IDEAS home Printed from https://ideas.repec.org/a/nat/natcom/v14y2023i1d10.1038_s41467-023-41188-8.html
   My bibliography  Save this article

Dominance vs epistasis: the biophysical origins and plasticity of genetic interactions within and between alleles

Author

Listed:
  • Xuan Xie

    (Zhejiang University - University of Edinburgh Institute, Zhejiang University School of Medicine)

  • Xia Sun

    (Zhejiang University - University of Edinburgh Institute, Zhejiang University School of Medicine
    University of Edinburgh)

  • Yuheng Wang

    (Zhejiang University - University of Edinburgh Institute, Zhejiang University School of Medicine
    University of Edinburgh)

  • Ben Lehner

    (The Barcelona Institute of Science and Technology
    Universitat Pompeu Fabra (UPF)
    ICREA
    Wellcome Genome Campus Hinxton)

  • Xianghua Li

    (Zhejiang University - University of Edinburgh Institute, Zhejiang University School of Medicine
    Wellcome Genome Campus Hinxton
    University of Edinburgh
    Biomedical and Health Translational Centre of Zhejiang Province)

Abstract

An important challenge in genetics, evolution and biotechnology is to understand and predict how mutations combine to alter phenotypes, including molecular activities, fitness and disease. In diploids, mutations in a gene can combine on the same chromosome or on different chromosomes as a “heteroallelic combination”. However, a direct comparison of the extent, sign, and stability of the genetic interactions between variants within and between alleles is lacking. Here we use thermodynamic models of protein folding and ligand-binding to show that interactions between mutations within and between alleles are expected in even very simple biophysical systems. Protein folding alone generates within-allele interactions and a single molecular interaction is sufficient to cause between-allele interactions and dominance. These interactions change differently, quantitatively and qualitatively as a system becomes more complex. Altering the concentration of a ligand can, for example, switch alleles from dominant to recessive. Our results show that intra-molecular epistasis and dominance should be widely expected in even the simplest biological systems but also reinforce the view that they are plastic system properties and so a formidable challenge to predict. Accurate prediction of both intra-molecular epistasis and dominance will require either detailed mechanistic understanding and experimental parameterization or brute-force measurement and learning.

Suggested Citation

  • Xuan Xie & Xia Sun & Yuheng Wang & Ben Lehner & Xianghua Li, 2023. "Dominance vs epistasis: the biophysical origins and plasticity of genetic interactions within and between alleles," Nature Communications, Nature, vol. 14(1), pages 1-15, December.
  • Handle: RePEc:nat:natcom:v:14:y:2023:i:1:d:10.1038_s41467-023-41188-8
    DOI: 10.1038/s41467-023-41188-8
    as

    Download full text from publisher

    File URL: https://www.nature.com/articles/s41467-023-41188-8
    File Function: Abstract
    Download Restriction: no

