A molecular tool for parentage analysis in the Mediterranean mussel ( Mytilus galloprovincialis )

A molecular tool for parentage analysis in the

Mediterranean mussel (Mytilus galloprovincialis)

Ania Pino-Querido1,2, Jose M Alvarez-Castro1,2, Manuel Vera1,*, Belen G Pardo1, Jose Fuentes3

& Paulino Martınez1

1Department of Genetics, University of Santiago de Compostela, Lugo, Spain2Instituto Gulbenkian de Ciencia, Oeiras, Portugal3Centro de Investigacions Marinas, Xunta de Galicia, Vilanova de Arousa, Pontevedra, Spain

Correspondence: A Pino-Querido, Instituto Gulbenkian de Ciencia, Rua da Quinta Grande 6, 2780-256 Oeiras, Portugal. E-mail:

[email protected]

*Current address: Departament of Biology, University of Girona, Girona, Spain

Abstract

Estimation of selection response and genealogical

tracing in family mixtures require an appropriate

tool for parentage analysis. In this study, we tested

19 marker loci for parentage analysis allocation in

Mediterranean mussel (Mytilus galloprovincialis). To

this aim, we reared families in tanks isolated from

wild mussel seed, analysed them using the 19 mar-

ker loci and characterized their performances based

on Mendelian rules. Probabilities of exclusion of a

false parent were estimated for different groups of

loci and contrasted to the real paternity assignment.

Based on this, we chose nine microsatellites with

the highest exclusion probabilities and a real pater-

nity assignment of 99.6%. Next, we analysed 600

individuals reared as in the usual production pro-

cess, where contamination from wild seed is likely.

We obtained a real assignment of 94.7% and were

able to identify individuals from the wild as the most

likely hypothesis to explain the observed incompati-

bilities with candidate parents. This information

was used to evaluate parental contribution in off-

spring obtained from gamete mixtures of several

parents, which bestowed results of interest for

future breeding programs of Mediterranean mussel.

Keywords: Mediterranean mussel, Mytilus gallo-

provincialis, parentage analysis, microsatellite, SNP,

genotyping errors, null alleles

Introduction

The Mediterranean mussel (Mytilus galloprovincial-

is) is a widespread mollusc originating in the

Mediterranean basin that can now be found along

the European Atlantic coasts and in several coun-

tries of the Pacific and Indian oceans (Gerard,

Bierne, Borsa, Chenuil & Feral 2008; Kijewski,

Wijsman, Hummel & Wenne 2009; Kijewski,

Smietanka, Zbawicka, Gosling, Hummel & Wenne

2011) due to its capacity to adapt to new habitats

(Branch & Steffani 2004). This species is among

the most cultivated molluscs, being recently intro-

duced for commercial purposes in several countries

(Wilkins, Fujino & Gosling 1983; Lee & Morton

1985; Geller 1999; Wonham 2004). Although the

major producers – China and Spain – have not reg-

ularly supplied records to the international organi-

zations, its production in 2002 was above 200 000

tons in Spain and 600 000 tons in China, which

shows the high economic relevance of the Med-

iterranean mussel (http://www.fao.org/fishery/-

culturedspecies/Mytilus_galloprovincialis/en).

In Europe, mussel culture is performed using dif-

ferent approaches and technologies but always in

natural environments such as fjords, r�ıas and

other type of embayment. The seed required for

the growing phase is always supplied from the

wild, either from natural populations in rocks at

the seashore or using different collector devices

such as ropes and nets. Then, seed is wrapped

with fine nets to different cultivation structures

(ropes, lines or poles) or just laid out in the bottom

of subtidal cultivation leases. Thus, all mussels,

those in the cultivation system and those living in

the rocks at the seashore, pertain to the same pan-

mictic population. As a consequence, and in spite

of recent efforts (see http://www.blueseedproject.

com/), no hatchery facilities exist in Europe for

© 2014 John Wiley & Sons Ltd 1

Aquaculture Research, 2014, 1–15 doi:10.1111/are.12329

this species, precluding the application of genetic

breeding programs for improving production.

In Spain, toxin accumulation is the most impor-

tant problem in this species and red tides occur

quite regularly along time causing mussel produc-

tion cancellation for long periods and the subse-

quent losses to producers (see http://www.

intecmar.org/Deinteres/toxsgal/Default.aspx?sm=c;

Blanco, Moro~no & Fern�andez 2005). Breeding pro-

grams could be a solution for diminishing toxin

accumulation in this species, as successfully imple-

mented in several shellfish species for different traits

(e.g. Langdon, Evans, Jacobson & Blouin 2003;

Kube, Appleyard & Elliott 2007; Gjedrem, Robinson

& Rye 2012). For carrying out this approach it

would be essential to ascertain the genetic compo-

nent associated with phenotypic variation in this

trait and to then estimate its potential response to

selection. Genetic breeding programs are usually

addressed through familiar or mass selection sup-

ported by appropriate molecular tools to trace gene-

alogies and to organize broodstock (Gjedrem 2000,

2010). Furthermore, heritability estimations are

based on phenotypic correlations between relatives

of multiple families reared in common environ-

ments, for which genealogy tracing is essential

(Falconer & Mackay 1996). Therefore, developing a

suitable molecular tool for parentage analysis is a

requirement to address these issues.

Microsatellites and single nucleotide polymor-

phisms (SNPs) are the most appropriate markers

for parentage evaluation because of their abun-

dance, polymorphism and codominance (Liu &

Cordes 2004). Several microsatellite (Presa, Perez

& Diz 2002; Yu & Li 2007a; Pardo, Vera, Pino-

Querido, �Alvarez-Dios & Martinez 2011) and SNP

(Vera, Pardo, Pino-Querido, Alvarez-Dios, Fuentes

& Martinez 2010) markers have been reported in

Mediterranean mussel, which have been mainly

applied in population genetics studies (Varela,

Gonzalez-Tizon, Marinas & Martinez-Lage 2007;

Diz & Presa 2009; Kijewski et al. 2009). Similar

studies have also been conducted in other species

of the Mytilus genus, where cross-amplification

between mussel species has been documented

(Presa et al. 2002; Yu & Li 2007a; Gardestr€om,

Pereyra & Andre 2008). DNA extraction has

sometimes been reported as a problem in these

studies, probably due to the abundance of muco-

polysaccharides in tissue samples (Vera et al.

2010; Pardo et al. 2011). Also heterozygote

deficiency has often been found in population genet-

ics analyses in this species, being attributed, at least

partially, to the presence of null alleles (Presa et al.

2002; Yu & Li 2007b; Gardestr€om et al. 2008; Diz

& Presa 2009). The exact causes of these features

are not yet well known (McInerney, Allcock, John-

son, Bailie & Prodohl 2011) and similar observa-

tions have also been reported for other mollusc

species (Hedgecock, Li, Hubert, Bucklin & Ribes

2004; Reece, Ribeiro, Gaffney, Carnegie & Allen

2004; Yu & Li 2007b; Gardestr€om et al. 2008).

Microsatellite loci have been used for parentage

analyses thus aiding to support breeding programs

in numerous aquaculture species (Liu & Cordes

2004; Vandeputte, Rossignol & Pincent 2011),

including bivalve molluscs (MacAvoy, Wood &

Gardner 2008; Pardo, Castro, Lopez, Pino-Querido,

Bouza, Fuentes, Villalba & Martinez 2008; Slab-

bert, Bester & D’Amato 2009; Lallias, Taris, Bou-

dry, Bonhomme & Lapegue 2010; An, Lee, Kim &

Myeong 2011). However, microsatellite markers

do not always fulfil the desirable properties for

parentage analyses (Vignal, Milan, SanCristobal &

Eggen 2002), and thus, SNPs have been suggested

as an alternative (Vignal et al. 2002; Morin, Luik-

art & Wayne 2004; Hill, Salisbury & Webb 2008;

Jones, Walsh, Werner & Fiumera 2009; Hara,

Watabe, Sasazaki, Mukai & Mannen 2010). SNPs

have already been successfully applied for parent-

age analyses in species of commercial interest like

pig (Sus scrofa) (Harlizius, Lopes, Duijvesteijn, van

de Goor, van Haeringen, Panneman, Guimaraes,

Merks & Knol 2011), sockeye salmon Oncorhyn-

chus nerka (Hauser, Baird, Hilborn, Seeb & Seeb

2011) and European bison (Bison bonasus) (Tok-

arska, Marshall, Kowalczyk, Wojcik, Pertoldi, Kris-

tensen, Loeschcke, Gregersen & Bendixen 2009).

