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Aquaculture 255 (2006) 1–29www.elsevier.com/locate/aqua-online

Review

Microsatellites and their genomic distribution, evolution, functionand applications: A review with special reference to fish genetics

Dimitry A. Chistiakov a,b,⁎, Bart Hellemans b, Filip A.M. Volckaert b

a Department of Pathology and Laboratory Medicine, University of Cincinnati Medical Center, 231 Albert Sabin Way, Cincinnati,OH 45267-0529, USA

b Laboratory of Aquatic Ecology, Katholieke Universiteit Leuven, Ch. de Bériotstraat 32, B-3000 Leuven, Belgium

Received 31 March 2005; received in revised form 17 November 2005; accepted 25 November 2005

Abstract

Microsatellites represent codominant molecular genetic markers, which are ubiquitously distributed within genomes. Due totheir high level of polymorphism, relatively small size and rapid detection protocols, these markers are widely used in a variety offundamental and applied fields of life and medical sciences. In the field of aquaculture, microsatellites represent workhorsemarkers, which are useful for the characterization of genetic stocks, broodstock selection, constructing dense linkage maps,mapping economically important quantitative traits, identifying genes responsible for these traits and application to marker-assistedbreeding programmes. In this review, genomic distribution, function, evolution and practical applications of microsatellites areconsidered, with special emphasis on fish genetics and aquaculture.© 2005 Elsevier B.V. All rights reserved.

Keywords: Evolution; Functional relevance; Genomic distribution; Microsatellite; SSR; Teleostei

Contents

1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22. Genomic distribution of microsatellites . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23. Evolution of microsatellites . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34. Function of microsatellites . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5

4.1. DNA structure. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54.2. DNA recombination. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54.3. DNA replication. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54.4. Gene expression. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5

5. Development of type I (coding) and type II

⁎ CorCincinn

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0044-8doi:10.

(non-coding) markers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7

responding author. Department of Pathology and Laboratory Medicine, University of Cincinnati Medical Center, 231 Albert Sabin Way,ati, OH 45267-0529, USA. Tel.: +1 513 558 4402; fax: +1 513 558 2141.ail address: [email protected] (D.A. Chistiakov).

486/$ - see front matter © 2005 Elsevier B.V. All rights reserved.1016/j.aquaculture.2005.11.031

mailto:[email protected]

http://dx.doi.org/10.1016/j.aquaculture.2005.11.031

2 D.A. Chistiakov et al. / Aquaculture 255 (2006) 1–29

6. Applications of microsatellites . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 96.1. Genetic mapping . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 96.2. Individual DNA identification and parentage assignment . . . . . . . . . . . . . . . . . . . . . . . . . . . . 126.3. Phylogeny, population and conservation genetics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 136.4. Molecular epidemiology and pathology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 156.5. Quantitative trait loci mapping. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 166.6. Marker-assisted selection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18

7. Prospects . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20Acknowledgements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21

1. Introduction

Microsatellites, or simple sequence repeats (SSRs),represent a unique type of tandemly repeated genomicsequences, which are abundantly distributed across gen-omes and demonstrate high levels of allele polymor-phism. They are codominant markers of relatively smallsize, which can be easily amplified with the polymerasechain reaction. These features provide the foundationfor their successful application in a wide range of fun-damental and applied fields of biology and medicine,including forensics, molecular epidemiology, parasito-logy, population and conservation genetics, genetic map-ping and genetic dissection of complex traits. In the fieldof fisheries and aquaculture, microsatellites are useful forthe characterization of genetic stocks, broodstock selec-tion, constructing dense linkage maps, mapping econom-ically important quantitative traits and identifying genesresponsible for these traits and application in marker-assisted breeding programmes. Although microsatel-lites are considered selectively neutral markers, theyoften represent functionally relevant polymorphisms.SSRs contribute to DNA structure, chromatin organi-zation, regulation of DNA recombination, transcriptionand translation, gene expression and cell cycle dyna-mics. To date, microsatellites are far from being com-pletely identified and characterised, providing intriguingperspectives for discovery of new properties and cha-racteristics of SSRs, which will help the design ofnew research fields and the practical use of thesemarkers.

2. Genomic distribution of microsatellites

Microsatellites are stretches of DNA consisting oftandemly repeated short units of 1–6 base pairs (bp) inlength. SSRs typically span between twenty and a fewhundred bases (Beckmann and Weber, 1992). They areubiquitous in prokaryotes and eukaryotes, present even

in the smallest bacterial genomes (Gur-Arie et al.,2000). The existence of SSRs in eukaryotic genomeshas been known since the 1970s (Bruford et al., 1996).Hamada et al. (1982) demonstrated a large number andwide occurrence of these sequences from yeast throughto vertebrates. Tautz and Renz (1984) hybridized differ-ent microsatellite sequences to genomic DNA from avariety of organisms and reported many types of simplesequences.

The majority of microsatellites (30–67%) found aredinucleotides. In the genome of vertebrates, (AC)n is themost common dinucleotide motif. It is 2.3-fold morefrequent than (AT)n, the second most general type ofdinucleotides (Toth et al., 2000). Interestingly, in pri-mates mononucleotide repeats are mostly representedby poly (A/T) tracts, which are the most frequent classesof SSRs (Beckmann and Weber, 1992). In total, higher-order SSR classes (tri-, tetra-, penta-and hexanucluo-tides) are about 1.5-fold less common in genomic DNAof vertebrates than dinucleotides (Toth et al., 2000). Inthe genome of Japanese pufferfish or fugu Takifugurubripes, dinucleotide repeats have the highest relativefrequency (34%) followed by tetranucleotides (21%),trinucleotides (19%), mononucleotides (16.5%), hexa-nucleotides (6%) and pentanucleotides (3%) (Edwardset al., 1998). In total, 1.29% of the genome of Japanesepufferfish consists of microsatellites. For the closelyrelated spotted green pufferfish Tetraodon nigroviridis,SSRs cover 3.21% of the genome (Crollius et al., 2000).In fugu, one microsatellite is found every 1.87 kilobases(kb) of DNA, and a CA repeat (the most common typeof tandem repeat) occurs every 6.56 kb of DNA. Inthree-spined stickleback Gasterosteus aculeatus, CAdinucleotides also are the most common type of micro-satellites, occuring approximately once every 14 kb(Peichel et al., 2001). For comparison, in the humangenome, one microsatellite was found every 6 kb andone CA repeat occurred every 30 kb of DNA (Beck-mann and Weber, 1992).

3D.A. Chistiakov et al. / Aquaculture 255 (2006) 1–29

Microsatellites can be found anywhere in the ge-nome, both in protein-encoding and noncoding DNA(Toth et al., 2000). In eukaryotic organisms, SSRs havebeen shown to be in excess in noncoding regions com-pared to a random distribution pattern (Metzgar et al.,2000). They are relatively rare in coding DNA, rangingbetween 7–10% in higher plants (Wang et al., 1994;Varshney et al., 2002) and between 9–15% in verte-brates (Moran, 1993; Jurka and Pethiyagoda, 1995; VanLith and Van Zutphen, 1996). Only 11.6% of a total of6042 microsatellites were found in protein-codingregions in the genome of Japanese pufferfish (Edwardset al., 1998). A relatively low frequency of SSRs incoding regions could be explained by negative selectionagainst frameshift mutations in the translated sequences(Metzgar et al., 2000; Li et al., 2004).

Di- and tetranucleotide motifs are mostly clusteredin noncoding regions. In vertebrates, they are distrib-uted 42- and 30-fold less frequently in exons than inintronic sequences and intergenic regions, respectively(Toth et al., 2000). Analysis of perfect dimeric SSRsshowed that the length distribution of microsatellitetracts with five and more repeat units in noncodingregions fits the unbiased single-step mutation model(Bell and Jurka, 1997). This model suggests thatrepeats change length by plus or minus one unit withequal probabilities, and that base substitutions destroylong perfect repeats, producing two shorter tracts ofperfect repeats (see later). As a consequence, longdimeric motifs are highly unstable within expressedsequences, while in noncoding regions most dinucleo-tide repeats can have surprisingly long stretches, prob-ably due to the high tolerance of noncoding DNA tomutations (Dokholyan et al., 2000). The potential sizeexpansion of di- or tetranucleotide microsatellites inuntranslated regions (UTRs) and introns could lead todisruption of native protein and/or formation of newgenes with frame-shift (Liu et al., 1999a). These pat-terns suggest that random distribution of such di- andtetranucleotide SSRs are strongly selected against(Bachtrog et al., 1999).

Dinucleotide repeats in 5′- and 3′-UTRs have beendescribed within genes of a variety of fish species,including channel catfish Ictalurus punctatus (Liu etal., 1999a), Atlantic salmon Salmo salar (Grimholt etal., 2002), zebrafish Danio rerio (Gerhard et al., 2000),Japanese flounder Paralichthys olivaceus (Hirono et al.,2000) and Nile tilapia Oreochromis niloticus (Mansouret al., 1998). Dinucleotide SSRs are also found inintrons. For example, intronic dinucleotide microsatel-lites have been detected in the growth hormone gene ofNile tilapia, barramundi Lates calcarifer, Japanese

flounder and Japanese pufferfish (Venkatesh and Bren-ner, 1997).

In contrast to other types of repeat motifs, triplets arefound in both coding and non-coding genomic regionswith a high frequency (Wren et al., 2000; Morgante etal., 2002). In all vertebrates, (G+C)-rich motifs (e.g.,CCG, CAG) are the most common among trinucleo-tides. These repeats dominate in exons, whereas theyare less common in intronic sequences (Toth et al.,2000). In humans, the expansion of trinucleotides,encoding polyproline (CCG)n, polyarginine (CGG)n,polyalanine [(GCC)n and (GCG)n] and polyglutamine(CAG)n tracts within exons has been described. Suchexpansions can lead to various neurodegenerative andneuromuscular disorders, including myotonic distrophy,fragile X syndrome, Huntington's disease and spinocer-ebellar ataxia (Jasinska et al., 2003; Brown and Brown,2004). Naturally occurring triplet repeat instability oftranscribed sequences occurs not only in humans, butalso in lower vertebrates. For example, length variabil-ity of the signal peptide-encoding region of the mela-noma receptor tyrosine kinase (XMRK) gene has beendescribed in the swordtail genus Xiphophorus (Schartlet al., 1998). The signal peptide contains a variablenumber of CTG repeats, which may differ in lengtheven between closely related individuals. The XMRKlocus, encoding a sex-linked oncogene, and responsiblefor melanoma formation, is located in a highly unstablegenomic region (Froschauer et al., 2001). Variability inthe signal peptide could influence the efficiency of thefunctional protein export through the cell membraneand therefore affect fitness traits of individuals expres-sing the melanoma phenotype. In channel catfish, vari-able (ACC)n repeats encoding a polythreonine tracthave been found within the RAD23B gene, which isimportant in the nucleotide excision repair system (Liuet al., 2001). A polymorphic (CAA)n trinucleotide motifencoding a polyglutamine stretch was observed in thesex-linked NROB1 (DAX1) gene of the European seabass Dicentrarchus labrax (Chistiakov and Hellemans,unpublished data).

