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Review
. 2019 Sep 9;16(4):20180069.
doi: 10.1515/jib-2018-0069.

A Survey of Gene Prioritization Tools for Mendelian and Complex Human Diseases

Olga Zolotareva  1 , Maren Kleine  2
Affiliations
Review

A Survey of Gene Prioritization Tools for Mendelian and Complex Human Diseases

Olga Zolotareva et al. J Integr Bioinform. .

Abstract

Modern high-throughput experiments provide us with numerous potential associations between genes and diseases. Experimental validation of all the discovered associations, let alone all the possible interactions between them, is time-consuming and expensive. To facilitate the discovery of causative genes, various approaches for prioritization of genes according to their relevance for a given disease have been developed. In this article, we explain the gene prioritization problem and provide an overview of computational tools for gene prioritization. Among about a hundred of published gene prioritization tools, we select and briefly describe 14 most up-to-date and user-friendly. Also, we discuss the advantages and disadvantages of existing tools, challenges of their validation, and the directions for future research.

Keywords: Data integration; Gene prioritization; Human diseases.

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Conflict of interest statement

Authors state no conflict of interest. All authors have read the journal’s Publication ethics and publication malpractice statement available at the journal’s website and hereby confirm that they comply with all its parts applicable to the present scientific work.

Figures

Figure 1:
Figure 1:
The scheme of a gene prioritization tools. Gene prioritization tools extract information about specified candidates and seed genes or phenotype terms from evidence sources and calculate a score that reflects how likely each gene is responsible for the development of a phenotype. In this example, genes which have alleles causing an early-onset autosomal dominant familial form of Alzheimer’s disease are used as seeds. Candidate genes were obtained from GWAS Catalog [29]. Each candidate gene has at least one variant associated with Alzheimer’s disease. The output of the program is a ranked list of candidate genes arranged according to calculated scores.
Figure 2:
Figure 2:
Data representation models utilized by gene prioritization tools. A. Relational data structure. The first and the third evidence sources provide relationships between genes labeled with G (seeds) or g (candidates) and diseases (d), the second source provides gene membership in pathways (p) and the last two evidence sources contain different kinds of interactions between genes. Vector representation of seed and candidate genes are shown on the left. The similarity between colorings of gene g7 and seed genes shows that g7 seems to be a promising candidate. B. Network data structure. Nodes depict genes, edges show relationships between genes. Seed genes are highlighted with red. Types of interactions and associations are shown on the right.
Figure 3:
Figure 3:
The process of gene prioritization tools selection for further detailed comparison.
See this image and copyright information in PMC

References

    1. Perez-Iratxeta C, Bork P, Andrade MA. Association of genes to genetically inherited diseases using data mining. Nat Genet 2002;31:316–9. - PubMed
    2. Perez-Iratxeta C, Bork P, Andrade MA. Association of genes to genetically inherited diseases using data mining. Nat Genet. 2002;31:316–9. - PubMed
    1. Aerts S, Lambrechts D, Maity S, Van Loo P, Coessens B, De Smet F, et al. Gene prioritization through genomic data fusion. Nat Genet 2006;24:537–44. - PubMed
    2. Aerts S, Lambrechts D, Maity S, Van Loo P, Coessens B, De Smet F. et al. Gene prioritization through genomic data fusion. Nat Genet. 2006;24:537–44. - PubMed
    1. Altshuler D, Daly MJ, Lander ES. Genetic mapping in human disease. Science 2008;322:881–8. - PMC - PubMed
    2. Altshuler D, Daly MJ, Lander ES. Genetic mapping in human disease. Science. 2008;322:881–8. - PMC - PubMed
    1. Thienpont B, Zhang L, Postma AV, Breckpot J, Tranchevent LC, Loo PV, et al. Haploinsufficiency of TAB2 causes congenital heart defects in humans. Am J Hum Genet 2010;86:839–49. - PMC - PubMed
    2. Thienpont B, Zhang L, Postma AV, Breckpot J, Tranchevent LC, Loo PV. et al. Haploinsufficiency of TAB2 causes congenital heart defects in humans. Am J Hum Genet. 2010;86:839–49. - PMC - PubMed
    1. Erlich Y, Edvardson S, Hodges E, Zenvirt S, Thekkat P, Shaag A, et al. Exome sequencing and disease-network analysis of a single family implicate a mutation in KIF1A in hereditary spastic paraparesis. Genome Res 2011;21:658–64. - PMC - PubMed
    2. Erlich Y, Edvardson S, Hodges E, Zenvirt S, Thekkat P, Shaag A. et al. Exome sequencing and disease-network analysis of a single family implicate a mutation in KIF1A in hereditary spastic paraparesis. Genome Res. 2011;21:658–64. - PMC - PubMed

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