In this study, we tested a total of 19 molecular

markers (11 microsatellite loci and 8 SNPs) for

parentage analysis in Mediterranean mussel. To

this aim, we reared a mixture of mussel families in

an isolated common environment (thus ensuring

that all offspring came from a small set of known

parents) to evaluate the genetic properties relevant

for parentage analysis in our set of markers. This

family analysis enabled us to accurately assess sys-

tematic genotyping errors and null allele frequen-

cies at those loci and check for Mendelian

segregation and linkage disequilibrium.

Then, paternity analysis was performed on a

large sample of hatchery produced mussels from

several families, reared under standard cultivation

conditions in the wild. In this complex scenario,

© 2014 John Wiley & Sons Ltd, Aquaculture Research, 1–152

Parentage analysis in Mediterranean mussel A Pino-Querido et al. Aquaculture Research, 2014, 1–15

we were able to allocate parents with confidence

and could identify wild (not hatchery-produced)

individuals attached to our culture ropes, provid-

ing very useful information for the production pro-

cess and for future genetic breeding programs in

this species.

Materials and methods

Biological samples and DNA extraction

Artificial crosses were carried out at the facilities

of the Centro de Investigaci�ons Mari~nas de Galicia

(CIMA). Two independent crosses corresponding to

the main objectives of our study were carried out

by mixing the gametes of several males and

females: (1) the first cross, C1, involved eight

breeders (4 males 9 4 females) and was designed

to evaluate the properties of tested markers for

parentage analysis; (2) the second cross, C2,

involved 29 breeders (19 males 9 10 females) and

was designed to evaluate the power of our parent-

age tool in the complex scenario of standard culti-

vation mussel rafts. To this aim, adult mussels

from several cultivation rafts in the Galician R�ıas

(NW Spain) were transported to CIMA facilities.

Spawning was induced following a thermal cycling

procedure between 10–20°C and breeders were

selected according to the quality of their gametes

(roundness of ovocytes and motility of sperm).

From each female, a sample of approximately

2 9 106 ovocytes was randomly taken, and all

ovocytes were mixed together (8 9 106 in C1 and

2 9 107 in C2) in 5 L plastic containers filled with

filtered (0.20 lm) and sterilized (UV) seawater.

Then, the mixture was fertilized with 20 mL of a

sperm suspension from males, transferred to a con-

ically based 300 L tank and left with a gentle aer-

ation for 48 h. After spawning, tissue samples

from males and females were collected for DNA

extraction. Resulting D-shape larvae were collected

from the 300L tank in a 100 lm screen and

reared, following standard procedures of bivalve

hatcheries, until settlement. Settled spat was

reared in a nursery in several PVC cylinders, each

provided with a nylon screen mesh fitted to it by

covering one of their ends and an air-lift device to

circulate the seawater, during 4 months. For C1,

250 offspring of approximately 1 cm length (maxi-

mum anterior-posterior dimension) were used

for genotyping and parentage analyses. For C2,

thousands of offspring were manually attached to

several cultivation ropes and transferred to a mus-

sel raft in the natural environment where they

were grown for 2 years. Along these 2 years, thin-

ning of the cultivation ropes was performed sev-

eral times according to mussel growth. At each

thinning out event, a biometric analysis of the

mussels in the ropes was performed and the small-

est size individuals (those of putative wild origin)

were culled out to ensure the highest proportion

of the C2 families in the ropes. After 2 years of

cultivation, a total of 600 individuals of length size

>6 cm were sampled from the ropes to check the

effectiveness of our parentage tool. We also sam-

pled 96 individuals shorter than 6 cm length size,

to evaluate their putative wild origin.

Finally, to evaluate genetic diversity in the

selected markers, a reference sample of 55 adult

mussels was analysed, including the 37 breeders

of C1 and C2 plus a group of 18 individuals also

coming from the Galician Atlantic coast (NW

Spain). Samples for DNA extraction were obtained

from gill tissue in the 55 wild individuals and 600

offspring coming from C2. The whole individual

was used for the 250 offspring coming from C1

because of their small size. All samples were stored

in pure ethanol previous to DNA extraction, which

was performed using the E.Z.N.A. E-96 mollusc

DNA kit (Omega Bio-Tech, Doraville, GA, USA).

This extraction procedure uses cetyltrimethyl

ammonium bromide (CTAB) together with the

selective DNA binding of Omega Bio-Tek’s HiBind

matrix. Samples were extracted with chloroform

that dilutes and wipes out mucopolysaccharides.

After ethanol precipitation, DNA was further puri-

fied using HiBind DNA spin columns.

Microsatellite and SNP analysis

A set of 11 reported microsatellites (Presa et al.

2002; Yu & Li 2007a; Gardestr€om et al. 2008;

Pardo et al. 2011) and eight SNPs (Vera et al.

2010) were used in this study. Singleplex PCR

reactions for microsatellites were performed in MJ

Research PTC-100 thermocyclers using the condi-

tions described by Pardo et al. (2011).

To make the analysis more cost-effective, two

multiplex microsatellite panels were designed

according to size range of alleles and fluorescent

labels: (1) MGE005, MGE007, Mgu2, MT203 and

Mg-USC22 and (2) MGE001, Mg-USC2, Mg-USC25,

Mg-USC42, Mg-USC31 and Mgu3. Following the

same criterion, SNPs were analysed following Vera

© 2014 John Wiley & Sons Ltd, Aquaculture Research, 1–15 3

Aquaculture Research, 2014, 1–15 Parentage analysis in Mediterranean mussel A Pino-Querido et al.

et al. (2010) in two SNaPshots by adding poly-

GACT tails of different sizes to get non-overlapping

fragments at the different loci: (1) MgUSC-SNP_3,

MgUSC-SNP_9, MgUSC-SNP_18 and MgUSC-

SNP_25; (2) MgUSC-SNP_5, MgUSC-SNP_15,

MgUSC-SNP_17 and MgUSC-SNP_20.

Both microsatellite and SNP loci were amplified

in 10 lL volume with 1 lL of DNA template

(30 ng) in 19 Qiagen Multiplex PCR MasterMix

(Valencia, CA, USA). This MasterMix contains

Multiplex PCR Buffer Kit, dNTP mix, MgCl2, Hot-

Start Taq DNA Polymerase, and PCR forward and

reverse primers in 0.2 mM concentration. Multiplex

PCR reactions were performed using a Verity TM

96-well Thermal Cycler (PE Applied Biosystems,

Foster City, CA, USA) using the following protocol

for microsatellites: initial denaturation at 95°C for

15 min, 35 cycles of denaturation at 94°C for 30 s,

50°C (annealing multiplex temperature) for 90 s

and extension at 72°C for 90 s. There was a final

extension step at 60°C for 30 min. SNaPshot reac-

tions for SNPs were performed in VerityTM 96-well

thermal cyclers as described by Vera et al. (2010).

PCR products were resolved by using an ABI

PRISM� 3730 automatic sequencer (Applied

Biosystems). Alleles were categorized using the soft-

ware GeneMapper v 4.0 (Applied Biosystems).

Genetic diversity and parentage assignment

Genetic diversity for each locus was estimated using

heterozygosity and allele number. To estimate the

potential of each locus and the combined potential

over loci for paternity allocation, cumulative exclu-

sion probabilities (EXCL1, EXCL2) were considered.

EXCL1 represents the probability of excluding a

false parent when no information exists of any true

parent, and EXCL2 is the probability of excluding a

false parent when the genotype of the other parent

is known. Both analyses were performed on the 55

wild individuals using CERVUS 3.0.3 (Kalinowski,

Taper & Marshall 2007). Parentage assignment

was performed following the exclusion-based

approach implemented in the program FAP

(Taggart 2007). All the 19 markers selected for the

study were evaluated in C1 cross assuming the four

females and four males as candidate parents. The

parentage analysis in C2 was performed using only

the set of markers finally chosen after discarding

the less effective ones.

