Comparison of RAPDs, AFLPs and SSR markers for the genetic ...

Food Microbiology 22 (2005) 561–568 

FOOD 

MICROBIOLOGY 

Comparison of RAPDs,AFLPs and SSR markers for the genetic 

analysis of yeast strains of Saccharomyces cerevisiae 

F. Javier Gallego a ,M. Angeles Pe´ rez b ,Yolanda Nu´ n˜ ez c ,Pilar Hidalgo b, 

a 

Departamento de Genética, Facultad de Biología, Universidad Complutense, 28040 Madrid, Spain 

b 

Instituto Madrileño de Investigación Agraria y Alimentaria (IMIA), Comunidad de Madrid, Finca El Encín, Apdo. 127, 28800 Alcalá de Henares, 

Madrid, Spain 

c 

Instituto Nacional de Investigación y Tecnología Agraria y Alimentaria (INIA), Autovía A-6, Km 7.5, 28040 Madrid, Spain 

Abstract 

Received 3 March 2004; received in revised form 8 November 2004; accepted 16 November 2004 

We evaluated the usefulness of different molecular techniques for the genetic analysis of Saccharomyces cerevisiae strains. Three 

commonly used PCR-derived genetic methods,random amplified polymorphic DNA (RAPDs),amplified fragment length 

polymorphism (AFLPs) and simple sequence repeats (SSRs; microsatellites),were used to characterize 27 wine yeast strains of S. 

cerevisiae from the ‘‘Denominacio´ n de Origen Vinos de Madrid’’ (Spain). Using these methods,we were able to overcome certain 

limitations associated with classical taxonomic methods. Based on the presence or absence of amplified fragments for each genotype, 

AFLPs and SSRs showed a similar discriminatory power superior to that of the RAPDs. Genetic relationships between strains were 

also estimated using the three methods. In general,very poor correlations were found,reflecting the different genomic regions for 

which the methods are screened. Results are discussed in terms of which molecular technique is most appropriate for use with a 

particular aspect of genetic evaluation. 

r 2005 Elsevier Ltd. All rights reserved. 

Keywords: RAPDs; AFLPs; SSRs; Molecular markers; Genetic analysis; Saccharomyces cerevisiae 

1. Introduction 

The use of molecular markers has provided important 

advances in the characterization and genetic identification 

of Saccharomyces cerevisiae strains,the species that 

plays the major role in the fermentation of alcoholic 

beverages. The usefulness of these molecular methods 

for the identification of S. cerevisiae at the strain level is 

of particular interest in enology,where they can be used 

to investigate the ecology and genetic diversity of a 

species that predominates during spontaneous fermentation 

of the must. Moreover,they can be used for 

typing,monitoring and controlling commercially selected 

strains of S. cerevisiae. 

Corresponding author. Tel.: +34 918879489; fax: +34 918879492. 

E-mail address: pilar.hidalgo@madrid.org (P. Hidalgo). 

0740-0020/$ - see front matter r 2005 Elsevier Ltd. All rights reserved. 

doi:10.1016/j.fm.2004.11.019 

ARTICLE IN PRESS 

www.elsevier.com/locate/fm 

PCR-based techniques,including interdelta analysis 

(Ness et al.,1993; Legras and Karst,2003) and the use 

of intro splice site primers (deBarros Lopes et al.,1996), 

randomly amplified polymorphic DNA (RAPDs) 

(Quesada and Cenı´ s,1995; Van der Westhuizen et al., 

1999),simple sequence repeats (SSRs) (Gallego et al., 

1998; Gonza´ lez Techera et al.,2001; Hennequin 

et al.,2001; Pe´ rez et al.,2001),and amplified fragment 

length polymorphism (AFLPs) (deBarros Lopes 

et al.,1999),have previously allowed the discrimination 

as well as estimation of genetic variation in S. cerevisiae 

strains. Differences exist,however,among the methods 

used with respect to complexity,cost,speed of use, 

and resolution power. This paper compares the usefulness 

of three PCR-based methods—RAPDs,AFLPs 

and SSRs—for the genetic analysis of S. cerevisiae 

strains.

