Aquaculture 241 (2004) 133–150

www.elsevier.com/locate/aqua-online

Applying microsatellites to the management of

farmed turbot stocks (Scophthalmus maximus L.)

in hatcheries

Yaisel Juan Borrella,b, Jorge Alvareza, Emilia Vazqueza,

Carlos Fernandez Patoc, Concha Martınez Tapiac,

Jose Antonio Sancheza, Gloria Blancoa,*

aLaboratorio de Genetica Acuıcola, Departamento de Biologıa Funcional,

Universidad de Oviedo, 33071 Oviedo, SpainbDepartamento de Bioquımica, Universidad de la Habana, Calle 25 entre J e I. # 455, Vedado, Plaza,

Ciudad de la Habana, CubacI.E.O. Planta de Experimentacion en Acuicultura bEl BocalQ, Apdo. 240, 39080 Santander, Spain

Received 5 April 2004; received in revised form 19 August 2004; accepted 23 August 2004

Abstract

Eight microsatellite loci reported previously to exhibit high levels of variability were assessed for

their utility for determining pedigrees in five turbot (Scophthalmus maximus L.) broodstocks from

two hatcheries in Spain and for estimating genetic relatedness between pairs of individuals with

unknown origin through relatedness coefficients. Discrepancies were found among expected levels

of correct parentage assignment obtained by simulation procedures (using Cervus or Probmax) and

those obtained working with real descendants. Besides the genetic variability levels found in

broodstocks, other factors such as mutation, occurrence of null alleles and genetic similarities among

parents of the same sex significantly affected parentage assignment test results. On the other hand,

selection of breeding pairs for avoiding inbreeding in progenies would be possible with more than

80% accuracy using relatedness coefficients below zero as the cut-off between the selected

0044-8486/$ -

doi:10.1016/j.

* Corresp

E-mail add

see front matter D 2004 Elsevier B.V. All rights reserved.

aquaculture.2004.08.021

onding author. Tel.: +34 985 103889; fax: +34 985 103534.

ress: gloriablanco@uniovi.es (G. Blanco).

Y.J. Borrell et al. / Aquaculture 241 (2004) 133–150134

individuals. In summary, microsatellite markers are useful tools for parentage studies in hatcheries

but their efficacy is not based only on the genetic variability found.

D 2004 Elsevier B.V. All rights reserved.

Keywords: Microsatellite loci; Turbot; Scophthalmus maximus; Aquaculture; Parentage; Relatedness; Pedigrees;

Cervus; Probmax

1. Introduction

The turbot Scophthalmus maximus (L.) is a commercially important flatfish with a wide

distribution throughout the eastern North Atlantic and Mediterranean Sea. The popularity

of this species in some fish markets and the significant decrease of wild catches over the

last decade have promoted an increase in hatchery-reared turbot production in European

countries (Josupeit, 1996; Coughlan et al., 1998). In 2000, Spain produced 70.3% (3650

mt) of the total European hatchery turbot production (Spanish General Secretariat of

Marine Fishing), but other countries like France and Norway are rapidly increasing their

turbot hatchery production. As a consequence of farmer’s interest in turbot production

after culture methodology was established, requests came to biologists and geneticists to

apply molecular tools to turbot breeding programs.

Genetic gains from a selective breeding program depend upon genetic variation,

selection intensity and heritability of the trait. High selection intensities can be practiced at

both family and individual level in fish; however, increases in inbreeding should be kept to

a minimum by avoiding mating of related individuals (Kincaid, 1983; Gjerde et al., 1983;

Su et al., 1996). In order to have pedigreed progeny from fish families, progeny groups

must be reared separately until they are large enough to be tagged using physical methods.

This approach is cost-, space- and labour-intensive and affordable under laboratory

conditions, but not at production farms. Recently, parentage assignment using genetic

markers has been suggested for use in aquaculture (Estoup et al., 1998; Norris et al., 2000;

Jackson et al., 2003). DNA microsatellites make possible retrospective assignment of

individuals to family groups reared communally (Garcıa de Leon et al., 1998; Estoup et al.,

1998; Perez-Enriquez et al., 1999; Norris et al., 2000; Sekino et al., 2003; Jackson et al.,

2003), or estimation of relatedness between pairs of individuals among different progeny

(Queller and Goodnight, 1989; Blouin et al., 1996; Norris et al., 2000). They also make

possible comparison of viability of offspring of different parental origins without bias from

unknown variations in laboratory systems or farm environments (Herbinger et al., 1999;

Selvamani et al., 2001; Boudry et al., 2002). However, some topics need still to be

empirically assessed or confirmed:

(a) Would a set of microsatellite loci, previously tested in wild populations and found

highly polymorphic, be useful for parentage studies in a particular hatchery? Would

they be equally useful for managing all broodstocks in the hatchery?

