A permutation solution to the problem of genetic differentiation is presented. The focus is on determining whether there are any differences between two populations. The ability to obtain plausible genetic heterogeneities by combining the information of several loci differs depending on the method used, so no statistical test uniformly dominates the others. The procedure proposed here is robust and flexible with regard to the possible configurations of the global alternative hypothesis. Moreover, such a procedure guarantees the rejection of the global hypothesis with probability α when all null (local) hypotheses are true. © 2004 Elsevier B.V. All rights reserved.
Nonparametric iterated combined tests for genetic differentiation
Solari A.
2006-01-01
Abstract
A permutation solution to the problem of genetic differentiation is presented. The focus is on determining whether there are any differences between two populations. The ability to obtain plausible genetic heterogeneities by combining the information of several loci differs depending on the method used, so no statistical test uniformly dominates the others. The procedure proposed here is robust and flexible with regard to the possible configurations of the global alternative hypothesis. Moreover, such a procedure guarantees the rejection of the global hypothesis with probability α when all null (local) hypotheses are true. © 2004 Elsevier B.V. All rights reserved.
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