Quantitative analysis of race-specific resistance to Colletotrichum lindemuthianum in common bean
Paula Rodrigues Oblessuc1,2,†, Renata Moro Baroni1,2,†, Guilherme da Silva Pereira3, Alisson Fernando
Chioratto4, Sérgio Augusto Morais Carbonell4, Boris Briñez1,2, Luciano Da Costa E Silva3, Antonio
Augusto Franco Garcia3, Luis Eduardo Aranha Camargo5, James D. Kelly6, Luciana Lasry BenchimolReis2*
1
Departamento de Genética e Evolução e Bioagentes, Instituto de Biologia, Universidade Estadual de
Campinas (UNICAMP), Campinas, São Paulo 13083 - 970, Brasil.
2
Centro de Recursos Genéticos Vegetais, Instituto Agronômico de Campinas (IAC), Campinas, São Paulo
13001-970, Brasil.
3
Departamento de Genética, Escola Superior de Agricultura Luiz de Queiroz (ESALQ/USP), Piracicaba,
São Paulo 13418-900, Brasil.
4
Centro de Grãos e Fibras, Instituto Agronômico de Campinas (IAC), Campinas, São Paulo 13001-970,
Brasil.
5
Departamento de Fitopatologia, Escola Superior de Agricultura Luiz de Queiroz (ESALQ/USP),
Piracicaba, São Paulo 13418-900, Brasil.
6
Department of Plant, Soil and Microbial Sciences, Michigan State University, East Lansing MI 48824
USA.
† These authors contributed equally to this study
*Corresponding author:
Dr. Paula Rodrigues Oblessuc
Departamento de Genética e Evolução e Bioagentes
Instituto de Biologia
Universidade Estadual de Campinas (UNICAMP)
Campinas - São Paulo
13083 - 970, Brasil.
e-mail: parobl@gmail.com
Phone: (+5519) 3202-1797; FAX: (+5519) 3202-1722
ABSTRACT
Molecular genetic maps continue to play a major role in breeding of crop species. The common bean
genetic map of the recombinant inbred line population IAC-UNA x CAL 143 (UC) has been used to detect
loci controlling important agronomic traits in common bean. In the current study, new microsatellite
markers were added to the UC map and the linkage analysis was refined using current genomic resources of
common bean, in order to identify quantitative resistance loci (QRL) associated with different races of the
anthracnose pathogen. A single race inoculation was conducted in greenhouse using four plants per plot.
Both race-specific and joint adjusted disease severity means, obtained from linear mixed model, were used
to perform multiple interval mapping (MIM) and multi-trait MIM (MTMIM). In total, 13 and 11 QRL were
identified by MIM and MTMIM analyses, respectively; with nine being observed in both analyses.
ANT02.1UC and ANT07.1UC showed major effects on resistance both for MIM and MTMIM. Common
major QRL for resistance to the three anthracnose races were expected, since high genetic pairwisecorrelation was observed between the race-specific and joint adjusted disease severity means. Therewith,
both ANT02.1 and ANT07.1 can be regarded as valuable targets for marker assisted selection; and so,
putative genes potentially involved in the resistance response were identified in these QRL regions. Minor
effect QRL were also observed, showing differential affects either on race-specific or multi-trait analyses
and may play a role on durable horizontal resistance. These results contribute to a better understanding of
the host-pathogen interaction and to breeding for enhancing resistance to C. lindemuthianum in common
bean.
Keywords: Phaseolus vulgaris; anthracnose; multi-trait Quantitative Resistance Loci mapping; multiple
interval mapping; candidate genes.
INTRODUCTION
Common bean (Phaseolus vulgaris L.) is an important source of protein, vitamins and minerals in human
diet worldwide (Broughton et al. 2003). Anthracnose is a serious seed-borne disease of common bean,
mainly in cool and highland environments in East Africa, Europe, and Latin America (Graham and Ranalli
1997). Yield losses caused by the anthracnose pathogen can be extremely high, reaching up to 100%
(Pastor-Corrales and Tu 1989). Anthracnose is caused by the specialized hemibiotrophic fungus
Colletotrichum lindemuthianum (Sacc. & Magnus) Briosi & Cavara, which exhibits highly variable genetic
diversity and co-evolved with common bean into Andean and Mesoamerican races (Pastor-Corrales et al.
1995; Melotto et al. 2000).
Currently 14 major race-specific resistance loci to C. lindemuthianum have been reported and are identified
by the Co symbol (Co-1 to Co-14) (Kelly and Vallejo 2004; Gonçalves-Vidigal et al. 2008; 2009; 2012;
Ferreira et al. 2013). Of these, only four loci Co-1, Co-12, Co-13 and Co-14 belong to the Andean gene
pool (Kelly and Vallejo 2004; Gonçalves-Vidigal et al. 2008; 2009; 2012). Genetic studies showed that Co1, Co-3, Co-4 and Co-5 loci have multiple allelic series (Melotto and Kelly 2000; Kelly and Vallejo 2004;
Gonçalves-Vidigal and Kelly 2006; Campa et al. 2009; Sousa et al. 2014), and mapping studies have
confirmed that these race-specific loci reside on different linkage groups (Ferreira et al. 2013). In fact,
some of these Co loci had already their location established on the common bean genetic map: Co-1 was
mapped to chromosome Pv01, Co-u on Pv02, Co-13 on Pv03, Co-3/9 and Co-10 on Pv04, Co-5 and Co-6
on Pv07, Co-4 on Pv08 and Co-2 on Pv11 (Kelly and Vallejo 2004; Campa et al. 2005; Rodríguez-Suárez
et al. 2007; Geffroy et al. 2008; Gonçalvez-Vidigal et al. 2011; Ferreira et al. 2013). With the exception of
the co-8 gene, all anthracnose resistance genes were shown to be dominant.