562 

2. Materials and methods 

2.1. Yeast strains 

Must samples undergoing spontaneous fermentation 

were collected from wineries (A,B and C) representing 

three viticultural areas (Arganda,Navalcarnero and San 

Martı´ n de Valdeiglesias) of the ‘‘Denominacio´ n de 

Origen Vinos de Madrid’’ (Spain). The number of tanks 

sampled in cellars A,B and C were two,four and two, 

respectively. Fermentation processes were conducted at 

temperatures between 18 and 20 1C. Populations of 

viable yeast cells in each step of the fermentation process 

were of the order of 10 7 cfu/mL. From these populations,a 

total of 27 S. cerevisiae (Kreger Van Rij,1984; 

Kurtzman and Fell,1998) strains were isolated and 

chosen at random for use in this study. These strains 

have been included in the culture collection of the IMIA 

(El Encı´ n,Madrid). The reference code,origin and 

source of each yeast strain are listed in Table 1. 

2.2. DNA extraction 

This process was carried out following the technique 

described by Pe´ rez et al. (2001). 

Table 1 

S. cerevisiae strains used in this study 

Strain 

(reference code) 

Viticultural 

area 

Winery Isolation 

sample 


Step of 

fermentation 

1a Arganda A 20 Middle 




5a Arganda A 20 End 




9a Arganda A 21 End 

1n Navalcarnero B 32 End 

2n Navalcarnero B 32 Middle 








1s San Martín C 46 Middle 


3s San Martín C 46 End 







F. Javier Gallego et al. / Food Microbiology 22 (2005) 561–568 

2.3. RAPD analysis 

RAPD amplifications were carried out according to 

the methodology described by Gallego and Martı´ nez 

(1997) using a DNA Thermal Cycler 9600 (Applied 

Biosystems) under the following conditions: a preliminary 

step of 2 min at 94 1C; 10 cycles each of 30 s at 94 1C, 

a ramp of 1.5 1Cs 1 to reach annealing temperature, 

1 min at 55 1C (decreasing 1 1C per cycle to a final 

temperature of 46 1C),and a ramp of 1.5 min to reach 

72 1C followed by 4.5 min at this temperature; 25 cycles 

each of 30 s at 94 1C,a ramp of 1.5 1Cs 1 to reach 

annealing temperature,1 min at 45 1C,and a ramp of 

1.5 min to reach 72 1C followed by 4.5 min at this 

temperature; and a final step of 1 min at 72 1C. 

Forty primers from sets B and C of Operon 

Technologies (Alameda,CA) were used in preliminary 

experiments in order to evaluate their performance. 

Thirty-two resulted in satisfactory amplifications and 

were selected for use in the remainder of the assay. 

Amplification products were separated by electrophoresis 

in 1.5% (w/v) agarose gels,and visualized under UV 

light after staining with ethidium bromide. 

2.4. AFLP analysis 

AFLP markers were developed following the protocol 

supplied by Applied Biosystems (Foster City,California,USA),based 

on Vos et al. (1995). DNA digestion 

was carried out using the restriction enzymes EcoRI and 

MseI. Six selective amplifications were performed using 

five primers. Two of the primers included the MseI 

adaptor sequence plus the selective CTT and CAA. The 

remaining three primers contained the EcoRI adaptor 

sequence plus AC,AT and AAG. The EcoRI selective 

primers were 5 0 -labeled with one of the following 

fluorescent dyes: 6-FAM,TET or HEX (Applied 

Biosystems). PCR products were separated by capillary 

electrophoresis in an ABI Prism 310 DNA Sequencer 

(Applied Biosystems). The number of fragments generated 

by each primer pair was obtained directly from 

Genescan Analysis software,using the local Southern 

method to size the fragments. 

2.5. SSR analysis 

SSR analysis was performed according to the 

methodology developed by Pe´ rez et al. (2001),using a 

panel of six effective microsatellite loci as sequencetagged 

site markers. 

