Marker-assisted selection strategies for developing resistant soybean plants to cyst nematode

Estratégias de seleção assistida por marcadores para desenvolvimento de plantas de soja resistentes ao nematoide de cisto

ARTICLE

Marker-assisted selection strategies for developing resistant soybean plants to cyst nematode

Estratégias de seleção assistida por marcadores para desenvolvimento de plantas de soja resistentes ao nematoide de cisto

Fernanda Abreu SantanaI, ^*; Martha Freire da Silva^II; Julierme Kellen Freitas Guimarães^II; Marcia Flores da Silva Ferreira^III; Waldir Dias Pereira^IV; Newton Deniz Piovesan^II; Everaldo Gonçalves de Barros^V

^IUniversidade Estadual do Norte Fluminense Darcy Ribeiro, Laboratório de Melhoramento Genético Vegetal, 28.013-602, Campos dosGoytacazes, RJ, Brazil

^IIUniversidade Federal de Viçosa, Instituto de Biotecnologia Aplicada à Agropecuária, 36.570-900, Viçosa, MG, Brazil

^IIIUniversidade Federal do Espírito Santo, AltoUniversitário, S/N, 29.500-000, Alegre, ES, Brazil

^IVEmbrapa Soja, C. P. 231, 86.001-970, Londrina, PR, Brazil

^VUniversidade Católica de Brasília, 70.790-160, Brasília, DF, Brazil

ABSTRACT

Resistant lines can be identified by marker-assisted selection(MAS), based on alleles of genetic markers linked to the resistance trait. This reduces the number of phenotypically evaluated lines, one of the limitations in the development of cultivars with resistance to soybean cyst nematode (SCN).This study evaluated the efficiency of microsatellites near quantitative traitloci (QTL) for SCN resistance, in the linkage groups (LG) G and A2 of soybean, for the selection of resistant genotypes in populations originated from crosses between the cultivars Vmax and CD201. The QTL of LG A2 was not detected in 'Vmax' (derived from PI 88788). In MAS, the microsatellites of LG G were efficient in selecting F_6:7 families with resistance and moderate resistance to SCN race 3. The selection efficiency of the microsatellites Sat_168, Satt309 and Sat_141 was greater than 93%.

Key words: MAS, Glycine max, SCN, microsatellites, QTL

RESUMO

A seleção assistida por marcadores (SAM) permite identificarlinhagens resistentes com base em alelos de marcadores genéticos ligados aocaráter, o que reduz o número de linhagens avaliados fenotipicamente, uma daslimitações ao desenvolvimento de cultivares resistentes ao nematoide de cistoda soja (NCS). Neste trabalho objetivou-se avaliar a eficiência demicrossatélites próximos a QTLs de resistência ao NCS, nos grupos de ligação (GL) G e A2 da soja, na seleção de genótipos resistentes em populaçõesoriginadas do cruzamento entre as cultivares Vmax e CD201. O QTL do GL A2 nãofoi detectado em 'Vmax' (derivada da PI 88788). A SAM por microssatélites do GLG foi eficiente na seleção de famílias F_6:7 resistentes emoderadamente resistentes à raça 3 do NCS. Os microssatélites Sat_168, Satt309e Sat_141 apresentaram eficiência de seleção maior que 93%.

Palavras-chave: SAM, Glycine max, NCS, microssatélites, QTLs

INTRODUCTION

The soybean cyst nematode (SCN), Heterodera glycines Ichinohe, is worldwidethe main pathogen of soybean (Wrather et al. 2001). The most efficient andeconomical control method is the use of resistant cultivars, together withrotation with non-host crops (Embrapa 2010). However, the development ofresistant cultivars is limited by factors such as phenotypic analysis ofsegregating populations, which is time consuming, labor-intensive and requiresmuch space in the greenhouse (Young and Mudge 2002, Cervigni et al. 2004, Concibido et al. 2004).

The development of 1, 000 microsatellites (Simple Sequence Repeat) led to theconstruction of an integrated and saturated consensus map for soybean (Creganet al. 1999a, Song et al. 2004). Thus, the markers near important QTL(Quantitative Trait Loci) can be used as anchors for locating regions in thelinkage map in different populations (Schuster et al. 2001).