Combined EXCL1 and EXCL2 probabilities were

obtained for different groups of loci to evaluate the

minimum number of loci to be used for the best

parentage tool with appropriate statistical power.

All selected markers were checked for Mendelian

segregation, and Hardy–Weinberg (HW) and geno-

typic equilibrium. Using the 19 tested markers we

assigned offspring to their parents with full confi-

dence and then chose the three largest full-sib

families (66, 42 and 40 offspring) to test Mende-

lian and independent segregation. Conformance to

Mendelian segregation was checked by chi-square

tests. Null alleles were re-coded to track their seg-

regation. Hardy–Weinberg and genotypic disequi-

librium was estimated by exact tests using the

program GENEPOP 4.1 (Raymond & Rousset

1995; Rousset 2008). The frequency of null alleles

at each locus from population data was obtained

following the method by Van Oosterhout, Hutchin-

son, Wills and Shipley (2004) as implemented in

the MicroCheker 2.2.3 software. In addition, fam-

ily data were used to detect systematic genotyping

errors and to estimate the frequency of null alleles

identified by systematic homozygote-homozygote

mismatches. Sequential Bonferroni correction (Rice

1989) was considered whenever multiple tests

were applied.

Results

Genetic diversity of microsatellites and SNPs

Genetic diversity parameters were estimated in a

natural sample of 55 adult individuals from the

Galician R�ıas. This area has been previously

reported as a single panmictic population using

microsatellites by Diz and Presa (2008, 2009).

Microsatellite loci showed 10.5 alleles on average,

ranging from 4 (Mg-USC25) to 17 (Mgu2)

(Table 1). Unbiased expected heterozygosity ranged

between 0.546 (MgUSC2) and 0.868 (MgUSC31),

with a mean of 0.724. Allele frequency distribu-

tions were highly heterogeneous, some loci dis-

playing rather homogeneous allelic frequencies

(Mg-USC31, MT203), while others sharply unbal-

anced (Mg-USC2, MGE005) (Fig. 1). Microsatellites

MGE007, Mgu2, MGE001, Mg-USC31, MT203

and MGE005 displayed nearly regular allelic series

in accordance with the reported repetition motifs

(Fig. 1). Most remaining microsatellites showed

irregular series, despite that they should be regular

according to their reported motif (Presa et al.

2002; Yu & Li 2007a; Pardo et al. 2011). For

SNPs, unbiased expected heterozygosity ranged



from 0.264 (MgUSC-SNP_15) to 0.504 (MgUSC-

SNP_25) with a mean of 0.425 (Table 1). All SNP

markers showed a biallelic pattern as previously

reported (Vera et al. 2010). Some loci showed sig-

nificant departures from Hardy–Weinberg (HW)

proportions in the wild population after Bonferroni

correction: the microsatellites MT203, Mgu2, Mg-

USC25, Mg-USC31, MgUSC2, MGE001 and Mgu3;

and the SNPs MgUSC-SNP_3, MgUSC-SNP_18 and

MgUSC-SNP_20. Microchecker 2.2.3 indicated that

most of these deviations were due to the presence

of null alleles (Table 1). No significant genotypic

disequilibrium was detected after Bonferroni cor-

rection between all pair-wise comparisons both for

microsatellites and SNPs starting from population

data.

Parentage analysis

Theoretical exclusion probabilities

Theoretical probabilities of excluding a false parent

without knowing the other one (EXCL1) and when

the other one is known (EXCL2), were obtained

using the program CERVUS 3.0. For microsatel-

lites, EXCL1 values ranged from 0.178 (Mg-USC2)

to 0.568 (Mg-USC31) and EXCL2 from 0.324

(Mg-USC25) to 0.726 (Mg-USC31) (Table 1).

The combined exclusion probabilities for the set of

11 microsatellite loci were EXCL1 = 0.9904 and

EXCL2 = 0.9996.

For SNPs, EXCL1 ranged from 0.034 (MgUSC-

SNP_15) to 0.125 (MgUSC-SNP_25) and EXCL2

from 0.114 (MgUSC-SNP_15) to 0.187 (MgUSC-

SNP_25) (Table 1). The combined exclusion poten-

tials for the eight SNPs were EXCL1 = 0.5431 and

EXCL2 = 0.7635. When gathering the 19 mark-

ers, the combined exclusion probabilities obtained

were EXCL1 = 0.9956 and EXCL2 = 0.9999

(Fig. 2).

Parentage analyses in an isolated environment: C1

Of the 250 offspring analysed in the C1 cross, six

were discarded due to low DNA quality. To iden-

tify the 16 expected families (4 ♂ 9 4 ♀) among

offspring, we first used the 11 microsatellite loci

following a Mendelian exclusion approach because

of their higher exclusionary potential. Parents

were allocated considering the couple with the

lowest Mendelian incompatibilities among all pos-

sible. A certain proportion of the progeny showed

one, two and up to three mismatches with the best

compatible couple due to the presence of null

Table 1 Genetic diversity estimates and probabilities of parental exclusion for the 19 microsatellite and SNP loci

analysed

Locus K N Ho He EXCL1 EXCL2 HW F(Null)

MGE005 15 55 0.691 0.690 0.312 0.505 0.819 �0.0129

MGE007 5 55 0.782 0.751 0.325 0.511 0.273 �0.0266

Mg-USC22 9 55 0.673 0.699 0.287 0.464 0.051 0.0084

MT203 16 55 0.673 0.865 0.564 0.723 0.009* 0.1061

Mgu2 17 51 0.314 0.780 0.42 0.61 0.000* 0.287

Mg-USC31 13 55 0.673 0.868 0.568 0.726 0.004* 0.1068

Mg-USC42 6 55 0.691 0.690 0.27 0.446 0.534 �0.0025

Mg-USC25 4 23 0.130 0.623 0.191 0.324 0.000* 0.3552

Mg-USC2 13 55 0.436 0.546 0.178 0.36 0.031* 0.0973

Mgu3 9 55 0.418 0.681 0.267 0.438 0.000* 0.1775

MGE001 8 54 0.500 0.668 0.261 0.436 0.004* 0.1303

MgUSC-SNP_3 2 55 0.218 0.500 0.123 0.186 0.000* 0.2483

MgUSC-SNP_9 2 55 0.455 0.488 0.117 0.183 0.782 0.0294

MgUSC-SNP_18 2 55 0.455 0.354 0.062 0.145 0.048* �0.2615

MgUSC-SNP_25 2 54 0.426 0.504 0.125 0.187 0.283 0.0714

MgUSC-SNP_5 2 54 0.389 0.338 0.056 0.14 0.419 �0.0941

MgUSC-SNP_20 2 55 0.345 0.499 0.122 0.186 0.031* 0.1401

MgUSC-SNP_15 2 55 0.309 0.264 0.034 0.114 0.332 �0.1688

MgUSC-SNP_17 2 55 0.582 0.456 0.102 0.175 0.067 �0.1634

K, number of alleles; N, number of individuals; Ho, unbiased observed heterozygosity; He, unbiased expected heterozygosity; EXCL1

and EXCL2, average exclusion probabilities when no parent is known and when one parent is known, respectively, using Cervus

3.0; HW, P-value of conformance to Hardy–Weinberg equilibrium after Bonferroni correction using Genepop v.4.1; F(Null),

frequency of null alleles calculated by Microchecker 2.2.3 using the Oosterhout estimator.

*P < 0.05 after Bonferroni correction.



alleles and some systematic genotyping errors (see

below). Following this approach, we could allocate

a single couple for all offspring analysed, except

one, compatible with two putative mothers (99.6%

parental assignation). Next, we introduced SNPs

genotyping data in the analysis to confirm paren-

tal microsatellite allocation and especially, to iden-

tify sources of error associated with SNPs. Family

allocation reached 100%, all individuals being

assigned to a single couple.

