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Author Spotlight: Cost-Effective Transcriptomic Drug Screening - Unlocking New Targets

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Measuring natural selection on the transcriptome.

John R Stinchcombe^1,2, John K Kelly³

¹Department of Ecology and Evolutionary Biology, University of Toronto, Toronto, ON, M5S3B2, Canada.

The New Phytologist

|June 6, 2025

Summary

This summary is machine-generated.

Understanding natural selection on gene expression in plants is crucial for fitness. New statistical and machine learning methods can now analyze complex transcriptomic data, overcoming challenges in field studies.

Keywords:

RNA‐seq coexpression networks eQTL fitness natural selection transcriptomes

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Area of Science:

Plant biology
Evolutionary genetics
Genomics

Background:

Gene expression patterns significantly influence plant phenotypes and fitness.
Characterizing natural selection on the transcriptome is an emerging field.
High dimensionality of transcriptomic data poses challenges for traditional selection analysis methods.

Purpose of the Study:

To review progress in estimating natural selection on plant transcriptomes.
To discuss challenges in analyzing high-dimensional gene expression data in field studies.
To explore statistical and machine learning approaches for transcriptome-wide selection analysis.

Main Methods:

Review of existing literature on natural selection and transcriptomics.
Discussion of multivariate statistical approaches.
Exploration of regularized regression, latent factor models, and machine learning techniques.
Consideration of methods to handle the large number of genes (traits) relative to sample size.

Main Results:

Existing data, though limited, can illustrate various analytical approaches.
Several statistical and machine learning methods are suitable for high-dimensional transcriptomic data.
Challenges include the vast number of genes compared to feasible field sample sizes.

Conclusions:

Methodological advancements are enabling the study of natural selection on gene expression.
Further development and application across species are needed.
Direct characterization of selection on gene expression in natural plant populations is a promising future direction.