(b) Should predictions about the utility of a set of microsatellite loci for parentage

studies, and improving hatchery management, be done taking into account only the

levels of genetic variation found in a broodstock? How close to reality are simulation

procedures for parentage assignment included in software such as Cervus?

Y.J. Borrell et al. / Aquaculture 241 (2004) 133–150 135

(c) What could be the influence of management (sex distribution in the broodstocks, the

crossing scheme, the number of crossings to obtain descendants, the number of

descendants per cross and thus the descendant genetic compositions, etc.) on a

hatchery strategy that it is based on the use of microsatellites for parentage studies to

avoid inbreeding or the use of separate ponds to rear descendants from different

families? What is the influence of the bparticular characteristicsQ exhibited by highly

polymorphic genetic tools such as microsatellites?

(d) How reliable are the relatedness coefficients (r) to identify bsafeQ mating pairs (no

inbreeding in the offspring) in the absence of pedigree information within

broodstocks?

In our study, we assessed whether eight microsatellite loci, reported previously to have

high levels of variability, are useful to determine pedigrees and genetic relatedness in five

turbot broodstocks (S. maximus L.) from two commercial turbot hatcheries in Spain using

both real and simulated progeny.

2. Materials and methods

2.1. Fish and progenies

Five broodstocks from 2 turbot hatcheries in Spain were used in this study: 1

broodstock (34 individuals) from hatchery 1 and 4 broodstocks from hatchery 2, with 28

(A broodstock), 25(B), 26(C) and 60(D) individuals, respectively. In hatchery 1, 5 males

and 5 females were individually mated (1 male�1 female) to create 5 families of full sibs

(Fig. 1a) from which 10 larvae of each family were analysed. For hatchery 2 (A

broodstock), a multifactorial cross using 6 males and 6 females were made obtaining 11

families (full and half sibs, see Fig. 1b) from which 10 larvae were analyzed.

2.2. Genetic analysis

Genomic DNAwas purified from a small piece of tail in the case of adult individuals or

complete larvae using ChelexR 100 (Walsh et al., 1991). Variation at eight microsatellite

loci was analysed by locus-specific PCR following the procedures proposed in those

studies where the loci were described: Smax-01, Smax-02, Smax-03 (Coughlan et al.,

1998), Sma1-125, Sma3-8, Sma3-12, Sma3-129 (Estoup et al., 1998) and F1-OCA19

(Iyengar et al., 2000). Aliquots of amplification products were resolved on 6% denaturing

polyacrylamide sequencing gels and visualised using an Open Gene Automated DNA

Sequencing System (one of the oligonucleotides marked with the Cy5 fluorescent dye).

2.3. Genetic diversity analysis

The number of alleles at each microsatellite locus (Na), the proportion of individual

samples that were heterozygous (direct count heterozygosity, Ho), and tests for linkage

disequilibria between microsatellite loci were calculated using the Fstat 2.9.3 program

Fig. 1. Schematic representation of family generation in turbot hatcheries. (a) Hatchery 1 and (b) hatchery 2A.

Y.J. Borrell et al. / Aquaculture 241 (2004) 133–150136

(Goudet, 2001; update from Goudet, 1995). The same statistical package was used to

estimate the total variation in gene frequencies (FIT) partitioned into components of

variation occurring within (FIS) and among (FST) samples for each locus following Weir

and Cockerham (1984). Significance levels of FIS were assessed through 800 random-

isations for the 0.05 signification level. Pairwise FST values between samples and p-values

before and after Bonferroni corrections were calculated using Fstat (for significance levels

of FST, multi-locus genotypes were randomised between pairs of samples with 200

permutations, and then the significance after Bonferroni correction was calculated) (Rice,

1989; Goudet, 2001). Genepop 3.3 (Raymond and Rousset, 1995) was used to assess

genetic differentiation between broodstocks.

2.4. Parentage assignments

To identify the most likely parents (father–mother pairs) among a set of possible parents,

we used Cervus (Marshall et al., 1998). This program calculates also the allelic frequencies,

observed and expected heterozygosities, and the polymorphic information content (PIC) for

every locus from each broodstock, estimating also the frequencies of possible null alleles,

from an algorithm, proposed by Summers and Amos (1997), which takes into account the

differences among the observed and expected frequencies of the homozygotes.

Y.J. Borrell et al. / Aquaculture 241 (2004) 133–150 137

The PIC (Botstein et al., 1980) is a metric which characterizes how informative a

genetic marker is; it was defined for measuring the usefulness of a codominant genetic

marker to identify the allele, transmitted by an heterozygote parent which is affected from

a genetic disease, whose expression is from the dominant allele. PIC is always smaller than

the heterozygosity, as some crossings among heterozygous individuals may not be

informative. For a locus with k alleles, PIC can be calculated as:

PIC ¼ 2Xk�1

i¼1

Xk

j¼iþ1

pipj 1� pipj� �

where pi is the population frequency of the i-th allele and k is the total number of alleles.

