What has population genomics told us about the dynamics of selection and plant adaptation?
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2026
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Abstract
Population genomics has transformed our understanding of how natural selection shapes plant genomes. The explosion of whole-genome resequencing data has enabled estimates of nucleotide diversity across genomes, clarified how selection interacts with recombination and demography, and broadened inference beyond crops and model species to virtually any plant system. Here, we synthesize key lessons and outstanding challenges concerning the action of natural selection on plant genomes. One message is that purifying selection is a pervasive selective force acting against deleterious mutations and structural variants, but its efficacy varies predictably and is often relaxed after demographic contractions, including domestication bottlenecks, range expansions, shifts in mating systems, and polyploid formation. Adaptation also routinely shapes genetic diversity, as evidenced by the detection of selective sweeps. Swept genes have functional biases, but many outstanding questions about adaptive variation remain, including the selection coefficients of adaptive alleles, whether selection typically acts on segregating variants or de novo mutations, and how often selection is polygenic. As with polygenic selection, our knowledge about balancing selection is limited, due in part to challenges in its detection. It is known, however, to act on disease resistance genes and genes that govern breeding systems. Finally, an emerging theme in plant population genomics is the interplay between introgression and adaptation, which is fueled by new methods, but it remains challenging to link confidently to fitness and also requires an expanded scale. This review reveals both the power and limits of population genomics for resolving the dynamics of selection on plant genomes.
| Reference Key |
openalex_W7169623340
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| Authors | Brandon S. Gaut, Stephen Wright, Tianpeng Wang, Peter L. Morrell |
| Journal | molecular biology and evolution |
| Year | 2026 |
| DOI |
10.1093/molbev/msag171
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| URL | |
| Keywords | Keywords not found |
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