A likelihood ratio test for detecting shifts in homeolog expression ratios in allopolyploids

Allopolyploids arise through hybridization between related species, carrying multiple sets of chromosomes from distinct progenitors, referred to as subgenomes. Within allopolyploids, duplicated genes across subgenomes, called homeologs, are thought to enhance environmental robustness by shifting their expression ratios depending on environmental and developmental changes. However, existing methods for detecting such ratio shifts, including HomeoRoq and Fisher's exact test, are limited to allopolyploids with two subgenome sets and thus cannot handle more complex cases. We present the HOmeolog Bias Identification Test (HOBIT), a statistical method for detecting shifts in homeolog expression ratios across different conditions using RNA-seq count data. HOBIT performs a likelihood ratio test for each homeolog, comparing a full model allowing homeolog expression ratios to vary across conditions with a reduced model assuming constant ratios. Simulation benchmarks for allotetraploids and allohexaploids demonstrated that HOBIT outperforms existing methods in both area under the receiver operating characteristic curve and F1 score. Application to real RNA-seq datasets from allotetraploid Cardamine flexuosa, allotriploid Cardamine insueta, and allohexaploid Triticum aestivum (wheat) produced biologically consistent results reflecting experimental settings. HOBIT provides a promising framework for uncovering homeolog regulation and adaptive responses in allopolyploids, without restrictions of ploidy complexities.

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Publication Details

Journal
New Phytologist
Published
2025-10-25
DOI
https://doi.org/10.1111/nph.70648
Primary Topic
Chromosomal and Genetic Variations
Type
article
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article

A likelihood ratio test for detecting shifts in homeolog expression ratios in allopolyploids

Kentaro K. Shimizu, Jun Sese, Jianqiang Sun
New Phytologist
Chromosomal and Genetic Variations
article

A likelihood ratio test for detecting shifts in homeolog expression ratios in allopolyploids

Kentaro K. Shimizu, Jun Sese, Jianqiang Sun
article en

Abstract

Allopolyploids arise through hybridization between related species, carrying multiple sets of chromosomes from distinct progenitors, referred to as subgenomes. Within allopolyploids, duplicated genes across subgenomes, called homeologs, are thought to enhance environmental robustness by shifting their expression ratios depending on environmental and developmental changes. However, existing methods for detecting such ratio shifts, including HomeoRoq and Fisher's exact test, are limited to allopolyploids with two subgenome sets and thus cannot handle more complex cases. We present the HOmeolog Bias Identification Test (HOBIT), a statistical method for detecting shifts in homeolog expression ratios across different conditions using RNA-seq count data. HOBIT performs a likelihood ratio test for each homeolog, comparing a full model allowing homeolog expression ratios to vary across conditions with a reduced model assuming constant ratios. Simulation benchmarks for allotetraploids and allohexaploids demonstrated that HOBIT outperforms existing methods in both area under the receiver operating characteristic curve and F1 score. Application to real RNA-seq datasets from allotetraploid Cardamine flexuosa, allotriploid Cardamine insueta, and allohexaploid Triticum aestivum (wheat) produced biologically consistent results reflecting experimental settings. HOBIT provides a promising framework for uncovering homeolog regulation and adaptive responses in allopolyploids, without restrictions of ploidy complexities.

New PhytologistVol. 249(1)
Kihara Institute for Biological Research (JP), National Agriculture and Food Research Organization (JP), University of Zurich (CH), Yokohama City University (JP), Chuo University (JP)
Openalex Percentile: Top 77%
Chromosomal and Genetic Variations
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A likelihood ratio test for detecting shifts in homeolog expression ratios in allopolyploids — Kentaro K. Shimizu, Jun Sese, et al. · New Phytologist (2025) | TGRS Research Map | TGRS