Further, different studies revealed that some of these anthracnose resistance loci are organized in clusters of
closely linked race-specific genes, what can be observed mainly in groups Pv04 and Pv07 (RodríguezSuárez et al. 2007; Campa et al. 2009; David et al. 2009). Pyramiding of Andean and Mesoamerican
specific resistance genes in a single cultivar has been proposed as a way to achieve durable resistance in
this crop (Kelly et al. 1995; Young et al. 1998; Ragagnin et al. 2009). The use of marker assisted selection
(MAS) for pyramiding resistance genes can clearly assure progress toward developing durable resistance in
common bean (Miklas et al. 2006; Garzon et al. 2008; Ragagnin et al. 2009; Ferreira et al. 2012; Madakbas
et al. 2013; Terán et al. 2013).
Alternatively, genes providing partial resistance have been shown in other crops to play an important role
in durable resistance, as defined by Johnson (1981). The implementation of this strategy to control
anthracnose in common bean has been limited by the lack of information concerning the existence of
partial resistance. The strict utilization of resistance genes (R genes) for crop protection generate a lack in
durability in some systems, primarily with respect to pathogens that have high evolutionary potential
(McDonald and Linde 2002; Poland et al. 2009). The prominence of single gene resistance (R gene and Avr
model) in the scientific literature has resulted from its simple Mendelian inheritance rather than its
prevalence in natural pathosystems (Gebhardt and Valkonen 2001). Non-Mendelian, quantitative variation
of resistance levels is frequently observed and this non-specificity or field resistance is assumed to be
controlled by polygenes or minor gene complexes. The distinction made between two types of resistance,
qualitative and quantitative, controlled by major R genes and by minor genes or polygenes, is often not
clear-cut at the phenotypic level, and race-specificity can also be a feature of quantitative disease resistance
(Young 1996; Caranta et al. 1997; Li et al. 2006; Marcel et al. 2008; Poland et al. 2009). In fact, studies
have been shown that a diversity of mechanisms is implicated in the overall resistance (Spoel and Dong
2012) and some overlapping with qualitative resistance and innate immunity (or non-specific basal
resistance) is observed (Poland et al. 2009; Spoel and Dong 2012).
In this context, multiple quantitative trait loci (QTL) models are useful tool to characterize and understand
the genetic architecture of complex traits. The well-known and powerful multiple interval mapping (MIM)
approach (Kao et al. 1999; Zeng et al. 1999) was recently improved by taking into account multiple traits
(MTMIM) (Da Costa E Silva et al. 2012). Additionally, mixed models have been successfully employed to
study genotype-environment interaction, since they provide great representation of the complex variancecovariance (VCOV) structure that follows from the patterns of genetic correlations between environments.
Quantitative resistance loci (QRL) mapping studies for anthracnose resistance in common bean have
identified two or more loci involved in resistance against a unique isolate of C. lindemuthianum (Geffroy et
al. 2000; Kelly and Vallejo 2004; Campa et al. 2009; Ferreira et al. 2013). Nkalubo et al. (2009) studied the
inheritance in market-class dry beans from Uganda and found that a large portion of the resistance response
could be explained by partial dominance suggesting there was also a significant role of minor genes with
additive effects.
Molecular genetic linkage maps remain a major tool for estimating the number and the effects of QRL that
are biologically relevant to resistance from the breeding perspective. Knowledge of the approximate QRL
locations has been used as a starting point for fine mapping and candidate gene approach, as well as for
accurate selection based on marker assisted selection (MAS) scheme in breeding programs (Dirlewanger et
al. 2012; Boopathi 2013). The segregating population developed by crossing different gene pool parents
IAC-UNA (Mesoamerican) x CAL 143 (Andean) (UC population) has contrasting features for several
agronomical traits such as reaction to different races of anthracnose, angular leaf spot (Pseudocercospora
griseola) and rust (Uromyces phaseoli). The UC map was generated as a large mapping population
currently developed with 380 recombinant inbred lines (RILs) advanced to the F 12 generation through selfbreeding. The current UC map has large gaps (Campos et al. 2011), so an updated version was needed for
mapping purposes. The objective of this study was to add new microsatellites (simple sequence repeats,
SSRs) markers to the UC map and to refine the linkage analysis using the current common bean genome
resources in order to identify QRL associated with different races of the anthracnose pathogen. The results
of this research may contribute to a better understanding of the host-pathogen interaction targeted at
resistance breeding.
MATERIALS AND METHODS
Mapping population
The IAC-UNA x CAL 143 (UC) recombinant inbred line (RIL) population used in this study was described
in previous studies of linkage and QTL mapping for disease resistance (Campos et al. 2011; Oblessuc et al.
2012a; 2013). The RIL population consists of 380 F12 lines developed by advancing the F2 generation
through the F8 by single pod descent (Funada et al. 2012) and from the F 8 to F12 by single seed descent.