The program Cervus can carry out a simulation to assess the significance of D values

(LOD difference). This program emulate the steps of paternity inference using allele

frequencies at loci screened and predicts the success rate of paternity analysis using

relaxed (80%) and strict (95%) criteria from D distribution. Thus, when working with real

data (descendants and parents), each descendant is assigned to the most-likely parent

(maximum LOD with significant D value).

Three different levels of assignment were assessed in this work and compared:

(a) The theoretical predictions of correct assignments in case of unknown parents using

allelic frequencies found in each broodstock under study (Cervus 1.0) (10,000

iterative simulation cycles of assignments). This value could be interpreted as a

measure of the markers’ efficiency for parentage studies, we sought to assess how

well it works in real situations?

(b) Correct assignment of bvirtualQ descendants generated using Probmax 1.02 routines

(Danzmann, 1997). In this case, we generated 250 descendants from 25 families (all

possible families from mating of 5 males and 5 females selected by random from

hatchery 1), and they were assigned among the 34 individuals that form the

broodstock in this hatchery. The most probable fathers (maximum LOD), after Cervus

assignation, were compared with the real parents to determine the percentage of

correct assignment. The same procedure was made for the four broodstocks from

hatchery 2. All bvirtualQ descendants were then assigned among the 28, 25, 26 and 60

individuals that form the four broodstocks in hatchery 2 (A, B, C and D, respectively).

(c) Correct assignment of both parents to the real descendants obtained through mating

of individuals in each hatchery (see fish and progenies). Fifty descendants from 5

families were assigned among 34 individuals in hatchery 1, and 108 descendants

from 11 families were assigned among 28 individuals that from broodstock A of

hatchery 2. Again, the most probable fathers (maximum LOD), after Cervus

assignation, were compared with the real parents to determine the percentage of

correct assignment.

2.5. Genetic relatedness

Relatedness (Queller and Goodnight, 1989) is a package that calculates average genetic

relatedness among sets of individuals defined by demographic variables, either on average

Y.J. Borrell et al. / Aquaculture 241 (2004) 133–150138

or by pairs. Relatedness, r, is estimated as:

r ¼

X

x

X

k

X

l

Fqy � Fq4� �

X

x

X

k

X

l

Fqx � Fq4ð Þ:

Where x is individuals, k loci and l allelic positions (E.g.: l=1 for haploid individuals

and 2 for diploid individuals). The variables in the equation are: Fqx: frequency, found in

the x individual, locus k and allelic position l (this frequency value in a diploid individual

must be 0, 0.5 or 1.0); Fqy: frequency of the same allele in the group of individuals with

which x is compared (those whose genetic relationship we seek to measure); Fq*:

frequency of this allele in all population, excluding al possible relations of x.

Theoretically, full-sib individuals share, on average, 50% of their genome constitution

and hence the reladtedness coefficient (r) among them should be near 0.5, while for pairs

of half-sibs, it is expected that rc0.25 and among pairs of unrelated individuals rc0

(Queller and Goodnight, 1989). We used this program to estimate r values among pairs of

offspring (in real progenies and also in those progenies generated by Probmax). Since the

degrees of genetic relationship that exists among them is known (full-sibs, half-sibs and

unrelated individuals), it is possible then to analyse whether there is concordance between

r values and the real genetic relatedness that exists among individuals. This evaluation is

then useful to see how reliable are r values to identify bsafeQ mating pairs (no inbreeding in

the offspring) in the absence of pedigree information within broodstocks in hatcheries.

2.6. Statistical procedures

The linear regression analysis and the regression curves estimates included in the SPSS

statistical package (version 10) were used to assess the influence of various parameters

(number of crosses carried out to obtain offspring, PIC, number of alleles present, etc.) on

the correct assignment percentages found. For the comparison of means, a simple ANOVA

included in the same statistic package was used, using the Bonferroni test for post-hoc

multiple comparison after tests for equal variances (SPSS, 10.0).

3. Results

3.1. Genetic variation in turbot broodstocks

Levels of genetic variability found in hatcheries 1 and 2 are presented in Table 1.

Among the three parameters used to compare genetic variability levels (mean number of

alleles per locus (Na), observed heterozygosity (Ho) and PIC), two (Na, PIC) exhibited

significant differences between broodstocks (Na: F=4.260, P=0.007; PIC: F=6.338,

P=0.001). Observed heterozygosity levels were not significantly different between groups

(Ho: F=2.037, P=0.110). The Bonferroni test used for post-hoc comparisons revealed that

differences in Na were detected between the hatchery 1 broodstock and the D broodstock

from hatchery 2 (P=0.017) and between the C and D broodstocks from hatchery 2

Table 1

Genetic variability in five broodstocks from two turbot hatcheries in Spain

Locus Size ranges Hatchery 1 Hatchery 2

(bp)A B C D

Na Ho PIC FIS Na Ho PIC FIS Na Ho PIC FIS Na Ho PIC FIS Na Ho PIC FIS

Smax-01 126–162 8 0.886 0.718 �0.160 6 0.714 0.639 �0.019 4 0.760 0.582 �0.150 8 0.731 0.700 0.037 3 0.700 0.583 �0.058