Loci were amplified using two multiple PCR reactions,including 

ScAAT2,ScAAT3,ScAAT5 in the first 

reaction (PCR1) and ScAAT1,ScAAT4,and ScAAT6 

in the second (PCR2). Each PCR reaction was 

performed in 25 mL final volume containing 10–400 ng 

of template DNA and 0.2 mM of each dNTP (Applied

Biosystems,Foster City,California,USA). PCR1 

reaction mix contained 5 pmol of ScAAT2 primer pair 

fluorescent-dye labeled with HEX (yellow),5 pmol of 

ScAAT3 primer pair fluorescent-dye labeled with 6- 

FAM (blue),7.5 pmol of ScAAT5 primer pair fluorescent-dye 

labeled with TET (green),and 1 U of Taq 

DNA polymerase (Biotools,Biotechnological and Medical 

Laboratories,S.A.,Madrid,Spain) in 1 reaction 

buffer (75 mM Tris HCl pH 9.0,50 mM KCl,20 mM 

(NH4)2SO4). PCR2 reaction mix contained 2.5 pmol of 

ScAAT4 primer pair fluorescent-dye labeled with TET, 

2.5 pmol of ScAAT6 primer pair fluorescent-dye labeled 

with HEX,10 pmol of ScAAT1 primer pair fluorescentdye 

labeled with 6-FAM,and 1.5 U of Taq DNA 

polymerase. 

The amplification reactions were carried out using a 

DNA Thermal Cycler 9600 (Applied Biosystems) under 

the following conditions: a preliminary step of 5 min at 

94 1C; 10 cycles each of 15 s at 94 1C,30 s at 58 1C 

(decreasing 1 1C per cycle to reach a final temperature of 

47 1C),and 30 s at 72 1C; 25 cycles each of 15 s at 94 1C, 

30 s at 48 1C,and 30 s at 72 1C; and a final step of 5 min 

at 72 1C. Amplifications were confirmed by running 

10 mL of the PCR product on 2.5% agarose gel. 

Aliquots (1–2 mL) of the PCR product were mixed 

with 12 mL of formamide and 0.5 mL of a red DNA size 

standard (Genescan-500 ROX,Applied Biosystems). 

Samples were denatured at 94 1C for 3 min prior to 

separation by capillary electrophoresis at 15 KV for 

24 min in an ABI Prism 310 DNA Sequencer (Applied 

Biosystems),and subsequently analysed using Genescan 

software (Applied Biosystems). 

The reproducibility of the three analysis techniques 

was assessed by comparing results from two additional 

independent DNA extractions from half of the study 

strains that were chosen randomly. 

2.6. Data analysis 

Data matrices were built based on the presence or 

absence of amplification products. Genetic distances 

were estimated from these matrices using Dice’s algorithm 

(Dice,1945). This coefficient was used for 

clustering data according to the UPGMA method. 

Bootstrap resampling of 1000 replicates was performed 

to test the robustness of the topology of the dendrograms. 

Differences between dendrograms were tested by 

generating cophenetic values for each dendrogram and 

assembling a cophenetic matrix for each marker type. 

The Mantel matrix correspondence test was then used to 

compare cophenetic matrices (Mantel,1967). Computing 

was performed using NTSYSpc software version 2.0 

(Rohlf,1993) and TFPGA software version 1.3 (Miller, 

1997). 

In order to evaluate the usefulness of each marker 

system,all of the following were calculated: the 


F. Javier Gallego et al. / Food Microbiology 22 (2005) 561–568 563 

arithmetic mean of Diversity Index per polymorphic 

locus (DIavp ¼ [1–Spi 2 ]/np,where p i is the allele 

frequency for the i allele and np is the number of 

polymorphic loci),the arithmetic mean of Effective 

Number of Alleles per polymorphic locus (ENA avp ¼ [1/ 

Sp i 2 ]/np),the total number of effective alleles per 

polymorphic locus (Ne ¼ sumat ENAavp),and the 

Assay Efficiency Index (Ai ¼ Ne/P). Ai combines the 

ENA identified per locus and the number of polymorphic 

bands detected in each assay (P). For dominant 

markers (RAPDs and AFLPs) where one of only two 

states (+,present or ,absent) can be distinguished at 

each position,we assumed that each band position 

corresponded to a locus with two alleles: presence and 

absence of the band. 

