Abstract
We demonstrate the effectiveness of a genetic algorithm for discovering multi-locus combinations that provide accurate individual assignment decisions and estimates of mixture composition based on likelihood classification. Using simulated data representing different levels of inter-population differentiation (Fst ∼ 0.01 and 0.10), genetic diversities (four or eight alleles per locus), and population sizes (20, 40, 100 individuals in baseline populations), we show that subsets of loci can be identified that provide comparable levels of accuracy in classification decisions relative to entire multi-locus data sets, where 5, 10, or 20 loci were considered. Microsatellite data sets from hatchery strains of lake trout, Salvelinus namaycush, representing a comparable range of inter-population levels of differentiation in allele frequencies confirmed simulation results. For both simulated and empirical data sets, assignment accuracy was achieved using fewer loci (e.g., three or four loci out of eight for empirical lake trout studies). Simulation results were used to investigate properties of the ‘leave-one-out’ (L1O) method for estimating assignment error rates. Accuracy of population assignments based on L1O methods should be viewed with caution under certain conditions, particularly when baseline population sample sizes are low (<50).
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References
Angers, B.A., L. Bernatchez, A. Angers & L. Desgroseillers. 1995. Specific microsatellite loci for brook charr reveal strong population subdivision on a microgeographic scale. J. Fish Biol. 47(Suppl. A): 177–185.
Aurelle, D., S. Lek, J.L. Giraudel & P. Berrebi. 1999. Microsatellites and artificial neural networks: Tools for the discrimination between natural and hatchery brown trout (Salmo trutta, L.) in Atlantic populations. Ecol. Model. 120: 313–324.
Belkhir, K., P. Borsa, J. Goudet, L. Chikhi & F. Bonhomme. 1996. genetix v. 3.0, logiciel sous Windows™ pour la genetique des populations. Montpellier, Laboratoire Genome et Populations, Universite Montpellier 2, France.
Bernatchez, L. & P. Duchesne. 2000. Individual-based genotype analysis in studies of parentage and population assignment: How many loci, how many alleles? Can. J. Fish. Aquat. Sci. 57: 1–12.
Brenner, C.H. 1998. Difficulties in the estimation of ethnic affiliation. Am. J. Hum. Genet. 62: 1558–1560.
Cornuet, J.M., S. Aulagnier, S. Lek, P. Franck & M. Solignac. 1996. Classifying individuals among infra-specific taxa using microsatellite data and neural networks. C. R. Acad. Sci. Paris, Life Sci. 319: 1167–1177.
Cornuet, J.M., S. Piry, G. Luikart, A. Estoup & M. Solignac. 1999. New methods employing multi-locus genotypes to select or exclude populations as origins of individuals. Genetics 153: 1989–2000.
Duda, R.O., P.E. Hart & D.G. Stork. 2000. Pattern Classification. 2nd edition, John Wiley and Sons, New York. 654 pp.
Girman, D.J., M.L.G. Mills, E. Geffen & R.K. Wayne. 1997. A molecular genetic analysis of social structure, dispersal, and interpack relationships of the African wild dog (Lycaon pictus). Behav. Ecol. Sociobiol. 40: 187–198.
Goldberg, D. 1989. Genetic Algorithms in Search, Optimization and Machine Learning, Addison-Wesley, Reading. 452 pp.
Gomulkiewicz, R., J.K.T. Brodziak & M. Mangel. 1990. Ranking loci for genetic stock identification by curvature methods. Can. J. Fish. Aquat. Sci. 47: 611–619.
Hansen, M.M., E. Kenchington & E.E. Nielsen. 2001. Assigning individual fish to populations using microsatellite DNA markers. Fish Fish. 2: 93–112.
Hansen, M.M., D.E. Ruzzante, E.E. Nielsen & K.L.D. Mensberg. 2000. Microsatellite and mitochondrial DNA polymorphism reveals life-history dependent interbreeding between hatchery and wild brown trout (Salmo trutta L.). Mol. Ecol. 9: 583–594.
Holland, J. 1994. Adaptation in Natural and Artificial Systems, MIT Press, Cambridge. 221 pp.
Jain, A.K. & D. Zongker. 1997. Feature selection: Evaluation, application and small sample performance. IEEE Trans. Patt. Anal. Mach. Intell. 19: 153–158.
Jain, A.K, R.P.W. Duin & J. Mao. 2000. Statistical pattern recognition: A review. IEEE Trans. Patt. Anal. Mach. Intell. 22: 4–37.
Letcher, B.H. & T.L. King. 1999. Targeted stock identification using multi-locus genotype ¡®familyprinting¡¯. Fish. Res. 43: 99–111.
Lewis, P.O. 1998. A genetic algorithm for maximum-likelihood inference using nucleotide sequence data. Mol. Biol. Evol. 15: 277–283.
Martinez, J.L., J. Dumas, E. Beall & E. Garcia-Vazquez. 2001. Assessing introgression of foreign strains in wild Atlantic salmon populations: Variation in microsatellites assessed in historic scale collections. Freshw. Biol. 46: 835–844.
Mitchell, M. & C.E. Taylor. 1999. Evolutionary computation: An overview. Annu. Rev. Ecol. Syst. 30: 593–616.
Neraas, L.P. & P. Spruell. 2001. Fragmentation of riverine systems: The genetic effects of dams on bull trout (Salvelinus confluentus) in the Clark Fork River system. Mol. Ecol. 10: 1153–1164.
Nielsen, E.E., M.M. Hansen, C. Schmidt, D. Meldrup & P. Grønkjaer. 2001. Population of origin of Atlantic cod. Nature 413: 272.
