Modified Hudson–Kreitman–Aguadé Test and Two-Dimensional Evaluation of Neutrality Tests

Abstract

Abstract There are a number of polymorphism-based statistical tests of neutrality, but most of them focus on either the amount or the pattern of polymorphism. In this article, a new test called the two-dimensional (2D) test is developed. This test evaluates a pair of summary statistics in a two-dimentional field. One statistic should summarize the pattern of polymorphism, while the other could be a measure of the level of polymorphism. For the latter summary statistic, the polymorphism-divergence ratio is used following the idea of the Hudson–Kreitman–Aguadé (HKA) test. To incorporate the HKA test in the 2D test, a summary statistic-based version of the HKA test is developed such that the polymorphism–divergence ratio at a particular region of interest is examined if it is consistent with the average of those in other independent regions.