exudates may affect resistance to Phytophthora

nicotianae needs to be investigated, especially given

past mention of their possible role in plant defense

(Severson et al. 1985; Jackson and Danehower 1996;

Wagner 1999). The reported isolation of genes

involved in cis-abienol synthesis (Sallaud et al.

2008) might permit plant transformation studies

designed to study the effect of either up-regulating

or down-regulating production of this compound on

partial resistance to Phytophthora. This possibility is

interesting from a plant biology perspective, but is

also important from the point of view of tobacco

cultivar development. Cis-abienol and Groups III–VI

96 Mol Breeding (2012) 29:89–98

123

sucrose esters are leaf surface chemistry traits that are

generally specific to Oriental and some cigar tobac-

cos. Their breakdown products contribute in a

substantial way to flavor and aroma of these tobacco

types (Severson et al. 1985), but are undesirable for

flue-cured or burley tobacco. If Abl does not have a

pleiotropic effect on black shank resistance, and there

is very close linkage between this gene and the gene

affecting resistance, significant effort might be needed

to break the linkage in order to utilize this genomic

region in breeding for disease resistance in flue-cured

or burley tobacco. Finally, information obtained

from the study presented here may be valuable in

preliminary efforts to better understand the molecu-

lar mechanism(s) behind quantitative resistance to

Phytophthora species in plants.

Acknowledgments This research was supported, in part, by

Altria Client Services Inc. and Philip Morris International.

References

Apple JL (1962) Transfer of resistance to black shank (Phytoph-thora parasitica var nicotianae) from Nicotiana plumba-ginifolia to N. tabacum. Phytopathology (Abstract)52:1

Bai D, Reeleder R, Brandle JE (1995) Identification of two

RAPD markers tightly linked with the Nicotiana debneyigene for resistance to black root rot of tobacco. Theor

Appl Genet 91:1184–1189

Bindler G, van der Hoeven R, Gunduz I, Plieske J, Ganal M,

Rossi L, Gadani F, Donini P (2007) A microsatellite marker

based linkage map of tobacco. Theor Appl Genet 114:

341–349

Burk LG, Gerstel DU, Wernsman EA (1979) Maternal haploids

of Nicotiana tabacum from seed. Science 206:585

Carlson SR, Wolff MF, Shew HD, Wernsman EA (1997)

Inheritance of resistance to race 0 of Phytophthora par-asitica var. nicotianae from the flue-cured tobacco culti-

var Coker 371-Gold. Plant Dis 81:1269–1274

Chaplin JF (1962) Transfer of black shank resistance from

Nicotiana plumbaginifolia to flue-cured N. tabacum. Tob

Sci 6:184–189

Chaplin JF (1966) Comparison of tobacco black shank (Phy-tophthora parasitica var. nicotianae) resistance from four

sources. Tob Sci 10:55–58

Churchill GA, Doerge RW (1994) Empirical threshold values

for quantitative trait mapping. Genetics 138:963–971

Clayton EE (1958) The genetics and breeding progress in

tobacco during the last 50 years. Agron J 50:352–356

Crews JW, Wills WH, Laprade JL (1964) Black shank disease

reactions of six flue-cured tobacco varieties and the F1

hybrids among them. Tob Sci 8:128–132

Gwynn GR, Severson RF, Jackson DM, Stephenson MG

(1985) Inheritance of sucrose esters containing b-meth-

ylvaleric acid in tobacco. Tob Sci 29:79–81

Haldane JBS (1919) The combination of linkage values, and

the calculation of distances between the loci of linked

factors. J Genet 8:299–309

Heggestad HE, Lautz W (1957) Some results of studies on

resistance to tobacco black shank. Phytopathology 47:452

Holland JB, Nyquist WE, Cervantes-Martinez CT (2003)

Estimating and interpreting heritability for plant breeding:

an update. Plant Breed Rev 22:9–112

Jackson DM, Danehower DA (1996) Integrated case study:

Nicotiana leaf surface components and their effects on

insect pests and diseases. In: Kerstiens G (ed) Plant

cuticles—an integrated functional approach. BIOS Sci.

Publishers, Oxford, pp 231–254

Johnson E, Miklas PN, Stavely JR, Martinez-Cruzado JC

(1995) Coupling- and repulsion-phase RAPDs for marker-

assisted selection of PI 181996 rust resistance in common

bean. Theor Appl Genet 90:659–664

Johnson ES, Wolff MF, Wernsman EA, Atchley WR, Shew HD

(2002a) Origin of the black shank resistance gene, Ph, in

tobacco cultivar Coker 371-Gold. Plant Dis 86:1080–1084

Johnson ES, Wolff MF, Wernsman EA, Rufty RC (2002b)

Marker-assisted selection for resistance to black shank

disease in tobacco. Plant Dis 86:1303–1309

Julio E, Denoyes-Rothan B, Verrier JL, de Borne F (2006)

Detection of QTLs linked to leaf and smoke properties in

Nicotiana tabacum based on a study of 114 recombinant

inbred lines. Mol Breed 18:69–91

Kao CH, Zeng ZB (1997) General formulas for obtaining the

MLEs and the asymptotic variance-covariance matrix in

mapping quantitative trait loci when using the EM algo-

rithm. Biometrics 53:653–665

Kao CH, Zeng ZB, Teasdale RD (1999) Multiple interval map-

ping for quantitative trait loci. Genetics 152:1203–1216

Kasperbaur MJ, Collins GB (1972) Reconstitution of diploids

from leaf tissue of anther-derived haploids in tobacco.

