Measuring natural selection on the transcriptome

Abstract

The level and pattern of gene expression is increasingly recognized as a principal determinant of plant phenotypes and thus of fitness. The estimation of natural selection on the transcriptome is an emerging research discipline. We here review recent progress and consider the challenges posed by the high dimensionality of the transcriptome for the multiple regression methods routinely used to characterize selection in field experiments. We consider several different methods, including classical multivariate statistical approaches, regularized regression, latent factor models, and machine learning, that address the fact that the number of traits potentially affecting fitness (each expressed gene) can greatly exceed the number of plants that researchers can reasonably monitor in a field study. While such studies are currently few, extant data are sufficient to illustrate several of these approaches. With additional methodological development coupled with applications to a broader range of species, we believe prospects are favorable for directly characterizing selection on gene expression within natural plant populations.

Keywords: RNA‐seq; coexpression networks; eQTL; fitness; natural selection; transcriptomes.

Fig. 1

Schematic depicting the mapping from genotype to fitness. From left to right, we present a hypothetical case of two genotypes (G₁ and G₂, differing by a base‐pair, A vs T) and two environments (E₁ and E₂) to illustrate how genetic and environmental variation affect transcriptomes, phenotypes, and fitness. In the middle column, we highlight that genetic and environmental variation may lead to differences in expression in some tissues and stages (measured tissue/stage) but not in others (in this case, an unmeasured tissue/stage). Expression and environmental variation, in turn, both affect macroscopic phenotypes (z₁, z₂, z₃). In this case, we highlight that while z₁ and z₂ have been measured, it is likely that unmeasured phenotypes (z₃) are affected by expression and also affect fitness. In the arrows leading to fitness, we note that expression can affect fitness directly (dotted arrow) and via phenotypes (z₁ and z₂). Bars across the bottom are labeled with common analytical approaches to understanding expression, the genetic basis of traits, and selection. Key paths: (a) Groen et al. (2020), (b) Henry & Stinchcombe (2025), (c) Fig. 2, this paper, (d) Brown & Kelly (2022), (e) Josephs et al. (2015, 2020), (f) Lande & Arnold (1983), (g) Rausher (1992). Illustration by Martin R. Henry.

Fig. 2

Selection gradients for expression module ‘Red’ in Mimulus guttatus were positive for survival in all 3 years. The overall effect of Red on survival to flower (all years included) is significantly positive (F ₁₁₀₇ = 6.43, P = 0.013), although only the 2015 regression (portrayed with the square symbols and dashed line), where survival was generally low, is significant when considered in isolation (F _1,38 = 11.07, P < 0.002).