Sma3-8 190–208 6 0.800 0.647 �0.141 7 0.607 0.532 �0.054 5 0.480 0.350 �0.215 8 0.615 0.553 �0.020 3 0.433 0.297 �0.244

Sma3-12 89–109 5 0.543 0.516 0.129 6 0.607 0.532 �0.014 5 0.400 0.362 0.006 6 0.577 0.527 0.020 3 0.517 0.332 �0.286

Sma3-129 159–193 8 0.914 0.779 �0.119 8 0.536 0.625 0.204 6 0.720 0.596 �0.088 8 0.808 0.724 �0.045 5 0.783 0.590 �0.182

F1-OCA19 88–104 4 0.686 0.537 �0.144 5 0.607 0.601 0.101 5 0.600 0.457 �0.060 5 0.654 0.593 0.018 5 0.617 0.405 �0.196

Smax-02 119–160 12 0.943 0.723 �0.252 8 0.929 0.700 �0.234 6 0.800 0.633 �0.137 8 0.846 0.740 �0.073 7 0.767 0.600 �0.148

Smax-03 289–305 7 0.765 0.807 0.093 4 0.714 0.614 �0.039 4 0.720 0.570 �0.115 5 0.923 0.650 �0.290 4 0.833 0.583 �0.265

Sma3-125 120–152 5 0.943 0.723 �0.221 5 0.679 0.606 0.002 5 0.720 0.526 �0.172 6 0.615 0.648 0.138 3 0.483 0.409 0.081

Mean values 6.88 0.810 0.681 �0.105 6.13 0.674 0.606 �0.009 5.00 0.650 0.509 �0.118 6.75 0.721 0.642 �0.029 4.13 0.641 0.475 �0.156

Na: number of alleles per locus, Ho: observed heterozygosity, PIC: polymorphic information content.

All FIS values are not significant after 800 randomisations for the 0.05 significance level using Fstat 2.93.

Y.J.

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etal./Aquacultu

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139

Y.J. Borrell et al. / Aquaculture 241 (2004) 133–150140

(P=0.027). Significant PIC differences between broodstocks arose from differences

between the broodstock from hatchery 1 and the B (P=0.013) and D (P=0.002)

broodstocks from hatchery 2, while the C and D broodstocks from hatchery 2 are different

regarding mean PIC values (P=0.017). Two groups could then be defined regarding

variability levels; one including the broodstock from hatchery 1, and the A and C

broodstocks from hatchery 2, and another group where appear the B and D broodstocks

from hatchery 2 (Table 1). Significant departures from Hardy–Weinberg expectations were

not detected (FIS were not significant after randomisations, Table 1) and disequilibrium

linkage between loci was not detected.

Some of the microsatellite loci that presented high levels of variability in hatchery 1

revealed lower levels of variability in hatchery 2 (e.g.; Sma3-125 locus PIC=0.723 in

hatchery 1 and PIC=0.409 in the D broodstock from hatchery 2) (Table 1). In the hatchery

1 broodstock and the A and C broodstocks from hatchery 2, only some loci (Smax-01,

Sma3-129 and Smax-02) exhibit PIC values near 0.600–0.700, as had been previously

reported by other authors (Table 1).

Significant differences were detected between hatchery 1 broodstock and all brood-

stocks from hatchery 2 in terms of genic differentiation (distribution of alleles), while the

A and C broodstocks from hatchery 2 were significantly different from the D broodstock

(Table 2). FST values also revealed significant differences between the broodstock from

hatchery 1 and all broodstocks from hatchery 2 (Table 2).

3.2. Parentage assignments

The theoretical predictions (simulation based on allele frequencies, 10,000 assignment

cycles) of correct parentage assignments that could be obtained in a situation of unknown

parents reached 76.2% in hatchery 1 (Table 3) and varied from 75.1% for the C broodstock

to only 38.9% for the D broodstock in hatchery 2 (Table 3). Levels of genetic variability

(PIC) were positively related in all broodstocks with the frequencies of correct parentage

assignments expected after the simulation procedures carried out with the Cervus program

(b=164.34, F=18.92, P=0.0224).

Table 2

Significance at genic differentiation for each broodstock pair (above diagonal) and FST per pair of broodstocks

(below diagonal) from two turbot hatcheries in Spain

Hatchery 1 Hatchery 2

A B C D

Hatchery 1 – *** *** *** ***

Hatchery 2

A 0.1136* – n.s. n.s. ***

B 0.1613* 0.0025 – n.s. n.s.

