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Adaptation to novel environments requires genetic variation, but whether adaptation typically acts upon preexisting genetic variation or must wait for new mutations remains a fundamental question in evolutionary biology. Selection during domestication has been long used as a model to understand evolutionary processes, providing information not only on the phenotypes selected but also, in many cases, an understanding of the causal loci. For each of the causal loci that have been identified in maize, the selected allele can be found segregating in natural populations, consistent with their origin as standing genetic variation. The sole exception to this pattern is the well-characterized domestication locustga1(teosinte glume architecture1), which has long been thought to be an example of selection on a de novo mutation. Here, we use a large dataset of maize and teosinte genomes to reconstruct the origin and evolutionary history oftga1. We first estimated the age oftga1-maizeusing a genealogy-based method, finding that the allele arose approximately 42,000 to 49,000 y ago, predating the beginning of maize domestication. We also identifytga1-maizein teosinte populations, indicating that the allele can survive in the wild. Finally, we compare observed patterns of haplotype structure and mutational age distributions neartga1with simulations, finding that patterns neartga1in maize better resemble those generated under simulated selective sweeps on standing variation. These multiple lines of evidence suggest that maize domestication likely drew upon standing genetic variation attga1and cement the importance of standing variation in driving adaptation during domestication.
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This content will become publicly available on June 17, 2026
Cross-species modeling of plant genomes at single-nucleotide resolution using a pretrained DNA language model
Interpreting function and fitness effects in diverse plant genomes requires transferable models. Language models (LMs) pretrained on large-scale biological sequences can capture evolutionary conservation and offer cross-species prediction better than supervised models through fine-tuning limited labeled data. We introduce PlantCaduceus, a plant DNA LM that learns evolutionary conservation patterns in 16 angiosperm genomes by modeling both DNA strands simultaneously. When fine-tuned on a small set of labeledArabidopsisdata for tasks such as predicting translation initiation/termination sites and splice donor/acceptor sites, PlantCaduceus demonstrated remarkable transferability to maize, which diverged 160 Mya. The model outperformed the best existing DNA language model by 1.45-fold in maize splice donor prediction and 7.23-fold in maize translation initiation site prediction. In variant effect prediction, PlantCaduceus showed performance comparative to state-of-the-art protein LMs. Mutations predicted to be deleterious by PlantCaduceus showed threefold lower average minor allele frequencies compared to those identified by multiple sequence alignment-based methods. Additionally, PlantCaduceus successfully identifies well-known causal variants in bothArabidopsisand maize. Overall, PlantCaduceus is a versatile DNA LM that can accelerate plant genomics and crop breeding applications.
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- Award ID(s):
- 2145577
- PAR ID:
- 10618170
- Publisher / Repository:
- PNAS
- Date Published:
- Journal Name:
- Proceedings of the National Academy of Sciences
- Volume:
- 122
- Issue:
- 24
- ISSN:
- 0027-8424
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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