Identifying genes targeted by disease-associated non-coding SNPs with a protein knowledge graph

Genome-wide association studies (GWAS) have identified many single nucleotide polymorphisms (SNPs) that play important roles in the genetic heritability of traits and diseases. With most of these SNPs located on the non-coding part of the genome, it is currently assumed that these SNPs influence the...

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Vydané v:PloS one Ročník 17; číslo 7; s. e0271395
Hlavní autori: Vlietstra, Wytze J., Vos, Rein, van Mulligen, Erik M., Jenster, Guido W., Kors, Jan A.
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: San Francisco Public Library of Science 13.07.2022
Public Library of Science (PLoS)
Predmet:
Analysis
Biology and Life Sciences
Cardiovascular disease
Computer and Information Sciences
Gene expression
Genes
Genetic distance
Genome-wide association studies
Genomes
Graphs
Heritability
Identification
Identification and classification
Knowledge representation
Literature reviews
Nucleotides
Performance enhancement
Physiology
Prostate cancer
Proteins
Single nucleotide polymorphisms
Single-nucleotide polymorphism
Netherlands
ISSN:1932-6203, 1932-6203
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Popis
Shrnutí:Genome-wide association studies (GWAS) have identified many single nucleotide polymorphisms (SNPs) that play important roles in the genetic heritability of traits and diseases. With most of these SNPs located on the non-coding part of the genome, it is currently assumed that these SNPs influence the expression of nearby genes on the genome. However, identifying which genes are targeted by these disease-associated SNPs remains challenging. In the past, protein knowledge graphs have often been used to identify genes that are associated with disease, also referred to as “disease genes”. Here, we explore whether protein knowledge graphs can be used to identify genes that are targeted by disease-associated non-coding SNPs by testing and comparing the performance of six existing methods for a protein knowledge graph, four of which were developed for disease gene identification. We compare our performance against two baselines: (1) an existing state-of-the-art method that is based on guilt-by-association, and (2) the leading assumption that SNPs target the nearest gene on the genome. We test these methods with four reference sets, three of which were obtained by different means. Furthermore, we combine methods to investigate whether their combination improves performance. We find that protein knowledge graphs that include predicate information perform comparable to the current state of the art, achieving an area under the receiver operating characteristic curve (AUC) of 79.6% on average across all four reference sets. Protein knowledge graphs that lack predicate information perform comparable to our other baseline (genetic distance) which achieved an AUC of 75.7% across all four reference sets. Combining multiple methods improved performance to 84.9% AUC. We conclude that methods for a protein knowledge graph can be used to identify which genes are targeted by disease-associated non-coding SNPs.
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Competing Interests: WJ Vlietstra is an employee of Elsevier B.V., the owner of Pathway Studio. This does not alter our adherence to PLOS ONE policies on sharing data and materials.
ISSN:1932-6203
1932-6203
DOI:10.1371/journal.pone.0271395