Properties of neutrality tests based on allele frequency spectrum

TL;DR

This paper discusses the properties and development of linear and nonlinear neutrality tests based on allele frequency spectrum, emphasizing scalability and optimality for detecting deviations from neutrality in population genetics.

Contribution

It introduces a comprehensive framework for linear and nonlinear neutrality tests, including the derivation of nonlinear optimal tests for polynomial functions of the frequency spectrum.

Findings

Emphasizes importance of scalability with sample size in test design.

Provides a general framework for nonlinear neutrality tests.

Derives nonlinear optimal tests for polynomial functions.

Abstract

One of the main necessities for population geneticists is the availability of statistical tools that enable to accept or reject the neutral Wright-Fisher model with high power. A number of statistical tests have been developed to detect specific deviations from the null frequency spectrum in different directions (i.e., Tajima's D, Fu and Li's F and D test, Fay and Wu's H). Recently, a general framework was proposed to generate all neutrality tests that are linear functions of the frequency spectrum. In this framework, a family of optimal tests was developed to have almost maximum power against a specific alternative evolutionary scenario. Following these developments, in this paper we provide a thorough discussion of linear and nonlinear neutrality tests. First, we present the general framework for linear tests and emphasize the importance of the property of scalability with the sample size (that is, the results of the tests should not depend on the sample size), which, if missing, can guide to errors in data interpretation. The motivation and structure of linear optimal tests are discussed. In a further generalization, we develop a general framework for nonlinear neutrality tests and we derive nonlinear optimal tests for polynomials of any degree in the frequency spectrum.