C 0.1033* �0.0040 0.0068 – ***

D 0.1878* 0.0190 �0.0030 0.0182 –

n.s.: not significant.

* Pb0.05.

*** Pb0.001.

Table 3

Frequencies of correct parentage assignment using eight microsatellite loci, in broodstocks from two turbot

hatcheries in Spain

Hatchery 1

(%)

Hatchery 2

A (%) B (%) C (%) D (%)

Theor. assign. (Cervus)a 76.1 69.5 59.0 75.1 38.9

Assignment of virtual descendants (Probmax + Cervus) 80.9 70.5 40.5 65.5 6.5

Assignment of real descendants (Cervus) 81.2 38.7

a Theoretically expected correct assignment through simulation (10,000 cycles, Cervus).

Y.J. Borrell et al. / Aquaculture 241 (2004) 133–150 141

Using Probmax, 10 descendants for 25 families were generated in each of the

broodstocks coming from hatcheries 1 and 2 (5 females and 5 males were chosen

randomly as parents among all individuals within the broodstocks). In hatchery 1, correct

assignment of these virtual descendants to their parents (80.9%) was concordant with

theoretically expected values after simulations (76.1%) (Table 3). However, large

discrepancies were found between the expected correct assignment values (simulations

with allele frequencies) and actual assignments of the virtual descendants for the B and D

broodstocks from hatchery 2 (Table 3). Nonetheless, we found a positive, and significant

association, among the PIC values for the respective broodstocks and correct assignment

percentages for virtual descendants (25 families in each case) (b=319.65, F=20.07,

P=0.021). This relationship also was observed for the PIC exhibited by male breeders in

the five broodstocks that were analysed (b=275.13, F=16.42, P=0.027), while this

relationship was positive, but not significant, for the female breeders in a similar analysis

(b=285.35, F=8.40, P=0.063) (Fig. 2). Also, a positive and significant relationship was

observed among the mean number of alleles, found in each of the broodstocks under

analysis, and the percentages of correct parentage assignments (b=24.12, F=34.24,

Fig. 2. Linear regression analysis of the relation between correct parentage assignment of virtual descendants and

broodstock’s mean PIC values and mean number of alleles per locus using eight microsatellite loci in five turbot

broodstocks.

Y.J. Borrell et al. / Aquaculture 241 (2004) 133–150142

P=0.010). This relationship is kept in each of the sexes: males (b=23.29, F=66.72,

P=0.004) and females (b=22.08, F=27.48, P=0.014) (Fig. 2).

The correct assignments of real offspring reached 81.2% in hatchery 1 (76.2%

theoretically expected after simulations, 80.9% working with virtual descendants) and,

surprisingly, only 38.7% in the A broodstock from hatchery 2 relative to 69.5% expected

by simulations (allele frequencies) or the 70.5% obtained during the assignments of virtual

descendants (Table 2).

In these hatcheries, real offspring were obtained through different hatchery practices. In

the 11 families obtained from 12 individuals from the A broodstock in hatchery 2, there

were full sibs, half sibs and unrelated individuals (Fig. 1). However, only 45.8% of the

assignments were correct if we consider for parentage analysis exclusively the four

families of full-sibs obtained in this hatchery (the type of kinship that exists in descendants

of hatchery 1).

To determine the of (a) the number of crosses that originated the families and (b) the

kinship among them, on the parentage assignment, 10 virtual offsprings of all possible

crossings in hatchery 1 (15 females and 19 males) were generated using Probmax and

parentage assignments were made. Percentages of correct assignment obtained with real

offspring (81.2%) and with descendants obtained with Probmax under similar conditions

(5 full-sib families chosen 9 times at random, average=82.5%) were not significantly

different (F=0.018, P=0.897) (Table 4). Correct assignments did not vary with increased

number of crosses that generated the offspring, either when we consider only full-sibs for

this analysis (F=0.0002, P=0.909) (Table 4), neither when half-sibs were considered

(F=0.0006, P=0.668) (Table 4). However, when percentages of correct assignments of

full-sibs (mean 82.5%) were compared with those obtained including all types of kinship

between descendants (mean 80.9%), they were different (F=155.90, P=0.000).

Among analysed real descendants in both hatcheries, Mendelian segregation of alleles

was generally fulfilled. However, in families 3 and 8 of hatchery 2, unexpected genotypes

were observed. This could not be caused by a handling error given the sample

characteristics (larvae in their first phases). In the case of family 3, we detected what

may be a mutation of the 88 or 102 alleles from the F1-OCA19 locus present in the male

Table 4

Percentage of correct assignments in hatchery 1 using different number of parental crosses and taking into account

different types of kinship among descendants (8 microsatellite loci, 10 descendants by family)

Crosses 5�5 10�10 15�15 15�19

(a) Full sibs 81.2 – – –

(b) Full sibsa 70.0 75.0 77.5 90.0 75.0 77.5 79.3 82.5 –

90.0 77.5 95.0 92.5 84.3 88.1

Mean 82.5 82.3 82.5 –

Alla 73.0 75.7 73.0 75.0 78.5 75.5 78.3 80.9 80.9

86.5 86.0 88.0 93.0 81.8 86.1

Mean 80.9 80.5 80.9 80.9

(a) Real descendants, (b) virtual descendants (Probmax).

