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Abstract BackgroundGenetic and epigenetic perturbation of cis-regulatory sequences can shift patterns of gene expression and result in novel phenotypes. Phased genome assemblies now enable the local dissection of linkages between cis-regulatory sequences, including their epigenetic state, and allele-specific gene expression to further characterize gene regulation and resulting phenotypes in heterozygous genomes. ResultsWe assembled a locally phased genome for a mandarin hybrid named ‘Fairchild’ to explore the molecular signatures of allele-specific gene expression. With local genome phasing, genes with allele-specific expression were paired with haplotype-specific chromatin states, including levels of chromatin accessibility, histone modifications, and DNA methylation. We found that 30% of variation in allele-specific expression could be attributed to haplotype associated factors, with allelic levels of chromatin accessibility and three histone modifications in gene bodies having the most influence. Structural variants in promoter regions were also associated with allele-specific expression, including specific enrichments of hAT and MULE-MuDR DNA transposon sequences. Integration of haplotype-resolved genetic and epigenetic landscapes with high-throughput phenotypic analysis of fruit traits in a panel of 154 accessions with mandarin and pummelo ancestry revealed that trait-associated variants were enriched in regions of open chromatin. Mining of trait-associated variants uncovered a Gypsy retrotransposon insertion in a gene that regulates potassium transport and may contribute to the reduction in fruit size that is observed in mandarins. ConclusionsUsing a locally phased assembly of a heterozygous cultivar of citrus, we dissected the interplay between genetic variants and molecular phenotypes to reveal cis-regulatory sequences with potential functional effects on phenotypes relevant for genetic improvement.
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Personal transcriptome variation is poorly explained by current genomic deep learning models
Abstract Genomic deep learning models can predict genome-wide epigenetic features and gene expression levels directly from DNA sequence. While current models perform well at predicting gene expression levels across genes in different cell types from the reference genome, their ability to explain expression variation between individuals due tocis-regulatory genetic variants remains largely unexplored. Here, we evaluate four state-of-the-art models on paired personal genome and transcriptome data and find limited performance when explaining variation in expression across individuals. In addition, models often fail to predict the correct direction of effect ofcis-regulatory genetic variation on expression.
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- Award ID(s):
- 2238125
- PAR ID:
- 10532413
- Publisher / Repository:
- Nature Genetics
- Date Published:
- Journal Name:
- Nature Genetics
- Volume:
- 55
- Issue:
- 12
- ISSN:
- 1061-4036
- Page Range / eLocation ID:
- 2056 to 2059
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1038/s41588-023-01574-w