Figure 1 Allelic frequency distributions of the 11 microsatellite loci analysed in the 55 wild individuals of Mytilus

galloprovincialis.



Genotyping errors and null alleles

A total of 3.7% and 1.5% mismatches were iden-

tified for microsatellites and SNPs, respectively,

assuming the family constitution inferred above.

This information was used to increase genotyping

accuracy, and to discard those loci with high

null allele frequency or prone to genotyping

errors. The major drawback, both for microsatel-

lite and SNPs, came from the presence of null

alleles. Null alleles represented 60.7% of incom-

patibilities for microsatellites and 97.9% for

SNPs. After family analysis, null alleles were

identified at microsatellites MGE005, MGE007,

Mgu2, Mg-USC25 and Mgu3, and at SNPs

MgUSC-SNP_3, MgUSC-SNP_17, MgUSC-SNP_20,

MgUSC-SNP_2 and MgUSC-SNP_25. The high

incidence of null alleles determined that 228 off-

spring carried null alleles in at least one locus,

and 33 individuals in three or more loci of 19

markers analysed. Furthermore, 16 individuals

were apparently homozygotes for null alleles (no

amplification) for a single locus in accordance

with the inferred heterozygote genotype for both

parents. According to family data, null allele fre-

quencies were high (>0.150) at Mg-USC25,

Mgu3 and Mgu2 microsatellite loci, and at

MgUSC-SNP_25, MgUSC-SNP_20 and MgUSC-

SNP_3 SNP loci. High null allele frequencies

were also estimated from population data at

these microsatellite and SNP loci, although popu-

lation estimates largely overrode family estimates

(nearly doubling them). One microsatellite

(MT203) which showed high allele frequencies

from population data did not show null alleles

from families (Table 1; Fig. 3).

Figure 2 Combined cumulative

exclusion probabilities for the 19

microsatellite and SNP markers

progressively included from the

highest to the lowest exclusion

potential.

Figure 3 Distribution of the abso-

lute null allele and genotyping

error frequencies for the microsat-

ellites and SNPs in the families

analysed. N-SA: Non-specific

amplifications. AA: adjacent alleles.

AD: Allele drop out. NA: Null

alleles.



The most frequent genotyping error among

microsatellite loci was unspecific amplifications

(19.8% of total mismatches; Fig. 3), which may be

erroneously confused with a true allele. This is

especially relevant when these peaks lay within the

allelic range, as for Mg-USC25 and Mgu2 loci. Sev-

eral microsatellites (MGE005, Mgu3, Mg-USC22,

Mg-USC31, Mg-USC42 and MT203) showed irreg-

ular allelic series including intermediate alleles,

which gave rise to erroneous genotyping (12.5%

mismatches). Allele dropout was detected at

MGE005, but especially at MGE001, representing

7.0% of the genotyping errors. After correcting all

these errors, only 11 mismatches could not be

assigned to specific genotyping errors and were

assumed to be caused by mutation, which renders

a mutation rate of 2.049 9 10�3. Genotyping

errors were much less frequent for SNPs than for

microsatellites, and only five mismatches were not

due to null alleles, representing non-systematic

errors attributable to technical issues.

Mendelian segregation and linkage disequilibrium

Mendelian segregation tests were performed using

the three largest families (66, 42 and 40 full sibs).

All loci without null alleles conformed to Mende-

lian segregation. Exact tests for linkage disequilib-

rium in the three aforementioned families rendered

P values <0.05 after Bonferroni correction for the

three pairs involving MGE005, MT203 and Mg-

USC31, suggesting linkage between them.

Molecular tool for parentage analysis

Using the theoretical combined exclusion probabili-

ties obtained with CERVUS 3.0 and the information

from null alleles and genotyping errors at each

locus, a set of six microsatellite markers was initially

selected among the whole set of 19 markers analy-

sed (Mg-USC31, MT203, Mgu2, MGE007, MGE005

and Mg-USC22) as the minimum loci for a tool with

high exclusionary power (EXCL1 = 0.963 and

EXCL2 = 0.995) for parentage allocation in Medi-

terranean mussel (Fig. 2). Parentage analyses per-

formed with FAP lead to 212 unambiguous

assignments (86.9%) among offspring. Thirty-two

individuals could not be assigned to a single couple

showing two possible fathers or mothers. At this

point, we added one by one the microsatellites with

higher potential for increasing the real assignment

power, but excluding those with high incidence of

genotyping errors. Thus, we obtained real assign-

ment power of 94.3% with seven markers

(EXCL1 = 0.973 and EXCL2 = 0.997), 98.7% with

eight markers (EXCL1 = 0.98 and EXCL2 = 0.998),

and 99.6% with nine markers (EXCL1 = 0.985 and

EXCL2 = 0.999), only one individual being not

assigned to a single couple. Therefore, we decided to

use the following nine-microsatellite panel for par-

entage analysis in the more complex C2 scenario:

Mg-USC31, MT203, Mgu2, MGE007, MGE005, Mg-

USC22, Mg-USC42 Mgu3 and MGE001 (Fig. 2).

Parentage analyses in the real mussel culture scenario:

C2

The microsatellite tool was tested in 600 individu-

als of the C2 cross. Both the higher number of

parents (190 putative families) and the possibility

of colonization from wild individuals represented a

challenge for parental allocation. Sixteen of the

600 C2 individuals were discarded due to low

DNA quality. Seventy-nine of the remaining 584

individuals (13.5%) showed two or more Mende-

lian incompatibilities with the 29 candidate par-

ents suggesting a wild origin as the most plausible

hypothesis. Most individuals of the remaining 505

(478; 94.7%) were unambiguously assigned to a

single couple; 4.4% showed one Mendelian incom-

patibility with their putative parents and the

remaining 0.2% were compatible with more than

one father or mother.

We detected null alleles by systematic homozy-

gote-homozygote mismatches at a frequency of

4.4% according to the family allocation performed.

Consistently with our previous analysis in the C1

scenario, we detected 148 putative genotyping

errors (3.3%), most of them due to intermediate

alleles (81.8%), followed by allele drop out

(16.2%). Finally, we analysed a sample of 96

culled small individuals from our ropes to confirm

its putative wild origin. These individuals are rou-

tinely discarded for production and assumed to

have settled from nature. Our parentage tool

enabled us to identify 84.3% of them not being

compatible with any C2 couple, 3.1% were unam-

biguously assigned to a single couple of candidate

parents, and the remaining 12.5% showed only

one Mendelian incompatibility not attributable to

null alleles or identifiable genotyping error. Thus,

we predicted that as much as 15.6% of the indi-

viduals discarded (mean size <6 cm) could actually

be individuals belonging to the families produced

by us in the hatchery.



Parental contribution and family structure

The parentage analyses carried out using C1 and

C2 crosses enabled us to evaluate parental contri-

butions, an interesting issue if mussel seed is pro-

duced at hatchery facilities in the future. All

breeders contributed to offspring both in C1 and

C2 crosses. In C1, contribution was much more

unbalanced within females (range: 0.9–70.9%)

than within males (5.9%–44.0%). The dominant

female contributed to the three largest families,

which included 29.6, 18.8 and 17.9% of the anal-

ysed offspring, the remaining families ranging

between 0.9 and 7.6% (Fig. 4). Thirteen of the 16

expected families (81.3%) were identified among

the 244 offspring analysed (Fig. 4). The contribu-

tion pattern of females and males in C2 families

was similar to C1, contribution of females ranging

from 0.9% to 47.8%, while ranging from 0.2% to

24.3% within males. The percentage of identified

breeding couples was much lower for C2, a total

of 118 families among the 190 possible ones

(62.1%), because of the lower ratio between sam-

ple size and number of families (C1: 15.3 versus

C2: 3.07). However, the contribution of families

was more balanced for C2 (range from 0.2% to

9.4%; Fig. 5). Ten families showed 10 offspring or

more, accounting for a total of 198 offspring

(42.2%), the largest family showing 44 offspring.

Discussion

Technical issues on Mediterranean mussel genetic

markers

It is known that allele categorization of microsatel-

lite loci in mollusc is not a straightforward task

(Lemer, Rochel & Planes 2011; McInerney et al.

