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Comparing Copy Number Variations and SNPs02:26

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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 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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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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KDSNP: A kernel-based approach to detecting high-order SNP interactions.

Kento Kodama1, Hiroto Saigo1

  • 11 Department of Bioscience and Bioinformatics, Kyushu Institute of Technology, 680-4 Kawazu, Iizuka 820-8502, Fukuoka, Japan.

Journal of Bioinformatics and Computational Biology
|November 4, 2016
PubMed
Summary
This summary is machine-generated.

This study introduces a novel method to detect complex genetic interactions (epistasis) between single nucleotide polymorphisms (SNPs) for human disease research. The approach accurately identifies interacting SNPs, even at higher orders, improving genotype-phenotype correlation.

Keywords:
EpistasisGWASgene–gene interactionkernel ridge regressionpolynomial kernel

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Area of Science:

  • Genetics and Bioinformatics
  • Computational Biology
  • Statistical Genomics

Background:

  • Current genotype-phenotype correlation methods struggle with complex human diseases due to limited success in elucidating genetic factors.
  • Single nucleotide polymorphism (SNP) interactions, or epistasis, present a significant challenge, with existing approaches often limited to detecting only lower-order interactions.
  • The combinatorial complexity of searching for high-order SNP interactions hinders comprehensive genetic analysis.

Purpose of the Study:

  • To develop an empirical approach for determining the true degree of epistasis among SNPs.
  • To overcome computational limitations in detecting high-order SNP interactions.
  • To improve the accuracy of genotype-phenotype correlation in complex diseases.

Main Methods:

  • An empirical approach utilizing ridge regression with polynomial kernels and model selection was developed.
  • The method determines the true degree of epistasis among SNPs.
  • A sliding window approach was proposed for scenarios with limited sample sizes to maintain a sufficient sample/SNP ratio.

Main Results:

  • Computer experiments with simulated data demonstrated the method's ability to correctly predict the number of interacting SNPs when sample size is adequate.
  • Computational experiments using heterogeneous stock mice data successfully identified subregions containing known causal SNPs.
  • The analysis suggested the presence of additional interacting candidate causal SNPs near known causal genes.

Conclusions:

  • The proposed method effectively detects higher-order SNP interactions, advancing the understanding of genetic factors in complex diseases.
  • The approach shows promise for identifying novel genetic interactions and causal SNPs in both simulated and real biological data.
  • Available software facilitates the application of this method in genetic research.