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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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Sequencing of the human genome has opened up several best-kept secrets of the genome. Scientists have identified thousands of genome variations that exist within a population. These variations can be a single nucleotide or a larger chromosomal variation.
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A Bayesian Model for SNP Discovery Based on Next-Generation Sequencing Data.

Yanxun Xu1, Xiaofeng Zheng2, Yuan Yuan3

  • 1Department of Statistics, Rice University Houston, TX.

IEEE International Workshop on Genomic Signal Processing and Statistics : [Proceedings]. IEEE International Workshop on Genomic Signal Processing and Statistics
|January 5, 2016
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Summary
This summary is machine-generated.

We developed a Bayesian method to detect single-nucleotide polymorphisms (SNPs) using next-generation sequencing data. This approach effectively identifies potential SNPs, with over 95% overlap with existing databases.

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Area of Science:

  • Genomics
  • Bioinformatics
  • Molecular Biology

Background:

  • Single-nucleotide polymorphisms (SNPs) are common DNA variations influencing disease susceptibility.
  • Accurate SNP detection is crucial for biomedical research and personalized medicine.
  • Next-generation sequencing (NGS) technologies generate vast amounts of genomic data.

Purpose of the Study:

  • To propose a novel Bayesian approach for enhanced SNP detection using NGS data.
  • To improve the accuracy and efficiency of identifying genetic variations.
  • To identify potential novel SNPs for further research.

Main Methods:

  • A Bayesian statistical model was developed to compute posterior probabilities of nucleotide variations.
  • The method analyzes genomic sequence data from NGS platforms.
  • The approach was applied to prostate cancer (PC3) and embryonic stem cell (H1) lines.

Main Results:

  • The Bayesian method demonstrated high accuracy in SNP detection.
  • Over 95% concordance was observed between the identified SNPs and the dbSNP database.
  • The analysis identified potential novel SNPs not present in current databases.

Conclusions:

  • The proposed Bayesian approach offers an effective strategy for SNP detection with NGS data.
  • This method can aid in discovering new SNPs and understanding their role in diseases.
  • The findings contribute to advancing genomic research and disease association studies.