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Related Concept Videos

Comparing Copy Number Variations and SNPs02:26

Comparing Copy Number Variations and SNPs

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.
Copy number variations or CNVs are the structural variations that cover more than 1kb of DNA sequence. The single nucleotide polymorphism (SNP), on the other hand, is a single nucleotide change or a point mutation that is found in more than 1%...
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.
GWAS does not require the identification of the target gene involved in...
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Genome comparison is one of the excellent ways to interpret the evolutionary relationships between organisms. The basic principle of genome comparison is that if two species share a common feature, it is likely encoded by the DNA sequence conserved between both species. The advent of genome sequencing technologies in the late 20th century enabled scientists to understand the concept of conservation of domains between species and helped them to deduce evolutionary relationships across diverse...
Single Nucleotide Polymorphisms-SNPs01:05

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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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Published on: June 21, 2018

Comparing gene set analysis methods on single-nucleotide polymorphism data from Genetic Analysis Workshop 16.

Nathan L Tintle1, Bryce Borchers, Marshall Brown

  • 1Department of Mathematics, Hope College, 27 Graves Place, Holland, Michigan 49423, USA. tintle@hope.edu.

BMC Proceedings
|December 19, 2009
PubMed
Summary
This summary is machine-generated.

Gene set analysis (GSA) using gene enrichment analysis (GSEA) or Fisher's exact test (FET) is not optimal for single-nucleotide polymorphism (SNP) data. The SUMSTAT method offers greater power and robustness for SNP analysis in genome-wide association studies.

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Area of Science:

  • Genetics
  • Bioinformatics
  • Statistical genomics

Background:

  • Gene set analysis (GSA) is increasingly applied to single-nucleotide polymorphism (SNP) data in genome-wide association studies (GWAS).
  • Commonly used statistics, gene set enrichment analysis (GSEA) and Fisher's exact test (FET), were originally developed for gene expression data.
  • Previous research indicated that GSEA and FET exhibit limitations in power and robustness when analyzing gene expression data.

Purpose of the Study:

  • To evaluate the effectiveness of GSEA and FET for analyzing SNP data in GWAS.
  • To determine if the power and robustness issues observed with GSEA and FET in gene expression data analysis extend to SNP data.
  • To compare the performance of GSEA and FET against an alternative method, SUMSTAT, for SNP data analysis.

Main Methods:

  • Comparative analysis of statistical methods for gene set analysis on SNP data.
  • Application of GSEA, FET, and SUMSTAT to real SNP data from the Framingham Heart Study.
  • Evaluation using simulated SNP data to assess power and type I error rates.

Main Results:

  • SUMSTAT identified a significantly larger number of significant gene sets compared to GSEA and FET when analyzing real SNP data.
  • In analyses of simulated data, SUMSTAT demonstrated superior statistical power.
  • SUMSTAT provided better control over the type I error rate compared to GSEA and FET.

Conclusions:

  • GSEA and FET are suboptimal for gene set analysis of SNP data in GWAS.
  • The SUMSTAT method offers increased power and robustness for SNP data analysis.
  • Adopting SUMSTAT over GSEA or FET in GSA for GWAS can potentially enhance the discovery of significant gene sets.