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The development of molecular typing techniques applied to the study of population genetic diversity originates data with increasing precision but at the cost of some ambiguities. As distinct techniques may produce distinct kinds of ambiguities, a crucial issue is to assess the differences between frequency distributions estimated from data produced by alternative techniques for the same sample. To that aim, we developed a resampling scheme that allows evaluating, by statistical means, the significance of the difference between two frequency distributions. The same approach is then shown to be applicable to test selective neutrality when only sample frequencies are known. The use of these original methods is presented here through an application to the genetic study of a Munda human population sample, where three different HLA loci were typed using two different molecular methods (reverse PCR-SSO typing on microbeads arrays based on Luminex technology and PCR-SSP typing), as described in details in the companion article by Riccio et al. [The Austroasiatic Munda population from India and its enigmatic origin: An HLA diversity study. Hum. Biol. 38:405-435 (2011)]. The differences between the frequency estimates of the two typing techniques were found to be smaller than those resulting from sampling. Overall, we show that using a resampling scheme in validating frequency estimates is effective when alternative frequency estimates are available. Moreover, resampling appears to be the unique way to test selective neutrality when only frequency data are available to describe the genetic structure of populations.

Allele Frequency Estimation from Ambiguous Data: Using Resampling Schema in Validating Frequency Estimates and in Selective Neutrality Testing