3. Results 

3.1. Efficiency of polymorphism detection 

The level of polymorphism detected with each marker 

system is summarized in Table 2,as well as a 

comparison between systems. 

A total of 155 bands were amplified by the 32 

primers used in the RAPD analysis (Fig. 1). Eight 

primers—OPB (01,06,07,12,13,15) and OPC (01, 

03)—allowed the intraspecific differentiation of the 

yeasts,with a total of 13 polymorphic bands. By 

combining the electrophoretic profiles of seven primers 

(OPB01,OPB06,OPB07,OPB13,OPB15,OPC01 and 

OPC03),we were able to use these polymorphisms to 

differentiate 13 of the 27 S. cerevisiae strains in this 

study. The number of amplified bands varied between 

primers,ranging between two and 10,with sizes from 

400 up to 2000 bp. Duplicate analysis of random strains 

revealed no significant differences in banding patterns, 

although some bands varied in intensity. 

Analysis of AFLP banding patterns (Fig. 2) yielded 

137 DNA fragments,with sizes ranging from 130 to 

410 bp. A total of 15 of these bands were polymorphic, 

allowing the differentiation of 19 of the 27 strains in this 

study. Information from four additional primer combinations 

(MseI-CTT/EcoRI-AC, MseI-CTT/EcoRI-AT, 

MseI-CAA/EcoRI-AC and MseI-CAA/EcoRI-AT) was 

also used in the differentiation process. The repetition of 

this AFLP analysis obtained similar results. 

SSR analysis (Fig. 3) detected a number of alleles for 

each locus,ranging from four (loci ScAAT5 and 

ScAAT6) to 10 (loci ScAAT1 and ScAAT3),with 39 

total alleles identified. Twenty of the 27 S. cerevisiae 

strains in this study were differentiated. To complete the 

SSR analysis,information from three microsatellites 

was combined: ScAAT1,ScAAT3 and ScAAT4. Amplification 

products varied in size from 165 to 445 bp. 

Despite this allelic diversity,percent heterozygosity

564 

Fig. 1. Patterns of amplified DNA obtained with nine S. cerevisiae 

strains,in reactions with primer OPC-01. Lane M: Size marker f 174 

DNA, Hae III. Polymorphic bands are indicated by arrows. 

between loci ranged from 7% at locus ScAAT6 to 67% 

at locus ScAAT1. Duplicate analysis of random strains 

resulted in identical profiles. 

Information content,measured as IDavp,was higher 

for the SSR analysis (0.62),although Ai was highest 

using AFLP analysis (4.0),followed by the SSR method 

(3.4) and finally RAPDs (2.1). Values obtained for the 

average ENA avp were greatest using SSR analysis at 3.4, 

with the extremes measuring 1.16 and 6.08. Values 

obtained using AFLP and RAPD analysis were lower, 

averaging 1.6 and 1.3,respectively. 

3.2. Genetic similarity 

A summary of genetic similarity estimates,calculated 

for each marker system,is recorded in Table 3. Average 

estimates obtained using RAPD and AFLP analysis 

were both high (higher than 0.981),with a small range in 

variation. Estimates obtained from the SSR analysis 

were significantly lower,with a mean value of 0.437 and 



Table 2 

Level of polymorphism detected and comparison of informativeness obtained with RAPD,AFLP,and SSR methods in the 27 Sacharomyces 

cerevisiae strains studied 

variation ranging from 0.000 to 1.000. The correlation 

among similarity matrices derived for each method 

(Table 4) was very low in all cases,with AFLP/SSR 

correlation (0.43) being the greatest. 

Genetic similarity trees for each marker type individually,and 

as a whole,are presented in Figs. 4a–d. 

