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PERSPECTIVE
Molecular pedigree analysis in natural populations
of fishes: approaches, applications, and practical
considerations
A.J. Wilson and M.M. Ferguson
Abstract: Molecular markers can provide information on the family structure of natural fish populations through
molecular pedigree analysis. This information, which is otherwise difficult to obtain, can give important insights into
the expression and evolution of phenotypic traits. We review the literature to provide examples of how molecular
pedigree analysis has been used extensively to examine patterns of distribution, dispersal, and social behaviour in fishes
and how it provides a tool for the estimation of quantitative genetic parameters. Although multiple methodologies can
be used to examine family structure, the efficacy of any molecular pedigree analysis is generally dependent on prior
consideration of interrelated statistical and biological factors. Statistical issues stem from the choice of molecular
marker type and marker set used, in addition to sampling strategy. We discuss these considerations and additionally
emphasize the utility of supplemental nongenetic data for increasing the efficacy of pedigree analysis. We advocate
that, where possible, a priori knowledge of the study system’s biology should be used to inform study design and
further highlight the need for additional empirical testing of methodologies.
Résumé : Les marqueurs moléculaires peuvent renseigner sur la structure familiale des populations naturelles de
poissons par l’analyse moléculaire de la filiation. Ces données, difficiles à obtenir autrement, fournissent une image
précieuse de l’expression et de l’évolution des caractéristiques phénotypiques. Notre revue de la littérature illustre, par
des exemples, comment l’analyse moléculaire de la filiation a souvent été utilisée pour étudier les patterns de répartition, de dispersion et de comportement social chez les poissons et comment elle peut fournir un outil pour l’estimation
des paramètres génétiques quantitatifs. Bien que de multiples méthodologies puissent servir à étudier la structure
familiale, l’efficacité d’une analyse moléculaire de filiation dépend de la prise en considération antérieure des facteurs
statistiques et biologiques interreliés. Les problèmes statistiques dépendent du choix du type de marqueur moléculaire
et de l’ensemble de marqueurs utilisé, ainsi que de la stratégie d’échantillonnage. Nous examinons toutes ces considérations et nous mettons aussi en relief l’utilité des données supplémentaires non génétiques pour augmenter l’efficacité
de l’analyse de filiation. Nous suggérons que, lorsque c’est possible, la connaissance a priori de la biologie du système
étudié serve à la planification de l’étude. Nous insistons sur la nécessité de faire des tests empiriques additionnels de
ces méthodologies.
[Traduit par la Rédaction]
Wilson and Ferguson
Introduction
Knowledge of pedigree structure is of considerable value
in the study of phenotypic evolution. In the most general
sense, knowledge of family relationships allows us to study
the nature of phenotypic inheritance, while in a social context, expression of behavioural traits might be expected to
vary according to the degree of relatedness between organReceived 19 June 2002. Accepted 7 September 2002.
Published on the NRC Research Press Web site at
http://cjfas.nrc.ca on 30 October 2002.
J16954
A.J. Wilson1 and M.M. Ferguson. Department of Zoology,
University of Guelph, Guelph, ON N1G 2W1, Canada.
1
Corresponding author (e-mail: [email protected]).
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isms interacting (Hamilton 1964). Although a pedigree can
frequently be tracked and controlled in laboratory studies,
the same is often not true in natural animal populations. In
some cases, and particularly in some taxa (notably birds and
mammals), pedigree might sometimes be inferred by observation (e.g., Kempenaers et al. 1992), but fishes generally
possess a suite of characteristics that make them less suitable for this approach.
In fishes, inferences about parent–offspring or sibling relationships are typically impeded by a lack of parental care,
coupled with a tendency towards polygamous mating systems. Though some species do provide care of offspring, a
relatively high incidence of alloparental care means that even
in these cases putative relationships should be treated with
caution (Wisenden 1999). Even if relatives can be identified,
subsequent tracking of individuals is problematic in most
fishes. Not only do many species show high mobility and
DOI: 10.1139/F02-127
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dispersal (particularly marine taxa), but also physical tagging or marking of individuals is not always possible. Although a range of tag types can be used effectively in larger
fish, this is not true for smaller individuals less than a few
centimetres in length (see Frederick (1997) and references
therein). Thus, small size of offspring precludes physical
marking or tagging of what may often be the most dispersive
of age groups. These difficulties, compounded with the practical issues presented by aquatic environments, mean that
observational methods of pedigree determination are usually
not a viable option. Molecular data might therefore offer a
solution in that they can be used to directly examine pedigree at the genetic level.
Molecular pedigree analysis is the use of molecular data
to infer pedigree and can be thought of as an assessment of
phylogeny at the extreme micro-evolutionary level (Avise
1994). Included in this are all methods that use molecular
data to test hypothesized relationships between individuals.
In addition to the testing of specified relationships (e.g., putative sibships or parent–offspring relationships), we take
molecular pedigree analysis to include estimation of the level
of relatedness between individuals. In the latter context it is
useful to define the pairwise coefficient of relatedness r. This
has been variously defined in the literature; we follow the
notation of recent authors (e.g., Van de Casteele et al. 2001)
in using r to denote the expected fraction of alleles in the genome that two individuals have identical by descent.