3. Evolution of microsatellites

The key feature of SSRs as molecular markers istheir hypermutability and, hence, their hypervariabilityin species and populations. The microsatellite mutationrate is estimated at 10−2–10−6 per locus per generation(Ellegren, 2000), which is several orders of magnitudegreater than that of regular nonrepetitive DNA (10−9;Li, 1997). Analysis of (AC)n microsatellites in fivevertebrate classes (mammals, birds, reptiles, amphibians


and fishes) showed that length is a major factor influ-encing mutation rate. A directional mutation toward anincrease in microsatellite length was also observed(Neff and Gross, 2001).

Two models (DNA polymerase slippage and unequalrecombination) have been suggested to explain micro-satellite generation and evolution. The first modelinvolves transient dissociation of the replicating DNAstrands with subsequent reassociation (Schlötterer andTautz, 1992; Richards and Sutherland, 1994). Theslipped structure can be stabilized by hairpin, triplexor quadruplex arrangement of DNA strands (Sinden,1999). (CCG)n× (CGC)n, (AGG)n×(CCT)n and someother triplet motifs are shown to have high hairpin-forming potential and, therefore, could form stable al-ternative structures, which are implicated in the tripletexpansion diseases (Usdin, 1998; Sinden, 1999). SinceDNA repeat regions represent preferred target sites formutations during DNA replication, microsatellite stabil-ity is controlled at multiple steps in vivo through theDNA mismatch repair (MMR) system, as shown forEscherichia coli, yeast and humans (Sia et al., 1997).MMR proteins are found in a wide variety of taxa andare responsible for the correction of replication mistakesand suppresion of the recombination between divergedsequences (Kolodner and Marsischky, 1999). If theMMR system is defective, coding sequences with tan-dem repeats become subject to mutations, for examplein tumour tissues (Sia et al., 1997). High-frequencymicrosatellite instability, therefore, plays a pivotal rolein carcinogenesis (Atkin, 2001). Both minor and majorMMR genes contain short (A)n tracts in their codingregions, which are highly vulnerable to spontaneousdeletion or insertion mutations, that could result in theinactivation of the MMR gene and hence cause MMRdeficiency (Chang et al., 2001).

Although a number of experimental findings argue infavour of the above model, nonreciprocal recombina-tion (gene conversion) also may play a role in geneticinstability of some SSRs, including triplet motifs(Jakupciak and Wells, 2000). Gene conversion mechan-isms were found to be involved in the differentiationand evolution of paralogous sequences (duplicated lociwithin species) in members of the family Salmonidaederived from tetraploidization. This observation wastaken from the comparative sequence analysis of asimple microsatellite locus Str1INRA, which containslong flanking sequences, including both coding andnoncoding regions, in different species of subfamilySalmoninae (Angers et al., 2002). Replication slippageand recombination could interact, affecting stability ofmicrosatellite loci. For example, such “repair-slippage”

mechanisms are probably involved in the instability oftri-and dinucleotides in yeast (Richard and Dujon,1997), polarity of substitutions within repeats in (CA)nstretches in ovine and bovine genomes (Brohede andEllegren, 1999), conservation of the dinucleotide micro-satellite locus Loc6 in lamniform sharks (Martin et al.,2002) and evolution of intronic microsatellites andsome haplotypes within orthologous Rhesus (RH)genes in vertebrates (Okuda and Kajii, 2002).

Slipped-strand mispairing during DNA replication islikely to represent the predominant mutational mecha-nism for microsatellites (Schlötterer and Tautz, 1992). Itresults in the nascent strand having a different numberof repeats from the template strand once DNA replica-tion is complete. This mutation process allows the samemicrosatellite allele to arise multiple times, therebygenerating size homoplasy. Homoplasy represents sim-ilarity of traits or genes for reasons other than coances-try (e.g., convergent evolution, parallelism, evolutionaryreversals, horizontal gene transfer, gene duplications).Homoplasy can violate a basic assumption of the analy-sis of genetic markers, in which variants of similarphenotype (e.g., base pair size) are assumed to derivefrom a common ancestor (Sanderson and Hufford,1996).

Various mutational models that account for this ho-moplasy have been proposed. The stepwise mutationalmodel (SMM) assumes that all mutational events in-volve a change in a single repeat only (Kimura andOhta, 1978; Bell and Jurka, 1997), whereas the two-phase mutational model (TPM) allows a proportion ofmutations to involve changes greater than single repeats(Di Rienzo et al., 1994). In contrast, the infinite-allelemodel (IAM) refuses homoplasy events, suggesting thatevery mutation results in the creation of a new allele(Kimura and Crow, 1964). Determining which muta-tional model is most appropriate is important, becauseestimation of microsatellite-specific genetic distancesamong populations relies on the underlying assump-tions of the chosen model.

During DNA replication, longer stretches of repeatedunits pose more of a problem to DNA polymerase thando shorter stretches, making longer alleles more proneto slipped-strand mispairing. In addition, larger numb-ers of repeats provide more opportunities for misalign-ment during the reannealing of the nascent strand(Eisen, 1999). Therefore, there is thought to be arough threshold of minimum repeat number belowwhich a microsatellite is not likely to mutate or bevariable. Estimations of slippage frequency during po-lymerase chain reaction (PCR) showed that such athreshold exists and is equal to four and eight repeats


for (CA/GT)n dinucleotides and (A/T)n motifs, respec-tively (Shinde et al., 2003). Measurements in slimemold Neurospora found that microsatellites areexpected to be variable if they have a mean of 5.1repeats (Dettman and Taylor, 2004).

4. Function of microsatellites

SSRs typically represent selectively neutral DNAmarkers. However, multiple studies proved the func-tional relevance of a significant number of SSRs.

4.1. DNA structure

Microsatellites are involved in forming a wide vari-ety of unusual DNA structures with simple and complexloop-folding patterns. Double-stranded alternating pu-rine and pyrimidine sequences such as the (dC−dA)×(dG−dT)32 form left-handed Z-DNA structures in vitro(Rich et al., 1984). Several microsatellite sequences,such as (GAA)n, (AC)n and composite (GT)n×(GA)nsimple repeats, also exhibit non-B-DNA structuralproperties (Epplen et al., 1996).

Telomeric and centromeric chromosome regionshave been shown to be rich in long arrays of a varietyof mono-, di-, tri-, tetra-and hexanucleotide motifs.Satellite sequences enriched by AT-dinucleotides havebeen found in the centromeric DNA of various gobiidspecies (Canapa et al., 2002). They are consideredimportant for the control of centromeric chromatincompactness in these fishes. A cryptic RRY(i) micro-satellite located close to the centromeric region of anacrocentric chromosome pair was characterized in At-lantic salmon (Martinez et al., 2001). The expandedstretches of a simple repeat sequence (TTAGGG)n ori-ented in the 5′ to 3′ direction towards the end ofeukaryotic chromosomes constitute a substantial por-tion of the repetitive DNA in telomeric regions (Hen-derson, 1995). For example, the telomeric repeat in theNile tilapia varies in size from 4 to 10 kb (Chew et al.,2002). Telomere-associated repeats can be related tonucleolus organizing regions (NORs), as seen in rain-bow trout Oncorhynchus mykiss (Abuin et al., 1996),lake trout Salvelinus namaycush (Reed and Phillips,1995) and Nile tilapia (Foresti et al., 1993). The(TTAGGG)n hexamer sequence is recognized by ribo-nucleoprotein polymerase, a telomerase, which synthe-sizes telomere repeats onto the chromosome ends toovercome the loss of sequences during DNA replica-tion, whereas other proteins prevent nucleolytic degra-dation and confer stability of chromosomes (Fang andSech, 1995; Martins et al., 2004). Therefore, SSRs play

an important role in the organization of the chromo-some structure.

4.2. DNA recombination

SSRs are considered hot spots for recombination(Jeffreys et al., 1998). Dinucleotide motifs are preferen-tial sites for recombination events due to their highaffinity for recombination enzymes (Biet et al., 1999).Some SSR sequences, such as GT, CA, CT, GA andothers, may influence recombination directly throughtheir effects on DNA structure (Biet et al., 1999). Theorthologous rhesus (Rh) genes, which are responsiblefor the determination of the blood group in highervertebrates, were found to contain multiple microsatel-lites in their introns (Okuda et al., 2000). These SSRswere shown to be associated with the assignment ofsome Rh phenotypes, and to be involved in the molec-ular evolution of the human Rh gene family and itsorthologs in other eukaryotes and Archaea via replica-tion slippage and recombination (gene conversion)mechanisms (Fujiwara et al., 1999; Okuda and Kajii,2002).

4.3. DNA replication

SSRs may influence DNA replication. For example,in rat cells, DNA amplification is terminated within aspecific fragment which consists of a d(GA)27×d(TC)27tract. This sequence is situated at the end of an ampliconand forms a loop, which serves as a stop signal for DNApolymerase (Li et al., 2002).

Human genes encoding important cell fidelity andgrowth factors, such as the B-cell leukemia/lymphoma2 (BCL2)-associated X protein, insulin-like growth fac-tor 2 receptor (IGF2R), breast cancer early onset protein2 (BRCA2) and transforming growth factor beta 2(TGF-β2), contain short repeated sequences. MMR de-ficiency causes frame-shift mutations, resulting in bothinsertions and deletions of repeat units within thesesequences that affect these genes and could thereforeinitiate tumorigenesis (Johannsdottir et al., 2000). Theseobservations suggest that microsatellites can affectenzymes controlling mutation rate and cell cycles(Chang et al., 2001).

4.4. Gene expression

Numerous data show that SSRs located in promoterregions can influence gene expression. The 5' upstreamregion of the insulin gene of Nile tilapia contains amicrosatellite close to the same position of a unique


minisatellite found only in humans and primates (Man-sour et al., 1998). The human insulin minisatellite ishighly polymorphic, and some of its alleles were shownto regulate the expression of the insulin gene (Melloul etal., 2002). An analogous function might be predictedfor the insulin microsatellite in Nile tilapia. In Japanesepufferfish, many SSRs have been found in the transcrip-tion factor-binding sites of promoter regions of manyimportant genes (Edwards et al., 1998). These SSRscould affect the sequence of the binding site and there-fore influence its affinity for the binding of acorresponding regulatory transciption factor. The tran-scription-regulating activity of the (CT)n tract in pro-moters of the HSP26 gene encoding a 26 kDa heat-shock protein in Drosophila (Sandaltzopoulos et al.,1995), the gene for glyceraldehyde-3-phospate dehy-drogenase in Aspergillus (Punt et al., 1990) and thepiypt1 gene of Phytophtora (Chen and Roxby, 1997)could serve as examples. The CT-element lying close tothe transcription start point of these genes was shown tobe the target sequence for binding transcription factorsand might be important in determining the frequency oftranscription initiation (Chen and Roxby, 1997).

In many cases, SSR repeat number could significant-ly influence gene expression level. Such an effect wasdescribed for a dinucleotide (CA/GT)n microsatellite inthe Nile tilapia prolactin 1 (PRL1) promoter (Streelmanand Kocher, 2002). Individuals homozygous for longmicrosatellite alleles express less PRL1 in freshwater,but more in half-seawater than fish with other geno-types. Interestingly, a similar activity was previouslyreported by Naylor and Clark (1990) for TG/CA repeatsequences in the promoter of the rat prolactin gene. Itsuggests the conservation of the regulatory function forCA/GT microsatellites in the PRL1 promoter over 300million years of vertebrate evolution.

