Title: Predictive Models of Genetic Redundancy in Arabidopsis thaliana

Abstract

Abstract Genetic redundancy refers to a situation where an individual with a loss-of-function mutation in one gene (single mutant) does not show an apparent phenotype until one or more paralogs are also knocked out (double/higher-order mutant). Previous studies have identified some characteristics common among redundant gene pairs, but a predictive model of genetic redundancy incorporating a wide variety of features derived from accumulating omics and mutant phenotype data is yet to be established. In addition, the relative importance of these features for genetic redundancy remains largely unclear. Here, we establish machine learning models for predicting whether a gene pair is likely redundant or not in the model plant Arabidopsis thaliana based on six feature categories: functional annotations, evolutionary conservation including duplication patterns and mechanisms, epigenetic marks, protein properties including posttranslational modifications, gene expression, and gene network properties. The definition of redundancy, data transformations, feature subsets, and machine learning algorithms used significantly affected model performance based on holdout, testing phenotype data. Among the most important features in predicting gene pairs as redundant were having a paralog(s) from recent duplication events, annotation as a transcription factor, downregulation during stress conditions, and having similar expression patterns under stress conditions. We also explored themore » potential reasons underlying mispredictions and limitations of our studies. This genetic redundancy model sheds light on characteristics that may contribute to long-term maintenance of paralogs, and will ultimately allow for more targeted generation of functionally informative double mutants, advancing functional genomic studies.« less

Authors:

Cusack, Siobhan A. ^[1]; Wang, Peipei ^[2]; Lotreck, Serena G. ^[3]; Moore, Bethany M.  ^[4];

• Search DOE PAGES for author "Moore, Bethany M."
• Search DOE PAGES for ORCID "0000-0002-2104-7292"

Meng, Fanrui ^[2]; Conner, Jeffrey K. ^[5]; Krysan, Patrick J. ^[6]; Lehti-Shiu, Melissa D. ^[2]; Shiu, Shin-Han  ^[7]

• Search DOE PAGES for author "Shiu, Shin-Han"
• Search DOE PAGES for ORCID "0000-0001-6470-235X"

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+ Show Author Affiliations

1. Cell and Molecular Biology Program, Michigan State University, East Lansing, MI, USA
2. Department of Plant Biology, Michigan State University, East Lansing, MI, USA
3. Department of Plant Biology, Michigan State University, East Lansing, MI, USA, Department of Computational Mathematics, Science and Engineering, Michigan State University, East Lansing, MI, USA
4. Department of Botany, University of Wisconsin-Madison, Madison, WI, USA
5. Department of Plant Biology, Michigan State University, East Lansing, MI, USA, Ecology, Evolution, and Behavior Program, Michigan State University, East Lansing, MI, USA, Kellogg Biological Station, Michigan State University, East Lansing, MI, USA
6. Department of Horticulture, University of Wisconsin-Madison, Madison, WI, USA
7. Cell and Molecular Biology Program, Michigan State University, East Lansing, MI, USA, Department of Plant Biology, Michigan State University, East Lansing, MI, USA, Department of Computational Mathematics, Science and Engineering, Michigan State University, East Lansing, MI, USA, Ecology, Evolution, and Behavior Program, Michigan State University, East Lansing, MI, USA

Publication Date:

Mon Apr 19 00:00:00 EDT 2021

Research Org.:

Great Lakes Bioenergy Research Center (GLBRC), Madison, WI (United States)

Sponsoring Org.:

USDOE Office of Science (SC), Biological and Environmental Research (BER); National Science Foundation (NSF)

OSTI Identifier:

1811204

Alternate Identifier(s):

OSTI ID: 1804107

Grant/Contract Number:  

SC0018409; IOS-1546617; DEB-1655386

Resource Type:

Published Article

Journal Name:

Molecular Biology and Evolution (Online)

Additional Journal Information:

Journal Name: Molecular Biology and Evolution (Online) Journal Volume: 38 Journal Issue: 8; Journal ID: ISSN 1537-1719

Publisher:

Oxford University Press

Country of Publication:

United States

Language:

English

Subject:

59 BASIC BIOLOGICAL SCIENCES; genetic redundancy; molecular evolution; machine learning