The IAC-UNA parent is a black seeded bean derived from the cross between the Mesoamerican lines DOR
41 x H11178-100. The other parent, CAL 143 is a large-seeded red striped Calima type bean, derived from
the cross of Andean AND277 to Bola Roja. IAC-UNA is resistant to races 04, 38 and 55 of C.
lindemuthianum; whereas CAL 143 is susceptible to the same three races.
Molecular marker genotyping
DNA of each RIL was extracted according to Hoisington et al. (1994) and used in PCR amplification of 94
polymorphic SSRs (Table S1) and three SCAR markers (SH13, SBA16 - Queiroz et al. 2004 - and PF5330 Mahuku et al. 2004). Reaction conditions were the same as described in the previous articles (Gaitán-Solís
et al. 2002; Queiroz et al. 2004; Mahuku et al. 2004 ; Caixeta et al. 2005; Hanai et al. 2007, 2010; Blair et
al. 2008; Campos et al. 2011). Amplicons were separated in 6% denaturing polyacrylamide gel
electrophoresis and visualized after silver staining (Creste et al. 2001) (Fig. S1). Data on these 94 SSRs
were added to that of 198 SSR reported previously (Campos et al. 2011), in order to construct an updated
version of the UC map.
Sequence and linkage analysis
All SSR markers with sequences available in NCBI (http://www.ncbi.nlm.nih.gov/) or PhaseolusGenes
(http://phaseolusgenes.bioinformatics.ucdavis.edu/) databases were located in the P. vulgaris chromosomes
using the native Phytozome’s BLAST and default algorithm parameters (http://www.phytozome.net/). The
criteria used to assign markers to putative chromosomes with E-values ≤ 1 x 10–15 and a minimum identity
of 70% between query and database sequences. This information was used in the first step of the map
construction, where markers previously mapped in the UC map (Campos et al. 2011) whose chromosomal
assignments agreed with the corresponding linkage groups were anchored in order to form a framework for
the inclusion of other markers. The OneMap R package (Margarido et al. 2007) was used to establish these
linkage frameworks, which had their ordering obtained using the ‘order.seq’ command and were checked
via heatmap plots visual inspection.
The second step in the development of the updated version of the UC map was to add SSRs to the
frameworks previously obtained using the ‘try.seq’ command of OneMap. Final order was verified by the
‘ripple’ command, with a window of six markers; and multipoint estimates of distance were obtained with
the ‘map’ command. The threshold for considering markers to be linked was a LOD score of 3.0 and a
maximum genetic distance of 37.5 cM using the Kosambi (1944) map function. The genomic information
was also used to assign markers to linkage groups, when the genetic threshold was unable to do so. The
linkage map design was made using the MapChart 2.2 (Voorrips 2002). The chi-square (χ²) test for 1:1
segregation ratios was performed for all polymorphic markers. The expected segregation ratios were tested
based on p-values after performing Bonferroni correction assuming a global significance level of 0.05.
Disease evaluation
Seeds of individual RILs were germinated on germination paper in a growth chamber at 25 °C with 12 hour
photoperiod during three days. Four seedlings per RIL were transplanted to boxes containing autoclaved
vermiculite (Plantmax®) as substrate, constituting an experimental plot. Three different RILs were grown
per box. The UC parents (IAC-UNA and CAL 143) and Pérola cultivar (highly resistant to anthracnose)
were used as check treatments (controls) and were randomly included among the plots. Plants were
inoculated seven days after transplanting, where each experiment consisted in a single race of C.
lindemuthianum inoculation: 04, 38 or 55.
Monosporic cultures of C. lindemuthianum were grown on PDA media (200 g L–1 potato, 30 g L–1 dextrose
and 30 g L–1 agar) and the conidia were collected in water suspension using a glass spreader. Plants were
sprayed with the spore suspension (106 spores mL–1) using a DeVilBiss apparatus (Fanem). Immediately
after inoculation, plants were kept for 48 hours under 95 - 100% relative humidity at 23 °C and 12 hour
photoperiod. Disease severity was evaluated seven to ten days after inoculation, using a diagrammatic scale
(Fig. S2) based on the 1 to 9 disease scores proposed by Pastor-Corrales et al. (1995).
Phenotypic data and QRL mapping analyses
The analysis of phenotypic data (disease severity scores) was performed in GenStat 14 th edition (Payne et
al. 2009) using Residual Maximum Likelihood (REML), and race-specific (marginal) and joint score
adjusted means were obtained. The linear mixed model was the following: yijk    ri  tij   ijk , where
yijk is the disease severity score of the
treatment
( k  1,, K ; K  4
th
race, t ij
is the effect of the j
) observation of the
),  is the general mean, ri
) in race i (
(
effect of the i
th
th
is the fixed
treatment in race i , and  ijk is the random residual
error that was assumed  ijk ~ N (0, 2 ) . Treatments were separated into two groups, being g ij
random genetic effect of the j
the
th
th
RIL genotype (
) in race , and
check ( j  J g  1,, J g  J c ; J g  J c  383
assumed g ~ N (0, G)
effects and G  G I  I J g
where g  ( g11,, g IJ g )'
the
the fixed effect of
) in race . It was
the vector of genotypic
the genetic variance-covariance (VCOV) matrix, in which  is
the direct (Kronecker) product and I J g is an identity VCOV matrix of genotypes. Three different models
for G I matrix were examined and compared via Akaike (AIC; Akaike 1974) and Bayesian (BIC; Schwarz
1978) information criteria. These models considered identical variances between races (identity matrix),
heterogeneous variances between races with no covariance (diagonal matrix), and heterogeneous variances
including covariance between races (unstructured matrix).