Transcribed microsatellites located in 5′ untranslatedregions (UTRs) could form specific and unusual DNAstructures. In that case, the length of the repeat regioncould affect the translation level from the target mRNA.The apparent correlation between gene expression andthe number of tandem GAA repeat motifs was observedfor the GAA repeat region which regulates expressionof the M9/pMGA gene family in the avian intracellularparasite Mycoplasma gallisepticum (Liu et al., 2000).

Intronic SSRs also can affect gene transcription. Forexample, such an effect was measured for the tetramericmicrosatellite located in intron 1 of the human tyrosinehydroxylase gene (Meloni et al., 1998) and the (CA)ndinucleotide repeat in the first intron of the humanepidermal growth factor receptor gene (Gebhardt etal., 1999). SSRs in introns might serve as target

sequences for binding a variety of expression-regulatingproteins, as was shown for certain (GT)n or mixed GT/GA stretches of intronic simple repeats. They have beenpreserved in immunologically relevant genes for at least70 million years and bind nuclear protein regulatorymolecules with high affinities (Epplen et al., 1993;1996). A number of such SSR repeats could also corre-late with the strength and affinity of the protein binding(Winter and Varshavsky, 1989).

Microsatellites situated in the 3′-UTR could affectgene expression through their influence on the stabilityof transcribed products. This role was found for a GA-rich repeatitive DNA segment in the 3′-UTR of the ratpolymeric immunoglobulin receptor gene (Fabregat etal., 2001) and the chicken elastin gene (Hew et al.,2000). Long stretches of such polypurine and pyrimi-dine repeat motifs at 3′-UTRs could destabilize thestructure of the 3′-end of a mRNA molecule andhence facilitate its availability for degradation by intra-cellular exonucleases (Wang et al., 2002). Otherwise,such repeats could affect mRNA stability, representingbinding sites for translation factors, as has been de-scribed for an AU-rich sequence in the 3′-UTR ofmRNA for human plasminogen activator inhibitortype 2 (Maurer et al., 1999).

In the European eel Anguilla anguilla, a highly var-iable region was found at the 3′-UTR of the TSHB geneencoding thyrotropin β subunit (Pradet-Balade et al.,1998). The region includes repeat units, which containCTG double repeats at their ends. The CTG doublerepeats are able to form unusual helix structures withindouble-stranded DNA (Chastain and Sinden, 1998).Such configurations may, therefore, promote geneticinstability and be responsible for heterogeneity in thenumber and length of the thyrotropin β mRNA in theEuropean eel (Pradet-Balade et al., 1997).

The above evidence suggests that SSR variation canproduce either drastic or quantitative variations in geneexpression. Because of genomic overabundance andhigh mutability of SSRs, changes in SSR array sizemay serve as a rich source of variation in fitness-relatedtraits in natural populations (Kashi et al., 1997; Streel-man and Kocher, 2002). Its role may be especiallyimportant for population survival and adaptation tospatially and temporarily varying environmental condi-tions (Blankenship et al., 2002). The presence of so-called contingency loci in many bacterial species mayexplain how microsatellite variability influences theadaptive evolution of microbial pathogens (Metzgarand Wills, 2000). These loci, containing tandem repeatswithin either a coding sequence or a promoter, can behypermutable. Altered numbers of repeats thus cause


switches in the reading frame of translation or changesin the level of promoter activity. The contingency lociare specifically associated with genes controlling theantigenic type and/ or phase state of pathogenic bacteria(Bayliss et al., 2001). Examples of genes carrying thesehypermutable repeats include those encoding surfacepolysaccharides of Haemophilus influenzae, variablesurface lipoproteins of Mycoplasma hyorhinis andopacity proteins of Neisseria sp. (Yogev et al., 1991;Bayliss et al., 2001). The hypervariability of these genesallows clonal lineages to switch rapidly between sero-types and thereby evade clearance by the host immunesystem, or to switch rapidly between different pheno-typic ‘phases’ required for the invasion of multipletissue types (Moxon et al., 1994).

5. Development of type I (coding) and type II(non-coding) markers

O'Brien (1991) divided molecular markers into typeI markers associated with genes of known functions andtype II markers associated with anonymous genomicsequences. Microsatellites usually represent type II mar-kers, since they commonly are located in noncodingintergenic regions. The fastest and simplest way todetect and characterize a large number of such type IImicrosatellites lies in the construction of small-insertgenomic libraries enriched in arrays of tandem repeats(Zane et al., 2002). The enrichment technique usuallyincludes selective hybridization of fragmented genomicDNA with a tandem repeat-containing oligonucleotideprobe and further PCR amplification of the hybridiza-tion products. Libraries highly enriched by tandemrepeats have been constructed for many organisms,including fishes. The high frequency of tandem repeatsin fish genomes provides a good opportunity to obtainlibraries significantly enriched in microsatellites. Forexample, libraries containing 74%, 95% and 96%clones with (CA)n repeats have been developed for theMediterranean angler fish Lophius sp. (Garoia et al.,2003), gilthead sea bream Sparus aurata (Zane et al.,2002), and Nile tilapia (Carleton et al., 2002), respec-tively. A library usually contains 1000–4000 recombi-nant clones. Screening of these clones typically yields10–15% unique polymorphic SSRs, resulting in theproduction of 100–500 non-redundant variable micro-satellites from a single library (Zane et al., 2002).

In addition, type II markers can be rapidly developedfrom SSRs isolated previously from closely relatedspecies (Bruford et al., 1996). Cross-species amplifica-tion of SSRs provides a possibility to superimpose thegenetic information or a genetic linkage map from one

species to another and therefore to use SSR markers forpopulation genetics, parentage analysis and other appli-cations without having to invest in the isolation ofpolymorphic microsatellites (Cairney et al., 2000;Leclerc et al., 2000). The presence of highly conservedflanking regions has been reported for some microsa-tellite loci in cetaceans (Schlötterer et al., 1991), turtles(FitzSimmons et al., 1995) and fishes (Rico et al., 1996;Angers and Bernatchez, 1996), allowing cross-amplifi-cation from species that diverged as long ago as 470million years.

Type I markers are more difficult to develop (Liu etal., 1999b). While non-gene sequences are free to mu-tate, causing higher levels of polymorphism, sequenceswithin protein-coding regions generally show lowerlevels of polymorphism because of functional selectionpressure. The most effective and rapid way for produc-ing type I microsatellites is the sequencing of clonesfrom cDNA libraries. Both 5′- and 3′-ends of a cDNAclone can be sequenced to produce expressed sequencetags (ESTs). An EST represents a short, usually 200–600 bp-long nucleotide sequence, which represents auniquely expressed region of the genome. If the ESTharbours any polymorphic type I marker [usually SSRand/or single nucleotide polymorphism (SNP)], it canbe mapped (see later). cDNA libraries can be routinelysequenced, rapidly producing a bulk of ESTs, withwhich to organize an EST collection. Such collectionsprovide a robust sequence resource that can be used forgene discovery, genome annotation and comparativegenetics (Rudd, 2003; Dunham, 2004; Ng et al., 2005).

EST sequences are archived in a special branch ofthe GenBank nucleotide database (dbEST) (http://www.ncbi.nlm.nih.gov/dbEST/index.html) (Wheeler et al.,2004). In November 2005, the EST database containedmore than 31.3 million sequence entries from around500 species. The numbers of ESTs for fish species aresummarized in Table 1; most of species listed representmodel organisms or economically important fishes.

SSRs can be searched for in these EST sequencedatabases. However, the major drawback for effectiveand rapid development of type I SSRs is access tosufficient sequence information. As shown in Table 1,for the channel catfish, around 45,000 EST sequenceshave been developed. This provides a serious source forextracting thousands of sequences containing putativeSSRs with the possibility of developing severalhundreds of polymorphic microsatellite markers. Forexample, sequence analysis of 1909 ESTs from a skincDNA library of Ictalurus punctatus revealed the pres-ence of 89 (4.7% of 1909) microsatellite-containinggenes (Karsi et al., 2002). Screening of 1201 ESTs

http://www.ncbi.nlm.nih.gov/dbEST/index.html

http://www.ncbi.nlm.nih.gov/dbEST/index.html

Table 1Fish taxa ranked by the number of ESTs available (November 2005)

Speciesa Familya # of ESTsb # of SSRsc Other sequencesc Totalc

Danio rerio (zebrafish) Cyprinidae 689,581 6055 362,754 1,052,335Oncorhynchus mykiss (rainbow trout) Salmonidae 239,327 968 2972 243,267Oryzias latipes (Japanese medaka) Adrianichthyidae 221,546 2 117,865 339,413Gasterosteus aculeatus (three-spined stickleback) Gasterosteidae 170,994 140 10,223 181,357Salmo salar (Atlantic salmon) Salmonidae 113,002 1696 1434 116,132Fundulus heteroclitus (mummichog) Fundilidae 55,116 83 494 55,693Ictalurus punctatus (channel catfish) Ictaluridae 44,476 3921 1614 50,011Haplochromis chilotes (cichlid fish) Cichlidae 35,747 – 1 35,748Takifugu rubripes (Japanese pufferfish) Tetraodontidae 25,850 430 66,805 93,085Eptatretus burgeri (inshore hagfish) Myxinidae 23,884 – 147 24,031Haplochromis sp. (red tail sheller) Cichlidae 14,073 – – 14,073Leucoraja erinacea (little skate) Rajidae 11,260 – 609 11,869Squalus acanthias (piked dogfish) Scualidae 10,878 8 134 11,020Petromyzon marinus (sea lamprey) Petromyzontidae 10,617 11 550 11,178Cyprinus carpio (common carp) Cyprinidae 10,612 29 1614 12,255Ictalurus furcatus (blue catfish) Ictaluridae 10,524 – 25 10,549Platichthys flesus (European flounder) Pleuronectidae 5,573 29 225 616Paralichtys olivaceus (Japanese flounder) Paralichtyidae 3874 87 368 4329Astatotilapia burtoni (cichlid fish) Cichlidae 3670 – 90 3760Hippoglossus hippoglossus (Atlantic halibut) Pleuronectidae 3146 29 180 3355Oncorhynchus tshawytscha (chinook salmon) Salmonidae 2301 78 591 2970Carassius auratus (goldfish) Cyprinidae 2080 21 789 2890Dicentrarchus labrax (European seabass) Moronidae 1770 208 286 1264Coregonus clupeaformis (lake whitefish) Salmonidae 1691 31 13 1735Pseudopleuronectes americanus (winter flounder) Pleuronectidae 1663 – 185 1668Osmerus mordax (rainbow smelt) Salmonidae 1587 – 15 1600Sparus aurata (gilthead seabream) Sparidae 1518 26 487 2007Gadus morhua (Atlantic cod) Gadidae 1422 12 1599 2954Perca fluviatilis (European perch) Percidae 1097 1 97 1195Epinephelus coioides (orange-spotted grouper) Serranidae 1007 1 167 1193Xiphophorus maculatus (Southern platyfish) Poeciliidae 847 266 167 1280Oncorhynchus nerka (sockeye salmon) Salmonidae 664 43 186 893Opsanus beta (gulf toadfish) Batrachoidiae 619 – 21 640Seriola quinqueradiata (five-ray yellowtail) Carangidae 558 5 178 741Ctenopharyngodon idella (grass carp) Cyprinidae 531 1 186 718Oreochromis niloticus (Nile tilapia) Cichlidae 294 235 3,543 4,072Anguilla japonica (Japanese eel) Anguillidae 196 37 323 556Gillichtys mirabilis (long-jawed mudsucker) Gobiidae 109 – 1 110Tetraodon fluviatilis (green pufferfish) Tetraodontidae 99 – 89 188Austrofundulus limnaeus (annual killifish) Ruvilidae 91 – 16 107Salvelinus alpinus (Arctic charr) Salmonidae 63 12 220 295Gillichthysseta (goby) Gobiidae 62 – 59 121Torpedo marmorata (spotted ray) Torpedinidae 41 – 123 164Siniperca chuatsi (Chinese perch) Percichthyidae 32 – 109 141Chiloscyllium plagiosum (white-spotted bambooshark) Hemiscylliidae 17 – 9 26Poecilia reticulata (guppy) Poeciliidae 15 137 339 491Cyclopterus lumpus (lumpsuker) Cyclopteridae 12 – 17 29Paramisgurnus dabryanus (Chinese loach) Cobitidae 11 1 24 36Xiphophorus maculatus x Xiphophorus helleri(Southern platyfish x green swordtail)