To date, investigation of behavioural traits, and in particular social behaviour, has provided the dominant rationale for
molecular pedigree analysis in natural populations. Ross
(2001) provides a useful review of the molecular ecology of
social behaviour, revealing a strong taxonomic bias of study
in this area towards eusocial insects (e.g., Queller and Goodnight 1989). In contrast, much work in fishes has focused on
the potential utility of molecular pedigree analysis in aquaculture (e.g., O’Reilly et al. 1998; Norris et al. 2000). In this
context molecular analysis might enable pedigree tracking to
facilitate optimization of selection regimes and broodstock
management procedures, while also allowing economical
common rearing practices. Parentage analysis has been carried out successfully in a number of commercially important
finfish species, including rainbow trout (Oncorhynchus mykiss;
Herbinger et al. 1995; Estoup et al. 1998; McDonald 2001),
Atlantic salmon (Salmo salar; O’Reilly et al. 1998; Norris et
al. 2000), and turbot (Scophthalmus maximus; Estoup et al.
1998) and also in shellfish (e.g., Evans et al. 2000). In situations where hatchery-based stocking programs are being
implemented, parentage analysis of offspring may also be
useful for examining introgression of hatchery stock into
natural populations or to evaluate performance of stocked
offspring (Letcher and King 2001). Other authors have considered the potential for using pairwise estimates of relatedness to avoid the risk of inbreeding depression in salmonid
breeding programs (Norris et al. 2000; McDonald 2001).
Here we restrict our attention to the use of molecular pedigree analysis in natural populations of fishes and do not focus
on aquaculture or stocking applications. Firstly, we present a
brief synopsis of available methodologies for molecular pedigree analysis, before reviewing the literature to illustrate the
utility of these methods in fishes. Despite the great potential
for this area of research to facilitate the study of phenotypic
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evolution in the field, there are also many prospective difficulties and practical considerations. Thus, in the latter part
of this article, we attempt to draw attention to these issues as
they pertain to molecular pedigree analysis in general and to
the study of fishes in particular.
Approaches to molecular pedigree analysis
The simplest approach to molecular pedigree analysis is
that of exclusion. For example, in parentage analysis, an offspring’s genotype is compared with that of a candidate
mother or father to determine if the latter could indeed be a
parent of the former. Given a codominant marker with Mendelian transmission, a parent and offspring must share at
least one allele at any given locus, and so any candidate parent that does not meet this criterion can be excluded. If one
parent is known, then this represents a particularly powerful
method of testing a putative parent–offspring relationship.
Exclusion can also be used to reconstruct pedigrees on a
larger scale, for example, by assigning parentage to many individuals when there are known genotypic pools of parents
and offspring (Danzmann 1997). Given enough genotypic
information and sampling of all possible parents, it is possible to assign any given offspring to a single parental pair by
exclusion.
However, difficulties arise when it is not possible to exclude all but a single parent (or parental pair) from the available genotypic data. In such cases, likelihood methods may
prove useful for distinguishing between nonexcluded candidates (Meagher and Thompson 1986). For example, SanCristobal and Chevalet (1997) presented an approach for
likelihood-based parentage identification from a finite set of
candidate parents. Several authors have presented additional
likelihood-based methods for parentage analysis that are
able to account for incomplete sampling of candidate parents
(Marshall et al. 1998; Gerber et al. 2000; Duchesne et al.
2002). In these cases, likelihood ratios of candidate parents
can be calculated and then comparisons made among all
candidates to find the most likely parent. Simulation procedures can then be used to determine the significance of results
(e.g., Marshall et al. 1998). Similarly, likelihood techniques
can be applied to test other putative relationships such as
sibships (Herbinger et al. 1997; Goodnight and Queller 1999).
An alternative approach for parentage analysis is that of
fractional allocation. Rather than determining a single most
likely parent (or sibling) from a set of candidates, this approach attempts to fractionally allocate offspring among
nonexcluded parents based on their probability of parentage
(Neff et al. 2001). Such models have obvious applications in
assessing relative mating success, for example, in studies of
intra-male competition and sperm competition. Recently, attention has also been given to alternative methodologies for
examining other types of relationships, notably for the reconstruction of sibships in the absence of parental information. For example, Painter (1997) explored the possibility of
sibship reconstruction using a Bayesian approach. Other authors have presented Markov chain Monte Carlo (MCMC)
algorithms for partitioning individuals into sibling groups
(Thomas and Hill 2000; Smith et al. 2001).
In some cases, determination of actual relationships is not
necessary, but whether or not individuals are related is still
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of interest. This can be addressed by the estimation of relatedness (r) between individuals, using one of several available estimators (e.g., Queller and Goodnight 1989; Ritland
1996a; Lynch and Ritland 1999). Average relatedness within
a specified group can be estimated, as can pairwise relatedness between two specified individuals. Given the expected
values of r for different classes of relationship (e.g., r = 0.5
for full siblings, r = 0.25 for half-siblings), it may also be
possible to use pairwise estimates of relatedness as a
computationally simple way of assigning pairs to relationship types (Blouin et al. 1996; McDonald 2001).
Applications in natural fish populations
Breeding systems and parental care
Molecular pedigree analysis provides a convenient method
for examination of fish breeding systems. For example, analysis of offspring sampled from male pouches has provided evidence for monogamy in seahorses (Jones et al. 1998;
Kvarnemo et al. 2000) and polyandry in pipefishes (Jones and
Avise 1997; McCoy et al. 2001). Similarly, in live-bearing
mosquito fish (Gambusia holbrooki), Zane et al. (1999) analyzed embryos from pregnant females and found evidence for
multiple paternity. In Atlantic salmon (Salmo salar), breeding
systems have also been investigated using parentage analysis.
Under simulated natural conditions, minisatellite loci were
used to infer paternity and so assess reproductive success of
precociously maturing male parr in the presence of adult
spawning pairs (Morán et al. 1996). By sampling progeny
from a single redd, Morán and García-Vázquez (1998) demonstrated the utility of this approach for detecting multiple paternity in natural populations. Subsequently more extensive
studies based on genotyping samples from natural redds have
found evidence for both multiple paternity and high reproductive success of precocious males (Thompson et al. 1998; Martinez et al. 2000). Parentage analysis has also revealed that
multiple mating is common in Atlantic salmon (Garant et al.