All: all types of kinship among descendants included.a In these cases, the results from different combinations of parents to form the families are shown, and then a

mean was calculated.

Table 5

Genotypes of F1-OCA19 locus for parents and descendants in family no. 3 from hatchery 2, in which a mutation

event was detected

Parents: female 96/96�male 88/102

Genotypes 10 descendants Observed Expected

88/96 5 5

102/96 4 5

96/98a 1 0

a Allele 98 could be a mutation in some of the paternal alleles.

Y.J. Borrell et al. / Aquaculture 241 (2004) 133–150 143

(Table 5). In family 8 from the same hatchery, a genetic distribution among descendants

for the Sma3-129 locus was obtained, which can only be explained by the presence of a

null allele in the female, which exhibited, however, a genotype which was apparently a

homozygote 189/189 (Table 6). However, when family 8 and the supposedly mutant

individual of family 3 were eliminated to carry out parentage assignment in Hatchery stock

2(A), only a 1.4% improvement was obtained in correct parentage assignments (40.1%).

3.3. Genetic relationships among individuals

The distribution of relatedness coefficients among virtual or real descendants for each

type of kinship (related—full-sibs and half-sibs—and unrelated individuals) in hatcheries

1 and 2 are shown in Figs. 3 and 4 (analysis is shown only for the A broodstock in

hatchery 2). In both case, means relatedness coefficients obtained were in agreement with

the theoretically expected averages for each kind of kinship, although some discrepancies

for half-sibs was observed. Full-sibs showed higher average r values (between 0.3630 and

0.4898), while unrelated individuals the lowest (between �0.1176 and �0.0782), and

these values were concordant with those theoretically expected (0.5 and 0, respectively)

(Figs. 3 and 4). However, mean relatedness coefficients (r) observed for half-sibs (between

0.1738 and 0.0220) are much lower than expected (0.25) (Figs. 3 and 4).

The criterion rV0, potentially vial to select pairs of individuals for mating to avoid

inbreeding in hatcheries, was tested here to assess whether it was supported by analysis of

the distribution of relatedness coefficients in samples a known kinship. In both cases (real

and virtual descendent), between 71% and 100% of pairs of individuals that exhibited a

relatedness coefficient of rV0 would be correctly identified as unrelated individuals (Figs.

Table 6

Genotypes of Sma3-129 locus for parents and descendants in family no. 8 from hatchery 2, in which a null allele

was detected

Parents: female 189/189a�male 165/171

Genotypes 10 descendants Observed Expected

189/165 1 5

189/171 2 5

165/165 5 0

171/171 2 0

a Female genotype should be: 189/ null.

Fig. 3. Relatedness coefficient distributions and means (mFS.D.) by kinship among virtual descendants generated using Probmax in turbot hatcheries (eight microsatellite

loci). (a) Hatchery 1 and (b) hatchery 2 (broodstock set A). In each graphic is shown the percentages of unrelated individuals found when rV0 or rbr m unrelated.

Y.J.

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144

Fig. 4. Relatedness coefficients distributions and means (mFS.D.) by type of kinship among real descendants in

turbot hatcheries (eight microsatellites loci). (a) Hatchery 1 and (b) hatchery 2 (broodstock set A). In each graphic

is shown the percentages of unrelated individuals found when rV0 or rbr m unrelated.

Y.J. Borrell et al. / Aquaculture 241 (2004) 133–150 145

3 and 4). If the selection criterion for potential crosses adopted for each broodstock is

established on the averages of relatedness that were found for unrelated individuals

(rVr(m) unrelated, between �0.1176 and �0.0782), instead of the rV0 value, the

percentages of individuals correctly identified as unrelated (81–100%) would be increased

(Figs. 3 and 4).

The analysis of relatedness coefficients among the individuals used as parents in the

broodstocks under analysis (whose origin is unknown) showed that 19.4% of the possible

Table 7

Ranks of relatedness coefficients in five broodstocks from two turbot hatcheries using eight microsatellite loci (%)

Ranks Hatchery 1 Hatchery 2

– A B C D

rV0 55.2 49.5 48.1 55.1 50.9

0brV0.25 25.4 22.2 30.3 20.6 27.4

0.25brV0.50 15.5 19 16.4 19.6 16.0

rN0.50 3.9 9.3 5.4 4.5 5.6

Mean r �0.0052 +0.0058 �0.0110 �0.0041 �0.0075

n* 34 (15–19) 28 (19–9) 25 (12–13) 26 (16–10) 60 (31–29)

n: number of analyzed breeders (*in parentheses, numbers of females and males in each broodstock).