Poeciliidae 6 – – 6

Periophthalmus modestus (shuttles hoppfish) Gobiidae 4 – 4 8Eptatretus cirrhatus (New Zealand hagfish) Myxinidae 2 4 3 9Labeo rohita (rohu) Cyprinidae 2 21 55 88Catla catla (Indian major carp) Cyprinidae 1 19 14 34Geotria australis (pouched lamprey) Petromyzontidae 1 – 8 9Lethenteron japonicum (Japanese lampey) Petromyzontidae 1 – 87 88Oncorhynchus keta (chum salmon) Salmonidae 1 16 335 351

Notes to Table 1:aTaxonomic assignment was performed through the NCBI taxonomy database (http://www.ncbi.nlm.nih.gov/Taxonomy/taxonomyhome.html,Wheeler et al., 2004).



from a channel catfish brain cDNA library yielded 88(7.3%) clones with microsatellites (Liu et al., 2001).Most brain microsatellites represented dinucleotides lo-cated in 3′-UTRs. However, 12 (14%) SSRs were tetra-meric, whereas 20 (23%) clones contained trinucleotiderepeats. A recent bioinformatic analysis of 43,033 ESTsfrom channel catfish revealed 4855 ESTs (11.3%) con-taining microsatellites (Serapion et al., 2004). 1312 ofthese ESTs fell into 569 contigs (e.g., locations within achromosome map, where contiguous DNA segmentsoverlap) and the remaining 3534 ESTs were singletons(e.g., unmapped to contigs, with unknown position in achromosome map). A total of 4103 unique microsatel-lite-containing genes were identified. The dinucleotideCA/TG and GA/TC pairs were the most abundantamong EST-derived microsatellites (Serapion et al.,2004).

A typical strategy for the development of EST-derived microsatellite markers (data mining) includespreliminary analysis of EST sequences from the DNAdatabase to remove poly(A) and poly(T) stretches.These mononucleotide repeats are very common inESTs developed from the 3′-ends of cDNA clonesand correspond to the poly(A)-tails in eukaryoticmRNA. Sequences are further screened for putativeSSRs. This supports identification of all SSR-contain-ing EST sequences. Following the identification ofmicrosatellite-containing ESTs, flanking primers shouldbe designed to amplify a microsatellite. In order tohypothesize about putative functions of SSR-containinggenes, these sequences are needed for comparison tothe database of amino acid sequences (Kantety et al.,2002; Thiel et al., 2003). UniProt/Swiss-Prot is anannotated protein sequence database (http://www.ebi.ac.uk/swissprot/) which is extremely helpful for thesepurposes.

6. Applications of microsatellites

SSRs are often highly polymorphic due to variationin the number of repeats (Amos and Pemberton, 1992).They can be simply and rapidly detected by the poly-merase chain reaction (PCR) using two unique oligonu-cleotide primers that flank the microsatellite and hencedefine the microsatellite locus. Because of their multi-allelic nature, codominant inheritance, small length,

Notes to Table 1:aTaxonomic assignment was performed through the NCBI taxonomy daWheeler et al., 2004).bDabData are taken from the dbEST database (http://www.ncbi.nlm.nih.gov/dcData are taken from GenBank database (http://www.ncbi.nlm.nih.gov/Genb

extensive genome coverage and relative abundance,microsatellites have been successfully applied in awide variety of research fields and practical disciplines(Powel et al., 1996).

6.1. Genetic mapping

Genetic mapping represents one of the major re-search fields in which microsatellite markers havebeen applied. SSRs remain the markers of choice forthe construction of linkage maps, because they arehighly polymorphic (and highly informative) and re-quire a small amount of DNA for each test (Fig. 1).Methods for microsatellite detection can be readily au-tomated. A disadvantage of microsatellites is that theyare mostly anonymous DNA fragments (Cullis, 2002).However, type II (noncoding) microsatellites are veryhelpful for building a dense linkage map frameworkinto which type I (coding) markers can then beincorporated.

Compared to type II markers, mapping type I mar-kers directly shows the location of genes within thelinkage map. The coding markers often represent genet-ic variations associated with interesting or economicallysignificant phenotypes. Therefore, enrichment of thelinkage map by type I loci greatly benefits the mappingand characterization of genes responsible for medically,agriculturally and evolutionarily important complextraits. This also provides a good opportunity for mark-er-assisted selection (MAS) in commercially significantspecies (Poompuang and Hallerman, 1997; Waldbieserand Wolters, 1999). For economically important fishes,EST-derived microsatellites have been isolated and usedfor genetic mapping in Atlantic salmon (Koop andDavidson, 2005), channel catfish (Liu et al., 2001;Waldbieser et al., 2001; Karsi et al., 2002), Europeansea bass (Chistiakov, pers. data), Nile tilapia (Cnaani etal., 2002), rainbow trout (Sakamoto et al., 2000) andzebrafish (Knapik et al., 1998).

Linkage maps are known as recombination maps anddefine the order and distance of loci along a chromo-some on the basis of inheritance in families or mappingpopulations. During meiosis, one random copy of eachchromosome pair is passed on to the gamete. Therefore,grandparental copies of genes located on different chro-mosomes are inherited independently, whereas genes on

tabase (http://www.ncbi.nlm.nih.gov/Taxonomy/taxonomyhome.html,

bEST/dbEST_summary.html).ank/GenbankSearch.html).

http://www.ebi.ac.uk/swissprot/

http://www.ebi.ac.uk/swissprot/

http://www.ncbi.nlm.nih.gov/Taxonomy/taxonomyhome.html

http://www.ncbi.nlm.nih.gov/dbEST/dbEST_summary.html

http://www.ncbi.nlm.nih.gov/Genbank/GenbankSearch.html

Fig. 1. Fifty F1 progeny and both parents (male and female) of European sea bass (Dicentrarchus labrax) from the linkage mapping panel VeneziaFbis (Chistiakov et al., 2005). The parents share microsatellite allele 171 and are diagnostic for the alleles 179 and 187 at locus DLA0014 (mapped tolinkage group 13). At the right are indicated the marker sizes of the standard (M) (in bp).


the same chromosome are not inherited independently,but are passed on together or “linked”. Only geneslocated next to each other are tightly linked. Crossing-over results from physical exchange of chromosomesegments between two homologous chromosomes ofmeiosis. Recombination results in the exchange ofgrandparental alleles of genes further apart on that chro-mosome (Hartl and Jones, 2001).

During linkage map construction, co-segregatingmarkers are placed into linkage groups, and the propor-tion of recombinants detected between linked markers isused as a measure of distance between them. Geneticdistance is usually measured in centimorgans (cM),where 1 cM is equivalent to 1% recombination betweenmarkers. Gene mapping algorithms analyse the co-seg-regation of markers in the families and assemble themarkers into linkage groups, followed by selecting themost likely order of markers within the same linkagegroup (Hartl and Jones, 2001).

Examples of microsatellite-based linkage maps forsome important teleost fish species are listed in Table 2.Consolidated linkage maps have been published forfishes, such as Arctic charr Salvenius alpinus (Woramet al., 2004), Atlantic salmon (Moen et al., 2004a, b),rainbow trout (Sakamoto et al., 2000; Nichols et al.,2003a, b), Xiphophorus sp. (Walter et al., 2004), zebra-fish (Woods et al., 2000), Japanese flounder (Coimbraet al., 2003) and Nile tilapia (Kocher et al., 1998;Agresti et al., 2000) (Table 2). These maps comprisedifferent types of markers, with a major contribution ofSSRs and amplified fragment length polymorphisms

(AFLPs) (Vos et al., 1995) (Table 2). SSRs andAFLPs represent DNA markers, which are extremelyuseful for constructing a primary framework map thatcould be further enriched with type I markers (ESTs,SNPs, genes). Zebrafish and rainbow trout linkagemaps can serve as examples of such “map evolution”.For example, Woods et al. (2000) used 616 of 2000microsatellites mapped by Shimoda et al. (1999) tobuild a linkage map additionally enriched with 1503coding markers.

The number of linkage groups in a linkage map doesnot always match the haploid number of chromosomes(Table 2). It may be due to the structure of the popula-tion used for construction of the map and the lowgenome coverage of the markers mapped.