2001; Taggart et al. 2001). In one study under near-natural
conditions, more than 50% of anadromous spawners of both
sexes contributed to more than one redd, with both males and
females mating with different partners at different sites
(Taggart et al. 2001). Similarly, reconstructing parental genotypes from progeny sampled from redds has been used to infer polyandry, polygyny, and the use of multiple redds by
individual females in chinook salmon (Oncorhynchus
tshawytscha; Bentzen et al. 2001).
Although marine species have received less attention in
general, several authors have used molecular methods to examine the breeding systems of Atlantic cod (Gadus morhua).
For example, Bekkevold et al. (2002) used microsatellitebased parentage analysis to examine correlates of male reproductive success in experimental spawning aggregations of
this species, and paternity inference using allozyme markers
has also been employed to examine sperm competition
(Rakitin et al. 1999). In a somewhat different approach,
Herbinger et al. (1997) used a likelihood ratio method and
microsatellite genotypes to determine possible half-sib and
full-sib relationships in a naturally spawned cohort of cod.
The authors concluded that the cohort was a fairly homogeneous mixture of largely unrelated individuals, suggesting it
Can. J. Fish. Aquat. Sci. Vol. 59, 2002
was unlikely to be derived from a small number of matings
(Herbinger et al. 1997).
In species that provide parental care, a large number of studies have used molecular data to scrutinize putative parent–
offspring relationships. Brood parasitism has been detected
by paternity analysis in a range of fishes, including cichlids
(Dierkes et al. 1999; Taborsky 2001), darters (DeWoody et
al. 2000a), gobies (DeWoody et al. 2000b; Jones et al.
2001), and sunfish (DeWoody et al. 1998, 2000c). Although
brood parasitism may most commonly take the form of male
sneaking, genetic analysis has also been used to detect egg
thievery, which is known to occur in some species
(Largiadèr et al. 2001). Neff et al. (2000) developed an allocation model for use with microsatellite data to estimate the
proportion of next-generation individuals actually descended
from putative parents. The utility of this model was demonstrated by application to genetic data from a nest of bluegill
sunfish (Lepomis macrochirus), a species in which so-called
“cuckolder” males mature precociously and steal fertilizations from “parental”-type males that build and defend nests.
The demonstrated ability of some fishes to discriminate kin
from non-kin raises the possibility that males might adjust
the level of parental care in response to their perceived levels of offspring paternity (Neff and Gross 2001). In a study
of darters and sunfish, DeWoody et al. (2001) tested whether
cannibalism by nesting males was directed towards unrelated
embryos in the nest. Genetic analysis of male stomach contents found evidence for filial cannibalism even when unrelated embryos were present.
Dispersal and distribution
Another application of pedigree analysis stems from the
potential to examine patterns of dispersal and to test hypothesized kin-biased patterns of distribution. Molecular markers
have been used in fishes to indirectly estimate levels of dispersal based on classical models of gene flow, and also using
assignment methods to determine an individual’s population
of origin (see Hansen et al. (2001) for a review). However,
pedigree analysis can also be used to examine aspects of dispersal. For example, Hansen et al. (1997) detected high levels of relatedness among young-of-the-year brown trout (Salmo
trutta) sampled from short stretches of Danish rivers. In one
case, estimates of relatedness suggested that 17 out of 18 individuals less than 1 year old probably represented only
three full-sib families. This finding was used to highlight a
potential difficulty for population genetics studies in systems
where limited dispersal might lead to nonrandom distributions of closely related individuals. In such systems, sampling on a limited spatial scale, or of a single nondispersed
age group, might cause bias in estimated population allele
frequencies because all individuals sampled come from only
a few families (Hansen et al. 1997). Both parentage analysis
and estimation of r have also been used specifically to test
hypothesized patterns of adult dispersal in vertebrate taxa
(e.g., Taylor et al. 1997; Ohnishi et al. 2000), although there
has been little work done in fishes. Intrasex levels of relatedness provided evidence of male-biased dispersal in Lake
Malawi cichlids (Knight et al. 1999). In another cichlid fish,
parentage analysis also demonstrated occasional exchange of
individuals between groups, and in this case, subsequent
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field experiments showed that females had the higher tendency to migrate (Schradin and Lamprecht 2000).
In addition to dispersal, molecular pedigree analysis has
been used to examine the possibility of kin-biased distributions. The expectation of kin bias in spatial distribution
stems from the concepts of inclusive fitness and kin selection that are central to our understanding of social behaviour
(Hamilton 1964). Certainly, laboratory studies have shown
that fish may benefit from living in kin-based groups (e.g.,
Brown et al. 1996). As a consequence, it might be expected
that spatial distribution and group structure in fishes could
be determined, at least in part, by relationships between individuals. However, in order to display kin-biased behaviours,
it is first necessary for an organism to discriminate kin from
non-kin.