Cophenetic correlations (Table 4) indicate the extent to 

which the dendrograms derived from the different 

techniques accurately represent estimates of the original 

similarity matrices. High cophenetic correlations were 

obtained for RAPD (0.81) and AFLP marker types 

(0.81),with SSR markers achieving an even higher 

correlation of 0.91. Nevertheless,low correlations were 

observed among the different dendrograms (Table 4), 

topping out at 0.56 for the AFLP/SSR correlation. 

4. Discussion 

RAPDs AFLPs SSRs 

Number of assay units 32 6 6 

Average number of bands per assay unit 4.8 22.8 6.5 

Polymorphic bands (%) 8.38 10.94 100 

Number of loci 155 137 6 

Average number of alleles per locus 1.08 1.10 6.5 

Number of strains discriminated 13 19 20 

Number of assay units necessary to achieve the highest discrimination of the strains 7 4 3 

DI avp 0.20 0.35 0.62 

ENA avp 1.3 1.6 3.4 

Ne 16.9 24.0 20.4 

Ai 2.1 4.0 3.4/10.2 

DIavp: diversity index per polymorphic locus.ENAavp: effective number of alleles per polymorphic locus.Ne: number of effective alleles per 

polymorphic locus.Ai: assay efficiency index. 

All three techniques employed in this study (RAPD, 

AFLP and SSR analysis) resulted in sufficient resolution 

to detect differences between the genetic profiles of 

various strains of the same species (S. cerevisiae). While 

the use of these methods may overcome certain 

limitations associated with classical taxonomic methods, 

differences between them exist with regards to the level 

of polymorphism detected,discriminatory power,effectiveness,and 

speed of use. 

AFLP analysis generated the greatest number of 

bands per assay unit,due to the high number of loci 

identified per assay. The most polymorphic bands, 

however,were obtained by microsatellite analysis 

(100%). Based on the small size of the S. cerevisiae 

genome and the number of amplification products 

obtained in this work,primers containing fewer selective 

bases will be used in order to increase the number of 

bands per assay unit.


Fig. 2. Patterns of amplified DNA of three S. cerevisiae strains,obtained by AFLPs. 

Fig. 3. Electrophoregram profiles of three S. cerevisiae strains generated by multiple PCR reaction amplifying the loci ScAAT2,ScAAT3 and 

ScAAT5. 

Table 3 

Comparison of genetic similarity estimates obtained from three PCRderived 

techniques,using Dice coefficient 

Minimum Maximum Mean 

RAPDs 0.983 1.000 0.992 

AFLPs 0.961 1.000 0.981 

SSRs 0.000 1.000 0.437 

Table 4 

Correlation between cophenetic matrices (above diagonal) and 

similarity matrices (below diagonal) obtained with different marker 

types 

RAPDs AFLPs SSRs 

RAPDs 0.81 0.35 0.50 

AFLPs 0.14 0.81 0.56 

SSRs 0.24 0.43 0.91 

Cophenetic correlation coefficients between the similarity and the 

corresponding cophenetic matrices are given in bold on the leading 

diagonal. 


While the SSR and AFLP techniques showed a higher 

discriminatory power than that of the RAPD analysis, 

all three techniques were able to detect genetic 

variability between S. cerevisiae populations in this 

study. AFLP and SSR analysis distinguished a combined 

total of 23 of the 27 strains tested. All three 

techniques resulted in identical DNA profiles for three 

groups of isolates (1a,6a and 7a; 4n and 5n; and 6 s and 

8 s). It is interesting to note that these three groups were 

isolated either from the same must or from different 

musts collected at the same winery (A,B,and C, 

respectively, Table 1). These observations may support 

the hypothesis that those isolates not characterized in 

this study were actually of the same strain. Further 

physiological and genetic analyses are needed to confirm 

this assumption. 