The Wald
statistic test was used to assess the significance (p < 0.001) of the fixed effects.
Genetic pairwise correlations (Pearson’s coefficient) were obtained between race-specific (marginal) and
joint disease severity score adjusted means. The correlations were calculated using the R software (www.rproject.org) and tested assuming global significance level of 0.01.
QRL mapping with each race-specific and joint disease severity score adjusted means was performed using
the single-trait MIM approach (Kao et al. 1999; Zeng et al. 1999). Model construction was based on
forward search for putative QRL by using score-based criterion (Zou et al. 2004; Da Costa E Silva et al.
2010) with genome-wide significance level of 0.15, with a window size of 10 cM and a search grid of 1
cM. After each search, locations of QRL in the model were refined and their effects were tested via
backward elimination by adopting a significance score level of 0.01. In addition, race-specific disease
severity score adjusted means were also investigated under the MTMIM approach (Da Costa E Silva et al.
2012). Steps followed to build the multi-trait multiple QRL model were similar to those applied in the
single race-specific and joint MIM analyses. MIM and MTMIM model summary procedures were used to
estimate additive effects, LOD scores, LOD-1.5 support intervals around QRL positions, and partition the
variance explained by each QRL in the full multiple QRL model ( h 2 ). QRL effect significances were
accessed by calculating empirical p-value estimated via score statistics resampling based on seemingly
unrelated regressions (Zellner, 1962). Following common practice in QTL mapping studies, the signs of the
additive effects of the QRL were used to identify the parental origin of the favorable alleles (Mangolin et
al. 2004; Lima et al. 2006). Breeding values provided by each final model (Zeng et al. 2000; Da Costa E
Silva et al. 2012) were evaluated to genotype rank correlations by calculating Spearman’s coefficient using
R software. The QRL were named according to protocol of Miklas and Porch (2010) and grouped by using
the support intervals overlapping. MIM and MTMIM analyses were performed using R package under
development in the Statistical Genetics Laboratory at ESALQ/USP (Da Costa E Silva et al., personal
communication).
QRL genes analysis
Putative genes in major QRLs were identified by locating their linked markers with available sequences in
GeneBank
(http://www.ncbi.nlm.nih.gov/)
or
PhaseolusGenes
(http://phaseolusgenes.bioinformatics.ucdavis.edu/) on common bean chromosomes using the Phytozome
database (http://www.phytozome.net/). A 10 Kb window search was performed around each marker. In
addition, putative functions of genes were inferred using the Pfam (http://pfam.sanger.ac.uk/ - Finn et al.
2010) protein family database annotation from Phytozome. Arabidopsis thaliana homologs of each gene
were identified using the protein phylogeny of Phytozome (E-values ≤ 1 x 10–5).
RESULTS
Phenotypic analysis
The UC parents showed contrasting disease reaction to all three races of C. lindemuthianum. The joint
disease severity score adjusted mean of the resistant parent IAC-UNA was almost five times lower (1.5)
than that of the susceptible parent CAL 143 (7.3) (Fig. S3). As expected the disease score of Pérola was
also very low (1.3). The majority of the RILs were considered resistant to anthracnose races, with the
disease severity score adjusted means of 3.4. Transgressive RILs were observed both towards susceptibility
and resistance only when inoculated with race 04 (Fig. S3). This race appeared to be the most aggressive;
since it caused symptoms with higher disease severity scores (Fig. S3). Nevertheless, RILs more
susceptible than the CAL 143 parent were observed in response to races 38 and 55, while the joint data
showed no transgressive RILs.
The phenotypic data was adequately analyzed by using mixed model approach, which includes significance
test for the fixed effects and the identification of an appropriate VCOV structure for the genetic random
effects. The Wald statistic test used to assess the significance of fixed effects showed differences (p <
0.001) between races for the RILs and the UC parent responses. Unstructured model, with six parameters to
estimate, was selected for the G I matrix since the smallest AIC (17924.38) and BIC (17968.75) values
were obtained; otherwise, larger criteria values were obtained for identity (AIC = 18259.11, BIC =
18271.78) and diagonal (AIC = 18101.56, BIC = 18126.91) matrices which had one and three parameters
to estimate, respectively. Structures allowing heteroscedasticity for the random residual error were tested;
however identity matrix resulted in smallest AIC and BIC and therefore was selected as the best error
structure model. The response to race 04 showed larger variation for the random genetic effect ( g ij ) than to
races 38 and 55, which implies the existence of a genotype-race interaction for the resistance.
Therewith, this heterogeneity of variance leads to heterogeneous covariances and correlations between
responses to races (Table S2). Genetic pairwise correlations between joint and race-specific disease
severity score adjusted means to the three races were high and significant (p < 0.001), although the
response to race 04 showed the lowest correlation in relation to the response to races 38 and 55 (Table S2).
Updated linkage map
All markers with sequence available on public database had their most probable chromosome assigned by
BLAST searches against the genome sequence of Phytozome (Table S3). This information was used to
assign markers to chromosomes and construct the linkage groups. As a result the re-analyzed UC map was
more consistent with the genome sequence, and some markers mapped to different chromosomes in relation
to the previous analysis. The newly assembled UC map included 24 newly mapped SSR loci and one
SCAR PF5330, with the greatest increase observed in chromosomes Pv03 and Pv11, with 24 and 17 markers
in the present analysis compared to ten and five, respectively, in the previous analysis (Table S4).
