Variance additivity of genetic populational parameter estimates obtained through bootstrapping

Carlini-Garcia, L. A.; Vencovsky, Roland; Coelho, Alexandre Siqueira Guedes

doi:10.1590/s0103-90162003000100015

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“…Petit and Pons (1998), applying bootstrap resampling to estimate diversity parameters, verified that the variance resulting from the simultaneous resampling of individuals and populations contains the population variance component once and the intrapopulation variance component twice. Carlini-Garcia (2003) observed that, when added up, the mean squares concerning the resampling of individuals and populations separately, correspond to the mean square obtained from the simultaneous resampling of individuals and populations. This was verified empirically for estimates of the parameters F, f and q, with real data, as well as with simulated data.…”

Section: Introductionmentioning

confidence: 90%

Factorial analysis of bootstrap variances of population genetic parameter estimates

2006

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We presented an alternative way to verify the relative contribution to the total variance, of the sources of variation due to populations (P), individuals within populations (I), the (P*I) interaction, and the standard error of the following parameter estimates: total (F) and intrapopulation (f) fixation indices, and divergence among populations (q). The knowledge of this relative contribution is important to establish sampling strategies of natural populations. To attain these objectives, the bootstrap method was used to resample simultaneously populations and individuals, considering different combinations of P and I. This procedure was repeated five times for a given combination of each analyzed data set. For each data set, five estimates of these variances were obtained for each combination of P and I, and a given parameter estimate. These variance estimates were submitted to an analysis of variance, considering a factorial structure. The sources of variation considered in this analysis were P, I and P*I. The coefficient of determination (R 2 ) was calculated for each source of variation. Sources of variation with greater R 2 are responsible for bigger errors of the estimates. The method applied was efficient for answering the questions initially proposed, and the results indicated that there are no ideal sample sizes for a species, but rather for a specific data set, because each data set has its own particularities. However, for investigations on the genetic structure of natural populations using population parameters, the number of populations to be sampled is a critical factor. Thus, more efforts should be made to increase the number of sampled populations, rather than the number of individuals within populations. A sampling strategy is given as a guide for investigations of this kind, when there is no previous knowledge about the genetic structure and the mating system of the populations.

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Section: Introductionmentioning

confidence: 90%