ArticlePLoS genetics2020
Pan-genomic open reading frames: A potential supplement of single nucleotide polymorphisms in estimation of heritability and genomic prediction.
Article in PLoS genetics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed, 11 citations in OpenAlex.
- Predictive models of the genetic bases underlying budding yeast fitness in multiple environments.NAR genomics and bioinformatics · 2026Article
- Predictive models of the genetic bases underlying budding yeast fitness in multiple environments.bioRxiv : the preprint server for biology · 2025Article
- Importance of genetic architecture in marker selection decisions for genomic prediction.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2023Article
- The pig pangenome provides insights into the roles of coding structural variations in genetic diversity and adaptation.Genome research · 2023Article
- Impact of linkage disequilibrium heterogeneity along the genome on genomic prediction and heritability estimation.Genetics, selection, evolution : GSE · 2022Article
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Authors and funding
2 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Pan-genomic open reading frames (ORFs) potentially carry protein-coding gene or coding variant information in a population. In this study, we suggest that pan-genomic ORFs are promising to be utilized in estimation of heritability and genomic prediction. A Saccharomyces cerevisiae dataset with whole-genome SNPs, pan-genomic ORFs, and the copy numbers of those ORFs is used to test the effectiveness of ORF data as a predictor in three prediction models for 35 traits. Our results show that the ORF-based heritability can capture more genetic effects than SNP-based heritability for all traits. Compared to SNP-based genomic prediction (GBLUP), pan-genomic ORF-based genomic prediction (OBLUP) is distinctly more accurate for all traits, and the predictive abilities on average are more than doubled across all traits. For four traits, the copy number of ORF-based prediction(CBLUP) is more accurate than OBLUP. When using different numbers of isolates in training sets in ORF-based prediction, the predictive abilities for all traits increased as more isolates are added in the training sets, suggesting that with very large training sets the prediction accuracy will be in the range of the square root of the heritability. We conclude that pan-genomic ORFs have the potential to be a supplement of single nucleotide polymorphisms in estimation of heritability and genomic prediction.
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