Interestingly, Pv02 and Pv07 showed a reduction in marker number compared to the previous map.
However, all gaps reported in the previous UC map (Campos et al. 2011) were narrowed (Fig. 1). B08a and
B08b were combined in Pv08, with a gap of 40.8 cM. Genome coverage was improved with an average of
20.2 markers per chromosome for a total map length of 2,058 cM (Table S3) with an average ratio of 245
Kb/cM, based on the common bean genome size of 504 Mb (Phaseolus vulgaris - JGI v1.0;
http://www.phytozome.net/). The segregation deviation from the expected 1:1 ratio was detected in 38.3%
of the markers based on the Bonferroni correction (Fig. S4).
MIM and MTMIM analyses
QRL analysis was performed using the MIM and MTMIM methods. MIM for each race-specific (marginal)
and joint score adjusted means was carried out for the identification of respective unique and common
effects, while MTMIM was performed over all three marginal means. The QRL were named according to
Miklas and Porch (2010), although no prior information from other QRL mapping studies could be
compared with the present work, since this is one of the few studies to analyze and map QRL for
anthracnose resistance regarding its quantitative inheritance in common bean. Thirteen QRL controlling
resistance to the three races were detected by the MIM analysis (Table 1; Fig. 2). A major QRL controlling
resistance to races 38 and 55 was observed on chromosome Pv02 (ANT02.1 UC) with high LOD scores
(11.21 and 15.97, respectively). Another QRL with major effect (LOD = 16.24) was detected on
chromosome Pv07 controlling resistance to race 04 (ANT07.1UC). The LOD score of this QRL was also
high (10.92) in the joint-MIM analysis, indicating that it plays an important role in resistance to all the
three races. The remaining QRL were found to be of minor effect (LOD scores ≤ 7.0), of which eight
comprise the resistance response to race 04, four to race 38 and five to race 55; whereas common QRL
between races were found (Table 1).
A larger number of QRL (nine) to race 04 compared to races 38 and 55 (five and six, respectively) is
supported by the variance-covariance matrix, in which race 04 showed the highest variance (Table S2). In
addition, race 04 showed smaller genetic correlation with races 38 and 55, while these last two exhibited
high genetic correlation between each other (Table S2). ANT06.1 UC was the only QRL associated with
resistance to only one race (race 55).
A total of seven QRL were identified in the joint-MIM analysis (Table 1). ANT04.1UC, ANT07.2UC and
ANT09.2UC were mapped exclusively in this analysis, whereas ANT02.1, ANT07.1, ANT07.3 UC, and
ANT08.2UC QRL were also identified in the multi-trait analysis. As expected, the MTMIM analysis
performed better since the race-specific responses were correlated, and the power to detect associations was
increased. A total of 11 QRL were mapped using MTMIM analysis (Table 2), of which nine were also
identified in the race-specific analysis (Table 1, underlined; Fig. 1). The total variation explained by the
multi-trait QRL was similar to those calculated by the race-specific analysis, with ANT02.1 and ANT07.1
remaining as major effect QRL. In addition, these major QRL also showed same effect directions but with
different relative effect values for each race (Table 2; Fig. 2). ANT07.3 had similar behavior in relation to
the effect signs, but with a minor effect on resistance (LOD = 7.19). The two QRL mapped only by
MTMIM were located on chromosome Pv02 (ANT02.3 and ANT02.4) and showed minor effect on
resistance (LODs of 4.50 and 5.92, respectively), and together with the remaining QRL presented
conditionally neutral effects for one or two of the races studied. ANT08.3 UC was the only QRL showing
opposite effects between races (antagonistic pleiotropy) and is important (LOD = 9.36) mostly because of
this strong interaction effect.
Spearman’s correlations indicated that the breeding values of RILs obtained from estimates of QRL
parameters to each race as well as to all races considered together (joint analysis) were correlated, whereas
highly correlation between the race 04 responses and joint-MIM analysis was observed (Fig. S5). In fact,
given the important role of the same major QRLs and hence high Spearman’s correlation, selecting RILs
from the UC population to a specific race may lead to resistance to the others races, although it may be
more prominent for races 38 and 55.
Genomic analysis of the major effect QRL
The markers associated with the QRL of major effect (ANT02.1and ANT07.1) were located on the
Phytozome v1.0 assembly of the common bean genome (www.phytozome.net/). Based on the position of
the markers (Table 3), the estimated genomic sizes of ANT02.1 and ANT07.1 QRL are 2.3 Kb and 1.2 Kb,
respectively. The order of markers on ANT02.1 differed from the genetic map; whereas in the genome
assembly the order was FJ014, BM164 and IAC255.1 (Table 3), in the genetic map it was IAC255.1, FJ014
and BM164 (Fig. 1). The markers IAC239 and PvM40 on ANT07.1 aligned with Pv07, while IAC18a
aligned to a region on Pv06, instead of Pv07.
