Effects of coancestry on accuracy of individual assignments to population of origin: examples using Great Lakes lake trout (Salvelinus namaycush).
Methods for assigning individuals to population of origin are widely used in ecological genetics, resources management, and forensics. Characteristics of genetic data obtained from putative source populations that enhance accuracy of assignment are well established. How non-independence within and among unknown individuals to be classified [i.e., gene correlations within individual (inbreeding) and gene correlations among individuals within group (coancestry)] affect assignment accuracy is poorly understood. We used empirical data for six microsatellite loci and offspring from full-sib crosses of hatchery strains of lake trout (Salvelinus namaycush; Salmonidae) representing known levels of coancestry (mean theta = 0.006 and 0.06) within families to investigate how gene correlations can affect assignment. Additional simulations were conducted to further investigating the influence of allelic diversity (2, 6 or 10 alleles per locus) and inbreeding (F = 0.00, 0.05, and 0.15) on assignment accuracy for cases of low and high inter-population variance in allele frequency (mean F (st) = 0.01 and 0.1, respectively). Inbreeding had no effect on accuracy of assignments. In contrast, variance in assignment accuracy across replicated simulations, and for each empirical case study increased with increasing coancestry, reflecting non-independence of probabilities of correct assignment among members of kin groups. Empirical estimates of assignment error rates should be interpreted with caution if appreciable levels of coancestry are suspected. Additional emphasis should be placed on sampling designs (spatially and temporally) that define or minimize the potential for sampling related individuals.
Guinand
Bruno
B
Department of Fisheries and Wildlife, Michigan State University, East Lansing, MI 48824-1222, USA. guinand@univ-montp2.fr
Scribner
Kim T
KT
Page
Kevin S
KS
Filcek
Kristi
K
Main
Laura
L
Burnham-Curtis
Mary K
MK
D000828:Animal Population Groups / Q000235:genetics*; D000818:Animals; D003198:Computer Simulation; D006114:Great Lakes Region; D007178:Inbreeding; D015810:Linkage Disequilibrium; D008957:Models, Genetic; D010802:Phylogeny; D018709:Statistics, Nonparametric; D014337:Trout / Q000145:classification / Q000235:genetics*