2011). Sokolov (2000) pointed out the presence of

mucopolysaccharides in mollusc as one of the

major causes for low DNA quality obtained for

PCR amplification. Several microsatellite loci anal-

ysed in our work showed genotyping errors that

could be due to DNA quality, compromising the

required accuracy for parentage allocation. Popa,

Murariu and Popa (2007) pointed out DNA ampli-

Figure 4 Family distribution of the 244 C1 offspring. Families are represented in the X axis using the male and

the female names.

Figure 5 Family distribution of the 478 C2 offspring. Families are represented in the X axis.



fication problems in freshwater species of mussel,

even using several mollusc-specific DNA extraction

methods. However, for saltwater mytilid species

like M. galloprovincialis, M. troussolus and M. edulis,

most population genetic studies did not report arti-

facts or genotyping errors either using specific

methods for mollusc (Varela et al. 2007; Diz &

Presa 2008, 2009; Gardestr€om et al. 2008;

Kijewski et al. 2009; Ouagajjou, Aghzar, Minam-

bres, Presa & Perez 2010), phenol chloroform

extraction (Presa et al. 2002) or chelex (Daguin,

Bonhomme & Borsa 2001). However, it is remark-

able that whenever family data were available,

this kind of problems was often detected, which

suggests to pay attention to technical issues (Reece

et al. 2004; MacAvoy et al. 2008). Our study rein-

forces this view, since our parentage analysis tool

enabled us to detect genotyping errors (likely due

to low quality DNA) that would otherwise remain

unnoticed.

Besides genotyping errors, we detected the

presence of null alleles as the most common

source of mismatches for parentage analysis using

microsatellite markers in Mediterranean mussel in

accordance with previous results in saltwater

bivalve mollusc (McGoldrick, Hedgecock, English,

Baoprasertkul & Ward 2000; Hedgecock et al.

2004; Reece et al. 2004; MacAvoy et al. 2008;

Wang, Wang, Wang & Guo 2010). Some authors

have attributed this fact to cryptic transposable

elements found in the flanking regions of microsat-

ellites (Lemer et al. 2011; McInerney et al. 2011),

but also the high abundance of SNPs reported in

molluscs (Curole & Hedgecock 2005; Zhang & Guo

2010) could be related to this observation deter-

mining lack of amplification if lying in the anneal-

ing primer sequences.

Since SNPs usually show less technical problems

than microsatellite loci (Ball, Stapley, Dawson,

Birkhead, Burke & Slate 2010), we inspected the

usefulness of the available SNPs for parentage

analyses in Mediterranean mussel (Vera et al.

2010) as previously reported in other species

(Harlizius et al. 2011; Hauser et al. 2011). In these

studies, up to 100 SNPs were necessary to attain

an allocation power between 95 and 100%, the

minimum number of SNPs required for effective

assignments being around 50 (Primmer, Borge,

Lindell & Saetre 2002; Aitken, Smith, Schwarz &

Morin 2004; Tokarska et al. 2009). Simulation

studies by Wang (2006) showed that 16 equally

frequent biallelic markers would provide the same

assignment power as three microsatellite loci with

10 equally frequent alleles each. We used eight

previously reported biallelic SNPs for a preliminary

evaluation of their performances for parentage

analysis in Mediterranean mussel. As for microsat-

ellites, the major problem observed at SNPs was

null allele frequency. In particular, two of the

highest polymorphic SNPs, MgUSC-SNP_3 and

MgUSC-SNP_25, showed high null allele fre-

quency. This problem had not been previously

reported when using SNPs for parentage analysis

in other species (Tokarska et al. 2009; Harlizius

et al. 2011; Hauser et al. 2011). The same expla-

nations indicated above for high null allele fre-

quency at microsatellites may also be responsible

for this observation at SNPs.

In the controlled C1 cross, we found 11 mis-

matches for microsatellites not attributable to sys-

tematic genotyping errors or null alleles. Their

occurrence is likely to be due to mutation, thus

rendering a mutation rate of 2.1 9 10�3, which

lays within the range reported for several species

of fish (Amos 1999; Castro, Bouza, Presa, Pino-

Querido, Riaza, Ferreiro, Sanchez & Martinez

2004; Castro, Pino, Hermida, Bouza, Riaza, Ferre-

iro, Sanchez & Martinez 2006). Concerning the

five mismatches detected at SNPs, if they were

attributed to mutation, the inferred rate from our

results (1.2 9 10�3) would be much higher than

expected according to previous data (Vignal et al.

2002). This suggests that these five non-system-

atic mismatches could be rather due to technical

problems. However, it should be noted that previ-

ous studies have reported an extremely high SNP

frequency in mollusc genomes – around one SNP

every 20–40 bp (Curole & Hedgecock 2005; Sau-

vage, Bierne, Lapegue & Boudry 2007; Zhang &

Guo 2010) – indeed much higher than that

observed in many other species, where SNPs occur

every 200–500 bp (Kruglyak & Nickerson 2001;

Morin et al. 2004).

Overall, we found that SNPs did not solve the

most frequent problems of microsatellites for par-

entage analysis in Mediterranean mussel. In addi-

tion, since much more SNPs are required for

parentage allocation, we do not find recommend-

able at this moment to use SNPs in this species,

despite their lower cost. It is possible that, when

genomic resources increase in this species by the

potential of new generation sequencing technolo-

gies, the scenario can change and SNPs will be

considered for molecular parentage estimation.



Real versus theoretical assignment of the

parentage analysis tool

Molecular markers have been profusely used to

support genetic breeding programs for aquaculture

species, and in particular, for analysing parentage

relationships (Gjedrem 2000, 2005; Dekkers

2004; Dunham 2004; Duchesne & Bernatchez

2007). In this work, we developed a parentage

tool that minimizes the cost while keeping a high

assignment potential, as a first step for future

breeding programs in this species. We started from

a large number of markers and successively

reduced their number towards a trade-off between

cost and reliability. In addition, selected markers

were combined in multiplexes to reduce costs.

In the simplest scenario (C1), the real assign-

ment power of the whole set of 19 markers was

100% in accordance with theoretical expectations

(EXCL1 = 0.9956 and EXCL2 = 0.9999). Even for

the nine microsatellites selected to make up

our paternity tool (EXCL1 = 0.9856 and

EXCL2 = 0.9992), only one of 244 analysed off-

spring showed ambiguous allocation (99.7%). In

the complex scenario (C2), the results were also

encouraging with 94.7% unambiguous assign-

ment. The systematic errors detected in the analy-

sis of C1 were of great value for the analysis of the

C2 scenario. The real assignment power for six

(86.9%) and seven (94.3%) microsatellite markers

is in contrast with the theoretical exclusion poten-

tial previously obtained (EXCL1 = 0.950 and

EXCL2 = 0.994 for six markers; EXCL1 = 0.9684

and EXCL2 = 0.9971 for seven). This fact suggests

that some of the assumptions under which the

theoretical estimates are performed do not strictly

hold. Relatedness among breeders seems to be an

unlikely explanation given the wild origin of the

breeders, but the suggestive linkage detected in

family segregation between three of the selected

loci (MGE005, MT203 and Mg-USC31) would vio-

late the assumption of independent segregation

and could explain this observation.

Microsatellite panels are profusely available in

the literature for the development of parentage

analysis tools (Jones & Ardren 2003; Jones, Small,

Paczolt & Ratterman 2010). Previous studies in

aquaculture species reported tools for parentage

analysis with 4 to 8 microsatellite markers leading

to a real assignment power close to 100% (Norris,

Bradley & Cunningham 2000; Borrell, Alvarez,

Vazquez, Pato, Tapia, Sanchez & Blanco 2004;

Castro et al. 2004, 2006; Borrell, Carleos, Asturi-

ano, Bernardo, Vazquez, Corral, Sanchez & Blanco

2007; Castro, Pino, Hermida, Bouza, Chavarrias,

Merino, Sanchez & Martinez 2007; de la Herran,

Robles, Navas, Mounim Hamman-Khalifa, Herrera,

Hachero, Jose Mora, Ruiz Rejon, Garrido-Ramos &

Ruiz Rejon 2008). For mollusc, however, this

methodology has been more difficult to achieve.