The gene content of ANT02.1 and ANT07.1 was assessed based on the genomic location of the markers
linked to the QRLs. A putative transcription factor (TF) belonging to the MYC TF family was identified on
ANT02.1, in addition to a gene with a domain of the placenta-specific 8 (PLAC8) gene family and a SCD1
stomatal cytokinesis defective gene. ANT07.1 contained genes encoding for a phloem protein 2-A13 (PP2A13), a membrane-associated progesterone binding protein 3 (ATMAPR3), and a disease resistance protein
of the TIR-NBS-LRR class (Table 3).
DISCUSSION
In order to develop durable horizontal resistance breeders need to pyramide minor additive effects with
those with major effects. Therefore, mapping favorable QRL alleles precisely on genetic linkage maps will
facilitate introgression into new cultivars, and can only be achieved by QRL mapping of crop plants
(Dirlewanger et al. 2012; Boopathi 2013). Genomic databases are a valuable resource to assign markers to
chromosomes and to identify candidate genes controlling the trait under study. Recently, the first efforts to
common bean genome sequencing had been performed by the Ibero-American Programme for Science,
Technology and Development (CYTED - http://www.cyted.org/) and the Applied Bean Genomics and
Bioproducts (http://www.beangenomics.ca/) groups. Therewith, this study used the first draft of the
common bean genome to gain insight into the genetic nature of race-specific resistance to C.
lindemuthianum.
Chromosomal localization of the SSR markers in the common bean genome enabled a refined analysis of
UC map. Markers were not uniformly distributed across the linkage groups, although similarity with
common bean chromosome sizes was observed (Pedrosa et al. 2003; Fonsêca et al. 2010). In addition,
correspondence between the UC map and other maps developed from different populations was observed
as common markers were positioned in the same portion of corresponding linkage groups (Blair et al. 2003;
Hanai et al. 2010; Garcia et al. 2011; Pérez-Vega et al. 2010; 2013), attesting to the transferability of the
markers. However, of the 94 polymorphic SSRs tested, only 24 were located on linkage groups. This could
be due, in part, to the use of 6% PAGE for resolving DNA fragments as this low resolution method could
difficult precise allele scoring. Notwithstanding, the usefulness of this updated version of the UC map was
confirmed by its effectiveness in identifying QRL to three races of C. lindemuthianum.
Resistance to all races was highly correlated most likely as the result of the 11 QRL identified in multi-trait
analysis, mainly the two common major QRL (ANT02.1 and ANT07.2). These loci may contain important
resistance genes acting in the resistance response to Andean races of C. lindemuthianum. The presence of
race-specific genes clustered on anthracnose resistance loci has been reported previously (Geffroy et al.
2000; Campa et al. 2009; Ferreira et al. 2013), as well as resistance to a single race was found in different
resistance loci (Gonçalves-Vidigal et al. 2008; Ferreira et al. 2013). For instance, the response to race 38
was conditioned by two independent dominant genes, one located on Pv11 (Co-2) and the other located on
Pv04 (Co-3) (Rodríguez-Suárez et al. 2007); as well as resistance to this race was also identified at the Co5 (Pv07), Co-6 (Pv07) and Co-4 (Pv08) (Ferreira et al. 2013). Race 38 of C. lindemuthianum is one of
commonest found in Northern Spain, and many studies have reported resistance to this race (Ferreira et al.
2013). A total of five QRL were mapped in response to this single race, with the ANT02.1 being the one
with major effect. The Co-u resistance gene conferring resistance to anthracnose isolates E4 and E42b was
located in the relative position of the I gene on Pv02 (Geffroy et al. 2008). Similarly, resistance genes to
race 55 identified in different bean genotypes were also conditioned by the Co-3 to Co-7, Co-9, Co-10 and
Co-12 loci (Gonçalves-Vidigal et al. 2008). In the present study, the major locus mapped in response to
this race was ANT02.1 among the total of the six QRL.
Interestingly, the greatest number of QRL (nine) was found for the most pathogenic race studied (race 04).
This was expected since the larger amount of variance from the phenotypic analysis indicated a more
quantitative response to race 04. In addition, while only one minor QRL was mapped exclusively in
response to races 38 or 55, four QRL were linked exclusively to race 04 in the race-specific analysis.
However, race 55 was the only one presenting a unique effect QRL (ANT06.1), that may contribute to the
resistance to this race, but with a minor effect, since the exclusive QRL mapped for race 04 (ANT02.2 UC,
ANT08.1UC, ANT09.1UC and ANT11.1UC) and race 38 (ANT08.3) was also identified in MTMIM or jointMIM analyses. ANT07.1 was another QRL linked to resistance to all races, but with major effects only
against race 04 in the race-specific analysis. In the MTMIM analysis, ANT07.1 also showed a high LOD
score for all the three races and same effect directions with different relative effect values for each race
response (Table 2), which may indicate the importance of this locus in general resistance to anthracnose.
The ANT02.1 also showed major effect and same directional additive effects for all three races.
The anthracnose resistance QRL ANT BJ was previously mapped on Pv02 in the BAT 93 x Jalo EEP558
(BJ) population (Greffroy et al. 2000). Since ANTBJ was linked to a cellulase gene (Cel marker) and four
putative genes with cellulose activity (Phvul.002G220300, Phvul.002G258400, Phvul.002G258500,
Phvul.002G261800) were located between 38.4 Mb and 42.8 Mb, in the region of the ANT02.1. It is
possible that ANTBJ and ANT02.1 correspond to the same QRL. Moreover, putative genes related to the
MYC TF family, the PLAC8 family and a SCD1 (Stomatal Cytokinesis-Defective 1) gene were also
identified in addition to the cellulase genes.
