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Genome-wide Association Studies-GWAS01:11

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Genome-wide association studies or GWAS are used to identify whether common SNPs are associated with certain diseases. Suppose specific SNPs are more frequently observed in individuals with a particular disease than those without the disease. In that case, those SNPs are said to be associated with the disease. Chi-square analysis is performed to check the probability of the allele likely to be associated with the disease.
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A single nucleotide polymorphism or SNP is a single nucleotide variation at a specific genomic position in a large population. It is the most prevalent type of sequence variation found in the human genome. Point mutations that occur in more than 1% of the population qualify as SNPs. These are present once every 1000 nucleotides on an average in the human genome. Replacement of a purine with another purine (A/G) or a pyrimidine with another pyrimidine (C/T) is known as a transition. In contrast,...
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The genome refers to all of the genetic material in an organism. It can range from a few million base pairs in microbial cells to several billion base pairs in many eukaryotic organisms. Genome assembly refers to the process of taking the DNA sequencing data and putting it all back together in a correct order to create a close representation of the original genome. This is followed by the identification of functional elements on the newly assembled genome, a process called genome annotation.
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A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports
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Identifying genes targeted by disease-associated non-coding SNPs with a protein knowledge graph.

Wytze J Vlietstra1,2, Rein Vos1,3, Erik M van Mulligen1

  • 1Department of Medical Informatics, Erasmus MC, University Medical Center Rotterdam, Rotterdam, the Netherlands.

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Summary

Protein knowledge graphs effectively identify genes targeted by disease-associated single nucleotide polymorphisms (SNPs). Combining methods significantly improved accuracy, offering a powerful tool for genetic research.

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Area of Science:

  • Genomics
  • Bioinformatics
  • Systems Biology

Background:

  • Genome-wide association studies (GWAS) identify single nucleotide polymorphisms (SNPs) linked to disease heritability.
  • Most disease-associated SNPs are non-coding, presumed to affect nearby gene expression, but target identification is challenging.
  • Protein knowledge graphs are utilized for disease gene identification.

Purpose of the Study:

  • To evaluate protein knowledge graphs for identifying genes targeted by disease-associated non-coding SNPs.
  • To compare the performance of six protein knowledge graph methods against state-of-the-art and nearest-gene baselines.

Main Methods:

  • Tested six protein knowledge graph methods, including four for disease gene identification.
  • Compared performance against guilt-by-association and nearest-gene baselines.
  • Evaluated methods using four reference sets and explored combining multiple methods.

Main Results:

  • Protein knowledge graphs with predicate information achieved an average AUC of 79.6%, comparable to state-of-the-art.
  • Graphs without predicate information performed similarly to the genetic distance baseline (AUC 75.7%).
  • Combining methods improved performance to an AUC of 84.9%.

Conclusions:

  • Protein knowledge graph methods are effective for pinpointing genes affected by non-coding disease-associated SNPs.
  • Incorporating predicate information enhances performance.
  • Method combination offers superior accuracy for SNP-gene target identification.