An assessment of introgressive hybridization between ...

An assessment of introgressive hybridization between ...

Estimating the number of successful spawners in a steelhead population from a sample of offspring
Background and Study Rationale
Determining genealogical relationships among individuals within a sample of fish without
parental information has many important conservation and management applications. Although
several methods are currently available to estimate these relationships, they are computationally
intensive and require highly informative genetic markers that are free of genotyping errors and
mutations in order to provide accurate reconstruction. In this study, we used a powerful new set
of 188 single nucleotide polymorphic markers (SNPs) developed for steelhead to compare the
effectiveness of existing sibling reconstruction computer programs in correctly reconstructing
family relationships using SNP genotypes from hatchery offspring with known parentage. We
also compared estimates of effective population size (Ne), a key parameter in assessing both the
short- and long-term viability of populations, derived from sibship assignment and linkage
disequilibrium against the known Ne values calculated from each parent/offspring group.
Verification of the accuracy and performance of these methods could provide opportunities for
assessing patterns of dispersal and estimating Ne in future research programs where field
collections only target one or two cohorts and may not include parents.

Jesse McCane, Pacific States Marine Fisheries Commission, Eagle, ID 83616
Craig A. Steele, Pacific States Marine Fisheries Commission, Eagle, ID 83616
Christine Kozfkay, Idaho Department of Fish and Game, Eagle, ID 83616
Matthew Campbell, Idaho Department of Fish and Game, Eagle, ID 83616

Results show COLONY to be more effective than KINGROUP at recreating full-sibling
families from offspring genotypes (Figure 1). As expected, the 188 combined marker set proved the
most effective, followed by the 95 PBT set and the 93 GSI set. For both COLONY and KINGROUP,
factoring in polygamy had a beneficial effect on the accuracy for nearly every group. Factoring
inbreeding into COLONY runs had either a detrimental effect or none at all. However, only looking
at accuracy measures can be misleading as type 1 and type 2 errors can cancel each other out in the
final estimate of the number of full-sibling families. Figures 2A and 2B show the combined number
of type 1 and type 2 errors committed by KINGROUP and COLONY runs, respectively. Type 1
error is defined as a test rejecting a true null hypothesis (not grouping two full-sibling individuals as
full-siblings), while type 2 error is defined as a test accepting a false null hypothesis (grouping two
individuals who are not full-siblings). Type 1 and type 2 errors are equally undesirable as an equal
number of each would result in a technically 100% accurate estimate of full-sibling families.
COLONY was also more effective than LDNE at estimating Ne from offspring genotypes
(Figure 3). Although COLONY underestimated Ne at times while overestimating it at others, the
known Ne value always fell within COLONYs 95% confidence intervals. Factoring polygamy in to
COLONY runs always resulted in an underestimate of Ne. In every case, LDNE provided an
underestimate of Ne (Figure 3).

Figure 2A. Number of type 1 and type 2 errors committed by KINGROUP during full-sibling family reconstruction.

Figure 3. COLONY and LDNE Ne estimates. Results are grouped by hatchery stock, then by marker set and program settings used. Red
lines indicate known Ne values for each hatchery stock. Error bars represent 95% CI reported by COLONY and LDNE.

Figure 1. Accuracy of COLONY and KINGROUP at re-creating known full-sibling families. Results are grouped by stock, then by marker
set and program settings used.