The MCY TF gene family is known to be involved in plant immune responses by activating the
transcription of jasmonic acid responsive defense genes (Kazan and Manners 2012). In common bean,
transcriptome analysis indicated positive regulation of gene expression in response to C. lindemuthianum
infection (Oblessuc et al. 2012b). Thus, the putative MYC gene identified in this study could be regarded as
a candidate gene of the resistance responses to the anthracnose pathogen. The SCD1 putative gene
identified in the ANT02.1 region could be acting in the bean immune response against anthracnose as well.
This gene was shown to act as a negative regulator of pathogen-associated molecular patterns (PAMP)
signaling pathways in Arabidopsis through inhibition of salicylic acid-mediated responses (Korasick et al.
2010). The PLAC8 genes are membrane-associated genes thought to be involved in plant growth (Wang et
al. 2008) and immunity (Staal et al. 2006), although little is known about its function.
Two major anthracnose resistance genes (Co-5 and Co-6) and two QRL were also previously mapped on
Pv07 (Geffroy et al. 2000; Kelly and Vallejo 2004; Campa et al. 2009; Souza et al. 2014). One QRL
(ANTBJ; Geffroy et al. 2000) and the Co-5 locus were linked to a Phaseolin (Phs) gene. In this study, three
Phs genes (Phvul.007G059800, Phvul.007G060000, Phvul.007G059700) were also located in the region of
the ANT07.1 (between 4.9 Mb to 5.1 Mb), suggesting that ANT07.1, Co-5 and the QRL mapped
previously by Geffroy et al. (2000) could be the same locus. Alternatively, since Co-5 was mapped between
markers BM183 and BM210 (Campa et al. 2009), which in the present analysis spans ANT07.1, ANT07.2
and ANT07.3 QRL, it is possible that Co-5 contains all three QRL.
Given the overall importance of the QRL on Pv07, a search for candidate genes in the ANT07.1 region was
also performed. The genes located closest to the SSR markers PvM40 (putative membrane-associated
progesterone binding protein 3; ATMAPR3) and IAC239 (putative Phloem protein 2-A13; PP2-A13) are
homologs of Arabidopsis genes involved in pathogen responses mediated by the salicylic acid pathway and
programmed cell death (Ascencio-Ibáñez et al. 2008). ATMAPR3 is a membrane protein that together with
PP2-A13 was shown to have their relative expression changed in response to infection by the Cabbage leaf
curl virus (Ascencio-Ibáñez et al. 2008). PP2-A13 is a myristoylation protein that also has been shown to
be induced in response to infection by the fungus Botrytis cinerea in Arabidopsis plants grown in compost
soil with resistance-inducing properties (Segarra et al. 2013).
The putative TIR-NBS-LRR gene was identified in the ANT07.1 QRL close to the IAC18a marker.
However, genome alignments assigned this marker to Pv06 rather than Pv07. NB-LRR proteins are
normally found in clusters in plant genomes and also it is a family with highly conserved domains.
Together with the unfinished common bean genome assembly, it is possible that the marker IAC18a
aligned with the Pv06 instead of Pv07 either by similarity between regions in these two chromosomes
containing NB-LRR gene clusters or due to the incomplete assembly of Pv07. The indication of a TIRNBS-LRR gene in the ANT07.1 supports a race specific R gene cluster in that region, as also suggested by
Campa et al. (2009).
Although resistance to anthracnose in common bean has been widely studied, molecular analysis offer
prospect for understanding the potential structural relationship between the genes underlying complete and
partial race-specific resistance to this pathogen. Observations of the performance of crop cultivars with
different types of resistance have led to the conclusion that quantitative resistance tends to be more durable
than typical R-gene mediated resistance. The results obtained in this study attempted to shed light on the
quantitative nature of anthracnose resistance in common bean.
The identification of QRL underlying resistance to different races, as was observed for ANT02.1 and
ANT07.1, with many race-specific loci such as Co-5 and Co-6 underlying resistance to unique races 38 and
55, are indicative of the complexity of the bean response to the anthracnose pathogen. In addition, the
identification of both QRL and race-specific loci by qualitative and quantitative analysis as the same
genome regions reinforce the importance of these loci to achieve durable resistance. In addition, differential
responses to a given QRL for each race observed here characterize QRL-race interaction and reveal the
genetic basis of genotype-race interaction highlighted during the phenotypic analysis. These QRL-race
interactions possibly justify the newly mapped QRL in MTMIM analysis and reinforce the importance of
race-specific genes in the control of resistance to anthracnose. Indeed, the identification of these major
QRL/race-specific loci indicates that these regions carry both race-specific R-genes as well as basal
resistance genes; what could be highlighted by the observation of TFs in these QRL. In addition, TFs could
be acting as partial resistance genes, as was observed for other crops (Miura et al. 2011) or even as major
genes in resistance (Lorenzo et al. 2004). Durable resistance may be achieved with a more comprehensive
knowledge of the C. lindemuthianum – P. vulgaris interaction by cloning and characterizing the actual
genes underlying the resistance in major QRL to better understand the pathways involved in the racespecific and defense response to anthracnose infection of common bean.