Kin recognition in many fishes has been demonstrated by
laboratory experimentation. For example, Arnold (2000) examined shoaling behaviour in rainbowfish (Melanotaemia
eachamensis) and found that females preferentially spend
time associating with relatives when in an all female shoal,
but avoided male relatives in a mixed shoal. The former result is consistent with the expectation of kin-biased behaviour, whereas the latter is suggestive of an innate tendency
towards inbreeding avoidance. In salmonids, numerous laboratory-based studies have demonstrated the existence of kin
recognition and kin-biased behaviour (see Brown and Brown
(1996) for a review). For example, in laboratory studies of
brown trout (Salmo trutta), increased aggression was detected in groups of mixed or unrelated juveniles compared
with groups of relatives (Olsen et al. 1996a). Such studies
have led to the hypothesis that kin discrimination in juvenile
salmonids might permit a reduction in territorial aggression,
thus lessening the costs associated with territory defense
(Brown and Brown 1996). However, the ability of fishes to
discriminate kin in laboratory studies does not prove that
kin-biased behaviour occurs under natural conditions in the
wild.
To date, genetic studies have provided some evidence for
kin-biased patterns of distribution in the field. For example,
aggregation of related individuals was detected in Eurasian
perch (Perca fluviatilis) using estimates of group relatedness
(Gerlach et al. 2001) and in tilapia using an index of genetic
similarity based on allele sharing (Pouyaud et al. 1999).
Conversely, despite a known preference by three-spined
sticklebacks (Gasterosteus aculeatus) to associate with kin
in the laboratory, no evidence for close relatedness among
individuals from within shoals of wild-caught juveniles was
found (Peuhkuri and Seppae 1998). Fontaine and Dodson
(1999) tested whether juvenile Atlantic salmon kin occupied
adjacent territories in order to profit from kin-biased behaviours. Microsatellite markers were used to determine pairwise coefficients of relatedness (r) using the estimator of
Queller and Goodnight (1989). There was no relationship
between r and geographical distance separating fish. Additionally, pairs of fish were classified as unrelated, half-siblings,
or full siblings according to the procedure of Blouin et al.
(1996). Little evidence was found for the idea of siblings defending adjacent territories, though many related pairs of fry
were detected when sampling was carried out soon after
emergence. In a contrasting study of the same species,
Mjølnerød et al. (1999) found that there was a significant as-
1699
sociation between genetic similarity and position in the
river. In particular, they found that genetically similar juveniles were found closer together than less related individuals. However, although the association was significant, it
was not strong, and the authors concluded that factors other
than relatedness are likely important for determining juvenile position in the river. Although this work did not directly
analyze pedigree, conclusions were based on pairwise bandsharing coefficients from multilocus genetic fingerprints that
typically show high correlation with relatedness.
Estimation of quantitative genetic parameters
Quantitative genetic parameters such as heritability and
genetic correlation are most easily estimated from the degree
of resemblance in a phenotypic trait between relatives (Falconer and Mackay 1996). Thus their estimation in natural
populations has been largely limited because of the lack of
pedigree information. It is therefore possible that molecular
pedigree analysis will greatly facilitate application of a
quantitative genetic framework to the study of phenotypic
traits (morphological, life history, and behavioural) in natural populations (see Ritland (2000) for a review). In fishes,
this may have considerable implications both for our understanding of phenotypic evolution and for management and
conservation practices. For example, intraspecific morphs
living in sympatry have been reported in several freshwater
fish species (e.g., Skúlason et al. 1996). Field-based estimates of heritability could be used to test for a genetic basis
to such phenotypic variation, as an alternative to currently
employed common garden rearing experiments. Similarly,
estimating genetic correlations between traits in natural fish
populations will allow testing of the genetic bases of tradeoffs that are believed to have a large role in the determination of teleost life histories (Roff 2002). In a more applied
context, management efforts frequently aim to conserve genetic diversity in an attempt to safeguard the evolutionary
potential of a species or population. However, molecular
measures of genetic variation that are typically used may
show little correlation with quantitative measures, such that
the former have limited value in predicting short-term evolutionary potential (Reed and Frankham 2001; McKay and
Latta 2002).
Molecular pedigree analysis might facilitate the estimation of quantitative genetic parameters via several methods.
For example, Ritland (1996b) showed how heritability could
be estimated from a regression of phenotypic trait similarity
on estimated relatedness between individuals and further developed his model to allow incorporation of sharing among
relatives of environmental effects, dominance, and levels of
inbreeding. The use of pairwise relatedness to estimate
quantitative genetic parameters has, however, been criticized
because such an approach loses valuable information from
multiple relationships and because families are weighted according to the number of pairs rather than according to information content (Thomas and Hill 2000). One alternative
is to use a MCMC procedure to partition the population into
full-sibling families within a single generation (Thomas and
Hill 2000). Reconstructed pedigrees can subsequently be
used to estimate genetic parameters in a conventional manner. Simulations show that in comparison to those derived
from pairwise techniques, estimates obtained in this manner
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may deviate less from those calculated from the known pedigree. This may be particularly true if there is prior information regarding the distribution of family sizes in a population
(Thomas and Hill 2000). Nevertheless, in a study of body
weight in Soay sheep, poor performance of this method was
found and attributed to insufficient amounts of marker data
and low numbers of relatives in the sample (Thomas et al.
2002).
Presently there has been very little empirical work that
applies marker-inferred relatedness or pedigree structure to
quantitative genetic study (but see Ritland and Ritland 1996;
Mousseau et al. 1998; Thomas et al. 2002). In the only study
of fish to date, Mousseau et al. (1998) estimated
heritabilities for weight, length, flesh color, and precocious
maturation in Chinook salmon (Oncorhynchus tshawytscha).
In this case, a captive population was used and it was known
that the population consisted of a mixture of full-sibs and
unrelated individuals, allowing the use of a maximum likelihood method to infer relatedness between pairs. The inferred
relatedness was then combined with phenotypic trait data in
a mixture model. Estimates of quantitative genetic parameters obtained in this way were found to fall within the range
reported previously for these traits in this species (Mousseau
et al. (1998) and references therein). Although further demonstrating the utility of marker-based approaches, this latter
methodology requires a known distribution of relatedness
(e.g., all individuals are full-sibs or unrelated), and so its applicability in natural populations may be less general.
