Molecular Ecology Resources (2008)
doi: 10.1111/j.1755-0998.2008.02128.x
COMPUTER PROGRAMS
Blackwell Publishing Ltd
P-LOCI:
a computer program for choosing the most
efficient set of loci for parentage assignment
S E A N E . M AT S O N ,* M A R K D . C A M A R A ,† W I L L E I C H E RT ‡ and M I C H A E L A . B A N K S ‡
*Department of Animal Sciences, Oregon State University, 2030 SE Marine Science Drive, Newport, OR 97365-5229, USA, †Shellfish
Genetics Program, USDA Agricultural Research Service, 2030 SE Marine Science Drive, Newport, OR 97365-5229, USA
‡Department of Fisheries and Wildlife, Coastal Oregon Marine Experiment Station, Oregon State University, 2030 SE Marine Science
Drive, Newport, OR 97365-5229, USA
Abstract
Determining how many and which codominant marker loci are required for accurate parentage assignment is not straightforward because levels of marker polymorphism, linkage,
allelic distributions among potential parents and other factors produce differences in the
discriminatory power of individual markers and sets of markers. P-LOCI software identifies
the most efficient set of codominant markers for assigning parentage at a user-defined level
of success, using either simulated or actual offspring genotypes of known parentage.
Simulations can incorporate linkage among markers, mating design and frequencies of
null alleles and/or genotyping errors. P-LOCI is available for windows systems at http://
marineresearch.oregonstate.edu/genetics/ploci.htm.
Keywords: codominant markers, linkage, microsatellite, offspring simulation, parentage assignment,
SNP
Received 23 September 2007; revision accepted 13 January 2008
Parentage assignment using codominant molecular markers
has become increasingly important for quantitative genetics,
animal breeding, molecular ecology and evolutionary
biology (Vignal et al. 2002; Jones & Ardren 2003; Anderson
& Garza 2006). Determining the most efficient set of marker
loci to use for a particular set of parents can save considerable
time, effort and funds. The minimum number of loci
necessary to accurately assign parentage depends on a
number of factors that affect their informativeness, including
allelic richness and diversity, linkage disequilibrium among
marker loci due to physical linkage and other sources,
number of parental pairs, mating design, frequency of
null alleles and genotyping errors, and unequal numbers
of offspring per family (Bernatchez & Duchesne 2000; Jones
& Ardren 2003; Dakin & Avise 2004; Anderson & Garza
2006; Kalinowski & Taper 2007; Kalinowski et al. 2007).
Few currently available parentage software packages
have multilocus predictive capabilities, and they do not
incorporate many of these important factors (Jones &
Ardren 2003; Taggart 2007). Most researchers and all currently
Correspondence: Sean E. Matson, Fax: 541-867-0105; E-mail:
matsonse@onid.orst.edu
© 2008 The Authors
Journal compilation © 2008 Blackwell Publishing Ltd
available parentage software assume markers are not
linked, even though physically linked markers carry
redundant information and are thus less informative in
combination than expected from single locus characteristics.
p-loci is the only program that uses linkage information
together with variable locus-specific frequencies of null
alleles and genotyping errors in the simulation of offspring
genotypes with variable number of offspring per family to
determine the minimum set of loci for assigning parentage.
Additionally, because the best combination of loci can
vary among populations, marker informativeness must be
re-evaluated for each study population, creating the need
for a quick and easy to use software tool. We created p-loci
to increase the efficiency of parentage assignment by
quickly identifying the best available set of codominant
molecular markers for parentage assignment in a specific
population. Figure 1 shows the p-loci interface with an
explanation of the controls.
p-loci identifies the smallest suite of codominant loci
required to assign diploid offspring to their parents at a
user-defined level of success through an iterative procedure.
