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This work was supported by grants from the National Research Foundation of Korea (NU) funded by the Korea government (MEST) (nos. 2009-0063342, 2009-0070968, 2009-0087951) and Yonsei University (no 2008-7-0284, 2008-1-0018) (I L), from the NSF, NIH, and Welch (F1515) and Packard Foundations (E.M.M), and from the ERC, MICINN, ICREA, AGAUR, and the EMBL-CRG Systems Biology Program (B L), T V is supported by a Marie Curie intra-European fellowship Some nematode strains used in this work were provided by the Caenorhabditis Genetics Center, which is funded by the NIH National Center for Research Resources (NCRR)

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Vavouri, SoultanaAuthorLehner BAuthor

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March 25, 2020
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Predicting genetic modifier loci using functional gene networks

Publicated to:Genome Research. 20 (8): 1143-1153 - 2010-08-01 20(8), DOI: 10.1101/gr.102749.109

Authors: Lee, I; Lehner, B; Vavouri, T; Shin, J; Fraser, AG; Marcotte, EM

Affiliations

Department of Biotechnology, College of Life science and Biotechnology, Yonsei University, Seodaemun-ku, Seoul 120-749, South Korea. insuksysbio@gmail.com - Author
Pompeu Fabra Univ UPF, Ctr Genom Regulat, EMBL CRG Syst Biol Res Unit, Barcelona 08003, Spain - Author
Univ Texas Austin, Ctr Syst & Synthet Biol, Inst Cellular & Mol Biol, Austin, TX 78712 USA - Author
Univ Texas Austin, Dept Chem & Biochem, Inst Cellular & Mol Biol, Austin, TX 78712 USA - Author
Univ Toronto, Donnelly CCBR, Toronto, ON M5S 3E1, Canada - Author
UPF, ICREA, Ctr Genom Regulat, Barcelona 08003, Spain - Author
Yonsei Univ, Coll Life Sci & Biotechnol, Dept Biotechnol, Seoul 120749, South Korea - Author
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Abstract

Most phenotypes are genetically complex, with contributions from mutations in many different genes. Mutations in more than one gene can combine synergistically to cause phenotypic change, and systematic studies in model organisms show that these genetic interactions are pervasive. However, in human association studies such nonadditive genetic interactions are very difficult to identify because of a lack of statistical power--simply put, the number of potential interactions is too vast. One approach to resolve this is to predict candidate modifier interactions between loci, and then to specifically test these for associations with the phenotype. Here, we describe a general method for predicting genetic interactions based on the use of integrated functional gene networks. We show that in both Saccharomyces cerevisiae and Caenorhabditis elegans a single high-coverage, high-quality functional network can successfully predict genetic modifiers for the majority of genes. For C. elegans we also describe the construction of a new, improved, and expanded functional network, WormNet 2. Using this network we demonstrate how it is possible to rapidly expand the number of modifier loci known for a gene, predicting and validating new genetic interactions for each of three signal transduction genes. We propose that this approach, termed network-guided modifier screening, provides a general strategy for predicting genetic interactions. This work thus suggests that a high-quality integrated human gene network will provide a powerful resource for modifier locus discovery in many different diseases.

Keywords

Biological networksCaenorhabditis-elegansConservationDatabaseGenome-wide predictionInteractome networkMapProtein complexesQuantitative traitsYeast

Quality index

Bibliometric impact. Analysis of the contribution and dissemination channel

The work has been published in the journal Genome Research due to its progression and the good impact it has achieved in recent years, according to the agency WoS (JCR), it has become a reference in its field. In the year of publication of the work, 2010, it was in position 3/160, thus managing to position itself as a Q1 (Primer Cuartil), in the category Biotechnology & Applied Microbiology.

From a relative perspective, and based on the normalized impact indicator calculated from the Field Citation Ratio (FCR) of the Dimensions source, it yields a value of: 5.43, which indicates that, compared to works in the same discipline and in the same year of publication, it ranks as a work cited above average. (source consulted: Dimensions Aug 2025)

Specifically, and according to different indexing agencies, this work has accumulated citations as of 2025-08-08, the following number of citations:

  • WoS: 62
  • Scopus: 65
  • Europe PMC: 52

Impact and social visibility

From the perspective of influence or social adoption, and based on metrics associated with mentions and interactions provided by agencies specializing in calculating the so-called "Alternative or Social Metrics," we can highlight as of 2025-08-08:

  • The use of this contribution in bookmarks, code forks, additions to favorite lists for recurrent reading, as well as general views, indicates that someone is using the publication as a basis for their current work. This may be a notable indicator of future more formal and academic citations. This claim is supported by the result of the "Capture" indicator, which yields a total of: 209 (PlumX).

It is essential to present evidence supporting full alignment with institutional principles and guidelines on Open Science and the Conservation and Dissemination of Intellectual Heritage. A clear example of this is:

  • The work has been submitted to a journal whose editorial policy allows open Open Access publication.

Leadership analysis of institutional authors

This work has been carried out with international collaboration, specifically with researchers from: Canada; Republic of Korea; United States of America.