Differences between diploid donors are the main contributing factor for subgenome asymmetry measured in either gene ratio or relative diversity in allopolyploids

Journal article

Ye, Xueling, Hu, Haiyan, Zhou, Hong, Jiang, Yunfeng, Gao, Shang, Yuan, Zhongwei, Stiller, Jiri, Li, Chengwei, Chen, Guoyue, Liu, Yaxi, Wei, Yuming, Zheng, You-Liang, Wang, You-Gan and Liu, Chunji. (2021). Differences between diploid donors are the main contributing factor for subgenome asymmetry measured in either gene ratio or relative diversity in allopolyploids. Genome. 64(9), pp. 847-856. https://doi.org/10.1139/gen-2020-0024
AuthorsYe, Xueling, Hu, Haiyan, Zhou, Hong, Jiang, Yunfeng, Gao, Shang, Yuan, Zhongwei, Stiller, Jiri, Li, Chengwei, Chen, Guoyue, Liu, Yaxi, Wei, Yuming, Zheng, You-Liang, Wang, You-Gan and Liu, Chunji
Abstract

[English] Subgenome asymmetry (SA) has routinely been attributed to different responses between the subgenomes of a polyploid to various stimuli during evolution. Here, we compared subgenome differences in gene ratio and relative diversity between artificial and natural genotypes of several allopolyploid species. Surprisingly, consistent differences were not detected between these two types of polyploid genotypes, although they differ in times exposed to evolutionary selection. The estimated ratio of shared genes between a subgenome and its diploid donor was invariably higher for the artificial allopolyploid genotypes than those for the natural genotypes, which is expected as it is now well-known that many genes in a species are not shared among all individuals. As the exact diploid parent for a given subgenome is unknown, the estimated ratios of shared genes for the natural genotypes would also include difference among individual genotypes of the diploid donor species. Further, we detected the presence of SA in genotypes before the completion of the polyploidization events as well as in those which were not formed via polyploidization. These results indicate that SA may, to a large degree, reflect differences between its diploid donors or that changes occurred during polyploid evolution are defined by their donor genomes.

[French] L’asymétrie des sous-génomes (AS) est généralement attribuée à l’acquisition, au cours de l’évolution, de réponses différentes à divers stimuli entre les sous-génomes présents chez un polyploïde. Dans ce travail, les auteurs ont comparé les différences entre sous-génomes pour ce qui est du ratio de gènes et de la diversité relative entre génotypes naturels et artificiels chez plusieurs espèces allopolyploïdes. Étonnamment, des différences ont régulièrement n’ont pas été détectées entre ces deux types de génotypes polyploïdes bien qu’ils diffèrent dans la durée de leur exposition à la sélection. Le ratio estimé de gènes partagés entre un sous-génome et le génome de l’espèce ancestrale était invariablement plus élevé chez les allopolyploïdes artificiels que chez les génotypes naturels, un résultat attendu puisqu’il est bien connu que plusieurs gènes ne sont pas présents chez tous les individus d’une espèce. Comme le parent diploïde exact n’est pas connu pour un sous-génome donné, les ratios estimés de gènes partagés avec les génotypes naturels pourraient également inclure des différences entre génotypes distincts de l’espèce diploïde ancestrale. De plus, les auteurs ont détecté la présence d’AS chez des génotypes avant le terme de la polyploïdisation de même que chez ceux qui n’étaient pas issus de la polyploïdisation. Ces résultats indiquent que l’AS pourrait, en grande partie, refléter des différences entre les espèces diploïdes ancestrales ou que les changements survenus au cours de l’évolution des polyploïdes sont définis par les génomes ancestraux. [Traduit par la Rédaction]