There is a remarkable case of 100% assignment

with only one marker for abalone (Haliotis midae;

Selvamani, Degnan & Degnan 2001), although in

most cases results have not been so outstanding.

A recent study for the same species reported 90

and 91% assignment using nine microsatellites

(Van Den Bergb & Roodt-Wilding 2010), and stud-

ies performed in eastern oyster (Crassostrea virgini-

ca) with nine microsatellites achieved 100%

(Wang 2006). Many other studies in mollusc have

shown lower performances – e.g. 49–65% of

assignment using four microsatellites in flat oyster

(Ostrea edulis; Lallias et al. 2010). More specifically

in mussel (Mytilus galloprovincialis), a previous

study reported a mere 62.5% of assignment with

10 microsatellite loci (Nguyen, Hayes, Guthridge,

Ab Rahim & Ingram 2011).

Parental contribution and family structure in

Mediterranean mussel

Molecular tools of parentage analysis enormously

facilitate assessing reproductive systems, particu-

larly in aquatic species (Lallias et al. 2010). These

studies have profusely been carried out in fish spe-

cies (reviewed by Coleman & Jones 2011) and to a

lesser extent in natural and cultured populations

of mollusc (Boudry, Collet, Cornette, Hervouet &

Bonhomme 2002; Petersen, Ibarra, Ramirez &

May 2008; Lallias et al. 2010). However, no such

study has been carried out for Mediterranean mus-

sel to date. Our results show that all individuals

contributed to progeny in both breeding scenarios.

Breeders of the simplest scenario (C1) showed

more heterogeneous individual contributions than

those of the most complex one (C2). Both scenar-

ios showed a more heterogeneous contribution of

females relative to males, although in C2 contribu-

tions were more balanced. This fact greatly

reduces the effective number of breeders in crosses

with multiple breeders due to unbalanced family

size. Studies on bivalve mollusc reared in captivity

documented very diverse reproductive patterns,

which putatively depend on factors such as sexual



dimorphism and spawning inducing systems.

Uneven contributions – including non-contributing

females – were reported for lion-paw scallop (Nodi-

pecten subnodosus) (Petersen et al. 2008) and flat

oyster (Ostrea edulis) (Lallias et al. 2010). In

mixtures of families of molluscs obtained from

directed crosses – as in our experimental design –

uneven contributions were also reported (Boudry

et al. 2002), suggesting that additional factors like

gamete quality, sperm competition and/or early

viability selection could play a role, as observed in

fish (see Coleman & Jones 2011). In our study,

families were obtained by pooling all sperm from

males, and thus, sperm competition may have rep-

resented the main problem as usually observed in

fish species (Campton 2004; Frost, Evans & Jerry

2006). Further studies are being carried out to

more deeply understand the different factors

involved in reproductive contribution of breeders.

Obtaining families by factorial crosses before pool-

ing all offspring would likely enhance the perfor-

mances regarding effective brood size by limiting

sperm competition. In any case, this is an essential

issue if Mediterranean mussel breeding programs

are finally carried out.

In the present study, a detailed evaluation of mi-

crosatellite and SNP markers was carried out for

developing a molecular traceability tool to be

applied to future genetic breeding programs in

Mediterranean mussel. Parentage analysis in an

isolated scenario enabled us to evaluate technical

and genetic properties of those markers for design-

ing a cost-effective marker tool. A final subset of

nine microsatellites providing a theoretical power

for parentage allocation above 0.99 was selected

and tested in a sample resembling the usual pro-

duction scenario. The results obtained were satis-

factory (94.6%), thus for aiding future breeding

programs in this species.

Acknowledgments

Authors acknowledge helpful comments from two

anonymous reviewers and are grateful to Luc�ıa In-

sua, Mar�ıa Portela and Susana S�anchez-Darriba

for their technical support in the genotyping work

and to Viki Gregorio, Rocio Rendo, Ram�on Gir�al-

dez and Juan Carlos Pazos for their technical assis-

tance in the hatchery and nursery work and in

the production of microalgal food. J.M. �Alvarez-

Castro and B.G. Pardo acknowledge support by the

autonomous government Xunta de Galicia

through an ‘Isigro Parga Pondal’ contract. J.M.�Alvarez-Castro acknowledges funding from project

BFU2010-20003, from the Spanish Ministry of

Science and Innovation. This study was supported

by the Cooperation Agreement EPITOX funded by

the Conseller�ıa de Industria of the Xunta de Gali-

cia (2008/CP392).

Conflict of interests

The authors declare no conflict of interests.

References

Aitken N., Smith S., Schwarz C. & Morin P.A. (2004)

Single nucleotide polymorphism (SNP) discovery in

mammals: a targeted-gene approach. Molecular Ecology

13, 1423–1431.

Amos W. (1999) A comparative approach to the study

of microsatellite evolution. In: Microsatellites, Evolution

and Applications (ed. by D.B. Goldstein & C. Schlotterer),

pp. 66–79. Oxford University Press, Oxford, UK.

An H.S., Lee J.W., Kim H.C. & Myeong J.I. (2011)

Genetic characterization of five hatchery populations of

the Pacific Abalone (Haliotis discus hannai) using mi-

crosatellite markers. International Journal of Molecular

Sciences 12, 4836–4849.

Ball A.D., Stapley J., Dawson D.A., Birkhead T.R., Burke

T. & Slate J. (2010) A comparison of SNPs and micro-

satellites as linkage mapping markers: lessons from the

zebra finch (Taeniopygia guttata). BMC Genomics 11,

218.

Blanco J., Moro~no A. & Fern�andez M.L. (2005) Toxic epi-

sodes in shellfish, produced by lipophitic phycotoxins:

an overview. Revista Galega dos Recursos Mari~nos

(Monog.) 1, 70.

Borrell Y.J., Alvarez J., Vazquez E., Pato C.F., Tapia C.M.,

Sanchez J.A. & Blanco G. (2004) Applying microsatel-

lites to the management of farmed turbot stocks

(Scophthalmus maximus L.) in hatcheries. Aquaculture

241, 133–150.

Borrell Y.J., Carleos C.E., Asturiano J.F., Bernardo D.,

Vazquez E., Corral N., Sanchez J.A. & Blanco G. (2007)

Use of microsatellites and a combinatorial optimization

approach in the acquisition of gilthead seabream (Spa-

rus aurata L.) broodstocks for hatcheries. Aquaculture

269, 200–210.

Boudry P., Collet B., Cornette F., Hervouet V. & Bonho-

mme F. (2002) High variance in reproductive success

of the Pacific oyster (Crassostrea gigas, Thunberg)

revealed by microsatellite-based parentage analysis of

multifactorial crosses. Aquaculture 204, 283–296.

Branch G.M. & Steffani C.N. (2004) Can we predict the

effects of alien species? A case-history of the invasion

of South Africa by Mytilus galloprovincialis (Lamarck).



Journal of Experimental Marine Biology and Ecology 300,

189–215.

Campton D.E. (2004) Sperm competition in salmon

hatcheries: the need to instituonalize genetically

benign spawning protocols. Transactions of the American

Fisheries Society 133, 1277–1289.

Castro J., Bouza C., Presa P., Pino-Querido A., Riaza A.,

Ferreiro I., Sanchez L. & Martinez P. (2004) Potential

sources of error in parentage assessment of turbot

(Scophthalmus maximus) using microsatellite loci. Aqua-

culture 242, 119–135.

Castro J., Pino A., Hermida M., Bouza C., Riaza A., Ferre-

iro I., Sanchez L. & Martinez P. (2006) A microsatellite

marker tool for parentage analysis in Senegal sole

(Solea senegalensis): genotyping errors, null alleles and

conformance to theoretical assumptions. Aquaculture

261, 1194–1203.

Castro J., Pino A., Hermida M., Bouza C., Chavarrias D.,

Merino P., Sanchez L. & Martinez P. (2007) A micro-

satellite marker tool for parentage assessment in gilt-

head seabream (Sparus aurata). Aquaculture 272,

S210–S216.