Microsatellite analysis resulted in the highest level of 

information content,measured as ID avp. This increased 

level of polymorphism may be due to some of the 

mechanisms responsible for generating SSR allelic 

diversity: replication slippage and errors during

566 

crossing-over in meiosis. In our study,the microsatellite 

analysis yielded nearly twice as much information as the 

AFLP markers,and three times more information than 

the RAPDs. This high level of polymorphism provided 

by the SSR technique is similar to that reported in other 

comparative studies (in soybean, Powell et al.,1996; in 

maize, Pejic et al.,1998; inMusa, Crouch et al.,1999; in 

table grapes, Cenı´ sandSa´ nchez Escribano,1999). 

The Assay Efficiency Index (Ai),or the ENA 

identified per assay,is of particular interest,as it 

combines the ENA identified per locus and the number 

of polymorphic bands detected per assay. For our study, 

this index allowed us to compare techniques that detect 

multiple alleles and one or two bands per assay,such as 

SSR analysis,with techniques that detect two alleles and 

multiple bands per assay,such as RAPD and AFLP 

analysis. The highest value of Ai was obtained by the 

AFLP method,with 4.0,compared with 3.4 for SSRs 



1a 

2a 

6a 

69 

1a 

2a 

6a 

7a 

8a 

9a 

6s 

8s 

3a 

2n 

5a 

4n 

7s 

5n 

8n 

7n 

6n 

9n 

1n 

3n 

3s 

5s 

4a 

2s 

9s 

4s 

1s 

53 

61 

75 

7a 

8a 

9a 

3a 

5a 

4a 

1s 

4s 

9s 

7n 

9n 

2n 

8n 

1n 

3n 

6n 

4n 

5n 

2s 

7s 

3s 

6s 

8s 

5s 

0.988 0.991 0.994 0.997 1.000 0.975 0.981 0.987 0.994 1.000 

(a) (b) 

1a 

6a 

7a 

50 

87 

66 

90 

1a 

6a 

7a 

9a 

8a 

2a 

4a 

1s 

9s 

2s 

4s 

6s 

8s 

5a 

1n 

2n 

3n 

6n 

9n 

3a 

3s 

4n 

5n 

8n 

7s 

7n 

5s 

98 

54 

88 

99 

76 

8a 

9a 

2a 

5a 

3a 

4a 

4s 

9s 

2s 

1s 

6s 

8s 

1n 

3n 

6n 

9n 

2n 

4n 

5n 

8n 

7s 

7n 

5s 

3s 

0.234 0.425 0.617 0.808 1.000 0.964 0.973 0.982 0.991 1.000 

(c) (d) 

Fig. 4. Dendrograms showing the clustering of the 27 Sacharomyces cerevisiae strains in this study. Dendrograms were created using (a) RAPD 

analysis (b) AFLP analysis,(c) SSR analysis,and (d) the combination of the three marker types. The numbers at the forks indicate the percentage of 

a group’s occurrence in a bootstrap resampling of 1000 trees. 

and 2.1 for RAPDs,in agreement with the results 

obtained by other authors (in maize, Pejic et al.,1998). 

This is due to the simultaneous detection of a large 

number of polymorphic bands per assay unit by AFLP 

markers. 

Based on the above results,it appears that microsatellite 

analysis is capable of revealing the highest level 

of information per single marker,whereas AFLP 

markers can detect the highest number of polymorphisms 

in a single assay. However,the Assay Efficiency 

Index can be experimentally modified,and will increase 

in the case of SSR analysis if multiplex PCR is adopted. 

In this particular study,two types of multiple PCR 

reactions were used,each permitting the amplification of 

three loci. As a result,the number of assay units 

decreased from six to two,in turn causing Ai to increase 

from 3.4 to 10.2 for microsatellite analysis. Thus,SSR 

markers obtained the highest value for Ai,making them

the most efficient markers in the study. This value may 

rise even higher if attempts to optimize this technique 

into a single multiple reaction involving all six microsatellites 

prove successful. 

