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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. 

Pleiotropy—when a single gene controls two or more seemingly unrelated traits—has been shown to impact genes with effects on flowering time, leaf architecture, and inflorescence morphology in maize. However, the genome-wide impact of biological pleiotropy across all maize phenotypes is largely unknown. Here, we investigate the extent to which biological pleiotropy impacts phenotypes within maize using GWAS summary statistics reanalyzed from previously published metabolite, field, and expression phenotypes across the Nested Association Mapping population and Goodman Association Panel. Through phenotypic saturation of 120,597 traits, we obtain over 480 million significant quantitative trait nucleotides. We estimate that only 1.56–32.3% of intervals show some degree of pleiotropy. We then assess the relationship between pleiotropy and various biological features such as gene expression, chromatin accessibility, sequence conservation, and enrichment for gene ontology terms. We find very little relationship between pleiotropy and these variables when compared to permuted pleiotropy. We hypothesize that biological pleiotropy of common alleles is not widespread in maize and is highly impacted by nuisance terms such as population structure and linkage disequilibrium. Natural selection on large standing natural variation in maize populations may target wide and large effect variants, leaving the prevalence of detectable pleiotropy relatively low.