Practical considerations
With one known parent, exclusion of a candidate parent
for an individual is often possible based on examination of
genotypes at only one or two marker loci (e.g., DeWoody et
al. 2000b; Evans and Magurran 2001). However, in largerscale pedigree analyses, where many individuals are being
considered and the aim is pedigree reconstruction or examination of patterns of relatedness, considerable amounts of
information will be required. Advances in molecular techniques have enabled the collection of large amounts of data,
but the widespread application of large-scale pedigree analysis has been limited to some extent by a lack of available
software dedicated to handling and processing this information. Increasingly, computer programs are being made publicly available (Table 1), providing the researcher with an
expanding range of tools for various types of analyses, including testing putative pairwise relationships as well as parentage assignment, sibship reconstruction, and relatedness
estimation. Nevertheless, successful implementation of these
methodologies depends on careful consideration of several
other issues. These include the choice of marker system
(e.g., Gerber et al. 2000) and statistical features of the methodology employed (Bernatchez and Duchesne 2000) and
ecological features of the study population such as size, geographical range, patterns of dispersal, and demographic variables. In the following section, we draw attention to some of
these issues in the hope that their prior consideration will be
of benefit to future study design.
Choice of marker system
Microsatellites have largely emerged as the marker type of
Can. J. Fish. Aquat. Sci. Vol. 59, 2002
choice for pedigree analysis, as they have for related applications such as population assignment testing (see Hansen et
al. 2001). This class of molecular marker has been extensively reviewed elsewhere (e.g., Estoup and Angers 1998),
but their particular suitability for pedigree analysis stems
from the combination of high variability with codominant
expression. In general, an increase in molecular variability
will result in an increase in exclusion probability, defined as
the average capability of a marker system to exclude any
given relationship (Gerber et al. 2000). Although development costs are high, these are offset to some degree by the
possibility for successful cross-amplification of loci across
related species demonstrated in many fish taxa (e.g., Olsen
et al. 1996b; Wenburg et al. 1996; Iyengar et al. 2000). Nevertheless, microsatellite loci may not be available in every
instance, and it is important to note that other marker systems can also be used.
In principle, molecular pedigree analysis is possible using
any type of molecular marker system. Early r estimators
were first developed for use with protein polymorphisms
(Queller and Goodnight 1989). More recent work has centered on the use of DNA-based marker systems, including
minisatellites, random amplified polymorphic DNAs (RAPDs),
and amplified fragment length polymorphisms (AFLPs), as
well as microsatellites. These latter systems are based on the
polymerase chain reaction (PCR) and thus facilitate nonlethal sampling as ample quantities of DNA can be extracted
from small pieces of tissue such as fin clips or scales (Yue
and Orban 2001). Codominant marker systems are preferable in that they allow tracking of both maternally and paternally derived alleles. However, the use of dominant markers
has also been limited by a lack of statistical methodology
(see Table 1). For example, efficient estimators of pairwise
relatedness have not been developed for use with dominant
markers. Nevertheless, dominant marker information can be
used effectively for exclusion-based pedigree analyses, including large-scale parentage assignment (e.g., Danzmann
1997). More recently, Gerber et al. (2000) extended the likelihood-based methodology of parentage assignment to dominant markers. Dominant markers (specifically AFLPs) were
shown to be less efficient than microsatellites in this context,
but effective parentage assignment was possible with the
former, particularly if there was careful selection of loci
(Gerber et al. 2000). Thus, for exclusion-based methods and
for parentage assignment, inexpensive dominant marker systems represent a viable alternative when microsatellite loci
are unavailable.
Design of marker set
For any chosen marker system, careful consideration and
choice of loci is necessary to maximize both efficiency and
success of pedigree analysis. In large-scale pedigree analysis,
performance of any approach will increase with the amount
of molecular information available. For example, using an
increased number of loci with higher numbers of alleles per
locus will increase the likelihood of successful parentage assignment (e.g., Marshall et al. 1998) and will increase both
accuracy and precision of relatedness estimation (e.g.,
Ritland 1996a). However, given the costs and practical constraints associated with data collection, it is of interest to examine how much information is minimally required.
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Comment
Can also be used to output pairwise relatedness estimates
Examining group genetic similarity with fragmentary data
sets
Pairwise relatedness estimation according to the estimator
of Lynch and Ritland (1999)
Pairwise and group relatedness according to the estimator
of Queller and Goodnight (1989)
Likelihood based, can be used to test multiple types of
relationship (e.g. parentage, full-sib, half-sib)
Testing putative pairwise sibling relationship
Testing putative pairwise sibling relationships
MCMC algorithms for partitioning individuals into either
full sibships or kin groups of unspecified relatedness
Reconstructs genotypes of unknown parents from known
parent and progeny genotypes
Likelihood based, allows incomplete sampling of candidate parents
Likelihood based, allows incomplete sampling of candidate parents
Likelihood based, allows incomplete sampling of candidate parents
Exclusion based approach to parentage assignment
Note: Data type is denoted as C, codominant only, or C/D, codominant or dominant marker types.
C
C/D
BURIAL v.1.0
MarQ v.1.0
C
DELRIOUS
Marker-assisted heritability
estimation
C
RELATEDNESS v.5.0
Relatedness estimation
C
KINSHIP v.1.3
C
RELPAIR v.0.90
Testing pairwise
relationships
C
C
GERUD v.1.0
RELATIVE v.1.10
C/D
FaMoz
C
C
PAPA v.1.0
Pedigree 2.0
C
CERVUS v.2.0
Parentage analysis,
assignment, and
reconstruction
Sibship analysis and
reconstruction
Data
C/D
Program
PROBMAX
Function
Table 1. Computer programs available for molecular pedigree analysis.