Another important factor to consider when evaluating 

marker efficiency is the ability to determine relationships 

between yeast strains based on an estimation of 

genetic similarity (Figs. 4a–c). Genetic similarity coefficients 

were obtained (Table 3) for all three PCR-derived 

techniques,reflecting the extreme variability and high 

resolving power of these methods. These same results, 

including lower similarity estimates for SSR analysis 

than for both RAPDs and AFLPs,have been reported 

in equivalent studies performed using other organisms 

(in soybean, Powell et al.,1996; in maize, Pejic et al., 

1998; in Musa, Crouch et al.,1999). These findings 

suggest that differences exist between a technique that 

amplifies unique sequences,such as microsatellite 

analysis,and other methods that amplify multiple 

sequences,such as RAPD and AFLP techniques. 

Poor correlation between estimates of genetic similarity 

based on the three different techniques evaluated 

in this study indicates that these methods may selectively 

screen for different regions of the genome. To further 

explore this issue,as well as the close similarity between 

the strains assayed,a greater number of loci should be 

analysed. In an effort to increase the integrity of our 

analysis,a single dendrogram was constructed that 

combines the information obtained from all three 

marker types (Fig. 4d). This genetic tree revealed that 

only the topology of some groupings is conserved,with 

non-significant groups appearing random regardless of 

the number of markers studied. Further physiological 

experimentation is needed in order to determine if the 

conserved groups are correlated with traits of interest. 

Based on the results of this study,RAPD markers 

were the least polymorphic markers of those evaluated, 

and consequently had the least resolving power. 

Although the amount of variability detected with 

RAPD analysis is dependent upon the selection of 

appropriate primers,this method has the advantage of 

being inexpensive and simple to perform,and does not 

require a previous knowledge of the genome. Problems 

with reproducibility have plagued the use of this 

technique in the past,due to the low temperature of 

the hybridization of the primers. However,we were able 

to limit these problems in this study,as evidenced by the 

results of our duplicate analysis,by careful DNA 

preparation,strict adherence to amplification protocols 

and a rigorous interpretation of the results. 

The AFLP technique resulted in high resolution and 

good reproducibility in this study,a finding substantiated 

by the recent use of AFLP markers in the 

identification and intraspecific differentiation of S. 

cerevisiae strains (deBarros Lopes et al.,1999). This 

technique,however,is much more technically complex 



than the use of SSR markers,requiring numerous 

experimental steps at a higher cost per informative 

marker. Despite these limitations,the AFLP technique 

has great value as a tool for use in genetic mapping and 

evolutionary studies,as it can test a large number of loci 

distributed randomly throughout a genome. Unlike 

microsatellites,AFLP markers are not highly variable, 

providing a less biased estimate of population variability. 

SSR analysis revealed the highest genetic variability in 

the microbial population studied,also achieving the 

greatest discriminatory power. It also proved to be the 

most efficient method,with the highest number of 

effective alleles per assay. We also found the results of 

SSR to be faster and easier to interpret than the other 

techniques studied,making it an ideal technique for use 

in the characterization and identification of S. cerevisiae 

strains. Successful SSR analysis,however,depends on 

the proper design and synthesis of primers,requiring a 

considerable amount of effort in the selection of 

polymorphic microsatellites that afford specific amplifications. 

Our study benefited from an available panel of 

six microsatellite loci and the appropriate primers 

needed for its specific amplification (Pe´ rez et al.,2001). 

In summary,SSR amplifications is a simple and 

effective method,with a high degree of discrimination 

and reproducibility,that can be used in the identification 

and intraspecific differentiation of S. cerevisiae strains,a 

species of great importance in industrial fermentations. 

Acknowledgments 

This research was supported by grants SC94-126 from 

the Programa Sectorial I+D Agrario y Alimentario and 

RM00-001 from the Accio´ n Estratégica ‘‘Conservacio´ n 

de los recursos gene´ ticos de interés agroalimentario’’ del 

Plan Nacional de Investigacio´ n Cientı´ fica,Desarrollo e 

Innovacio´ n Tecnolo´ gica (I+D+I) from the Instituto 

Nacional de Investigacio´ n y Tecnologı´ a Agraria y 

Alimentaria (INIA) (Spain). 

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