For some aquaculture species, such as rainbow trout(Sakamoto et al., 2000), zebrafish (Singer et al., 2002),Japanese flounder (Coimbra et al., 2003), tilapia(Agresti et al., 2000), Arctic charr (Woram et al.,2004) and European sea bass (Chistiakov et al., 2005),sex-specific maps have been developed (Table 2). Link-age map length differs between sexes. In species withthe XY sex determination system, the female map isusually longer than the male map because of higherrecombination rates in females compared to males. Inzebrafish and rainbow trout, the male recombinationrate close to the centromere is greatly reduced comparedto the female (Sakamoto et al., 2000; Singer et al.,2002). The molecular basis of suppression in recombi-nation remains unclear. Lindahl (1991) proposed somegeneral explanations, while Sakamoto et al. (2000)

Table 2Microsatellite-based-linkage maps of some economically important marine species

Species Linkage map Diploidchromosomenumber (2n)a

Reference

Number of markers mapped Number oflinkage groups

Length, cM

Arctic charr(Salvenius alpinus)

327 (184 SSRs+129 AFLPs+13ESTs+phenotypic marker Sex)

46 3900 (male)9920 (female)

80 Woram et al., 2004

Atlantic salmon(Salmo salar)

Male—251 (31 SSRs+215 AFLPs) 31 103 60 Moen et al., 2004aFemale—230 (31 SSRs+199 AFLPs) 25 910

Channel catfish(Ictalurus punctatus)

263 (19 type I SSRs+243type II SSRs+1 EST)

32 1958 56 Waldbieser et al.,2001

Yellowtail (Seriola sp.) Male (S. lalandi): 175 SSRs 21 548.3 48 Ohara et al., 2005Female (S. quinqueradiata):122 SSRs

25 473.3

Three-spined stickleback(Gasterosteus aculeatus)

227 SSRs 26 886 42 Peichel et al., 2001

Nile tilapia(Oreochromis niloticus)

162 (59 STRs+103 AFLPs) 30 704 44 Kocher et al., 1998Male (O. aureus×O. niloticus F1):214 (60 SSRs+154 AFLPs)

24 1632 Agresti et al., 2000

Female (O. mossambicus):62 (13 SSRs+42 AFLPs)

14 514

Hybrid O. aureus×O. niloticus

525 SSRs+21 genes 24 1311 Lee et al., 2005

Rainbow trout(Oncorhynchus mykiss)

208 (191 SSRs+3 RAPD+7genes+7 allozymes)

29 463.2 (male) 60 Sakamoto et al.,2000

1152.8 (female)1359 (973 AFLPs+226 SSRs+72 VNTRs+38 SINE markers+29 genes+12 minisatellites+5 RAPD+4 allozymes)

40 (30 major) 4590 Nichols et al.,2003a

European sea bass(Dicentrarchus labrax)

162 SSRs 25 815 48 Chistiakov et al.,2005

567 (male)906 (female)

Red sea bream(Pagrus major)

Male: 100 SSRs 26 609.1 48 Inami et al., 2005Female: 89 SSRs 28 (female) 708.5 (female)

Platyfish (Xiphophorus sp.) 290 (256 SSRs+22 allozymes+11 genes)

24 2178 48 Walter et al., 2004

Zebrafish (Danio rerio) 102 SSRs 25 1320 50 Knapik et al., 1996705 SSRs 25 2350 Knapik et al., 19982119 (616 SSRs+1503 genesand ESTs)

25 2400 Woods et al., 2000

2000 SSRs 25 2295 Shimoda et al., 1999141 SSRs 25 2582.7 (female) Singer et al., 2002

942.5 (male)1635.6 (sex-averaged)

Japanese flounder(Paralichtys olivaceus)

Male—231 (82 SSRs+149 AFLPs) 25 741.1 48 Coimbra et al., 2003Female—304 (101 SSRs+203 AFLPs) 27 670.4

aData are taken from the Animal Genome Size Database (http://www.genomesize.com/fish.htm). Abbreviations: AFLP, amplified fragment lentgthpolymorphism; VNTR, variable number of tandem repeats; STS, sequence tagged site; RAPD, randomly amplified polymorphic DNA; IRS, internalrepetitive sequence.


focused their suggestions for salmonids on the model oftetravalent formation, which hinders crossovers be-tween homologous chromosomes via structural con-straints in distal regions in males.

Fishes have some of the most complex sex determi-nation systems known in the animal kingdom (Schartl,2004). Identification of sex-determining loci is ham-

pered in fish species due to the absence of heteromorphicsex chromosomes (Traut and Winking, 2001), variabil-ity of genetic sex determination (Volff and Schartl,2001) and ability to switch sex depending on the envi-ronmental conditions (Baroiller and D'Cotta, 2001).However, applying microsatellites provides a good op-portunity to find a sex-determining locus due to

http://www.genomesize.com/fish.htm


specific features in the heterogametic sex such as anobvious reduction in recombination between markerslinked to the sex-determination region in male com-pared to female meioses (Naruse et al., 2000) andthe consistently heterozygous status of males forunique alleles in this region (Peichel et al., 2004).Using this approach, a sex-determining locus hasbeen found in medaka Oryzias latipes (Naruse etal., 2000) and three-spined stickleback (Peichel etal., 2004), species without distinct sex chromosomes.

6.2. Individual DNA identification and parentageassignment

Microsatellites represent codominant single-locusDNA markers. For each SSR, a progeny inherits oneallele from the male parent and another allele from thefemale parent. This simple inheritance pattern can ex-plain the extreme popularity of polymorphic SSR loci inpaternity testing. Using a panel of several microsatelliteloci, a unique combined SSR genotype profile can beproduced for each individual tested. The genotype pro-file is highly discriminating, which suggests that a ran-dom individual would have a low probability ofmatching a given genotype.

Microsatellites are extensively exploited for paternityand relatedness analysis of natural populations, hatcherybroodstocks and trade control of fish products, includingthose from aquaculture (Liu and Cordes, 2004). Appro-priate mathematical tools are available to evaluate ge-netic relatedness and inheritance in these systems(Luikart and England, 1999; Blouin, 2003; Jones andArdren, 2003). A suitable methodology should be cho-sen for accurate and correct analysis of genotyping datato reconstruct parentage and pedigree structure in wildpopulations and broodstocks. Existing analyticalpackages combine several different approaches in par-entage analysis such as merging likelihood techniqueswith tools assessing statistical confidence in parentalassignments that provides a significant power in par-entage reconstruction (Jones and Ardren, 2003).

An example of successful application of microsatel-lite markers in relatedness testing was described byHerbinger et al. (1995), who analyzed a rainbow troutbroodstock in a small hatchery in Canada. Using onlyfour of five microsatellite markers, they were able tomatch 91% of offspring to one or two parental couplesof 100 possible parental pairs (ten sires× ten dams) and,in addition, to estimate parental effects on progenygrowth and survival. Applications of SSRs have beenreported to determine paternity and reproductive contri-bution in wild and farmed populations of various eco-

nomically significant species such as bluegill sunfishLepomis macrochirus (Neff, 2001), red sea breamPagrus major (Doyle et al., 2001), turbot Scophthalmusmaximus (Castro et al., 2004), chinook salmon Oncor-hynchus tshawytscha and rainbow trout (Bentzen et al.,2001), Atlantic salmon (Norris et al., 2000; King et al.,2001), European sea bass and gilthead sea bream(Kotoulas and Tsigenopoulos, pers. comm.).

Due to the small size of SSRs, they are relativelystable in degraded DNA (Schneider et al., 2004). Thisis one reason why polymorphic SSRs are widely usedin forensic science for individual DNA identification.In addition, they show a high degree of allelic vari-ability, and hence uniqueness. An interesting exampleof the application of microsatellites to resolve a case offishing tournament fraud in Finland was reported byPrimmer et al. (2000). Genotyping data provided ahighly significant power for excluding the possibility(PN0.9999) of a 5.5 kg Atlantic salmon originatingfrom the fishing competition location, Lake Saimaa(south east Finland). Assignment of the suspect fishto neighbouring natural samples was done with threemethods: using a Bayesian-based approach, based onreference population allele frequencies and based ongenetic distance, all using the software GeneClass(Cornuet et al., 1999). When presented with this evi-dence, the offender confessed to purchasing the salmonat a local fish shop, and criminal charges were made. Asimilar strategy could be also used, for example, incases of illegal poaching, fraud with food authenticityand the mix-up of commercial fish catches (Fig. 3), inorder to assign or exclude individuals from originatingfrom a claimed population (Poetsch et al., 2000).

In addition, microsatellite loci remain relatively sta-ble in bone remnants and dental tissue, providing thebasis for the successful application of ancient DNA formolecular analysis (Burger et al., 1999). Successfulextraction and amplification of nuclear DNA fromthe β-globin gene region containing a polymorphic(AT)χ(T)γ microsatellite from 12,000 year-old humanbone specimens has been reported (Beraud-Colomb etal., 1995). Application of microsatellites obtained fromhistorical fish scale collections has helped to explaindemographic declines in abundance, which resulted inthe complete collapse of populations of lake trout in theupper Laurentian Great Lakes of North America duringthe past 40 years (Guinand et al., 2003). Other caseswhere DNA from old fish scales was used to characterisehistoric populations involve northern pike Esox lucius(Miller and Kapuscinski, 1996; Larsen et al., 2005) andbrown trout Salmo trutta (Hansen, 2002). Also the mem-brane lining of fish otoliths contains DNA, which can be


used to genotype historic populations (Atlantic codGadus morhua: Hutchinson et al., 2003; New Zealandsnapper Pagrus auratus: Hauser et al., 2002).

Analysis of nuclear microsatellites from ancienthuman and animal remnants represents an essentialstep to understand the genetic diversity in current popu-lations and to provide substantial perspectives for theanalysis of archeological issues, establishment of an-cient baselines, heritable diseases, determination of re-latedness and establishment of genealogies inprehistoric populations (Zierdt et al., 1996).

6.3. Phylogeny, population and conservation genetics

The molecular structure and genetic variability ofmicrosatellites is extensively exploited in evolutionarystudies of a wide variety of fish species. The vast ma-jority of these studies attempt to infer phylogeneticrelationships from microsatellite data at levels belowthe species level (Goldstein et al., 1999; Heath et al.,2001; Reusch et al., 2001) or for recently divergedspecies (McCartney et al., 2003; Stamford and Taylor,2004), using variability within stretches of tandemrepeats, which evolve significantly more rapidly thanflanking regions. However, the high incidence of homo-plasy (e.g., false equality of alleles based on independentmutation to the same size) with increasing evolutionarydistance, may undermine the confidence of the inferredphylogenetic hypotheses, compromise the accuracy andlimit the depth of phylogenetic inference (Jarne andLagoda, 1996). Another obvious problem with usingSSRs for phylogenetic inference is that primers devel-oped from one taxon may not work well on all the taxafor which genotypes are required. Although cross-species ampification is common, limits on the utility of

North Sea

Fig. 2. Microsatellite genotypes (scored at dinucleotide locus F14) of commSea (n=29). Homozygotes are indicated with an arrow. A larger number of ithe higher allelic diversity. At the right are indicated the marker sizes of the

primers for amplifying homologous loci in divergenttaxa are evident. Even when it is possible to amplifysomething in divergent taxa, the sequences may not besimilar enough to permit confident assessment of orthol-ogy. Flanking regions of microsatellites have proventheir value in establishing phylogenetic relationshipsbetween species and families, because they evolvemuch more slowly than numbers of tandem repeats.For example, a phylogeny of cichlid fishes was studiedbased on information from DNA sequences of the flank-ing region of a (CA)n microsatellite locus TmoM27,which showed particular conservation in severallineages of cichlids diverged more than 80–100 millionyears ago (Zardoya et al., 1996). Analysis revealed thatthe repeat region was nearly lost in the ancestor tocichlids and then amplified extensively in African taxa(Streelman et al., 1998). Indian and Malagasy cichlidsformed a basal, paraphyletic group, while African andNeotropical cichlids were both monophyletic and sistergroups (Zardoya et al., 1996; Streelman et al., 1998).The authors suggested that marker TmoM27 could bewidely applied in phylogenetic studies in other perci-form fishes.

Phylogeographical applications of microsatellites areeminently suitable, where population structure is ob-served over a large geographical scale (Koskinen etal., 2002; Gum et al., 2005) (Fig. 2). The latter studyon grayling Thymallus thymallus shows that there isstrong admixture among major lineages in contactzones between drainages zones. Microsatellites areeven more revealing over shorter geographical dis-tances, where a few cases of panmixia (Dannewitz etal., 2005) and numerous cases of isolation by distancepatterns (Ruzzante et al., 1999; O'Reilly et al., 2004),clinal variation (Nielsen et al., 2004), fragmentation

Adriatic Sea

105 bp

100 bp

94 bp

75 bp

50 bp

M

on soles (Solea solea) collected in the North Sea (n=27) and Adriaticndividuals from the North Sea population are heterozygous because ofstandard (M) (in bp).