Coleman S.W. & Jones A.G. (2011) Patterns of multiple

paternity and maternity in fishes. Biological Journal of

the Linnean Society 103, 735–760.

Curole J.P. & Hedgecock D. (2005) Estimation of prefer-

ential pairing rates in second-generation autotetraploid

pacific oysters (Crassostrea gigas). Genetics 171, 855–

859.

Daguin C., Bonhomme F. & Borsa P. (2001) The zone of

sympatry and hybridization of Mytilus edulis and M.

galloprovincialis, as described by intron length polymor-

phism at locus mac-1. Heredity (Edinb) 86, 342–354.

Dekkers JC (2004) Commercial application of marker-

and gene-assisted selection in livestock: strategies and

lessons. Journal of Animal Science 82(E-Suppl) E313–

E328.

Diz A.P. & Presa P. (2008) Regional patterns of microsat-

ellite variation in Mytilus galloprovincialis from the Ibe-

rian Peninsula. Marine Biology 154, 277–286.

Diz A.P. & Presa P. (2009) The genetic diversity pattern

of Mytilus galloprovincialis in Galician Rias (NW Iberian

estuaries). Aquaculture 287, 278–285.

Duchesne P. & Bernatchez L. (2007) Individual-based

genotype methods in aquaculture. In: Aquaculture Gen-

ome Technologies (ed. by J. Liu), pp. 87–108. Blackwell

Publishing, Oxford, UK.

Dunham R.A. (2004) Aquaculture and Fisheries Biotechnol-

ogy. Genetic Approaches. Auburn University, Auburn,

AL, USA.

Falconer D.S. & Mackay T.F. (1996) Introduction to Quan-

titative Genetics. Prentice Hall, Essex, UK.

Frost L.A., Evans B.S. & Jerry D.R. (2006) Loose of

genetic diversity due to hatchery culture practices in

barramundi (Lates calcarifer). Aquaculture 261, 1056–

1064.

Gardestr€om J., Pereyra R.T. & Andre C. (2008) Charac-

terization of six microsatellite loci in the Baltic blue

mussel Mytilus trossulus and cross-species amplification

in North Sea Mytilus edulis. Conservation Genetics 9,

1003–1005.

Geller J.B. (1999) Decline of a native mussel masked

by sibling species invasion. Conservation Biology 13,

661–664.

Gerard K., Bierne N., Borsa P., Chenuil A. & Feral J.P.

(2008) Pleistocene separation of mitochondrial lin-

eages of Mytilus spp. mussels from Northern and

Southern Hemispheres and strong genetic differentia-

tion among southern populations. Molecular Phyloge-

netics and Evolution 49, 84–91.

Gjedrem T. (2000) Genetic improvement of cold-water

fish species. Aquaculture Research 31, 25–33.

Gjedrem T. (2005) Selection and Breeding Programs in

Aquaculture AKVAFORSK. Institute of Aquaculture

Research, Aas, Norway.

Gjedrem T. (2010) The first family-based breeding pro-

gram in aquaculture. Reviews in Aquaculture 2, 2–15.

Gjedrem T., Robinson N. & Rye M. (2012) The impor-

tance of selective breeding in aquaculture to meet

future demands for animal protein: a review. Aquacul-

ture 350–353, 117–129.

Hara K., Watabe H., Sasazaki S., Mukai F. & Mannen

H. (2010) Development of SNP markers for individual

identification and parentage test in a Japanese black

cattle population. Animal Science Journal 81, 152–

157.

Harlizius B., Lopes M.S., Duijvesteijn N., van de Goor

L.H., van Haeringen W.A., Panneman H., Guimaraes

S.E., Merks J.W. & Knol E.F. (2011) A single nucleotide

polymorphism set for paternal identification to reduce

the costs of trait recording in commercial pig breeding.

Journal of Animal Science 89, 1661–1668.

Hauser L., Baird M., Hilborn R., Seeb L.W. & Seeb J.E.

(2011) An empirical comparison of SNPs and micro-

satellites for parentage and kinship assignment in a

wild sockeye salmon (Oncorhynchus nerka) population.

Molecular Ecology Resources 11(Suppl 1), 150–161.

Hedgecock D., Li G., Hubert S., Bucklin K. & Ribes V.

(2004) Widespread null alleles and poor cross-species

amplification of microsatellite DNA loci cloned from

the Pacific oyster, Crassostrea gigas. Journal of Shellfish

Research 23, 379–385.

de la Herran R., Robles F., Navas J.I., Mounim Hamman-

Khalifa A., Herrera M., Hachero I., Jose Mora M., Ruiz

Rejon C., Garrido-Ramos M. & Ruiz Rejon M. (2008) A

highly accurate, single PCR reaction for parentage

assignment in Senegal sole based on eight informative

microsatellite loci. Aquaculture Research 39, 1169–

1174.

Hill W.G., Salisbury B.A. & Webb A.J. (2008) Parentage

identification using single nucleotide polymorphism



genotypes: application to product tracing. Journal of

Animal Science 86, 2508–2517.

Jones A.G. & Ardren W.R. (2003) Methods of parentage

analysis in natural populations. Molecular Ecology 12,

2511–2523.

Jones B., Walsh D., Werner L. & Fiumera A. (2009)

Using blocks of linked single nucleotide polymorphisms

as highly polymorphic genetic markers for parentage

analysis. Molecular Ecology Resources 9, 487–497.

Jones A.G., Small C.M., Paczolt K.A. & Ratterman N.L.

(2010) A practical guide to methods of parentage

analysis. Molecular Ecology Resources 10, 6–30.

Kalinowski S.T., Taper M.L. & Marshall T.C. (2007)

Revising how the computer program CERVUS accom-

modates genotyping error increases success in pater-

nity assignment. Molecular Ecology 16, 1099–1106.

Kijewski T., Wijsman J.W.M., Hummel H. & Wenne R.

(2009) Genetic composition of cultured and wild

mussels Mytilus from The Netherlands and transfers

from Ireland and Great Britain. Aquaculture 287,

292–296.

Kijewski T., Smietanka B., Zbawicka M., Gosling E.,

Hummel H. & Wenne R. (2011) Distribution of Mytilus

taxa in European coastal areas as inferred from molec-

ular markers. Journal of Sea Research 65, 224–234.

Kruglyak L. & Nickerson D.A. (2001) Variation is the

spice of life. Nature Genetics 27, 234–236.

Kube P.D., Appleyard S.A. & Elliott N.G. (2007) Selective

breeding greenlip abalone (Haliotis laevigata): prelimin-

ary results and issues. Journal of Shellfish Research 26,

821–824.

Lallias D., Taris N., Boudry P., Bonhomme F. & Lapegue

S. (2010) Variance in the reproductive success of flat

oyster Ostrea edulis L. assessed by parentage analyses

in natural and experimental conditions. Genet Res

(Camb) 92, 175–187.

Langdon C., Evans D., Jacobson D. & Blouin M. (2003)

Yields of cultured pacific oysters, Crassostrea gigas

Thunberg, improved after one generation of selection.

Aquaculture 220, 227–244.

Lee Y. & Morton B. (1985) The introduction of the Medi-

terranean mussel (Mytilus galloprovincialis) into Hong

Kong. Malacological Review 18, 107–109.

Lemer S., Rochel E. & Planes S. (2011) Correction

method for null alleles in species with variable micro-

satellite flanking regions, a case study of the black-

lipped pearl oyster Pinctada margaritifera. Journal of

Heredity 102, 243–246.

Liu Z.J. & Cordes J.F. (2004) DNA marker technologies

and their applications in aquaculture genetics. Aquacul-

ture 238, 1–37.

MacAvoy E.S., Wood A.R. & Gardner J.P.A. (2008)

Development and evaluation of microsatellite markers

for identification of individual Greenshell (TM) mussels

(Perna canaliculus) in a selective breeding programme.

Aquaculture 274, 41–48.