Table 8

Ranks of relatedness coefficients in five broodstocks from two turbot hatcheries using eight microsatellite loci, by

sex (%)

Ranks Hatchery 1 Hatchery 2

– A B C D

Females rV0 42.8 39.8 36.3 43.8 34.1

Males rV0 58.5 68.8 57.7 50.3 66.6

Y.J. Borrell et al. / Aquaculture 241 (2004) 133–150146

crosses in hatchery 1 (relatedness coefficients rz0.25) posed risk because of inbreeding in

the offspring generation. In the broodstocks of hatchery 2, the risks were higher (28.3% in

the A broodstock, 21.8% in B, 24.1% in C and 21.6% in D) (Table 7). Relatedness

coefficients in broodstocks from the two hatcheries showed that broodstocks with higher

numbers of pairs with low genetic relatedness are in the hatchery 1 broodstock (55.2%)

and in the C broodstock from hatchery 2 (55.1%) (Table 7).

Sex is an important element in parentage assignment, since sex helps in the assignment

procedures. This means that where the more probable parent (higher LOD) is selected,

then a second parent from the other sex group of individuals is selected. The analysis of

relatedness coefficients between broodstocks taking into account the sexes showed that

females were always more related. (Note in Table 8 that females always exhibited the

lower percentages of individuals with relatedness coefficients below zero, thus

determining the Ne for the management design.) In the cases of D and B broodstocks

from hatchery 2, the difference is quite significant, since they exhibited the lowest

relatedness coefficients among females below zero among all sets under study (34.1% and

36.3%, respectively).

No relationship was found between the percentages of unrelated individuals (assuming

rV0 as the cut-off) in these five broodstocks and the percentages of correct parentage

assignments using virtual descendants carried out with Cervus (Fig. 5). However, a similar

analysis, in this case taking into account sexes, showed that correct parentage assignments

had a positive and significant relationship with the percentages of females that appear in

Fig. 5. Linear regression analysis between correct parentage assignment and percentages of unrelated broodstocks

following rV0 using eight microsatellite loci in five turbot broodstocks from two turbot hatcheries.

Y.J. Borrell et al. / Aquaculture 241 (2004) 133–150 147

broodstocks with rV0 among them (unrelated) (b=6.42, F=11.87, P=0.041). There was no

relationship with the same estimated value in males (b=�1.29, F=0.35, P=0.596) (Fig. 5).

4. Discussion

We assessed the usefulness of eight microsatellite loci reported previously to exhibit

high levels of variability to determine pedigrees in five turbot (Scophthalmus maximus L.)

broodstocks from two hatcheries in Spain and to estimate genetic relatedness between

pairs of individuals with unknown origin. Estoup et al. (1998) analysed 45 individuals of

natural origin selected as breeders in a French hatchery, and found that the Sma1-125

(PIC=0.795), Sma3-8 (PIC=0.855), Sma3-12 (PIC=0.775) and Sma3-129 (PIC=0.886)

loci presented high PIC. Iyengar et al. (2000) found observed heterozygosity of 0.880 for

the F1-OCA19 locus in wild individuals. To keep variability levels in a hatchery

comparable to those exhibited by wild individuals, appropriate management is needed in

terms of adequate numbers of effective parents to create the broodstocks to avoid

significant genetic variability reduction (Estoup et al., 1998). The broodstocks analysed in

this study could exhibit loss of variability that may be a consequence of one or more

domestication generations, especially in broodstocks from hatchery 2.

In this study, heterogeneity was found regarding variability of the eight microsatellite

loci in the broodstocks from two turbot hatcheries. Some broodstocks, such as those from

hatchery 1 and the A and C broodstocks from hatchery 2, showed higher levels of

variability, and theoretically correct parentage assignment results were expected over 70%,

Meanwhile, the B and D broodstocks from hatchery 2 showed lower levels of variability

(PICb0.600) and lower results in the assignment tests. Each broodstock represents,

therefore, a unique entity with peculiar characteristics in order of genetic composition,

which will depend on the origin of the individuals (natural or cultivated individuals),

culture cycles which they have undergone, and type of handling established to obtain those

breeders. In this context, data about microsatellite variability taken from the literature may

serve only as a reference for selecting a wide range of marker with an expected high

variability that should be confirmed during a preliminary study of the genetic composition

of the broodstocks of interest.

We found some elements, apart from the genetic variability present in a certain stock

(PIC, number of alleles), that significantly influenced the frequency of correct parentage

assignments. The experimental design used in this work, in which breeders and offspring

are available and the origin of all descendants is known (Norris et al., 2000 published an

equivalent design), allowed identification of mutations and null alleles (Pemberton et al.,

1995; McGoldrick et al., 2000) as factors affecting parentage studies using microsatellite

loci. The mating designs used to generate the offspring, and the genetic similarity among

breeders of the same sex also could affect frequencies of correct parentage assignment

tests using real offspring.