Ritland (1996b)
http://genetics.forestry.ubc.ca/ritland/programs.html
Schönfisch et al. (2001)
http://www.uni-tuebingen.de/uni/bcm/BURIAL/index.html
Stone and Björklund (2001)
http://www.zoology.uu.se/zooeko/JonS/DELRIOUS/delirious.htm
Queller and Goodnight (1989)
http://www.bioc.rice.edu/Keck2.0/labs/
Goodnight and Queller (1999)
http://www.bioc.rice.edu/Keck2.0/labs/
Göring and Ott (1997)
ftp://linkage.rockefeller.edu/software/relative
http://www.sph.umich.edu/statgen/boehnke/relpair.html
Smith et al. (2001)
email: [email protected] or [email protected]
Jones (2001)
http://www.bcc.orst.edu/~jonesa/
Gerber et al. (2000)
http://www.pierroton.inra.fr/genetics/labo/Software/Famoz/index.html
Duchesne et al. (2002)
http://www.bio.ulaval.ca/navigation/frame-principal-departement.html
Marshall et al. (1998)
http://helios.bto.ed.ac.uk/evolgen/cervus/cervusregister.html
Danzmann (1997)
http://www.uoguelph.ca/~rdanzman/software/
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In parentage assignment work, simulation approaches should
be used to assess the likely performance of any given marker
set with specified allelic distributions (e.g., Danzmann 1997;
Marshall et al. 1998). However, although such methods are
useful for determining the probable success rate with a specified marker set, they are of limited use in deciding a priori
how many loci are needed and which loci should be used.
More recently, Bernatchez and Duchesne (2000) developed a
model to predict the probability of assigning parents to a parental couple under the maximum likelihood method of parentage assignment proposed by SanCristobal and Chevalet
(1997). Under this model, the probability of assignment is a
function of the number of candidate parents, the number of
loci used, and the mean number of alleles per locus. Given a
desired level of assignment success, it should thus be possible to design an appropriate marker set (Bernatchez and
Duchesne 2000). Therefore, this work represents an important step towards improving the design of parentage studies.
Similar statistical considerations apply when estimating
relatedness. A general feature of all pairwise estimators is a
large sampling variance (Ritland 2000; Van de Casteele et al.
2001). This variance might lead to a failure to detect kinbiased patterns of distribution and dispersal or an inability to
use relatedness estimates for discriminating efficiently between classes of relatives (Blouin et al. 1996; McDonald
2001). Sampling variance decreases with increasing marker
information so that the precision of r can be increased by
use of more loci; though it is also important to note that loci
provide information about relatedness roughly in proportion
to the number of alleles (Ritland 2000). In addition to large
sampling variances, relatedness estimators can also be subject to bias, which may be an inherent property of the estimator (Ritland 1996a) or result from the necessity of
estimating population allele frequencies from sample frequencies (Queller and Goodnight 1989). Making the appropriate choice of r estimator might reduce both bias and
sample variance. Lynch and Ritland (1999) argued that their
estimator was better than earlier estimators (e.g., Queller and
Goodnight 1989; Ritland 1996a), because it yielded similar
or lower levels of sampling variance while also being
computationally simpler. However, in a subsequent comparison, Van de Casteele et al. (2001) found that the most appropriate choice of estimator for a given data set can depend on
the number and variability of the marker loci used, frequency distribution of alleles at each locus, and population
composition. The conclusion that there is generally no single
best-performing estimator has led to the recommendation
that simulations should be performed to decide which estimator to use for a given marker data set (Van de Casteele et
al. 2001). To date, few studies have done this (but see
Thomas et al. 2002), and the actual and relative performance
of relatedness estimators in the field is largely unknown.
Data quality and marker characteristics
In addition to the amount of information required, data
quality is important in all applications of pedigree analysis.
Genotyping error is to be expected in any large-scale study
and can lead to false exclusion in pedigree analyses
(O’Reilly et al. 1998). Fortunately, methods and computer
programs designed for performing large-scale parentage
analysis typically allow for these possible errors (Danzmann
Can. J. Fish. Aquat. Sci. Vol. 59, 2002
1997; SanCristobal and Chevalet 1997; Goodnight and
Queller 1999). Provided that genotyping errors are allowed
at a rate greater than zero, then the success of likelihood approaches to parentage assignment may be fairly insensitive
to variation (e.g., Marshall et al. 1998). However, this is not
true for relatedness estimation in which it is assumed that error rates are negligible (Van de Casteele et al. 2001), and
genotyping errors are also likely to impact the accuracy of
likelihood procedures for sibship reconstruction (C.
Herbinger, Department of Biology, Dalhousie University,
Halifax, N.S., Canada, personal communication). Thus, particular care must be taken to select loci that can be scored
consistently, and in some cases, the choice of a suitable
marker set might reflect a trade-off between the aims of using the most informative loci and minimizing genotyping error rates.
Furthermore, methodologies for molecular pedigree analysis assume a number of marker characteristics that typically
include independence, selective neutrality, an absence of null
alleles, and a negligible mutation rate (e.g., Marshall et al.
1998; Van de Casteele et al. 2001). Wherever possible, the
validity of these assumptions should be tested, and loci that
do not meet the criteria should be excluded from subsequent
analysis.