A novel panel of 188 steelhead SNPs were used; 93 SNPs optimized for the Columbia
basin Genetic Stock Identification (GSI) program and 95 SNPs optimized for the Snake
River Parentage Based Tagging (PBT) program. Six groups of 93 hatchery broodstock juveniles
with known parentage were sampled for this project Pahsimeroi, Sawtooth, East Fork Salmon
River, Squaw Creek, Dworshak and Grande Ronde Lyons Ferry. Each hatchery stock of juveniles
was analyzed using the 93 GSI and 95 PBT SNP marker sets separately as well as with the
combined 188 marker set.
Two Programs were compared for recreating full-sibling families from offspring genotypes:
COLONY 2.0 (Jones and Wang 2010) and KINGROUP (Konovalov et al. 2004). COLONY 2.0
runs were performed assuming monogamy without inbreeding, polygamy without inbreeding, and
polygamy with inbreeding for the purpose of comparison. KINGROUP runs were performed using
the full sibling primary hypothesis and an unrelated null hypothesis. For the KINGROUP
polygamy runs, the null hypothesis was set to half siblings.
COLONY 2.0 was also compared to the program LDNE (Waples and Do 2008) for the ability
to estimate Ne from offspring genotypes. COLONY Ne estimates were obtained from the run settings
described previously. LDNE Ne estimates were obtained with the default program settings using the
0.02 lowest allele frequency output.

Figure 2B. Number of type 1 and type 2 errors committed by COLONY during full-sibling family reconstruction.

Jones, O.R. and J. Wang. 2010. COLONY: A program for parentage and sibship inference from multilocus genotype
data. Molecular Ecology Resources 10:551-555.
Konovalov, D.A., C. Manning, and M.T. Henshaw. 2004. Kingroup: A program for pedigree relationship reconstruction
and kin group assignments using genetic markers. Molecular Ecology Notes 4:779-782.
Laurie-Ahlberg, C.C. and B.S. Weir. 1979. Allozymic variation and linkage disequilibrium in some laboratory populations
of Drosophila melanogaster. Genetics 92:1295-1314.
Luikart, G., N. Ryman, D.A. Tallmon, M.K. Schwartz, and F.W. Allendorf. 2010. Estimation of census and effective
population sized: the increasing usefulness of DNA-based approaches. Conservation Genetics 11:355-373.
Waples, R.S. and C. Do. 2008. LDNE: A program for estimating effective population size from data on linkage
disequilibrium. Molecular Ecology Resources 8:753-756.
Waples, R.S. and Waples, R.K. 2011. Inbreeding effective population size and parentage analysis without parents.
Molecular Ecology Resources 11:162-171.

This work is part of a BPA funded collaborative project with the Columbia River InterTribal Fish Commissions genetics lab in Hagerman, Idaho to evaluate and implement new
genetic technologies for managing and conserving steelhead and Chinook salmon
populations in the in the Snake River basin (2010-00-026 and 2010-00-031).

Inbreeding effective population size refers to the size of an ideal population that would allow the
same accumulation of inbreeding as the actual population of interest. Inbreeding occurs when an
offspring inherits two copies of a gene from its parents which are identical by descent (IBD)- that is,
they are both directly descended from a single allele present in one of the founders of that population
(perhaps the parents are cousins and each inherited the particular allele from the same grandfather).
Inbreeding effective population size is thus a measure that emphasizes the effect that small population
size has on the chances of relatives mating with each other. Such matings lead to a loss of
heterozygosity in the population. Thus, this effective size gives an indication of the likely loss of
heterozygosity across all alleles in the population.
Single-sample estimators of NE provide an estimate of the effective number of parents that
produced the progeny from which the sample is drawn, and hence, they can be associated with the
inbreeding effective size (Laurie-Ahlberg & Weir 1979). Waples and Waples (2011) suggest that a
single-sample estimator of contemporary inbreeding effective population size can be obtained from a
random sample of progeny if sibling relationships can be accurately determined within the sample.
Importantly, they also suggest that it is not necessary to know the number of adults that produced no
offspring in order to provide an unbiased estimate of inbreeding effective population size. Initial
results, especially those from COLONY, using these new SNP marker sets developed for steelhead are
promising. They demonstrated high accuracy in estimating the number of families contributing to
juvenile offspring sample sets and they provided reasonable estimates of inbreeding effective
population size for the six hatchery populations tested.
Further testing under more varied conditions is necessary before it can be determined whether
these methodologies can be applied to wild populations. Specifically, additional testing is needed under
more complex mating system scenarios; when multiple cohorts are present; when
a larger number of parents contribute offspring; and with varying sizes of sampled

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