ArticleBMC genomics2022
eQTLs are key players in the integration of genomic and transcriptomic data for phenotype prediction.
Article in BMC genomics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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4 citing papers in PubMed, 13 citations in OpenAlex.
- Incorporating transcriptomic data into genomic prediction models to improve the prediction accuracy of phenotypes of efficiency traits.Genetics, selection, evolution : GSE · 2025Article
- Transcriptome-based prediction for polygenic traits in rice using different gene subsets.BMC genomics · 2024Article
- RNAseq based variant dataset in a black poplar association panel.BMC research notes · 2023Article
- Adding gene transcripts into genomic prediction improves accuracy and reveals sampling time dependence.G3 (Bethesda, Md.) · 2022Article
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4 authors at 2 institutions in 1 country.
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Abstract
backgroundMulti-omics represent a promising link between phenotypes and genome variation. Few studies yet address their integration to understand genetic architecture and improve predictability.
resultsOur study used 241 poplar genotypes, phenotyped in two common gardens, with xylem and cambium RNA sequenced at one site, yielding large phenotypic, genomic (SNP), and transcriptomic datasets. Prediction models for each trait were built separately for SNPs and transcripts, and compared to a third model integrated by concatenation of both omics. The advantage of integration varied across traits and, to understand such differences, an eQTL analysis was performed to characterize the interplay between the genome and transcriptome and classify the predicting features into cis or trans relationships. A strong, significant negative correlation was found between the change in predictability and the change in predictor ranking for trans eQTLs for traits evaluated in the site of transcriptomic sampling.
conclusionsConsequently, beneficial integration happens when the redundancy of predictors is decreased, likely leaving the stage to other less prominent but complementary predictors. An additional gene ontology (GO) enrichment analysis appeared to corroborate such statistical output. To our knowledge, this is a novel finding delineating a promising method to explore data integration.
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