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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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Genetic screens are tools used to identify genes and mutations responsible for phenotypes of interest. Genetic screens help identify individuals or a group of people at risk of developing  genetic diseases and help them with early intervention, targeted therapy, and reproductive options.
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In Vivo Functional Study of Disease-associated Rare Human Variants Using Drosophila
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Recent advances in network-based methods for disease gene prediction.

Sezin Kircali Ata1, Min Wu2, Yuan Fang3

  • 1School of Computer Science and Engineering Nanyang Technological University (NTU).

Briefings in Bioinformatics
|December 4, 2020
PubMed
Summary

Network-based computational methods offer a low-cost alternative for disease gene prediction, overcoming limitations of genome-wide association studies (GWAS). This review categorizes and analyzes these methods for improved accuracy.

Keywords:
disease gene predictiongraph representation learningnetwork-based methods

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Area of Science:

  • Computational biology
  • Bioinformatics
  • Genomics

Background:

  • Genome-wide association studies (GWAS) are costly and yield many false positives for disease-gene association.
  • Researchers require complementary, low-cost methods to validate disease-gene links.
  • Molecular networks capture complex biological interactions crucial for disease understanding.

Purpose of the Study:

  • To provide a comprehensive review of network-based computational methods for disease gene prediction.
  • To empirically analyze and compare the performance of 14 state-of-the-art network-based methods.
  • To identify potential research directions in network-based disease gene prediction.

Main Methods:

  • Categorization of existing network-based methods into network diffusion, traditional machine learning with graph features, and graph representation learning.
  • Empirical performance evaluation of 14 selected methods across seven different diseases.
  • Comparative analysis of method strengths and weaknesses based on empirical findings.

Main Results:

  • Network-based approaches offer a viable, cost-effective alternative to traditional GWAS for disease gene discovery.
  • Distinct performance characteristics were observed among different categories of network-based methods.
  • Empirical analysis provided insights into the efficacy of various state-of-the-art techniques.

Conclusions:

  • Network-based computational methods are essential for advancing disease gene prediction.
  • The study highlights the utility of molecular network data in identifying disease-associated genes.
  • Future research should focus on refining these network-based approaches for enhanced predictive power.