    File URL: https://libkey.io/10.1038/s41467-023-41188-8?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Xianghua Li & Jasna Lalić & Pablo Baeza-Centurion & Riddhiman Dhar & Ben Lehner, 2019. "Author Correction: Changes in gene expression predictably shift and switch genetic interactions," Nature Communications, Nature, vol. 10(1), pages 1-1, December.
    2. Jonathan Frazer & Pascal Notin & Mafalda Dias & Aidan Gomez & Joseph K. Min & Kelly Brock & Yarin Gal & Debora S. Marks, 2021. "Disease variant prediction with deep generative models of evolutionary data," Nature, Nature, vol. 599(7883), pages 91-95, November.
    3. Cristopher V. Van Hout & Ioanna Tachmazidou & Joshua D. Backman & Joshua D. Hoffman & Daren Liu & Ashutosh K. Pandey & Claudia Gonzaga-Jauregui & Shareef Khalid & Bin Ye & Nilanjana Banerjee & Alexand, 2020. "Exome sequencing and characterization of 49,960 individuals in the UK Biobank," Nature, Nature, vol. 586(7831), pages 749-756, October.
    4. David Porubsky & Shilpa Garg & Ashley D. Sanders & Jan O. Korbel & Victor Guryev & Peter M. Lansdorp & Tobias Marschall, 2017. "Dense and accurate whole-chromosome haplotyping of individual genomes," Nature Communications, Nature, vol. 8(1), pages 1-10, December.
    5. Xianghua Li & Jasna Lalić & Pablo Baeza-Centurion & Riddhiman Dhar & Ben Lehner, 2019. "Changes in gene expression predictably shift and switch genetic interactions," Nature Communications, Nature, vol. 10(1), pages 1-15, December.
    6. Xianghua Li & Ben Lehner, 2020. "Biophysical ambiguities prevent accurate genetic prediction," Nature Communications, Nature, vol. 11(1), pages 1-11, December.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Andreas Wagner, 2023. "Evolvability-enhancing mutations in the fitness landscapes of an RNA and a protein," Nature Communications, Nature, vol. 14(1), pages 1-11, December.
    2. Dick Schijven & Sourena Soheili-Nezhad & Simon E. Fisher & Clyde Francks, 2024. "Exome-wide analysis implicates rare protein-altering variants in human handedness," Nature Communications, Nature, vol. 15(1), pages 1-12, December.
    3. Daniel J. Diaz & Chengyue Gong & Jeffrey Ouyang-Zhang & James M. Loy & Jordan Wells & David Yang & Andrew D. Ellington & Alexandros G. Dimakis & Adam R. Klivans, 2024. "Stability Oracle: a structure-based graph-transformer framework for identifying stabilizing mutations," Nature Communications, Nature, vol. 15(1), pages 1-15, December.
    4. Henry Webel & Lili Niu & Annelaura Bach Nielsen & Marie Locard-Paulet & Matthias Mann & Lars Juhl Jensen & Simon Rasmussen, 2024. "Imputation of label-free quantitative mass spectrometry-based proteomics data using self-supervised deep learning," Nature Communications, Nature, vol. 15(1), pages 1-15, December.
    5. Kian Hong Kock & Patrick K. Kimes & Stephen S. Gisselbrecht & Sachi Inukai & Sabrina K. Phanor & James T. Anderson & Gayatri Ramakrishnan & Colin H. Lipper & Dongyuan Song & Jesse V. Kurland & Julia M, 2024. "DNA binding analysis of rare variants in homeodomains reveals homeodomain specificity-determining residues," Nature Communications, Nature, vol. 15(1), pages 1-19, December.
    6. Wenkai Han & Ningning Chen & Xinzhou Xu & Adil Sahil & Juexiao Zhou & Zhongxiao Li & Huawen Zhong & Elva Gao & Ruochi Zhang & Yu Wang & Shiwei Sun & Peter Pak-Hang Cheung & Xin Gao, 2023. "Predicting the antigenic evolution of SARS-COV-2 with deep learning," Nature Communications, Nature, vol. 14(1), pages 1-14, December.
    7. M. L. Richter & I. K. Deligiannis & K. Yin & A. Danese & E. Lleshi & P. Coupland & C. A. Vallejos & K. P. Matchett & N. C. Henderson & M. Colome-Tatche & C. P. Martinez-Jimenez, 2021. "Single-nucleus RNA-seq2 reveals functional crosstalk between liver zonation and ploidy," Nature Communications, Nature, vol. 12(1), pages 1-16, December.