Keywordsasymmetric evolution; subgenome asymmetry; diploid donor; polyploids; progenitor; évolution asymétrique; asymétrie des sous-génomes; diploïde ancestral; polyploïde; progéniteur
Year2021
JournalGenome
Journal citation64 (9), pp. 847-856
PublisherCanadian Science Publishing
ISSN0831-2796
Digital Object Identifier (DOI)https://doi.org/10.1139/gen-2020-0024
PubMed ID33661713
Scopus EID2-s2.0-85114299889
Page range847-856
FunderCommonwealth Scientific and Industrial Organization (CSIRO)
Publisher's version
License
All rights reserved
File Access Level
Controlled
Output statusPublished
Publication dates
Online04 Mar 2021
Publication process dates
Accepted09 Feb 2021
Deposited04 Aug 2023
Grant IDR-10191-01
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Gao, Shang, Wu, Ryan, Stiller, Jiri, Zheng, Zhi, Zhou, Meixue, Wang, You-Gan and Liu, Chunji. (2020). Identifying barley pan-genome sequence anchors using genetic mapping and machine learning. Theoretical and Applied Genetics. 133(9), pp. 2535-2544. https://doi.org/10.1007/s00122-020-03615-y
Natural mortality estimation using tree-based ensemble learning models
Liu, Chanjuan, Zhou, Shijie, Wang, You-Gan and Hu, Zhi-Hua. (2020). Natural mortality estimation using tree-based ensemble learning models. ICES Journal of Marine Science. 77(4), pp. 1414-1426. https://doi.org/10.1093/icesjms/fsaa058
Profile-guided three-phase virtual resource management for energy efficiency of data centers
Ding, Zhe, Tian, Yu-Chu, Tang, Maolin, Li, Yuefeng, Wang, You-Gan and Zhou, Chunjie. (2020). Profile-guided three-phase virtual resource management for energy efficiency of data centers. IEEE Transactions on Industrial Electronics. 67(3), pp. 2460-2468. https://doi.org/10.1109/TIE.2019.2902786
Response of water quality to land use and sewage outfalls in different seasons
Xu, Jing, Jin, Guangqiu, Tang, Hongwu, Mo, Yuming, Wang, You-Gan and Li, Ling. (2019). Response of water quality to land use and sewage outfalls in different seasons. Science of the Total Environment. 696, p. Article 134014. https://doi.org/10.1016/j.scitotenv.2019.134014
A new hybrid model to predict the electrical load in five states of Australia
Wu, Jinran, Cui, Zhesen, Chen, Yanyan, Kong, Demeng and Wang, You-Gan. (2019). A new hybrid model to predict the electrical load in five states of Australia. Energy. 166, pp. 598-609. https://doi.org/10.1016/j.energy.2018.10.076
Sweepstakes reproductive success is absent in a New Zealand snapper (Chrysophrus auratus) population protected from fishing despite “tiny” Ne/N ratios elsewhere
Jones, Andy, Lavery, Shane D., Le Port, Agnès, Wang, You-Gan, Blower, Dean and Ovenden, Jennifer. (2019). Sweepstakes reproductive success is absent in a New Zealand snapper (Chrysophrus auratus) population protected from fishing despite “tiny” Ne/N ratios elsewhere. Molecular Ecology. 28(12), pp. 2986-2995. https://doi.org/10.1111/mec.15130
A review of the Behrens–Fisher problem and some of its analogs : Does the same size fit all?
Paul, Sudhir, Wang, You-Gan and Ullah, Insha. (2019). A review of the Behrens–Fisher problem and some of its analogs : Does the same size fit all? Revstat Statistical Journal. 17(4), p. 563–597.
Incorporating social objectives in evaluating sustainable fisheries harvest strategy
Wu, Jiafeng, Wang, Na, Hu, Zhi-Hua, Hong, Zhenjie and Wang, You-Gan. (2019). Incorporating social objectives in evaluating sustainable fisheries harvest strategy. Environmental Modeling and Assessment. 24(4), pp. 381-386. https://doi.org/10.1007/s10666-019-9651-9
Significance tests for analyzing gene expression data with small sample sizes
Ullah, Insha, Paul, Sudhir, Hong, Zhenjie and Wang, You-Gan. (2019). Significance tests for analyzing gene expression data with small sample sizes. Bioinformatics. 35(20), pp. 3996-4003. https://doi.org/10.1093/bioinformatics/btz189
Robust Estimation Using Modified Huber’s Functions With New Tails
Jiang, Yunlu, Wang, You-Gan, Fu, Liya and Wang, Xueqin. (2019). Robust Estimation Using Modified Huber’s Functions With New Tails. Technometrics. 61(1), pp. 111-122. https://doi.org/10.1080/00401706.2018.1470037
Variable selection in rank regression for analyzing longitudinal data
Fu, Liya and Wang, You-Gan. (2018). Variable selection in rank regression for analyzing longitudinal data. Statistical Methods in Medical Research. 27(8), pp. 2447-2458. https://doi.org/10.1177/0962280216681347
Assessing temporal variations of Ammonia Nitrogen concentrations and loads in the Huaihe River Basin in relation to policies on pollution source control
Xu, Jing, Jin, Guangqiu, Tang, Hongwu, Zhang, Pei, Wang, Shen, Wang, You-Gan and Li, Ling. (2018). Assessing temporal variations of Ammonia Nitrogen concentrations and loads in the Huaihe River Basin in relation to policies on pollution source control. Science of the Total Environment. 642, pp. 1386-1395. https://doi.org/10.1016/j.scitotenv.2018.05.395
Genomic prediction of breeding values using a subset of SNPs identified by three machine learning methods
Li, Bo, Zhang, Nanxi, Wang, You-Gan, George, Andrew W., Reverter, Antonio and Li, Yutao. (2018). Genomic prediction of breeding values using a subset of SNPs identified by three machine learning methods. Frontiers in Genetics. 9, p. Article 237. https://doi.org/10.3389/fgene.2018.00237
