An Experimental-Design Perspective on Population Genetic Variation

TL;DR

This paper investigates how evolution shapes population genomes to ensure the broad validity of adaptations, using experimental design principles to analyze genetic variation across multiple species.

Contribution

It introduces a novel hypothesis linking population-wide genome patterns to experimental design concepts, and demonstrates this through analysis of large genomic datasets.

Findings

Genetic variation patterns resemble Erdos-Renyi-Gilbert random graphs.

Derived mutation rates and probabilities from genome data.

Supports hypothesis that evolution promotes externally valid adaptations.

Abstract

We consider the hypothesis that Evolution promotes population-wide genome patterns that, under randomization, ensures the External Validity of adaptations across population members. An adaptation is Externally Valid (EV) if its effect holds under a wide range of population genetic variations. A prediction following the hypothesis is that pairwise base substitutions in segregating regions must be 'random' as in Erdos-Renyi-Gilbert random graphs, but with edge probabilities derived from Experimental-Design concepts. We demonstrate these probabilities, and consequent mutation rates, in the full-genomes of 2504 humans, 1135 flowering plants, 1170 flies, 453 domestic sheep and 1223 brown rats.