    8. Ruoyu Tian & Tian Ge & Hyeokmoon Kweon & Daniel B. Rocha & Max Lam & Jimmy Z. Liu & Kritika Singh & Daniel F. Levey & Joel Gelernter & Murray B. Stein & Ellen A. Tsai & Hailiang Huang & Christopher F., 2024. "Whole-exome sequencing in UK Biobank reveals rare genetic architecture for depression," Nature Communications, Nature, vol. 15(1), pages 1-12, December.
    9. Naman S. Shetty & Mokshad Gaonkar & Nirav Patel & Akhil Pampana & Nehal Vekariya & Peng Li & Garima Arora & Pankaj Arora, 2024. "Determinants of transthyretin levels and their association with adverse clinical outcomes among UK Biobank participants," Nature Communications, Nature, vol. 15(1), pages 1-7, December.
    10. Guoling Li & Xue Dong & Jiamin Luo & Tanglong Yuan & Tong Li & Guoli Zhao & Hainan Zhang & Jingxing Zhou & Zhenhai Zeng & Shuna Cui & Haoqiang Wang & Yin Wang & Yuyang Yu & Yuan Yuan & Erwei Zuo & Chu, 2024. "Engineering TadA ortholog-derived cytosine base editor without motif preference and adenosine activity limitation," Nature Communications, Nature, vol. 15(1), pages 1-10, December.
    11. Matteo Cagiada & Sandro Bottaro & Søren Lindemose & Signe M. Schenstrøm & Amelie Stein & Rasmus Hartmann-Petersen & Kresten Lindorff-Larsen, 2023. "Discovering functionally important sites in proteins," Nature Communications, Nature, vol. 14(1), pages 1-13, December.
    12. Ziyi Zhou & Liang Zhang & Yuanxi Yu & Banghao Wu & Mingchen Li & Liang Hong & Pan Tan, 2024. "Enhancing efficiency of protein language models with minimal wet-lab data through few-shot learning," Nature Communications, Nature, vol. 15(1), pages 1-13, December.
    13. Yinglu Cui & Yanchun Chen & Jinyuan Sun & Tong Zhu & Hua Pang & Chunli Li & Wen-Chao Geng & Bian Wu, 2024. "Computational redesign of a hydrolase for nearly complete PET depolymerization at industrially relevant high-solids loading," Nature Communications, Nature, vol. 15(1), pages 1-12, December.
    14. Weikang Gong & Yan Fu & Bang-Sheng Wu & Jingnan Du & Liu Yang & Ya-Ru Zhang & Shi-Dong Chen & JuJiao Kang & Ying Mao & Qiang Dong & Lan Tan & Jianfeng Feng & Wei Cheng & Jin-Tai Yu, 2024. "Whole-exome sequencing identifies protein-coding variants associated with brain iron in 29,828 individuals," Nature Communications, Nature, vol. 15(1), pages 1-13, December.
    15. Mofan Feng & Xiaoxi Wei & Xi Zheng & Liangjie Liu & Lin Lin & Manying Xia & Guang He & Yi Shi & Qing Lu, 2024. "Decoding Missense Variants by Incorporating Phase Separation via Machine Learning," Nature Communications, Nature, vol. 15(1), pages 1-17, December.
    16. Remo Monti & Pia Rautenstrauch & Mahsa Ghanbari & Alva Rani James & Matthias Kirchler & Uwe Ohler & Stefan Konigorski & Christoph Lippert, 2022. "Identifying interpretable gene-biomarker associations with functionally informed kernel-based tests in 190,000 exomes," Nature Communications, Nature, vol. 13(1), pages 1-16, December.
    17. Yaan J. Jang & Qi-Qi Qin & Si-Yu Huang & Arun T. John Peter & Xue-Ming Ding & Benoît Kornmann, 2024. "Accurate prediction of protein function using statistics-informed graph networks," Nature Communications, Nature, vol. 15(1), pages 1-12, December.
    18. Nicki Skafte Detlefsen & Søren Hauberg & Wouter Boomsma, 2022. "Learning meaningful representations of protein sequences," Nature Communications, Nature, vol. 13(1), pages 1-12, December.
    19. Sina Majidian & Mohammad Hossein Kahaei & Dick de Ridder, 2020. "Minimum error correction-based haplotype assembly: Considerations for long read data," PLOS ONE, Public Library of Science, vol. 15(6), pages 1-12, June.
    20. Xiao-Yu He & Bang-Sheng Wu & Liu Yang & Yu Guo & Yue-Ting Deng & Ze-Yu Li & Chen-Jie Fei & Wei-Shi Liu & Yi-Jun Ge & Jujiao Kang & Jianfeng Feng & Wei Cheng & Qiang Dong & Jin-Tai Yu, 2024. "Genetic associations of protein-coding variants in venous thromboembolism," Nature Communications, Nature, vol. 15(1), pages 1-12, December.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:nat:natcom:v:14:y:2023:i:1:d:10.1038_s41467-023-41188-8. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.nature.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.