Norris, A.T., D.G. Bradley & E.P. Cunningham. 2000. Parentage and relatedness determination in farmed Atlantic salmon (Salmo salar) using microsatellite markers. Aquaculture 182: 73–83.
Olsen, J.B., P. Bentzen & J.E. Seeb. 1998. Characterization of seven microsatellite loci derived from pink salmon. Mol. Ecol. 7: 1087–1089.
Olsen, J.B., P. Bentzen, M.A. Banks, J.B. Shaklee & S. Young. 2000. Microsatellites reveal population identity of individual pink salmon to allow supportive breeding of a population at risk of extinction. Trans. Amer. Fish. Soc. 129: 232–242.
O’Reilly, P.T., L.C. Hamilton, S.K. McConnell & J.W. Wright. 1996. Rapid analysis of genetic variation in Atlantic salmon (Salmo salar) by PCR multiplexing of dinucleotide and tetranucleotide microsatellite. Can. J. Fish. Aquat. Sci. 53: 2292–2298.
Paetkau, D., W. Calvert, I. Stirling & C. Strobeck. 1995. Microsatellite analysis of population structure in Canadian polar bears. Mol. Ecol. 4: 347–354.
Pella, J.J.&G.B. Milner. 1987. Useofgenetic marksinstock composition analysis. pp. 247–276. In: N. Ryman & F. Utter (ed.) Population Genetics and Fisheries Management. Univeristy of Washington Press, Seattle, WA.
Potvin, C. & L. Bernatchez. 2001. Lacustrine spatial distribution of landlocked Atlantic salmon populations assessed across generations by multi-locus individual assignment and mixed-stock analysis. Mol. Ecol. 10: 22375–22388.
Pritchard, J.K., M. Stephens & P. Donnelly. 2000. Inference of population structure using multi-locus genotype data. Genetics 155: 945–959.
Queller, D.C. & K.F. Goodnight. 1989. Estimating relatedness using genetic markers. Evolution 43: 258–275.
Rannala, B. & J.L. Mountain. 1997. Detecting immigration using multi-locus genotypes. Proc. Natl. Acad. Sci. USA 94: 9197–9202.
Roques, S., P. Duchesne & L. Bernatchez. 1999. Potential of microsatellites for individual assignment: The North Atlantic redfish (genus Sebastes) species complex as a case study. Mol. Ecol. 8: 1703–1717.
Scribner, K.T., J.R. Gust & R.L. Fields. 1999. Isolation and characterization of novel salmon microsatellite loci: Cross-species amplification and population genetics applications. Can. J. Fish. Aquat. Sci. 53: 833–841.
Shao, J. 1993. Linear model selection by cross-validation. J. Amer. Stat. Assoc. 88: 486–494.
Shriver, M.D., M.W. Smith, L. Jin, A. Marcini, J.M. Akey et al. 1997 Ethnic-affiliation estimation by use ofpopulation-specific DNA markers. Amer. J. Hum. Genet. 60: 957–964.
Smouse, P. E. & C. Chevillon. 1998. Analytical aspects of population-specific DNA fingerprinting for individuals. J. Hered. 89: 143–150.
Smouse, P.E., R.S. Spielman & M.H. Park. 1982. Multiple-locus allocation of individuals to groups as a function of the genetic variation within and differences among human populations. Amer. Nat. 119: 445–463.
Smouse, P.E., R.S. Waples & J.A. Tworek. 1990. Agenetic mixture analysis for use with incomplete source population data. Can. J. Fish. Aquat. Sci. 47: 20–634.
Sokal, R.R. & J.F. Rohlf. 1995. Biometry. 2nd edition, Freeman, USA. 887 pp.
Stefanini, M.F. & A. Camussi. 2000. The reduction of large molecular profiles to informative components using a genetic algorithm. Bioinformatics 16: 923–931.
Taylor, E.B., A. Kuiper, P.M. Troffe, D.J. Hoysak & S. Pollard. 2000. Variation in developmental biology and microsatellite DNA in reproductive ecotypes of kokanee, Oncorhynchus nerka: Implications for declining populations in a large British Columbia lake. Conserv. Genet. 1: 213–249.
Taylor, E.B., Z. Redenbach, A.B. Costello, S.J. Pollard & C.J. Pacas. 2001. Nested analysis of genetic variation in northwestern North American char, Dolly Varden (Salvelinus malma) and bull trout (S. confluentus). Can. J. Fish. Aquat. Sci. 58: 406–420.
Trunk, G.V. 1979. A problem of dimensionality: A simple example. IEEE Trans. Patt. Anal. Mach. Intell. 1: 306–307.
Waser, P.M. & C. Strobeck. 1998. Genetic signatures of interpopulation dispersal. Trends Ecol. Evol. 13: 43–44.
Weir, B.S. 1979. Inferences about linkage disequilibrium. Biometrics 25: 235–254.
Weir, B.S. & C.C. Cockerham. 1984. Estimating F-statistics for the analysis of population structure. Evolution 43: 1358–1370.
Wright, S. 1965. The interpretation of population structure by F-statistics with special regards to system of mating. Evolution 19: 395–420.
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Guinand, B., Scribner, K.T., Topchy, A., Page, K.S., Punch, W., Burnham-Curtis, M.K. (2004). Sampling issues affecting accuracy of likelihood-based classification using genetical data. In: Gharrett, A.J., et al. Genetics of Subpolar Fish and Invertebrates. Developments in environmental biology of fishes, vol 23. Springer, Dordrecht. https://doi.org/10.1007/978-94-007-0983-6_20
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DOI: https://doi.org/10.1007/978-94-007-0983-6_20
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