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Discovering Alzheimer Genetic Biomarkers Using Bayesian Networks.

Fayroz F Sherif1, Nourhan Zayed2, Mahmoud Fakhr2

  • 1Systems and Computer Department, Electronics Research Institute (ERI), Giza 12622, Egypt ; Systems and Biomedical Department, Faculty of Engineering, Cairo University, Giza 12316, Egypt.

Advances in Bioinformatics
|September 15, 2015
PubMed
Summary
This summary is machine-generated.

Researchers used Bayesian networks to identify novel single nucleotide polymorphism (SNP) biomarkers for Alzheimer's disease (AD). This approach effectively detects causal SNPs and gene-SNP interactions, improving early AD diagnosis and treatment strategies.

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Area of Science:

  • Genetics and Genomics
  • Computational Biology
  • Neurodegenerative Diseases

Background:

  • Single nucleotide polymorphisms (SNPs) are a major source of human genetic variation and are associated with complex diseases like Alzheimer's disease (AD).
  • Identifying SNP biomarkers is crucial for the early diagnosis and treatment of AD.
  • Bayesian networks (BNs) offer a framework for modeling gene and SNP interactions.

Purpose of the Study:

  • To apply Bayesian network structure learning algorithms to whole genome sequencing (WGS) data.
  • To detect causal SNPs and gene-SNP interactions associated with Alzheimer's disease.
  • To identify novel SNP biomarkers for AD.

Main Methods:

  • Whole genome sequencing (WGS) data analysis.
  • Application of Bayesian network structure learning algorithms, including Markov blanket-based methods, Naïve Bayes, and Tree Augmented Naïve Bayes.
  • Focus on polymorphisms within the top ten genes linked to AD by genome-wide association (GWA) studies.

Main Results:

  • Several novel SNP biomarkers (rs7530069, rs113464261, rs114506298, rs73504429, rs7929589, rs76306710, and rs668134) were identified and found to be significantly associated with Alzheimer's disease.
  • Bayesian network methods demonstrated effectiveness in identifying AD-causal SNPs with acceptable accuracy.
  • Markov blanket-based methods outperformed Naïve Bayes and Tree Augmented Naïve Bayes, achieving higher accuracy (66.13%) and sensitivity (88.87%) compared to Naïve Bayes (61.58% and 59.43%).

Conclusions:

  • Bayesian networks are effective tools for identifying causal SNPs and gene-SNP interactions relevant to Alzheimer's disease.
  • The identified SNP set, particularly those found using Markov blanket methods, shows a strong association with AD.
  • This study highlights the potential of BN in advancing the discovery of genetic biomarkers for improved AD diagnosis and treatment.