Marker loci should be tested for departures from Hardy–
Weinberg equilibrium proportions of genotypes using available population genetic computer programs (e.g., Raymond
and Rousset 2001). Such departures might result from mutations, undetected null alleles, and selection. Similarly, it is
prudent to examine population genetic structure of natural
study systems before large-scale pedigree analysis. Not only
can this help to inform sampling strategy (see below), but
also restricted gene flow can result in patterns of population
genetic structure that violate the assumption of random mating. Although loose linkage between a few pairs of markers
may not seriously bias multilocus likelihood procedures, it is
likely to lead to a general decrease in the precision of relationship estimation (Thompson and Meagher 1998) and should
also be tested for.
Design of sampling strategy
Sampling strategy includes decisions as to how many individuals should be sampled, as well as where and when sampling should take place. In allocating effort and resources,
the trade-off between the number of individuals sampled and
the amount of molecular data obtained from each individual
warrants consideration. For example, if pedigree analysis is
being performed to estimate quantitative genetic parameters,
then beyond a few loci, the number of phenotypes sampled
might typically provide a greater constraint on statistical
power than the amount of genotypic data (Ritland 2000).
Another consideration is that the estimation of population allele frequencies from finite samples can introduce bias into
pedigree analysis procedures (e.g., Queller and Goodnight
1989). In this context, larger sample sizes are expected to
provide more accurate estimates of population allele frequencies.
The number of individuals sampled is also critical in determining the success of pedigree reconstruction, because in
most natural fish populations, sampling of candidate parents
(or siblings) is likely to be incomplete. Marshall et al. (1998)
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Wilson and Ferguson
demonstrated how the success of likelihood-based paternity
inference will decline as the number of candidate males increases and as the proportion of those candidates sampled
decreases. Although the number of candidates will be a feature of a system that cannot be controlled, sampling strategy
will dictate the proportion of those candidates that are likely
to be sampled. We illustrate the effect of this latter consideration (Fig. 1), showing the success of paternity inference
(defined as a percentage of paternity tests resolved with 80%
confidence; Marshall et al. 1998) as a function of the proportion of candidate males. The allelic distributions used in
this simulation were taken from McDonald (2001), in which
12 microsatellite loci were found sufficient for assigning
97% of rainbow trout to a single parental pair based on exclusion alone. Simulations were performed using CERVUS
2.0 (Marshall et al. 1998), and we assume that the maternal
parent is also unknown, as is likely the case in most fish
populations. Clearly, despite the use of a potentially powerful marker set in this case, success declines dramatically as
the proportion of candidate parents actually sampled drops.
This feature will be typically confounded with the effect of
population size, as constraints on the absolute number of individuals sampled will mean that in a larger population, the
proportion of candidates sampled will also be smaller. Thus
population size has major implications for any attempt at
pedigree reconstruction. Population size in fishes can be estimated using a variety of methods. Direct counts may be
possible in some cases (for example, using underwater visual census methods or counting fish passing through ladders), though frequently mark–recapture or depletion-based
estimates of abundance might be used (King 1995).
Assuming normal constraints on fieldwork, sampling a
high proportion of candidate relatives is clearly facilitated by
a small population size, as well as by low rates of adult mortality. Furthermore, the presence of actual parents in a system will be dependent on rates of emigration from the study
area. Thus, although molecular pedigree analysis can in itself be used to examine patterns of dispersal (discussed above),
prior knowledge of dispersal may also be used to inform
spatial sampling strategy. Again, this may be of particular
importance if pedigree analysis is being used for the estimation of quantitative genetic parameters. Effective use of relatedness to estimate heritability is contingent upon the
presence of significant actual variance of relatedness within
the sample (Ritland 1996b), whereas estimation from reconstructed sibships is facilitated by the presence of large families (Thomas et al. 2000). Thus, in mobile animals, sampling
of an insufficient number of relatives among a large number
of unrelated individuals could prevent effective estimation of
genetic parameters. In philopatric fish such as salmonids, it
has been suggested that this problem might be avoided by
sampling near to breeding areas (Mousseau et al. 1998).
The researcher should also use predictable aspects of life
cycle or biology to inform temporal aspects of sampling
strategy. For example, the presence of discrete breeding seasons in many fish species facilitates highly efficient sampling approaches such as testing putative sibships by
collecting embryos from a nest before emergence and dispersal (e.g., Thompson et al. 1998). In migratory species,
sampling in a given spatial area may only be possible at certain times, or actual pedigree structure of a sampled popula-
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Fig. 1. The effect of varying the proportion of all candidate
males sampled on the expected success of paternity inference.
Success of paternity inference is indicated as the percentage of
paternity tests resolved with 80% confidence, for numbers of
candidate males of 1000 (open square), 500 (solid triangle),
250 (open circle), 100 (solid square), 50 (open triangle), and
10 (solid circle). Simulations were performed according to the
procedure of Marshall et al. (1998) using allelic distributions for
12 microsatellite loci genotyped in rainbow trout, Oncorhynchus
mykiss (McDonald 2001).
tion might vary predictably as the result of temporal changes
in the spatial overlap of generations. Seasonal variation in
climatic conditions is also an important consideration, because weather conditions can affect both the spatial distribution of fishes and the practicality of sampling them.
Availability of supplemental information
The efficiency of pedigree analysis is typically increased
by the use of supplemental information about individuals.
For example, in parentage and sibship analysis, age and sex
information is routinely used to determine a priori whether
an individual should be considered as a candidate relative
(e.g., mother, father, or sibling) of another. Where possible,
supplemental information should therefore be collected, particularly in attempts to explicitly reconstruct pedigree, though
a lack of supplemental data will not preclude all applications
of molecular pedigree analysis. For example, estimation of
relatedness does not necessarily require any a priori grouping of individuals, though such groupings (based on age or
sex) are certainly useful to test specific hypotheses (e.g.,
sex-biased dispersal; Knight et al. 1999).
