ArticlePLoS genetics2021
Haplotype associated RNA expression (HARE) improves prediction of complex traits in maize.
Article in PLoS genetics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed, 9 citations in OpenAlex.
- PlantCAD2: A DNA foundation model for interpreting genomes across flowering plants.Cell genomics · 2026Article
- Predicted protein 3D structures provide essential insights into the genetic architecture underlying phenotypic diversity in maize.Genome research · 2026Article
- PlantCAD2: A Long-Context DNA Language Model for Cross-Species Functional Annotation in Angiosperms.bioRxiv : the preprint server for biology · 2025Article
- Predicting Gene Expression Responses to Cold inGenes · 2025Article
- Leveraging prior biological knowledge improves prediction of tocochromanols in maize grain.The plant genome · 2023Article
- Combining GWAS and TWAS to identify candidate causal genes for tocochromanol levels in maize grain.Genetics · 2022Article
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Authors and funding
4 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Genomic prediction typically relies on associations between single-site polymorphisms and traits of interest. This representation of genomic variability has been successful for predicting many complex traits. However, it usually cannot capture the combination of alleles in haplotypes and it has generated little insight about the biological function of polymorphisms. Here we present a novel and cost-effective method for imputing cis haplotype associated RNA expression (HARE), studied their transferability across tissues, and evaluated genomic prediction models within and across populations. HARE focuses on tightly linked cis acting causal variants in the immediate vicinity of the gene, while excluding trans effects from diffusion and metabolism. Therefore, HARE estimates were more transferrable across different tissues and populations compared to measured transcript expression. We also showed that HARE estimates captured one-third of the variation in gene expression. HARE estimates were used in genomic prediction models evaluated within and across two diverse maize panels-a diverse association panel (Goodman Association panel) and a large half-sib panel (Nested Association Mapping panel)-for predicting 26 complex traits. HARE resulted in up to 15% higher prediction accuracy than control approaches that preserved haplotype structure, suggesting that HARE carried functional information in addition to information about haplotype structure. The largest increase was observed when the model was trained in the Nested Association Mapping panel and tested in the Goodman Association panel. Additionally, HARE yielded higher within-population prediction accuracy as compared to measured expression values. The accuracy achieved by measured expression was variable across tissues, whereas accuracy by HARE was more stable across tissues. Therefore, imputing RNA expression of genes by haplotype is stable, cost-effective, and transferable across populations.
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