ACKNOWLEDGMENTS
The authors thank to São Paulo Research Foundation - FAPESP for the fellowship to PRO and RMB
(2009/02411-2 and 2008/52541-7). This work was also supported by São Paulo Research Foundation FAPESP (2010/51673-7).
AUTHOR CONTRIBUTION STATEMENT
PRO and RMB conceived and conducted inoculation experiments and the RIL population genotyping. PRO
drafted the manuscript. GSP and AAFG performed all the phenotypic, linkage and QTL mapping analyses,
as well as the genomic analysis. BB helped in the mapping construction analysis. LDCES helped with the
QTL mapping analysis. AFC and SAMC are the associated common bean breeders, participating of the
discussions. LEAC participated in the initial design of the project, discussions and in the editing of the
manuscript. JDK provided the anthracnose isolates and helped with the manuscript edition. LLBR
conceived the project and coordinated the sponsoring project through FAPESP agency, helped with data
interpretation and editing of the manuscript. All authors have read and approved the final version of the
manuscript.
CONFLICT OF INTEREST
The authors declare that they have no conflict of interests.
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TABLES
Table 1 QRL positions, QRL peak closest marker, LOD scores, additive effects, and heritability (h2) for
resistance to anthracnose races in UC population using the MIM approach.
Table 2 QRL positions, QRL peak closest marker, joint (LODj) and individual (LODi) LOD scores,
additive effects, and heritabilities (h2) for resistance to anthracnose races in UC population using the
MTMIM approach.
Table 3 Genome locations of the molecular markers mapped on the two major anthracnose resistance QRL
ANT02.1 and ANT07.1; and the putative genes located in a 10 Kb window of these markers, based on the
Phytozome P. vulgaris genome database.
FIGURE LEGENDS
Figure 1 The updated UC linkage map and the QRL for anthracnose resistance. Full boxes delimit QRLs
peaks for all mapping analyses, and whiskers delimit the inferior and superior LOD-1.5 support intervals
for the peaks. Black box-and-whiskers plots represent the QRL detected in MTMIM analysis, while red and
orange plots represent specific QRL detected in respective joint and race 55 MIM analyses exclusively.
Additional stacked blocks show the significant (‘+’ or ‘–’) or non-significant (‘○’) effects for races 04, 38
and 55 (from top to bottom) for QRL detected by MTMIM analysis; a single block shows the effect for
QRL detected by only MIM analyses. This representation was based on Malosetti et al. (2008). The map
was drawn with MapChart (Voorrips 2002).
Figure 2 LOD score profiles of the QRL mapping for anthracnose resistance in IAC-UNA x CAL 143
(UC) RIL population. LOD scores (y-axis) were obtained by multiple interval mapping (MIM) for each
race-specific and joint adjusted means and by multi-trait MIM (MTMIM) based on marker distances (xaxis) of the updated UC genetic map. Colored lines represent MIM profiles for the races 04 (green), 38
(blue), 55 (orange) and for the joint analysis (red); black lines represent MTMIM profiles. The triangles
indicate the position of maximum LOD scores for each QRL. Full boxes delimit QRL peaks for all
mapping analyses, and whiskers delimit the inferior and superior LOD-1.5 support intervals for the peaks.
The figure was obtained using the R software (www.r-project.org).
SUPPLEMENTAL MATERIAL
Table S1 Microsatellite (SSR) markers scored in the UC RILs mapping population.
Table S2 Genetic pairwise correlations (Pearson’s coefficient) between specific-race and joint disease
severity score adjusted means; and estimated genetic unstructured variance-covariance matrix G I for races
(in bold).
Table S3 Molecular marker location on the common bean genome of Phytozome v1.0
Table S4 Comparison between the previous UC map (Campos et at. 2011) and the updated UC map.
Figure S1 SSR segregation in the RILs and parental genotypes of the UC population. Polymorphism of
SSR markers (A) IAC128 and (B) PvM98 are shown in denaturing polyacrylamide gel silver stained. The
bands corresponding to the population parents IAC-UNA (U) and CAL 143 (C) alleles in addition to the
RILs are shown as a representative sample of the UC population.
Figure S2 Diagrammatic scale for the anthracnose disease scores. The scale was developed based on the
overall UC RILs symptoms to all the three races (04, 38 and 55) using the 1 – 9 scale. The scores range
from 1 (absence of symptoms) to 9 (strongest symptoms, with the death of the plant).
Figure S3 Anthracnose disease score distribution for the UC RILs evaluated for each race 04, 38 and 55.
The score frequencies were based on the joint and race-specific (marginal) disease severity adjusted means
from the linear mixed model. The disease score for the RILs parents IAC-UNA (U) and CAL 143 (C) are
indicated by the arrows. The histograms were obtained using the R software (www.r-project.org).
Figure S4 Segregation deviation analysis. (A) Logarithm scale for the p-values (y-axis) and (B) the
frequency of alleles (y-axis), both obtained for each marker mapped in all 11 linkage groups (x-axis) for
the 1:1 segregation test; α referred to the significance established for the χ2 test after Bonferroni correction
and α* is the significance for the χ2 test of 0.05. The figure was obtained using the R package qtl (www.rproject.org).
Figure S5 Spearman’s correlations for genotype ranks based on the breeding values provided by each final
MIM model for resistance to anthracnose races in UC RIL population. The correlation values may be
viewed on the top of the figure; and the scatter plots may be observed on the bottom of the figure. The
figure was obtained using the R package psych (www.r-project.org).