Several QTL linked to the resistance to different SCN races were identified andvalidated in different soybean genotypes (Concibido et al. 2004). One of theQTL with major effect in LG G, designated rhg1, confers resistance toseveral races (Chang et al. 1997, Concibido et al. 1997, Meksem et al. 2001, Yue et al. 2001, Glover et al. 2004, Silva et al. 2007b), while another in LGA2 (Rhg 4), with major effect, confers specific resistance to race 3(Mahalingam and Skorupska 1995, Webb et al. 1995, Chang et al. 1997, Meksem etal. 2001, Wang et al. 2004).

Marker-assisted selection (MAS) is an important tool to overcome difficulties of phenotypicselection, in the identification of SCN-resistant lines in segregating populations(Young and Mudge 2002, Concibido et al. 2004) and represents a usefulalternative in the development of resistant cultivars.

This study evaluated the effectiveness of using microsatellite near the loci rhg1 and Rhg4, for the selection of soybean lines resistant to SCN race 3.

MATERIAL AND METHODS

Plant material

From crosses between isolines derived from the cultivars Vmax (resistant to SCNraces 3 and 14) and CD 201 (SCN-susceptible), 65 F₅soybeanpopulations were obtained by the single pod descent (SPD) method). Thesepopulations were derived from the breeding program for soybean quality of theinstitute BIOAGRO at the Federal University of Viçosa (UFV) (Figure 1A).

Marker-assisted breeding strategy

The selected microsatellites were chosen for being in regions close to QTL for SCNresistance, i.e., in the region from 0.0 to 10.06 cM of the LG G on theconsensus map, comprising the region of the SSR Satt163, Satt038, Satt275, Sat_168, Satt309, Sat_141, and Sat_163, as well as the region of LG A2, from 51.57 to 58.44 cM, with the SSR Sat_157, Sat_162, BLT 065, Satt187, GMENOD 2B(Song et al. 2004).

The polymorphic microsatellites between the parents “Vmax” and “CD201” wereamplified in DNA seed bulks of each of the 65 F₅ populations andseven F₅ populations were selected on microsatellite alleles closeto the resistance QTL. Of these seven, four populations were simultaneouslyselected on polymorphism of microsatellites of LG G and A2 and three on microsatellitepolymorphism in LG A2 only. The selected populations were sown in bulks by theSPD method to obtain the F₆ generation. At harvest, the plants werethreshed separately and 64 F_6:7 families were obtained (Figure 1B).These families were phenotyped for race 3 (HG Type 5.7) of SCN.

Phenotypic evaluation

The experiment was carried out in a greenhouse (25-30 ºC and 16 hours of light) ofEmbrapa Soja, in Londrina, Paraná. The experiment was arranged in a completelyrandomized design with seven replications.

Seeds from 64 F_6:7 families were sown separately in plastic pots withsand. The same procedure was applied to the seven soybean lines ('Peking', PI88788, PI 90763, PI 437654, PI 209332, PI 89772, and PI548316), to classify theHG types of the SCN populations, as proposed by Niblack et al. (2002), and to the susceptibility control Lee 74.Three days after germination, the seedlings were transplanted to 1 kg claypots, containing a soil-sand mixture (1:3). At transplanting, each plant wasinoculated with 4, 000 SCN eggs of race 3 as described by Dias et al. (1998).The soybean plants were grown in a greenhouse for 28 days. Thereafter, leavesof each plant were collected for DNA extraction and recovery of the nematodefemales.

For female extraction, each plant was carefully removed from the pot and the rootsystem washed on sieves of 20 and 100 mesh under a strong water jet. Afterquantifying the females with a gridded acrylic plate and stereoscopicmicroscope, the female index (FI) was calculated for each F_6:7 family.The reaction of the F_6:7 families was classified by the criterion ofSchmitt and Shannon (1992), i.e., families with FI<10% were considere dresistant; 10% < FI < 30% moderately resistant; and FI > 31%susceptible.

Genotypic analysis

For DNA extraction, leaves were collected from plants of different families prior tophenotypic analysis. The extraction followed the protocol of Doyle and Doyle(1990), modified by Abdelnoor et al. (1995). From each F_6:7 family, DNA bulks with seven plants were obtained for subsequent genotyping with themicrosatellites Satt 309, Sat_141 and Sat_168 (Figure 1B).