Dividend growth and equity premium predictability
Zhu, Min, Chen, Rui, Du, Ke and Wang, You-Gan. (2018). Dividend growth and equity premium predictability. International Review of Economics and Finance. 56, pp. 125-137. https://doi.org/10.1016/j.iref.2017.10.020
Robust Regression with Data-Dependent Regularization Parameters and Autoregressive Temporal Correlations
Wang, Na, Wang, You-Gan, Hu, Shuwen, Hu, Zhi-Hua, Xu, Jing, Tang, Hongwu and Jin, Guangqiu. (2018). Robust Regression with Data-Dependent Regularization Parameters and Autoregressive Temporal Correlations. Environmental Modeling and Assessment. 23(6), pp. 779-786. https://doi.org/10.1007/s10666-018-9605-7
Analysis of spatial data with a nested correlation structure
Adegboye, Oyelola, Leung, Denis and Wang, You-Gan. (2018). Analysis of spatial data with a nested correlation structure. Journal of the Royal Statistical Society Series C: Applied Statistics. 67(2), pp. 329-354. https://doi.org/10.1111/rssc.12230
Working correlation structure selection in generalized estimating equations
Fu, Liya, Hao, Yangyang and Wang, You-Gan. (2018). Working correlation structure selection in generalized estimating equations. Computational Statistics. 33(2), pp. 983-996. https://doi.org/10.1007/s00180-018-0800-4
Selection of working correlation structure in generalized estimating equations
Wang, You-Gan and Fu, Liya. (2017). Selection of working correlation structure in generalized estimating equations. Statistics in Medicine. 36(14), pp. 2206-2219. https://doi.org/10.1002/sim.7262
Blockwise AICc for model selection in generalized linear models
Song, Guofeng, Dong, Xiaogang, Wu, Jiafeng and Wang, You-Gan. (2017). Blockwise AICc for model selection in generalized linear models. Environmental Modeling and Assessment. 22(6), pp. 523-533. https://doi.org/10.1007/s10666-017-9552-8
A comment on Koh’s “The optimal design of fallible organizations : Invariance of optimal decision threshold and uniqueness of hierarchy and polyarchy structures”
Zhu, Min, Liu, Chang and Wang, You-Gan. (2017). A comment on Koh’s “The optimal design of fallible organizations : Invariance of optimal decision threshold and uniqueness of hierarchy and polyarchy structures”. Social Choice and Welfare. 48(2), pp. 385-392. https://doi.org/10.1007/s00355-016-1009-5
The Buckley–James estimator and induced smoothing
Wang, You-Gan, Zhao, Yudong and Fu, Liya. (2016). The Buckley–James estimator and induced smoothing. Australian and New Zealand Journal of Statistics. 58(2), pp. 211-225. https://doi.org/10.1111/anzs.12155
Maximum likelihood estimation of natural mortality and quantification of temperature effects on catchability of brown tiger prawn (penaeus esculentus) in Moreton Bay (Australia) using logbook data
Kienzle, Marco, Sterling, David, Zhou, Shijie and Wang, You-Gan. (2016). Maximum likelihood estimation of natural mortality and quantification of temperature effects on catchability of brown tiger prawn (penaeus esculentus) in Moreton Bay (Australia) using logbook data. Ecological Modelling. 322, pp. 1-9. https://doi.org/10.1016/j.ecolmodel.2015.11.008
Otolith morphology of four mackerel species (Scomberomorus spp.) in Australia : Species differentiation and prediction for fisheries monitoring and assessment
Zischke, Mitchell T., Litherland, Lenore, Tilyard, Benjamin R., Stratford, Nicholas J., Jones, Ebony L. and Wang, You-Gan. (2016). Otolith morphology of four mackerel species (Scomberomorus spp.) in Australia : Species differentiation and prediction for fisheries monitoring and assessment. Fisheries Research. 176, pp. 39-47. https://doi.org/10.1016/j.fishres.2015.12.003
Efficient parameter estimation via Gaussian copulas for quantile regression with longitudinal data
Fu, Liya and Wang, You-Gan. (2016). Efficient parameter estimation via Gaussian copulas for quantile regression with longitudinal data. Journal of Multivariate Analysis. 143, pp. 492-502. https://doi.org/10.1016/j.jmva.2015.07.004
Mixture of time-dependent growth models with an application to blue swimmer crab length-frequency data
Lloyd-Jones, Luke R., Nguyen, Hien D., McLachlan, Geoffrey J., Sumpton, Wayne and Wang, You-Gan. (2016). Mixture of time-dependent growth models with an application to blue swimmer crab length-frequency data. Biometrics. 72(4), pp. 1255-1265. https://doi.org/10.1111/biom.12531
Movement and growth of the coral reef holothuroids Bohadschia argus and Thelenota ananas
Purcell, Steven W., Piddocke, Toby P., Dalton, Steven J. and Wang, You-Gan. (2016). Movement and growth of the coral reef holothuroids Bohadschia argus and Thelenota ananas. Marine Ecology Progress Series. 551, pp. 201-214. https://doi.org/10.3354/meps11720
Improved confidence intervals for the linkage disequilibrium method for estimating effective population size
Jones, A. T., Ovenden, J. R. and Wang, Y.-G.. (2016). Improved confidence intervals for the linkage disequilibrium method for estimating effective population size. Heredity. 117(4), pp. 217-223. https://doi.org/10.1038/hdy.2016.19