In either simulation or real progeny mode, the user provides
a parental file consisting of the candidate parents’ multilocus
2 COMPUTER PROGRAMS
Fig. 1 Screenshot of p-loci software interface, showing where to enter input files and other important operating information.
marker genotypes at all loci to be evaluated and a mating
design file specifying how the parents are paired. When
the mating structure is not known, the user submits an
all-combination mating file. p-loci simulates offspring
genotypes using those files and optional linkage and error
information, and then attempts to assign them to their
parents based on using an exclusion algorithm (Jones &
Arden 2003). The accuracy of these assignments is evaluated
against the known pedigrees of simulated or actual progeny.
p-loci was created primarily for use with microsatellite data,
but works with any codominant genetic markers. Figure 2
is a conceptual model of p-loci, showing information flow.
p-loci simulates biologically realistic offspring genotypes
through a computationally intensive but genetically realistic
‘brute force’ procedure by first building virtual gametic
haplotypes from each parent. For each virtual offspring,
the program first randomly chooses one allele from the
current parent at the first locus in each linkage group and
then ‘walks’ along the virtual parental meiotic chromatid.
Cross-over probabilities between adjacent markers are
determined by recombination fractions calculated from
linkage map distances. If a linkage map is not provided,
the program assumes independent marker segregation
and assembles each haplotype choosing each allele at each
locus with equal probability. The two haplotypes are then
combined into a diploid offspring according to the mating
design.
p-loci accommodates different male and female maps
in either Kosambi or Haldane distances (Liu 1997; Lynch &
Walsh 1998). Linkage phase among marker alleles in specific
parents is assumed as their order of entry in the parental
genotype file. If the user knows the phase, they can enter it
as such, although the true phase is usually unknown, and
therefore arbitrarily represented in genotype data. The
user can also vary the number of offspring produced per
family. This may be desirable if some families are expected
to be over-represented in the offspring pool, or to model
variability in the best marker set, due to variance in relative
contribution of specific parents to the offspring population.
p-loci can realistically incorporate two types of error when
simulating offspring genotypes: segregating null alleles
and random-genotyping errors. p-loci optionally introduces
© 2008 The Authors
Journal compilation © 2008 Blackwell Publishing Ltd
COMPUTER PROGRAMS 3
Fig. 2 Conceptual model of p-loci showing information flow, signified by arrow
direction. Program operation is depicted as
follows: (1) p-loci simulates offspring (or
they are provided by the user); (2) Those
offspring are assigned to candidate parental
pairs, as denoted in the mating design; (3)
The locus selection routine ranks locus sets
by assignment success, and either accepts
the locus set as fulfilling the assignment
success criterion, or reinitiates the assignment
routine to perform all possible combinations
of the next level (e.g. from pairs to triplets),
or the current locus combination fulfills the
criterion, the program stops and reports.
null alleles at user-specified frequencies and creates a
modified parental genotype file in which a proportion of
the homozygous parents at each locus are re-coded as
heterozygotes with an undetectable null allele. The simulated
offspring that inherit these null alleles are treated by the
assignment algorithm as homozygotes for the detectable
allele. Null allele frequencies and genotyping error rates
must be estimated by the user a priori using other available
software (e.g. van Oosterhout et al. 2004; Kalinowski et al.
2007). The user can also designate individual parents a priori
as null homozygotes.
p-loci optionally incorporates microsatellite marker typing
errors by randomly adding or subtracting a user-defined
number of base pairs to the offspring alleles, producing
mismatches and potential misassignments that realistically
compromise the discriminatory value of error-prone loci.
To mitigate errors in real or simulated data sets that prevent
assignment via exclusion, the user can enter a maximum
number of loci at which offspring are allowed to mismatch
potential parents and still be assigned to them. The conservative user can also have p-loci determine a marker set
with one more locus than is needed to reach the assignment
success criterion.
If information regarding the rates of null alleles, typing
errors or linkage relationships among markers is not available,
the user may wish to genotype a small number of offspring
of known parentage from all crosses (e.g. offspring of
controlled crosses or observed matings) at all loci in order
to produce an input file containing actual offspring geno© 2008 The Authors
Journal compilation © 2008 Blackwell Publishing Ltd
types rather than simulated ones. Actual offspring genotypes
will inherently exhibit the effects of the aforementioned
complicating factors.