Crop Sci 12:98–101

Lander ES, Botstein D (1989) Mapping Mendelian factors

underlying quantitative traits using RFLP linkage maps.

Genetics 121:185–199

Lander ES, Green P, Abrahamson J, Barlow A, Daly MJ,

Lincoln SE, Newburg L (1987) MAPMAKER: an inter-

active computer package for constructing primary genetic

linkage maps with experimental and natural populations.

Genomics 1:174–181

Lewis RS (2005) Transfer of resistance to potato virus Y

(PVY) from Nicotiana africana to Nicotiana tabacum:

possible influence of tissue culture on the rate of intro-

gression. Theor Appl Genet 110:678–687

Milla SR, Levin JS, Lewis RS, Rufty RC (2005) RAPD and

SCAR markers linked to an introgressed gene condition-

ing resistance to Peronospora tabacina Adam. in tobacco.

Crop Sci 45:2346–2354

Moon H, Nicholson JS (2007) AFLP and SCAR markers linked

to tomato spotted wilt virus resistance in tobacco. Crop

Sci 47:1887–1894

Moon HS, Nicholson JS, Heineman A, Lion K, van der Hoeven

P, Hayes AJ, Lewis RS (2009a) Changes in genetic

diversity of US flue-cured tobacco germplasm over seven

decades of cultivar development. Crop Sci 49:498–508

Moon HS, Nifong JM, Nicholson JS, Heineman A, Lion K,

van der Hoeven R, Hayes AJ, Lewis RS (2009b)

Mol Breeding (2012) 29:89–98 97

123

Microsatellite-based analysis of tobacco (Nicotianatabacum L.) genetic resources. Crop Sci 49:2149–2159

Moore EL, Powell NT (1959) Dominant and modifying genes

for resistance to black shank of tobacco. Proc Assoc

Southern Agric Workers, p. 211

Nielsen MT (1992) Sources of resistance to black shank and

black root rot diseases. CORESTA Info Bull 3(4):144–150

Nishi T, Tajima T, Noguchi S, Ajisaka H, Negishi H (2003)

Identification of DNA markers of tobacco linked to bac-

terial wilt resistance. Theor Appl Genet 106:765–770

Piepho HP, Gauch HG (2001) Marker pair selection for map-

ping quantitative trait loci. Genetics 157:433–444

Ren N, Timko MP (2001) AFLP analysis of genetic poly-

morphism and evolutionary relationships among culti-

vated and wild Nicotiana species. Genome 44:559–571

Sallaud C, Rontein D, Tissier A (2008) Genes coding for cis-

labda-12,14-dien-8 alpha-ol (cis-abienol) synthase and

syn-copalyl-8-ol diphosphate synthase and uses thereof.

International Patent Application PCT/FR2007/051654

Schuelke M (2000) An economic method for the fluorescent

labelling of PCR fragments. Nat Biotechnol 18:233–234

Severson RF, Johnson AW, Johnson DM (1985) Cuticular

constituents of tobacco: factors affecting their production

and their role in insect and disease persistence and smoke

quality. Rec Adv Tob Sci 11:105–174

Silber G, Heggestad HE (1963) Comparative black shank

resistance of Beinhart-1000 (Quin Diaz), N.C. 5346, and

Rg in F1 generation involving various tobacco types. Tob

Sci 7:144–147

Smith TE, Clayton EE (1948) Resistance to bacterial wilt and

black shank in flue-cured tobacco. Phytopathology 38:

227–229

Sullivan MJ, Melton TA, Shew HD (2005) Managing the race

structure of Phytophthora parasitica var. nicotianae with

cultivar rotation. Plant Dis 89:1285–1294

Tisdale WB (1931) Development of strains of cigar wrapper

tobacco resistant to black shank (Phytophthora nicotianaeBreda de Haan). Florida Univ Agr Expt Stat Bull 226:1–45

Valleau WD, Stokes GW, Johnson EM (1960) Nine years

experience with the Nicotiana longiflora factor for resis-

tance to Phytophthora parasitica var. nicotianae in the

control of black shank. Tob Sci 4:92–94

Vontimitta V, Danehower DA, Steede T, Moon HS, Lewis RS

(2010) Analysis of a Nicotiana tabacum L. genomic region

controlling two leaf surface chemistry traits. J Agric Food

Chem 58:294–300

Voorrips RE (2002) MapChart: software for the graphical

presentation of linkage maps and QTLs. J Hered 93:77–78

Wagner G (1999) Leaf surface chemistry. In: David DL,

Nielsen MT (eds) Tobacco production, chemistry and

technology. Blackwell, Malden, pp 292–303

Wang S, Basten CJ, Gaffney P, Zeng ZB (2007) Windows

QTL Cartographer 2.5 User manual. available online at:

http://statgen.ncsu.edu/qtlcart/wqtlcart.htm. NC State

University Bioinformatics Research Center, Raleigh

Wills WH (1971) A comparison of black shank resistance of

tobacco measured in inoculated whole plants and excised

leaf tissue. Tob Sci 15:47–50

Yi YH, Rufty RC, Wernsman EA (1998) Identification of

RAPD markers linked to the wildfire resistance gene in

tobacco. Tob Sci 42:52–57

Zeng ZB (1994) Precision mapping of quantitative trait loci.

Genetics 136:1457–1468

98 Mol Breeding (2012) 29:89–98

123