(Lemaire et al., 2005), hybridisation (Gum et al., 2005)and cryptic speciation (Fillatre et al., 2003) have beenidentified (see later). In those cases, differences in themicrosatellite allelic composition of populations areconverted into evolutionary distances. Microsatellitegenotypes are particularly helpful to detect structure inclosely related populations, regardless of whether theyare in evolutionary equilibrium.

The levels of genetic diversity between fish popula-tions revealed with microsatellite markers are muchhigher than those obtained with phenotypic or allozymemarkers (Miller and Kapuscinski, 1996; Shaw et al.,1999; Triantafyllidis et al., 2002; Corujo et al., 2004).Compared with allozyme markers, microsatellites ex-hibit higher levels of polymorphism and abundance ingenomic DNA (Schlötterer, 2000). Most microsatellitesare considered neutral markers, whereas allozymes aresometimes the target for natural selection. In that case,no deviation from the expectations under a neutralmodel is expected by selection acting on the microsa-tellite itself. However, if a microsatellite locus is linkedto a genomic region which is the target for naturalselection, then this microsatellite will show deviationfrom neutral expectation (Schlötterer et al., 1997;Lemaire et al., 2000).

SSR loci are more sensitive than allozymes for theevaluation of the dynamics of populations, includingdemographic bottlenecks (Spencer et al., 2000; Guinandand Scribner, 2003; Ramstad et al., 2004), populationsize fluctuations and effective population sizes (Gold etal., 2001; Bérubé et al., 2002; Waples, 2002). Commonmeasures of genetic diversity are heterozygosity (theproportion of heterozygous individuals in the popula-tion), allelic diversity (number of alleles at a locus in thepopulation), and the proportion of polymorphic loci(Pujolar et al., 2005). Marked decreases in the observedheterozygosity and reduced number of observed allelesof tested SSRs might be attributed to the action ofpopulation genetic bottlenecks. Since they evolve102–103 times faster than single-copy nuclear DNA,they are a powerful tool for analyzing recent and con-temporary events (Ellegren, 2000). For example,screening of microsatellites linked to the Y chromo-some enabled observation of fine genetic structure ofhuman populations as well as directions of migrationand timing of post-glacial human expansion in Europe(Rootsi et al., 2004). In salmonids, SSRs have beensuccessfully used for defining temporal intervals andexplaining mechanisms of severe decline of populationsof brown trout in Denmark (Hansen et al., 2002) andlake trout in the North American Great Lakes (Guinandand Scribner, 2003; Guinand et al., 2003). However,

size homoplasy problems in PCR-based microsatelliteassays may affect the inference of recent populationhistory (Estoup et al., 2002). Homoplasy may contrib-ute to biased genetic analyses of natural populationsand, hence, limit the use of microsatellites for the iden-tification of conservation units.

Genome-wide scans using microsatellite markerscould be applied for a search of locus-specific signa-tures of positive directional selection in natural popula-tions of any species for which a high-density geneticmap is available (Storz, 2005). The expansion and fix-ation of adaptive mutations is associated with jointfixation of linked neutral variants, or genetic hitch-hik-ing. Genetic hitch-hiking results in a reduced level ofpolymorphism and a skewed distribution of allele fre-quencies at linked neutral markers (Kim and Stephan,2000). The basic starategy of how to use whole-genomescreens to detect loci under positive selection, wasexplained and referred to as hitch-hiking mapping(Harr et al., 2002). For example, a whole-genomescreen of DNA polymorphisms was recently performedin humans and found evidence for selective sweeps, orloci which are driven by positive adaptive selection, innon-African populations (Storz et al., 2004).

In selected cases, it is possible to identify candidategenes, which are responsible for divergent selection innatural populations (Hendry et al., 2000; Santiago andCaballero, 2005). A multilocus scan of microsatellitevariation in a southeast Asian population of the malariaparasite Plasmidium falciparum identified evidence fora selective sweep within a 100-kb region on chromo-some 4 containing the dihydrofolate reductase (dhfr)gene; point mutations in this gene are known to beresponsible for resistance to the antimalarial drug pyri-methamine (Nair et al., 2003).

For spatially separated populations that inhabit dif-ferent environments or sympatric populations that usedifferent ecological niches, is it possible to find chro-mosome regions conferring adaptive biodiversity bycomparing relative levels of differentiation among mul-tiple unlinked loci (Charlesworth et al., 1997). Theoccurrence of highly polymorphic microsatellites inthe untranslated regions of ESTs is a potentially usefulsource of gene-associated polymorphisms representinggenetic signatures of divergent selection. For example,analysis of 95 EST-microsatellites, isolated during asalmonid EST screening project (Rise et al., 2004), inSalmo salar populations inhabiting contrasting naturalenvironments and geographically distinct regions,resulted in the selection of nine loci linked to candidategenes associated with adaptive divergence (Vasemägi etal., 2005).


Conservation and fisheries genetics focus on theeffects of inbreeding, demography, contemporary genet-ic structuring and adaptation on the long-term survivalof a species. Stock identification is a big issue (Fergu-son et al., 1995), helping wildlife managers to protectbiodiversity by identifying series of conservation unitssuch as evolutionarily significant units (ESUs), man-agement units (MUs) and action units (AUs) (Wan etal., 2004).

If populations within species show significant adap-tive differentiation to different habitats (ecologicalniches) or significant genetic diffrentiation, they mayjustify management as separate evolutionary lineagestermed ESUs (Moritz, 1994). The ESU concept wasdeveloped to assign units for protection below the tax-onomical level. The identification of an ESU preferablydepends on significantly differentiated genetic structuredetected by presumably neutral markers. In that case,SSRs represent markers of choice for identifying ESUs.However, to find a true ESU, multiple, prefererablydifferent kinds of markers should be exploited, sincesize homoplasy and null (e.g., non-amplifiable) allelescould affect PCR-based microsatellite analysis (Brownet al., 2005). For example, mitochondrial and microsa-tellite DNA markers revealed four genetically differen-tiated lineages of European grayling (Thymallusthymallus) in central and nothern Europe, whichevolved in geographical isolation during the Pleistoceneand could be recognized as the ESUs (Gum et al.,2005). The genotype data should be complementedwith ecological and biological (e.g., life-history andbehavioural) evidence (Crandall et al., 2000; Fraserand Bernatchez, 2001).

Genetic analyses often reveal differences betweensampled populations with substantial but noncompletephylogenetic separation, which have minor but statisti-cally significant differences in allele frequency of nu-clear or mitochondrial loci. These populations aretermed MUs (Wan et al., 2004). The MU is consideredto be a conservation unit level below that of the ESU,which is based on multiple evidence such as molecularmarkers, habitat use and adaptive characters. Using anMU is focused on the monitoring of the contemporarypopulation structure and requires defining genetic struc-ture of currently fragmented populations compared tothe ESU, which is derived from historical genetic dif-ferentiation (Moritz, 1994). Using an MU designationdoes not imply detailed genetic structure of populations,but indicates the populations to be treated as a unit. AUsdisplay genetic patterns of living populations. Microsa-tellites could be more successfully applied for identify-ing MUs and AUs than mitochondrial DNA markers,

since mitochondrial DNA has maternal inheritance.Conservation strategies depend on neither paternal normaternal variation, but focus on using biparental poly-morphism of nuclear DNA to reflect characteristicsneeded to cope with environmental conditions (Zhangand Hewitt, 2003). A high mutation rate of SSR locialso supports use of these markers in the genetic anal-ysis of very recent events in the dynamics of popula-tions, e.g., to MUs and AUs.

6.4. Molecular epidemiology and pathology

Genomic instability of microsatellites has been ex-tensively evaluated in the field of carcinogenesis, wherechromosomal rearrangements (e.g., translocations,insertions and deletions of genomic regions) occur(Charames and Bapat, 2003). Carcinogenic eventsoften happen within a genomic region harbouring atumour suppressor gene and hence inactivate the gene(Grady, 2004). Carcinogenic rearrangements are associ-ated with loss of heterozygosity (LOH) in microsatel-lites located within the affected chromosome region.Thus, detecting microsatellite LOH in tumour tissuescontributes not only to molecular diagnosis of cancer,but also points the possible location of a tumour sup-pressor gene (Presneau et al., 2003).

Zebrafish is a relevant vertebrate system for model-ing human cancer, displaying many similarities in tu-morigenic pathways, despite the evolutionarydivergence of tetrapods and fishes more than 300 mil-lion years ago. The zebrafish (whose cell-cycle genes,tumor suppressor genes and oncogenes are homologousto those found in humans and other mammals) hasenormous potential as a vertebrate system in which toidentify novel molecular pathways of oncogenesis, es-pecially because they are prone to develop tumors(Amatruda et al., 2002; Rubinstein, 2003). Zebrafishalso have played an important role as a vertebratemodel system for carcinogenesis regarding environmen-tal effects, genetic susceptibility, and environmental–genomic interactions (Amatruda et al., 2002).

The instability of triplet motifs was found in lowervertebrates, including fishes (Schartl et al., 1998; Liu etal., 2001). A variable number of trinucleotide repeatsoccurred within the coding region of functionally im-portant genes expressed in the brain of adult fishes suchas the channel catfish orthologue of the RAD23B gene(Liu et al., 2001) and the zebrafish Clock gene (Saleemet al., 2001). A RAD23B gene product functions in thenucleotide exclusion repair (NER) pathway. NERdefects are associated with higher incidence of muta-genesis and carcinogenesis and cause Xeroderma


pigmentosum, an autosomal recessive disease inhumans (Sancar and Hearst, 1993). The Clock locusregulating circadian rhythms is highly conserved invarious organisms. Alterations of circadian rhythmscould be related to a large number of diseases, includingpsychiatric disorders in humans (King and Takahashi,2000). The polyglutamine (CAG)n tract at the Clockgene is highly polymorphic in Drosophila, rangingfrom 25 to 33 pure glutamine repeats. In zebrafish, the(CAG)n stretch includes up to 51 repeat units (Saleem etal., 2001). However, it is shortest and non-polymorphicin human. The lack of polymorphism may indicate thatvariation at this locus is deleterious to the individual andhence not tolerated. The above examples suggest thatexpansion of trimeric repeats within exons is tightlyregulated during molecular evolution and that regulato-ry mechanisms preserve coding sequences from theuncontrolled extension of triplet motifs.

Since microsatellite markers are usually selectivelyneutral and often represent non-functional sequences,they cannot be defined as loci directly responsible fordisease phenotype. However, SSR markers, showinglinkage and association with disease, can be in stronglinkage disequilibrium with other functional geneticvariations which truly cause the pathological pheno-type. These disease-associated markers typically arerepresented by single nucleotide polymorphisms, orSNPs. They are often functionally relevant and, there-fore, could be responsible for determination of the path-ogenic phenotypes (Schork et al., 2000).