After p-loci either produces the simulated offspring
file or is provided with actual offspring genotypes of
known parentage, the user initiates the marker evaluation
algorithm, and p-loci first assigns all offspring using
each marker individually by checking each offspring for
Mendelian compatibility with each parental pair in the
mating file. Assignments are successful only when a single
compatible parental pair is identified. If more than one
compatible pair is found, or if an offspring is misassigned
when checked against its known parentage information,
that individual assignment is unsuccessful. This information is used to rank individual loci by their assignment
success rate. The software subsequently examines all
possible marker pairs, triplets, etc., and stops when it
reaches the user-provided level of assignment success.
The program then produces a report that includes the ranks
of individual loci and their assignment scores, followed
by the best pair, triplet and so on. p-loci can automatically
produce and analyse multiple sets of simulated offspring
and produce a summary report that includes the average
rankings of individual loci among runs and how often a
particular locus appeared in the best marker set. After
using p-loci to determine the best set of loci, the user can
assign actual progeny to their parents using a variety of
methods and software, of which Jones & Ardren (2003)
provide a thorough review.
4 COMPUTER PROGRAMS
We tested p-loci with actual and simulated microsatellite
and single nucleotide polymorphism (SNP) data, varying
levels of polymorphism, distribution of alleles among
parents, number of parents, mating design complexity, degree
of linkage among markers, and locus-specific frequencies
of null alleles and genotyping errors. p-loci reported
increasing assignment success with increasing allelic
richness and more heterogeneous allelic distributions
among potential parents. Assignment success decreased
with increasing number of potential parents, increasing
complexity of the mating design, and higher frequencies
of null alleles and genotyping errors. In general, unlinked
markers provided a higher level of assignment success
than linked ones, all other factors being equal.
p-loci also chose different marker sets in different parental
populations of Pacific oysters, Crassostrea gigas (S.E. Matson
and M.D. Camara, C. Langdon and F. Evans, unpublished
data), using genotype data from breeding experiments, microsatellite markers, and microsatellite linkage map information
(Hubert & Hedgecock 2004). We used an early version of
p-loci to determine the best available suite of microsatellites
(Li et al. 2003; Magoulas et al. 1998; McGoldrick et al. 2000)
for assigning 1200 offspring to 20 pairs of parents, and performed parentage analysis with a 98.5% success rate using
papa software (Duchesne et al. 2002) and four loci.
Single locus ranks within populations were similar to
those obtained from ranking loci by Shannon diversity
index computed with microsatellite analyser (Dieringer
& Schlotterer 2003). However, we found that the best suite
of loci often consists not of only the top-ranked individual
loci, but rather a mixture of top- and middle-ranked markers.
This is most likely due to random allelic associations
among loci, and LD in the parental population that make
the information carried by some marker sets redundant
and others complementary.
Our preliminary results have important implications.
The top ranked individual loci do not necessarily constitute
the smallest group of loci for assignment, and that group is
not necessarily the best for all populations, making p-loci
an important tool for efficient parentage analysis.
Acknowledgements
This research was funded by Oregon Sea Grant, the California
Department of Water Resources (Environmental Services), the
USDA Agricultural Research Service (CRIS Project #5358-31000001-00D), and a Mamie Markham Research Award at OSU-HMSC.
We thank Chris Langdon and Ford Evans for use of their oyster
genotype data.
Mandatory USDA-ARS Disclaimer Any use of trade, firm, or
corporation names in this publication is for the information and
convenience of the reader. Such use does not constitute an official
endorsement or approval by the US Department of Agriculture or
the Agricultural Research Service of any product or service to the
exclusion of others that may be suitable.
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© 2008 The Authors
Journal compilation © 2008 Blackwell Publishing Ltd