Microsatellite DNA provides essentially limitless, highly varied information within species.  That this provides a means for distinguishing not only among populations but also individuals has not escaped current theoretic interest (Smouse and Chevillon 1998, Waser and Strobeck 1998).  Here, we present a C++ computer program named WHICHRUN that uses multilocus genotypic data to allocate individuals to their most likely source population.

Requirements

Program runs on Windows95, 98 or NT (including Macintosh emulations of these operating systems) and has no specific hardware requirements.

Input File

WHICHRUN requires baseline genotype data for all potential source populations as well as genotype data for candidate individuals for which population origin is to be determined.  Data should be provided in simple ASCII format as required for GENEPOP (Raymond and Rousset 1995). WHICHRUN's help file describes preparing input data in detail, and the download includes a sample baseline, unknown and output file.

Whichrun Bayes (v4.2)

If you have Whichrun 4.0, be sure to download version 4.2, which corrects an error that was affecting the jackknife results.

Theory and Program Outline

It is assumed that each baseline population (B₁..B_k) has Hardy-Weinberg-Castle (H-W-C) genotype frequencies and that genetic loci employed are independent.  The likelihood that an individual sample (s_1..n) may come from each of the source populations (B_{1.. k}) is presumed to be equal to the H-W-C frequency of its specific genotype at each locus in each respective source population.  Thus, for homozygotes the likelihood that a sample (s₁) is an element (e) of baseline population B₁ is p₁²  (the square of its allele frequency (p₁) in population B₁) or for heterozygotes, s₂ e B₁ = 2 p₁q₁ (q₁ being the frequency of an alternate allele in population B₁) and the likelihood that s_n e B_k = p_k² or 2 p_kq_k.  Likelihood values for each locus are multiplied to give a series of multi-locus likelihood functions for assignment to each of the source populations.  Alternate hypotheses that individual samples in question may come from each source population are considered in three ways:

1. Multi-locus likelihood functions may be grouped to form ratios considering all possible pairs of baseline populations under consideration.  If the ratio of the most likely allocation grouped with the second most likely allocation approaches one, there is ambiguity in the assignment of the particular sample under study.  Conversely, samples for which this ratio yields a large result in comparison to all other ratios can be assigned to a single population with more confidence.  For the two populations considered in the ratio, the chance of error is equal to the inverse of this ratio.  Stringency for population allocation can be applied by defining a selection criterion for the log₁₀ of this ratio.  For example, by selecting only assignments that have a log of the odds (LOD) ratio of at least 2, all results will have a 1/100 chance of error or less.

2. Multi-locus likelihood functions may be grouped according to maximum likelihood format according to the equation L(n)/L(max).   This yields a series of ratios between 1 (most likely) and close to 0 (least likely). Analysis of variance of log transformed data followed by a Tukey’s multiple comparison enables evaluation of statistical significance in the classical sense.

3. Jackknife iterations provide an empirical means for evaluating baseline data and the chances of correct allocation.  Iterations sample individuals from the baseline one at a time, recalculating allele frequencies in the absence of each individual genotype sampled before determining most likely population origin for that individual.  Experimenting with alternate loci and populations enables one to determine which population comparisons and loci combinations enable reliable population re-allocation.

Reporting options and special cases

Sample ID, genotypic data, and multilocus likelihoods for population allocation can be displayed for verification.  A critical population routine allows one to select a target population for calculation of LOD scores.  All scores are then calculated with the critical population as the numerator in the ratio.  A special case where test samples may have an allele or pair of alleles not observed in one or all of the baseline populations are treated as follows.  For source populations in which the allele is not observed an estimated allele frequency of 1/(2N + 1) is applied.  This hypothesizes that the non-observance of the allele in question is due to sampling error and that the allele in question would have been observed in the baseline population if one more allele had been sampled.  Note that this estimation may introduce substantial bias if baseline population size (N) is small as would be likely for any allele frequency estimation given small N, particularly when dealing with highly polymorphic marker types.  The program implements a warning describing this consideration when small baseline population sizes (N < 30) are encountered.  Alternatively, if sampling error is low, an unknown sample allele not observed in a baseline population may constitute strong evidence that the sample in question may indeed not originate from the particular baseline population under consideration.  Any alleles for which the 1/(2N+1) estimation is necessary are noted on the genotype output.

It is obvious that a technique such as WHICHRUN will only be effective if there is reasonable reproductive isolation among populations under consideration.  Three other considerations are also important.  First, the rate of accumulation of variance for molecular loci employed should be closely matched with estimated divergence times among populations under study.  For example, highly polymorphic microsatellites prone to homoplasy would not be suited for diagnosis among populations that have been diverged for substantial evolutionary time.  However, highly polymorphic microsatellites are likely one of a few molecular marker types that have sufficient information to resolve diagnosis among recently diverged populations such as the global radiation of Drosophila melanogaster which is estimated to have occurred within the last 10,000 to 15,000 years (David and Capy 1988; Benassi and Veuille 1995). Second, the accuracy of determination is crucially dependent upon the lack of differential sampling error among baseline allele frequencies.  While this problem is partially addressed through ensuring that sample size is equal for all populations, highly polymorphic marker types such as microsatellites require substantial sampling.  Third, for population origin diagnoses where source populations are recently diverged, there will be a number of loci that have not accumulated differences in the time since divergence.  As a result, simply increasing the number of loci employed may not necessarily increase the power of diagnosis.  For closely related populations, additional loci that have marked differences in allele frequency profiles among populations will be necessary to achieve increased power.

Authors

Michael A. Banks¹ and Will Eichert^2
1 Hatfield Marine Science Center, Oregon State University, 2030 S. Marine Science Drive, Newport, OR, 97365, USA

² Bodega Marine Laboratory, University of California at Davis,
  Bodega Bay, CA 94923 USA

Michael.Banks@oregonstate.edu

wfeichert@ucdavis.edu

Published reference

Banks, M.A. and W. Eichert. 2000. WHICHRUN (Version 3.2) a computer program for population assignment of individuals based on multilocus genotype data. Journal of Heredity. 91:87-89.

Note: Copyright has been awarded to the American Genetics Association.

Thanks

From The Bodega Marine Laboratory, University of California at Davis, P.O. Box 247, Bodega Bay. USA. We thank V.K. Rashbrook, H.A. Fitzgerald and J. Olsen, for beta testing various versions of this program and a number of useful suggestions and improvements that resulted from our collaboration. We are also grateful to F.J. Saminiego for discussion on statistical aspects during the development of WHICHRUN. Research and development of WHICHRUN was supported by funds attained from the California Department of Water resources and the US Fish and Wildlife Service.

References

Benassi, V. and Veuille, M.  1995.  Comparative population structuring of molecular and allozyme variation of Drosophila melanogaster Adh between Europe, West Africa and East Africa.  Genetics Research.  65:95-103.

David, J.R. and Capy, P.  1988.  Genetic variation of Drosophila melanagaster natural populations.  Trends in genetics.  4:106-111.

Raymond, M. and Rousset, F.  1995.  GENEPOP (Version 1.2): Population genetics software for exact tests and ecumenicism.  Journal of Heredity.  86:248-250.

Smouse, P.E. and Chevillon, C.  1998.  Analytical aspects of population-specific DNA fingerprinting for individuals.  Journal of Heredity.  89:143-150.

Waser, PM, and Strobeck, C.  1998.  Genetic signatures of interpopulation dispersal.  Trends in Ecology and Evolution.  13:43-44.

*Updated February 17th 2000

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