Modularity, Noise and natural selection

TL;DR

This paper investigates how modularity and noise affect the estimation of covariance matrices in biological systems, demonstrating that controlling noise significantly improves the accuracy of reconstructing selection gradients.

Contribution

It introduces methods for controlling noise in covariance matrix estimates and shows their effectiveness in improving selection gradient reconstructions.

Findings

Controlling noise enhances the accuracy of selection gradient estimates.

Modularity and noise levels influence the reliability of covariance matrix analyses.

Noise control methods produce more plausible biological interpretations.

Abstract

Most biological systems are formed by component parts that to some degree are inter-related. Groups of parts that are more associated among themselves and are relatively autonomous from others are called modules. One of the consequences of modularity is that biological systems usually present an unequal distribution of the genetic variation among variables. Estimating the covariance matrix that describes these systems is a difficult problem due to a number of factors such as poor sample sizes and measurement errors. We show that this problem will be exacerbated whenever matrix inversion is required, as in directional selection reconstruction analysis. We explore the consequences of varying degrees of modularity and signal-to-noise ratio on selection reconstruction. We then present and test the efficiency of available methods for controlling noise in matrix estimates. In our simulations, controlling matrices for noise vastly improves the reconstruction of selection gradients. We also perform an analysis of selection gradients reconstruction over a New World Monkeys skull database in order to illustrate the impact of noise on such analyses. Noise- controlled estimates render far more plausible interpretations that are in full agreement with previous results.