McGoldrick D.J., Hedgecock D., English L.J., Baoprasertkul

P. & Ward R.D. (2000) The transmission of microsatel-

lite alleles in Australian and North American stocks of

the Pacific oyster (Crassostrea gigas): selection and null

alleles. Journal of Shellfish Research 19, 779–788.

McInerney C.E., Allcock A.L., Johnson M.P., Bailie D.A. &

Prodohl P.A. (2011) Comparative genomic analysis

reveals species-dependent complexities that explain dif-

ficulties with microsatellite marker development in

molluscs. Heredity (Edinb) 106, 78–87.

Morin P.A., Luikart G. & Wayne R.K. (2004) SNPs in

ecology, evolution and conservation. Trends in Ecology

& Evolution 19, 208–216.

Nguyen T.T.T., Hayes B.J., Guthridge K., Ab Rahim E.S. &

Ingram B.A. (2011) Use of a microsatellite-based pedi-

gree in estimation of heritabilities for economic traits in

Australian blue mussel, Mytilus galloprovincialis. Journal

of Animal Breeding and Genetics 128, 482–490.

Norris A.T., Bradley D.G. & Cunningham E.P. (2000)

Parentage and relatedness determination in farmed

Atlantic salmon (Salmo salar) using microsatellite

markers. Aquaculture 182, 73–83.

Ouagajjou Y., Aghzar A., Minambres M., Presa P. &

Perez M. (2010) Differential gene flow between popula-

tions of Mytilus galloprovincialis distributed along Ibe-

rian and North African coasts. Thalassas 26, 75–78.

Pardo B.G., Castro J., Lopez A., Pino-Querido A., Bouza

C., Fuentes J., Villalba A. & Martinez P. (2008) Identi-

fying familiar relationships in cohorts of Ostrea edulis

using microsatellite loci. Journal of Shellfish Research

27, 1038–1038.

Pardo B.G., Vera M., Pino-Querido A., �Alvarez-Dios J.A.

& Martinez P. (2011) Development of microsatellite

loci in the Mediterranean mussel, Mytilus galloprovin-

cialis. Molecular Ecology Resources 11, 586–589.

Petersen J.L., Ibarra A.M., Ramirez J.L. & May B. (2008)

An induced mass spawn of the hermaphroditic lion-

paw scallop, Nodipecten subnodosus: genetic assignment

of maternal and paternal parentage. Journal of Heredity

99, 337–348.

Popa OP, Murariu D & Popa LO (2007) Comparison of

four DNA extraction methods from invasive freshwater

bivalve species (Mollusca, Bivalvia) in Romanian

fauna. Travaux du Mus�eum National d’Historie Naturelle

“Grigore Antipa”, 50, 527–536.

Presa P., Perez M. & Diz A.P. (2002) Polymorphic micro-

satellite markers for blue mussels (Mytilus spp.). Con-

servation Genetics 3, 441–443.

Primmer C.R., Borge T., Lindell J. & Saetre G.P. (2002)

Single-nucleotide polymorphism characterization in

species with limited available sequence information:

high nucleotide diversity revealed in the avian gen-

ome. Molecular Ecology 11, 603–612.

Raymond M. & Rousset F. (1995) Genepop (version-1.2)

– population-genetics software for exact tests and ecu-

menicism. Journal of Heredity 86, 248–249.



Reece K.S., Ribeiro W.L., Gaffney P.M., Carnegie R.B. &

Allen S.K. Jr (2004) Microsatellite marker development

and analysis in the eastern oyster (Crassostrea virgini-

ca): confirmation of null alleles and non-Mendelian

segregation ratios. Journal of Heredity 95, 346–352.

Rice W.R. (1989) Analyzing tables of statistical tests.

Evolution 43, 223–225.

Rousset F. (2008) Genepop’007: a complete re-imple-

mentation of the genepop software for Windows and

Linux. Molecular Ecology Resources 8, 103–106.

Sauvage C., Bierne N., Lapegue S. & Boudry P. (2007)

Single Nucleotide polymorphisms and their relationship

to codon usage bias in the Pacific oyster Crassostrea

gigas. Gene 406, 13–22.

Selvamani M.J.P., Degnan S.M. & Degnan B.N. (2001)

Microsatellite genotyping of individual abalone larvae:

parentage assignment in aquaculture. Marine Biotech-

nology 3, 478–485.

Slabbert R., Bester A.E. & D’Amato M.E. (2009) Analyses

of genetic diversity and parentage within a South Afri-

can hatchery of the abalone Haliotis midae Linnaeus

using microsatellite markers. Journal of Shellfish

Research 28, 369–375.

Sokolov E.P. (2000) An improved method for DNA isola-

tion from mucopolysaccharide-rich molluscan tissues.

Journal of Molluscan Studies 66, 573–575.

Taggart J.B. (2007) FAP: an exclusion-based parental

assignment program with enhanced predictive func-

tions. Molecular Ecology Notes 7, 412–415.

Tokarska M., Marshall T., Kowalczyk R., Wojcik J.M.,

Pertoldi C., Kristensen T.N., Loeschcke V., Gregersen

V.R. & Bendixen C. (2009) Effectiveness of microsatel-

lite and SNP markers for parentage and identity analy-

sis in species with low genetic diversity: the case of

European bison. Heredity (Edinb) 103, 326–332.

Van Den Bergb R.-W. & Roodt-Wilding R. (2010) Parent-

age assignment in Haliotis midae L.: a precursor to

future genetic enhancement programmes for South

African abalone. Aquaculture Research 41, 1387–1395.

Van Oosterhout C., Hutchinson W.F., Wills D.P.M. &

Shipley P. (2004) MICRO-CHECKER: software for iden-

tifying and correcting genotyping errors in microsatel-

lite data. Molecular Ecology Notes 4, 535–538.

Vandeputte M., Rossignol M.-N. & Pincent C. (2011)

From theory to practice: empirical evaluation of the

assignment power of marker sets for pedigree analysis

in fish breeding. Aquaculture 314, 80–86.

Varela M.A., Gonzalez-Tizon A., Marinas L. & Martinez-

Lage A. (2007) Genetic divergence detected by ISSR

markers and characterization of microsatellite regions

in Mytilus mussels. Biochemical Genetics 45,

565–578.

Vera M., Pardo B.G., Pino-Querido A., Alvarez-Dios J.A.,

Fuentes J. & Martinez P. (2010) Characterization of

single-nucleotide polymorphism markers in the Medi-

terranean mussel, Mytilus galloprovincialis. Aquaculture

Research 41, e568–e575.

Vignal A., Milan D., SanCristobal M. & Eggen A. (2002)

A review on SNP and other types of molecular mark-

ers and their use in animal genetics. Genetics, Selection

Evolution 34, 275–305.

Wang J. (2006) Informativeness of genetic markers for

pairwise relationship and relatedness inference. Theo-

retical Population Biology 70, 300–321.

Wang Y., Wang X., Wang A. & Guo X. (2010) A 16-

microsatellite multiplex assay for parentage assignment

in eastern oyster (Crassostrea virginica Gmelin). Aqua-

culture 308, S28–S33.

Wilkins N.P., Fujino K. & Gosling E.M. (1983) The Medi-

terranean mussel Mytilus galloprovincialis Lmk in Japan.

Biological Journal of the Linnean Society 20, 365–374.

Wonham M.J. (2004) Mini-review: distribution of the

Mediterranean mussel Mytilus galloprovincialis (Bival-

via: Mytilidae) and hybrids in the Northeast Pacific.

Journal of Shellfish Research 23, 535–543.

Yu H. & Li Q. (2007a) Development of EST-SSRs in the

Mediterranean blue mussel, Mytilus galloproviancialis.

Molecular Ecology Notes 7, 1308–1310.

Yu H. & Li Q. (2007b) EST-SSR markers from the Pacific

oyster, Crassostrea gigas. Molecular Ecology Notes 7,

860–862.

Zhang L. & Guo X. (2010) Development and validation

of single nucleotide polymorphism markers in the east-

ern oyster Crassostrea virginica Gmelin by mining ESTs

and resequencing. Aquaculture 302, 124–129.



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A molecular tool for parentage analysis in the Mediterranean mussel ( Mytilus galloprovincialis )