Amplification reactions were performed in a final volume of 15 µL containing 10 mM Tris-HCl, pH 8.3, 50 mMKCl, 2 mM MgCl₂, 100 M of each deoxynucleotide, 0.3 µM of each primer, one unit of Taq polymerase, and 30 ng of DNA. The PCRprogram consisted of: 94 ºC for 4 min, 30 cycles of 94 ºC for 1 min, 55 ºC for1 min, 72 ºC for 2 min, and subsequent 72 ºC for 7 min. The amplificationproducts were separated by electrophoresis in 10% native polyacrylamide gelsusing 1X TAE buffer (1 mM Tris- acetate 40 mM, EDTA) at 140 volts, subsequentlystained with ethidium bromide (10 mg mL^-1) and photographed.

Statistical analysis

For analysis of variance and establishment of the genetic parameters, we usedsoftware Genes (Cruz 2013). The efficiency of selection (SE) of themicrosatellite loci linked to SCN resistance was based on the comparisonbetween the phenotypic and genotypic analyses and was calculated as describedby Silva et al. (2007b):

SE = 100 [MFMF + mfmf)/(MM + mm)]

where:

MFMF - number of families selectedcorrectly as resistant, based on the marker and phenotypic analysis;

mfmf - number of families selectedcorrectly as susceptible, based on the marker and phenotypic analysis, and

MM + mm - total of familiesselected as resistant and susceptible, based on markers only.

The SEwas calculated using both the criterion of resistance, considering an index ofparasitism (IP) of < 10 as well as moderate resistance, with IP < 30(Schmitt and Shannon 1992).

RESULTS AND DISCUSSION

Three microsatellites of LG G (Satt309, Sat_168 and Sat_141) andone from LG A2 (Satt187) were polymorphic in the parents. These wereused in the assisted selection performed in DNA seed bulks of each of the 65 F₅populations. Based on the resulting polymorphism, seven segregating F₅populations were selected by microsatellites, four of which of both LG andthree of LG A2 only. The other populations were not polymorphic for the testedmicrosatellites.

Phenotypic evaluation of the selectedpopulations

The 64 F_6:7families derived from the seven selected F₅ populations werephenotyped for resistance to SCN race 3 (HG type 5.7). The lowest and highest mean numbers offemales were found in population 1; transgressive segregation was observed inthis population only, for both reduction and increase of the number of females, compared to the means of the parents. In the other populations, transgressivesegregation occurred only for increase in the number of females (Table 1). Thistype of segregation was also observed for resistance to SCN race 14, which wasattributed to possible effects of gene interaction in the control of resistance(Silva et al. 2007a).

All populations except 3 and 4 had a higher mean number of females than thesusceptible parent (Table 1). The different resistance level of the families ineach population indicated that the parents (isolines) derived from Vmax mayhave failed to recover all resistance genes, particularly minor-effect genes.Another possibility is that the population size did not allow the detection ofa combination with all alleles. In this case, it would be necessary to studystrategies that would allow the evaluation of a greater number of genotypes.

By the classification based on the index of parasitism of F_6:7 families ofeach population (Table 2) and resistant or moderately resistant and susceptiblefamilies were identified in the four F₅ populations, selected on SSRpolymorphism of LG G and A2. However, the F_6:7 families derived fromthe three selected F₅ populations with LG A2 only were susceptible.

The results of the analysis of variance and estimates of genetic parameters of eachF₅ population are shown in Table 3. Significant genetic variance of1% was found among F_6:7 families, originating from the F₅populations selected on the basis of SSR of LG G and A2 (Table 3). This allowedthe selection for the best families in these populations. In the F_6:7familiesof the populations selected by the microsatellite of LG A2 (Satt187), nogenetic variability for resistance to race 3 was found (Table 3), indicatingthe absence of segregation of the resistance gene Rhg 4 of LG A2in these populations. Thus, LG A2 markers should not be used in marker-assistedselection in Vmax-derived populations (descendant from PI 88788). For thisreason, the discussion below focuses only on populations with significantgenetic variability.

The heritability estimates ranged from 75.9 to 95.7%. The high heritabilitydetected may be a result of the high level of homozygosis of the studiedfamilies, the small number of major genes involved and of the environmentalcontrol in the experiment. According to Falconer and Mackay (1996), heritabilityestimates depend on the plant material, the estimation method and theexperiment.

Webb etal. (1995) reported a broad-sense heritability of 97% for resistance to race 3in crosses with PI 437654. Therefore, the efficiency of phenotypic selection ishigh for this SCN race.