Genome-wide scans using SSRs often reveal linkageto susceptibility loci on chromosomes that harbourgenes contributing to genetic predisposition/resistanceto pathology. Further fine mapping with microsatellitemarkers located within the region of linkage oftenresults in identifying the susceptibility gene, but not inthe detection of the genetic variation, which directlycauses clinical outcomes of the disease. In some cases,only when a microsatellite marker has direct functionalsignificance, can it represent a genetic marker for dis-ease (Fornoni et al., 2002; Wang et al., 2002).

Microsatellite-based screening strategies can be usedin the fields of veterinary and medical parasitology andfor molecular studies of infectious diseases. Thisincludes mapping and further identification of genesresponsible for resistance to parasites and pathogensand the identification of genes controlling drug resis-tance in pathogenic organisms (Gasser, 1999; Naidooand Chetty, 1998; Behnke et al., 2003; Anderson,2004). Such approaches have been applied in a varietyof domesticated and farmed animals and plants (Naidooand Chetty, 1998; Yencho et al., 2000). In farmed fish-

es, they have been mostly performed on Salmonidae. Inrainbow trout, genomic DNA was screened for locicontrolling natural killer cell-like activity (Zimmermanet al., 2004) and linked to resistance to infectous hema-topoietic necrosis virus (Palti et al., 1999; Khoo et al.,2004), ceratomyxosis (Nichols et al., 2003b) and pan-creatic necrosis virus (Ozaki et al., 2001; Gibson,2002). In Atlantic salmon, polymorphic loci associatedwith resistance to infectious salmon anemia have beenidentified (Grimholt et al., 2003; Moen et al., 2004b).For an interspecific tilapia hybrid (Oreochromis mos-sambicus×O. aureus), a genome scan using microsatel-lite markers found five loci in different linkage groupslinked to innate immunity (Cnaani et al., 2004). Identi-fication of six microsatellite loci linked to resistance tored sea bream iridovirus, which causes high mortalitiesin cultured red sea bream in Japan, have also beenreported (Inami et al., 2005).

Transmission of diseases is a major problem in aqua-culture production, and determining the genetic archi-tecture of disease resistance traits is of great interest togeneticists working on aquaculture species. Due to therapid accumulation of genetic and genomic data andavailability of high-density microsatellite maps for cer-tain farmed fishes, the number of studies searching formarker loci and genes associated with resistance topathogens is expected to increase dramatically.

6.5. Quantitative trait loci mapping

A quantitative trait is one that has measurable phe-notypic variation owing to genetic and/or environmen-tal influences. The variation can be measurednumerically (for example, height, size or blood pres-sure) and quantified. Generally, quantitative traits arecomplex (multifactorial) and influenced by severalpolymorphic genes and by environmental conditions.A QTL is a genetic locus (gene), the alleles of whichaffect phenotypic variation. One or many QTLs cancontribute to a trait or a phenotype. When more thanone QTL influences a particular trait, each might have adifferent effect size, and the effects of individual QTLscan vary from strong to weak. The size and nature ofthese effects also can be affected by the genetic back-ground (the total genotype of the individual), and inter-actions between QTLs are common (Mackay, 2001).

To date, microsatellite-based strategies (scans acrossindividual chromosomes and a whole genome) repre-sent appropriate techniques to identify QTLs, particu-larly those that are associated with medically,economically and evolutionarily important complextraits. Due to the genome-wide distribution and high


levels of allelic polymorphism, SSR loci are very help-ful in coarse and fine linkage mapping approaches.Coarse mapping resolves detection of a putative QTLin a chromosomal region, usually within a range of 10–30 cM. For a given QTL, the likelihood of success andmapping resolution depends on the number of lociscreened and the magnitude of their effect on the traitof interest. Also important are recombination events inthe mapping population, the mode of expression of thetrait (dominant, recessive or additive), size of the map-ping population, and number of genes that define thequantitative trait (Glazier et al., 2002).

Characteristics and types of experimental crossesused to breed a mapping population are also crucial inprecise QTL mapping. Typically, approaches that in-crease the number of breakpoints in a mapping popula-tion provide higher mapping resolution, but also requirea larger number of animals to achieve significance for agiven size of a QTL effect. Lander and Kruglyak (1995)defined threshold values to assign significance of link-age of a marker to a given trait in a genome-widescreen: ‘highly significant’ refers to Pb4.9×10−6, ‘sig-nificant’ refers to 1.7×10−3 ×P×4.9×10−6 and ‘sug-gestive’ refers to P×1.7×10−3 after correction formultiple testing. Permutation tests represent a proventechnique to calculate threshold values adjusted formultiple testing (Doerge and Churchill, 1996).

All QTLs detected, including those only with sug-gestive significance, should be designated as proposedby Lander and Kruglyak (1995); it facilitates furtherconfirmation of such loci. It is wise to reconfirm anysignificant linkages before proceeding to finer mapping,to avoid unexpected effects. The reconfirmation can bedone with independent crosses but requires a simple testof the proposed chromosome interval for linkage to thequantitative trait. Another way includes analysis of acongenic strain produced by repeated backcrosses to aninbred strain with selection for a particular marker orchromosomal region, which shows a maximum strengthof linkage. This approach was widely applied in QTLmapping in rat and mouse, providing rapid confirmationof loci with modest genetic effect using a limited num-ber of animals (Nadeau et al., 2000).

Fine mapping narrows the putative location of a QTLwithin the chromosome to the interval of less than 1–5cM but requires more recombination events to separatethe genes that drive expression of the quantitative traitfrom closely linked markers. If there are several linkedQTLs, it is nessessary to perform more sensitive phe-notyping procedures. Creation of subcongenic strains,subdividing the crucial interval of linkage into severalsegments, greatly facilitates fine mapping and further

discovery of a suitable candidate gene (Nadeau et al.,2000). However, it is difficult to find a candidate geneeven when the crucial region was 0.5 cM if no signif-icant sequence information on that region is available.

Generation and analysis of knock-out and knock-in(for a given gene) animals (Hentschel and Bonventre,2005), transgenesis with bacterial artificial chromo-somes carrying the target gene (Yang and Seed, 2003),detection and evaluation of polymorphic markers withinthe candidate gene (Fanara et al., 2002), in vitro func-tional studies (Ponsuksili et al., 2005), examination ofgene function (Salvi and Tuberosa, 2005) and othertechniques can be used for further identification andconfirmation of the gene responsible for the quantitativetrait. However, identification of genes controlling com-plex traits often is complicated by the action of non-coding regulatory variants that are problematic for sim-ple functional characterization. Until 2002, a total of1700 genes responsible for a variety of genetic traitshave been detected in various species, and only 31 ofthem (including 7 human genes) have been defined forcomplex traits (Peltonen and McKusick, 2001; Glazieret al., 2002). Five complex QTLs have been defined inagricultural plants and only one QTL in farmed animals(Glazier et al., 2002). The latter involves the DGAT1gene encoding an enzyme which catalyzes the final stepin tryglyceride synthesis. Three polymorphisms withinthe gene are found to cosegregate with milk production,and one of them represents lysine-to-alanine amino acidsubstitution in codon 232, which underlies variation inmilk yield and composition (Coppieters et al., 1998).

For farmed fishes, the first mapping of an econom-ically important QTL was reported in 1998 (Jackson etal., 1998). To date, no QTL gene has been defined, butseveral microsatellite-based QTL screenings have beenperformed. Most of these mapping experiments havetargeted three salmonid species (Atlantic salmon, rain-bow trout and Arctic charr). These sceenings includesearches for QTLs related to temperature tolerance(Jackson et al., 1998; Danzmann et al., 1999; Perry etal., 2001; Cnaani et al., 2003; Somorjai et al., 2003),body weight (Cnaani et al., 2003; O'Malley et al., 2003;Borrell et al., 2004; Reid et al., 2005), body length(Borrell et al., 2004), spawning date (Sakamoto et al.,1999; O'Malley et al., 2003), embryonic developmentrate (Nichols et al., 2000; Robison et al., 2001) andcondition factor (Nakamura et al., 2001; Perry et al.,2003; Reid et al., 2005). In rainbow trout, the sex-linkedmicrosatellite marker OmyFGT19TUF showed signifi-cant association with fork length (FL) and upper ther-mal tolerance (UTT), explaining dependence of maleadvantages in FL and UTT compared to their female


sibs with the origin of the Y chromosome (Perry et al.,2005). A body weight QTL search has been reported forall three salmonids mentioned above (Reid et al., 2005).Several markers linked to feed conversion efficiencyhave been found in channel catfish (Karsi et al., 2000).

An interspecific hybrid tilapia stock (four-way crossbetween Oreochromis mossambicus, O. aureus, O. mos-sambicus and Sarotherodon galilaeus) has been suc-cessfully developed for linkage mapping and QTLanalysis (Agresti et al., 2000; Cnaani et al., 2003). Asearch for association with deleterious alleles and sex-ratio distortions revealed three microsatellite loci linkedto sex distortion genes in tilapia, and one of the markerswas likely to be related to a modifier of these genes(Shirak et al., 2002). A recent complex search for ge-netic linkage between DNA markers and QTLs forinnate immunity, response to stress, biochemical para-meters of blood, and fish size in the tilapia hybridrevealed 35 significant marker-trait associations involv-ing 26 microsatellite markers in 16 linkage groups(Cnaani et al., 2004). A different hybrid line was usedfor the detection of QTLs controlling body color (Howeand Kocher, 2003) and sex determination (Lee et al.,2003, 2004).

QTL mapping in natural populations represents apowerful tool to study the genetic architecture of fitnesstraits and reproductive isolation. This approach has notbeen extensively exploited yet since there are no well-developed genetic and genomic tools for most free-living species. The search for QTLs in wild populationsrequires the design or observation of appropriate crossesto create a suitable mapping population, which containsindividuals of measured phenotype and which can bepedigreed. The availability of a genetic map of variablemarkers is also crucial (Slate, 2005).

Marine three-spined stickleback represents a goodexample of an organism that successfully colonizednew freshwater lakes approximately 15,000 years agoand rapidly adapted to a diverse array of new environ-ments. Within these lakes, sympatric forms adapted to adifferent ecological niche-benthic forms have reducedbody armour, increased body length and decreasednumber of gill rakers, while limnetic forms more closelyresemble marine forms with a streamlined body, exten-sive armour and a large number of gill rakers. To dissectthe genetic architecture of the traits that cause reproduc-tive isolation, a mapping population was derived from abenthic female and limnetic male from Priest Lake(British Columbia), followed by backcrossing an F1male to a benthic female (Peichel et al., 2001). Amedium-density linkage map containing 227 microsa-tellites was constructed. The F2 progeny (n=92) was

genotyped to find QTLs linked to several gill raker andbody armour traits. A major QTL was mapped for everytrait, each explaining between 17% and 37% of thephenotypic variance. A new and relatively large (360F2 progeny) stickleback mapping population was re-cently produced using a marine female (from Japan)and benthic male (from Lake Paxton, British Columbia)to study the genetics of armour plate reduction (Colo-simo et al., 2004). QTLs were discovered for lateralplate number, with a major locus in linkage group 4accounting for 75% of the phenotypic variance. A sec-ond cross was generated from benthic and limnetic fish(Lake Frient, California). Three of the QTLs (includingthe major one) segregated in the second population(Colosimo et al., 2004). The authors also performed a‘complementation” cross between Paxton and Frientpopulations and found that the same gene within themajor QTL was responsible for the high plate number inthese populations. This indicates that a single genecausing a major shift in phenotype can explain casesof parallel evolution (Colosimo et al., 2004).

