Universality and predictability in molecular quantitative genetics

Abstract

Molecular traits, such as gene expression levels or protein binding affinities, are increasingly accessible to quantitative measurement by modern high-throughput techniques. Such traits measure molecular functions and, from an evolutionary point of view, are important as targets of natural selection. We review recent developments in evolutionary theory and experiments that are expected to become building blocks of a quantitative genetics of molecular traits. We focus on universal evolutionary characteristics: these are largely independent of a trait's genetic basis, which is often at least partially unknown. We show that universal measurements can be used to infer selection on a quantitative trait, which determines its evolutionary mode of conservation or adaptation. Furthermore, universality is closely linked to predictability of trait evolution across lineages. We argue that universal trait statistics extends over a range of cellular scales and opens new avenues of quantitative evolutionary systems biology. * * Authors with equal contributions.

Adaptive Evolution of Gene Expression in Drosophila

“…where the trait scale D 0 is defined such that U ij z1 for neutral evolution in the limit of long divergence times (details of this definition are given in Experimental Procedures and Box 1). The evolution of these divergence measures depends only weakly on the effect distribution of expression QTL and on the amount of recombination between these loci, which is key to quantitative genetics approaches (Lynch and Hill, 1986;Leinonen et al, 2013;Nourmohammad et al, 2013aNourmohammad et al, , 2013bHeld et al, 2014).…”

“…The inference of adaptation is based on a minimal dynamical model of selection: gene expression levels E evolve in a single-peak fitness seascape fðE; tÞ = f à À const:3ðE À E à ðtÞÞ 2 (Nourmohammad et al, 2013a;Held et al, 2014). This model is illustrated in Box 1 and formally defined in Experimental Procedures.…”

“…The simplest inference scheme is based on aggregate time-dependent expression divergence data; a probabilistic extension to individual genes is discussed later. Box 1 shows the analytical form of the rescaled trait divergence UðtÞ in a fitness seascape with positive stabilizing strength and driving rate (c > 0; y > 0; green solid line); the corresponding form U eq ðtÞ in a fitness landscape of the same stabilizing strength and zero driving rate (c > 0; y = 0; blue solid line) reaches the saturation value U stab (Nourmohammad et al, 2013a;Held et al, 2014). The saturation time is inversely proportional to the stabilizing strength t stab $ U stab $ 1=c and is shorter than expected from neutral evolution t $ 1 (gray solid line).…”

“…We infer directional selection driving adaptation, together with conservation under stabilizing selection, and we show that these forces act on different scales of evolutionary time. Our inference is based on theoretical results on the evolution of molecular quantitative traits (Held et al, 2014;Nourmohammad et al, 2013aNourmohammad et al, , 2013b, using solely the dependence of gene expression divergence on the divergence time of 7 Drosophila species. Moreover, the method only relies on the phenotypic observables and does not depend on number and effects of the underlying QTL; these molecular determinants of gene expression are often unknown and vary considerably among genes.…”

Antigenic evolution of human influenza H3N2 neuraminidase is constrained by charge balancing

“…Understanding the biophysical constraints on evolution is a key to building a predictive evolutionary model 17,21,[48][49][50][51][52] Through a systematic analysis of pairwise epistasis, this study shows that the local net charge is a major biophysical molecular phenotype that constrains the evolution of an antigenic region in human influenza H3N2 NA. An important feature for this antigenic region of interest is that the pairwise epistasis are highly conserved across diverse genetic backgrounds.…”