In this work, a mutation rate of 10�4 was found. This rate is within the range described

for microsatellite variation (Amos, 1999). This would determine that only one out of

10,000 individuals would not be assigned to any possible parents when the mutation

produce a new allele which can not be found in broodstocks, or it would be incorrectly

Y.J. Borrell et al. / Aquaculture 241 (2004) 133–150148

assigned if the change is towards one of the existing alleles. The assignment mistakes that

could be made due to null alleles makes a more important element. In our case, 70% of the

breeder’s offspring that had a null allele were incorrectly assigned. In this study, both

factors explain only 1.4% of the differences among correct parentage assignments

theoretically expected in A broodstock of hatchery 2 (69.5%) and those obtained when

considering a real situation (38.7%) (Table 3). In a situation in which a breeder that carries

a null allele is responsible for an important contribution to a group of mixed offspring

reared together, parentage inferences based on the microsatellite loci would be

significantly erroneous. Thus, those responsible for genetic programs in a hatchery must

be concerned about it. It is advisable to consider the possibility of null alleles’ when

considering of microsatellite loci to be used for parentage studies in a hatchery.

The problem of quantifying the influence of null alleles in a real situation is complex

because unequivocal detection requires analysing descendants of all breeders. Never-

theless, an estimate of the presence of null alleles can be carried out through an algorithm

proposed by Summers and Amos (1997), also available in the Cervus program. If in the

analysis for a certain locus in a natural or cultured population the program estimates that a

null allele with a frequency higher than 0.02 may exist, then must be considered the

possibility that this alleles exists with a high probability. In the A broodstock from

hatchery 2, the Sma3-129 and F1-OCA19 loci showed estimates for null alleles

frequencies of 0.1262 and 0.0749, respectively. In the C broodstock, the Sma3-125 locus

showed a frequency of a possible null allele of 0.0647. The algorithm works from

differences among expected and observed frequencies in homozygotes. These differences

also can originate because the sample is a mixture of individuals from different

populations (genetic subdivision) (Wahlund effect), because of selection at this locus or

genes linked to that locus, or because of sampling error (error type I). In the case of

population mixing, it is expected that more than one locus in the analysis shows the same

pattern.

Although the number of crosses which generates the families in a hatchery did not

significantly affect the correct assignments using microsatellite in hatchery 1, in the A

broodstock from hatchery 2, the genetic similarity among the females which participated

in the creation of the 11 families (e.g.: females 1 and 3) constitutes, in addition to

mutation and null alleles in that broodstock, a factor which could explain differences

between expected and observed correct parentage assignments. Genetic similarities

among breeders of the same sex (in this case, females) also explains the divergences

among correct assignments percentages expected after simulations in the B (59.5%) and

D (38.9%) broodstocks and those obtained using virtual descendants (B: 40.5% and D:

6.5%), as they where the broodstocks in which females are presumably more genetically

related.

The use of relatedness coefficients as a tool for the appropriate selection of breeding

pairs in hatcheries when their origin (pedigree) is unknown is a method that would

contribute to inbreeding avoidance (Norris et al., 2000). In this study, we tested certain

bcut-off valuesQ for relatedness coefficients (e.g.: rV0), which proved to have acceptable

levels of confidence (80%) when identifying unrelated individuals.

In each broodstock, there are always a limited number of possible pairing to generate

offspring, which depends on the number of individuals that comprise the broodstock and

Y.J. Borrell et al. / Aquaculture 241 (2004) 133–150 149

the sex distribution. For instance, in hatchery 1, breeders were 15 females and 19 males,

which would allow 285 different crosses. From them, applying the criterion of selecting

couples as individuals with rV0, 157 of them (55.2%) are reasonably out of inbreeding

risk. This number is enough for executing a genetic program in a hatchery and also, given

the high fecundity in fishes, to meet production goals. Nevertheless, a proposed crossing

between two individuals with rV0 to avoid inbreeding would not imply that better results

would be obtained in parentage assignments. In fact, if among the breeders, there are two

females (or two males) with the same genotype, the offspring of these families may present

very similar genotypes and the percentages of correct paternity assignment would be

affected.

In summary, microsatellite loci are useful tools to design broodstock management in a

hatchery through parentage studies (assignments and selection of appropriate pairs of

breeders to create progenies) if various elements are taken into account, such as the

previous genetic variability analysis in broodstocks with the selected loci (PIC and number

of alleles, in general and by sexes), the mating design used to obtain the offspring, the

possibility of mutations and presence of null alleles in those microsatellite loci in use and

the existing genetic relationships among breeders of the same sex.

Acknowledgements

This research was funded with grants from DGES project (MAR 1999-1190-CO2-01)

and AECI (PhD fellowship for Borrell Y.J.).

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