The cross between Japanese marine and Paxton ben-thic sticklebacks was used to investigate the geneticbackground of reduction or complete loss of pelvicspines in benthic stickleback populations (Shapiro etal., 2004). A major QTL was mapped to linkage group7. Within this QTL, a likely candidate gene (Pitx1),which causes a similar phenotype (asymmetry in pelviclimb reduction) in mice, was localized. However, noamino acid-changing mutation was found in the codingportion of Pitx1. Functional studies showed that Pitx1 isnot expressed in pelvic tissues of benthic fish, whereasmarine fish showed normal expression in pelvic tissue.Complementation tests showed that the same locus isresponsible for loss of pelvic structures in a sticklebackpopulation from Iceland (Shapiro et al., 2004). Thissuggests that a functional polymorphism, affectinggene expression, is located in the noncoding region ofPitx1. Hence, a second gene, contributing to the parallelevolution in stickleback, was found, providing an ex-ample of the successful application of the candidategene analysis to identify loci responsible for quantita-tive variation. In addition, QTL mapping experiments instickleback showed that replicate populations offeredconsiderable power for understanding evolutionarychanges.

6.6. Marker-assisted selection

Marker-assisted selection is based on the conceptthat it is possible to infer the presence of a gene fromthe presence of a marker tightly linked to the gene. For


this purpose, it is important to have high-density andhigh-resolution genetic maps, which are saturated bymarkers in the vicinity of a target locus (gene) thatwill be selected. The degree of saturation is the propor-tion of the genome that will be covered by markers atthe density such that the maximum separation betweenmarkers is no greater than a few centimorgans (usually1–2 cM), within which linkage of markers and QTLscan be detected. Strategies to find markers tightly linkedto the target gene are similar to those that are used forfine QTL mapping. Strategies, such as flanking markeranalysis (Dixon et al., 1995) and pooled sample map-ping (Churchill et al., 1993), are used to order thesemarkers. When implementing flanking marker analysis,a large segregating population is screened with markersflanking the target interval (usually 5–10 cM) in orderto identify individuals with a crossover within thatinterval. In pooled sample mapping, DNA from indivi-duals from a large segregating population that share agiven phenotype is pooled. DNA from each pool isanalysed with markers flanking the target gene. Theorder of markers then is determined from the proportionof pools that show a crossover with respect to themarkers used for analysis.

Once a tight linkage is found between a molecularmarker and a gene of interest, the inheritance of the genecan be traced in breeding programs. The availability of aphenotype, which can be clearly identified and quanti-fied, plays an important role in successful MAS pro-grammes. MAS should be performed with strong controlof the correlation and matching ratio between the phe-notype of interest and genotype information. Breedersselect animals or plants carrying beneficial genotypesand alleles of markers that associate with or contribute toa trait of interest. Successful implementation of MASrequires well-developed genomic tools, including op-tional information on genetic variations relevant to theQTL phenotype, mode of inheritance, interactions withother contributing QTLs and economical magnitude ofthe QTL studied (Poompuang and Hallerman, 1997). Toplan MAS, breeders also should take into account pos-sible interactions between QTLs, which could relate toeach other and have overlapping genetic backgrounds.In that case, MAS should preferably represent a complexselection index and take into consideration all econom-ically significant traits that interact.

Production (such as growth rate, stress response anddisease resistance) and reproductive (such as sex deter-mination and development rate) traits are extremelyimportant to breeders. Discovery of genes that controlthese features could greatly benefit MAS in breedingprograms. Some marker-assisted breeding approaches

have been implemented in agricultural plants, forwhich significant genetic and genomic knowledge isavailable (Koebner and Summers, 2002; Dale and Brad-shaw, 2003). For example, wheat lines resistant to leafrust, a world-wide disease caused by Puccinia reconditaf.sp. tritici, have been selected using molecular markerslinked to resistant alleles of genes Lr9, Lr10, Lr19, Lr21,Lr24, Lr28, Lr29 and Lr35 through construction andanalysis of near-isogenic and single-gene lines. Theselines differ in the presence or absence of the target geneand a small region flanking the target gene (Kolmer,2005). SSR markers located in the vicinity of thesegenes were converted into sequence-tagged sites, orSTS, representing short DNA sequences, which have asingle occurrence in the genome and whose location andbase sequence are known. The STS tightly linked toeach of the leaf rust protective genes are clearly associ-ated with the preferable phenotype and can be rapidlydetected by PCR (Singh et al., 2004; Blaszczyk et al.,2004). The Lr10 and Lr21 genes have been cloned andsequenced (Feuillet et al., 2003; Huang et al., 2003).Both genes have sequences encoding nucleotide-bind-ing site-leucine-rich regions, which are characteristic ofdisease resistance genes in plants. Leaf rust infectionsoccur when the resistance gene interacts with a specificantivirulence gene in the rust, while compatible infec-tions occur in the absence of an antivirulence gene.New races of rust develop by mutation and selectionof virulence against rust resistance genes in wheat(Kolmer, 2005).

MAS was successfully implemented in farmed ani-mals, such as cattle (Maillard et al., 2003), pig (Roths-child, 2003), sheep (Notter and Cockett, 2005) andchicken (Malek and Lamont, 2003), for which manyQTLs have been completed. It can be combined withcandidate gene analyses that have identified importantchromosomal regions and individual genes associatedwith traits of economic interest (Rothschild, 2003).

To date, most of genetic improvements in aquaculturehave been performed following the use of traditionalbreeding approaches. For example, the Norwegian se-lective breeding programme for Atlantic salmon hasfocused on growth rate, age at sexual maturity andlater, disease resistance and carcass quality traits. Theselection response for growth rate in salmon is generallymuch higher than that obtained for traditional livestockanimals. Gjerde and Korsvoll (1999) reported the aver-age genetic gain for growth rate of 14% per generationthrough six generations per selection. These authors alsoreported that four rounds of selection for delayed sexualmaturation have reduced the incidence of salmon be-coming sexually mature before they reach market size.


However, traits difficult to measure, such as diseaseresistance, low-heritability traits, sex-limited traits andtraits expressed late in life would benefit from the use ofgene technology (Poompuang and Hallerman, 1997).

In fish culture, DNA marker-based techniques havebeen applied in several cases, for example, in breedingprogrammes for Atlantic halibut Hippoglossus hippo-glossus (Jackson et al., 2003), channel catfish (Waldbie-ser and Wolters, 1999), European sea bass (García deLeón et al., 1995), Japanese flounder (Hara and Sekino,2003; Sekino et al., 2003) and salmonids (Herbinger etal., 1995; Fjalestad et al., 2003; Wilson et al., 2003).Apart from facilitating survival under variable environ-mental conditions, a higher genetic diversity within aprogeny array stemming from multiple matings byfemales might also serve to reduce the potential cost ofinbreeding and reduce the deleterious effects of geneticincompatibility between two partners (Jennions and Pet-rie, 2000).

There are not yet large-scale fish breeding pro-grammes using MAS. However, the current status ofgenetic information and advanced genomic tools avail-able for rainbow trout, Atlantic salmon and channelcatfish provide an opportunity for the successful appli-cation of the DNA marker-based technology to selectivebreeding in these species. Predictions of the benefits ofMAS in beef production are that genetic progress mayincrease by about 11% under specific circumstances(Gomez-Raya and Klemensdal, 1999). Estimates ofbenefits to aquaculture breeding are yet undefined.

Microsatellite markers are useful in early stages ofMAS for the primary selection of parents for furthercrossing and subsequent genetic characterization ofprogeny. For this, SSRs linked to the target QTLwould be used. Further improvements, such as enrich-ment of linkage maps with type I markers, constructionof high-resolution linkage maps, development of phys-ical maps and their integration with linkage maps, fineQTL mapping using the candidate gene approach, will

Fig. 3. Results of STRUCTURE clustering and assignment analysis (Pritchpopulations of four morphologically defined eel species (represented by four canguilla; yellow: American eel A. rostrata; blue: Japanese eel A. japonica; remay point to introgression (e.g., between A. anguilla and A. rostrata) (courtfigure legend, the reader is referred to the web version of this article.)

lead to the replacement of SSRs by other types ofgenetic markers (ESTs and SNPs) in later stages ofmarker-assisted breeding programmes. This wouldlead to even more precise selection by gene-assistedselection (GAS), based on the use of favorable haplo-types and genotypes derived from genes directly con-tributing to the target trait (Hulata, 2001).

7. Prospects

In this review, with special emphasis on fishes, weconsidered microsatellites as structural genomic compo-nents and as genetic markers which have specific evo-lutionary mechanisms, functions and applications.Microsatellite loci have a high utility for constructing agenetic framework onto which other markers and genesare incorporated using various mapping strategies (link-age mapping, physical mapping and comparative genet-ic and genomic tools). Using microsatellite markers willgreatly benefit the genetic dissection of complex andquantitative traits in order to map, identify and eventu-ally clone and characterize the candidate genes control-ling economically important traits. In aquaculture, SSRsrepresent the markers of choice for genetic monitoring offarmed stocks in view of breeding programs through theanalysis of genetic variablity and pedigree structure todesign beneficial crosses, select genetically improvedstocks, minimise inbreeding and increase selection re-sponse (Davis and DeNise, 1998; Knibb, 2000).

Large-scale DNA marker-based approaches derivedfrom population and conservation genetics will greatlyassist the management of wild fish stocks, such as troutand salmon. As advanced genomic resources becomemore and more available in model and economicallyimportant organisms, the possibility of applying themin related nonmodel species increases. For example,mapping studies in red deer have benefited fromresources created for cattle (Slate et al., 2002). Geneticand genomic information from rainbow trout and

ard et al., 2000). Bar plot of individual assignment scores for eightolours). Each bar represents a single eel. Green: European eel Anguillad: giant mottled eel A. marmorata. A colour different from the baselineesy of G. Maes). (For interpretation of the references to colour in this


Atlantic salmon can be used for comparative mappingin other salmonids and predictive genetic reconstruc-tion of their common ancestor (Rexroad et al., 2005).In these studies, the main strategy lies in the identifi-cation of common SSR type I markers with subsequentmapping and annotation of the ESTs harbouring theseSSRs to develop comparative anchor-tagged sequences(CATS). CATS primers anneal to conserved exonicregions, enabling the amplification and sequencing ofadjacent introns. Since the primers are in conservedregions, CATS loci can be useful for identifying SNPsfor comparative genomics projects (O'Brien et al.,1993).

Microsatellite-based techniques will be applied ingenome scans and QTL mapping in natural populationsto search for the genetic basis of adaptive selection andbiodiversity in an increasing number of species (Rogersand Bernatchez, 2005). As our knowledge of the ge-nome expands, we predict that more difficult and for-mal proofs of QTL identity will become unnecessary.New genetic and experimental tools will allow us torapidly identify more and more genes, which drivequantitative variation, and then to evaluate theirimportance.

Acknowledgements

This work was supported by the EU-funded researchprojects BASSMAP (FP5; Q5RS-2001-01701) andAQUAFIRST (FP6; STREP contract no. 513692). Wethank G. Maes for Fig. 3.

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