It is important to realize that such supplemental information can be difficult to obtain in many fish taxa. For example, sexual dimorphism of external morphology is common
but not ubiquitous in fishes, and in some cases, assigning
sex may be difficult or impossible without resorting to lethal
sampling procedures. In some species, sexual dimorphism
becomes more pronounced during spawning and thus sampling at this time might be appropriate. In other cases, or
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with juvenile individuals, sex determination may itself be
possible using molecular markers (e.g., Griffiths et al. 2000).
Age information is similarly of value in determining
whether a given individual should be included as a candidate
parent of another. This is particularly true in combination
with some knowledge of reproductive biology (e.g., likely
age of maturation). Similarly, in a long-lived species with
overlapping generations, sibship reconstruction (e.g., Smith
et al. 2001) might most appropriately be performed within a
single age cohort. Although it is possible that siblings and
half-siblings may actually differ in age, analysis without regard to age structure will risk confounding sibships with
parent–offspring relationships (C. Herbinger, Department of
Biology, Dalhousie University, Halifax, N.S., personal communication). Unfortunately, age determination in fishes is
notoriously labour intensive and further hindered by indeterminate growth rates and frequent overlap (both spatially and
temporally) of generations. Assuming that spawning occurs
in discrete periods, age determination of fishes may be possible using methods such as analysis of length–frequency
distributions or calcified structures such as otoliths and
scales (Casselman 1987). It should be noted, however, that
such methods are not perfect and validation is important in
all cases.
Summary and conclusions
Success of molecular pedigree analysis clearly requires
careful consideration of issues relating to the methodology
employed and to the biology of the study organism at both
individual and population levels. The primary result of failure to adequately consider both statistical and biological issues will be a lack of statistical power in testing hypotheses.
Although statistical issues can be complex, it is clear that
simulation approaches are useful in evaluating performance
and determining significance of results (Marshall et al. 1998;
Goodnight and Queller 1999; Van de Casteele et al. 2001).
Continued development of these approaches and, in particular, attempts to yield specific guidelines for the development
of marker sets (e.g., Bernatchez and Duchesne 2000) are of
immense value. Although it is to be expected that further improvement of statistical methodologies will increase the accuracy and precision of pedigree analysis, it is also possible
that new types of molecular markers may effect the development of this field. In this respect, we particularly support the
efforts to extend methodology of large scale pedigree analysis to dominant marker types (Gerber et al. 2000).
There is an urgent need for future research to focus on
empirical testing of both existing and new methodologies.
Simulation-based studies alone are clearly insufficient for
fully exploring the implications of an organism’s biology to
the efficacy of pedigree analysis. Although the choice of a
study system will normally be dictated by the presence of
biological features of interest, it is nevertheless important to
consider aspects of fish biology and ecology that might affect study design. Age and sex information may be trivial to
obtain in some taxa but this is not necessarily the case in
fishes, and prior knowledge of range, dispersal, and population size may also be relevant in many cases. Although this
information will not always be available, in general many
Can. J. Fish. Aquat. Sci. Vol. 59, 2002
applications of molecular pedigree analysis are likely to be
aided by prior biological knowledge of the study system.
These biological considerations suggest that although hypothesized relationships might be tested in many situations,
the ability to reconstruct pedigrees (for example, to estimate
quantitative genetic parameters or examine differential reproductive success of adults) may be more restricted. Hansen et
al. (2001) pointed out the difficulties of microsatellite-based
population assignment methods in marine systems where
high dispersal might often lead to a lack of detectable genetic structure, even over large geographical distances. We
suggest that this also poses problems for molecular pedigree
analysis in marine systems as sampling high proportions of
candidate relatives will be best managed in small closed
populations more typical of freshwater habitats. Although
molecular pedigree analysis may therefore be used under
semi-natural experimental conditions (e.g., Bekkevold et al.
2002), in situ application to marine and larger freshwater
habitats might be restricted to species with tendencies towards low dispersal or philopatry. However, small closed
populations are not without difficulties themselves. In particular, although efficient sampling may be facilitated, such
systems may also have less advantageous attributes, such as
high rates of drift (leading to loss of genetic variation) and
inbreeding (a violation of the typical assumption of random
mating; e.g., Queller and Goodnight 1989).
In fishes, efforts to validate results through observational
study are impeded by those very features that make molecular pedigree analysis such an attractive tool. This presents a
challenge for the critical evaluation of pedigree analysis
methodologies through empirical testing. To an extent, the
need for validation can be met by simulation studies and
also through application of analyses to captive fish populations with known true pedigrees (e.g., Smith et al. 2001; A.J.
Wilson, unpublished data). In natural populations, it may be
possible to evaluate methodologies by examining the congruence of results from multiple approaches, for example, by
comparing pedigree structures obtained by parentage analysis and sibship reconstruction.
Despite the potential pitfalls, knowledge of pedigree structure in natural fish populations can provide enormous insight
into phenotypic evolution, both by direct examination of putatively kin-biased behaviour and more generally by allowing application of a quantitative genetic framework to the
study of phenotype. Thus molecular pedigree analysis represents an important tool for extending evolutionary study from
the laboratory to the natural environment in which phenotypic evolution occurs.
Acknowledgements
This work was supported by an Natural Sciences and Engineering Research Council of Canada (NSERC) research grant to
MMF. We thank C. Herbinger, J. Hutchings, T. Nudds, and three
anonymous reviewers for helpful comments on the manuscript.
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