Genotypic evaluation of selected F_6:7families

The second genotypic evaluation assessed DNA bulks of seven plants from each F_6:7F₅ families derived from the selected populations. These familieswere genotyped using only the LG G microsatellites, since the F_6:7families of the populations selected by marker Satt 187 of LG A2 werehighly susceptible, indicating that this marker was not linked to SCNresistance in the studied populations.

Although the Satt187 marker of LG A2 is close to the resistance locus Rhg 4, there is no evidence that this resistance allele is present in the resistantparent Vmax, which was derived from the resistance source PI 88788. Glover etal. (2004) studied PI 88788 and identified QTL in LG G and J by compositeinterval mapping at the 5% level of significance, but mentioned no QTL for SCNresistance in the region of LG A2. Concibido et al. (1997) found no resistancelocus for race 3 in LG A2 either in the same resistance source. Therefore, thelocus for SCN race-3 resistance in LG A2 is probably absent in PI 88788.

However, LG A2 is important to control resistanceto SCN race 3 in different resistance sources, such as PI 209332 (Concibido etal. 1994), Peking (Mahalingam and Skorupska 1995, Chang et al. 1997, Meksem etal. 2001), PI 437654 (Webb et al. 1995, Prabhu et al. 1999), PI 90763 (Guo etal. 2005) and Hartwig (Silva et al. 2007b).

Genotype - phenotype analysis of F_6:7 families

The four phenotypically resistant F_6:7 families carried resistance alleles ofthe three microsatellite markers LG G (Satt309, Sat_168 and Sat_141), indicating that these markers are extremely efficient in the selection ofresistant families.

Of the four moderately resistant F_6:7 families, three carried resistancealleles of the LG G microsatellites, indicating that these markers did notdistinguish resistant from moderately resistant families. Within thesefamilies, plants with resistance, moderate resistance and moderatesusceptibility were observed. This demonstrates that in addition to theresistance QTL of LG G, other resistance (minor effect) QTL are needed forcomplete resistance to SCN race 3 in Vmax-derived populations. The QTL of LG Gis known to explain much of the resistance to SCN race 3 in differentresistance sources. Complete resistance however requires minor-effect genesthat are not always identified (Concibido et al. 2004).

It wasfound that five F_6:7 families derived from the four genotypedpopulations were classified as susceptible in spite of carrying resistancealleles of the evaluated microsatellites, indicating the existence ofsegregating resistance genes in these families, since plants with moderateresistance were found in the families.

Selection efficiency

The selection efficiency was evaluated considering all F_6:7 families ofthe four populations together. With the SSR Sat_168, a selectionefficiency of 97.05% was reached and 96.55 and 93.55%, with Satt309 and Sat_141, respectively. The pairwise combinations of the microsatellites raised the SE to100%, in all cases (Table 4).

The selection efficiency increased little when the criterion of moderate resistance (IP <30) was used for calculations (Table 4). In all cases, the discrimination ofresistant families did not raise the efficiency, unlike the correctclassification of the susceptible ones.

The sedata confirmed that these markers are very close to locus rhg1, whichhas been extensively studied for explaining a large phenotypic variation of theSCN resistance to race 3 and other races (Concibido et al. 2004), and provedtheir usefulness in the selection of the assessed populations and that they canbe used in other Vmax-derived populations.

Creganet al. (1999b) reported that the SSR Satt309 and Sat_168 are at a distance of0.4 cM from locus rhg1 and the use of one of these markers would ensuresuccess in genotypic selection. High selection efficiency was also reported byMudge et al. (1997) in the identification of race-3 resistant lines with SSRflanking only the region of gene rhg 1, with an accuracy of 98%. Silva etal. (2007b) also reported high selection efficiency (94%) for race-3 resistancein F_2:3 families with only one SSR of LG G: Satt309.

However, the high SE values reported in the literature were obtained in mappingpopulations, where the QTL was detected in the study population. Thisparticular study deals with a breeding population in advanced generations;nevertheless, it was possible to obtain high selection efficiency with the SSRof LG G, near locus rhg1.

ACKNOWLEDGEMENTS

The authors wish to thank the National Council for Scientific and Technological Development (CNPq) and the Brazilian Federal Agency for Support and Evaluation of Graduate Education (CAPES) for funding this study.

Received 07 March 2013

Accepted 20 January 2014

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Publication Dates

• Publication in this collection
  25 Nov 2014
• Date of issue
  Oct 2014

History

• Received
  07 Mar 2013
• Accepted
  20 Jan 2014