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Related Concept Videos

Genome-wide Association Studies-GWAS01:11

Genome-wide Association Studies-GWAS

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...
Single Nucleotide Polymorphisms-SNPs01:05

Single Nucleotide Polymorphisms-SNPs

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,...
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%...
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Multiple Allele Traits

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Multiple Allele Traits01:49

Multiple Allele Traits

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Polygenic Traits01:18

Polygenic Traits

When more than one gene is responsible for a given phenotype, the trait is considered polygenic. Human height is a polygenic trait. Studies have uncovered hundreds of loci that influence height, and there are believed to be many more. Due to the high number of genes involved, as well as environmental and nutritional factors, height varies significantly within a given population. The distribution of height forms a bell-shaped curve, with relatively few individuals in the population at the...

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Updated: Jun 16, 2026

Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry
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Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry

Published on: June 21, 2018

PCA-based bootstrap confidence interval tests for gene-disease association involving multiple SNPs.

Qianqian Peng1, Jinghua Zhao, Fuzhong Xue

  • 1Department of Epidemiology and Health Statistics, School of Public Health, Shandong University, Jinan 250012, PR China.

BMC Genetics
|January 27, 2010
PubMed
Summary

This study introduces the PCA-based bootstrap confidence interval test (PCA-BCIT) for genetic association studies. PCA-BCIT offers a valid and powerful approach for identifying disease-predisposing variants using multiple single-nucleotide polymorphisms (SNPs).

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Detection of Rare Genomic Variants from Pooled Sequencing Using SPLINTER
14:06

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Published on: June 23, 2012

Area of Science:

  • Genetics
  • Statistical genetics
  • Bioinformatics

Background:

  • Genetic association studies commonly use single-nucleotide polymorphisms (SNPs) but face multiple testing concerns.
  • Haplotype methods account for SNP correlations but can be limited by Hardy-Weinberg equilibrium assumptions and degrees of freedom.
  • Principal Component Analysis (PCA) approaches are promising but depend on the method of principal component (PC) extraction.

Purpose of the Study:

  • To develop and evaluate a novel Principal Component Analysis-based bootstrap confidence interval test (PCA-BCIT) for gene-disease association.
  • To compare different methods of extracting principal components (PCs) for use in the PCA-BCIT.

Main Methods:

  • Developed PCA-BCIT using PC scores to assess gene-disease association.
  • Evaluated three PC extraction methods: cases only (CAES), controls only (COES), and cases and controls combined (CES).
  • Assessed test performance through simulations and real-world data analysis (rheumatoid arthritis, heroin addiction).

Main Results:

  • Extraction of PCs using controls only (COES) demonstrated superior performance compared to CAES and CES.
  • PCA-BCIT maintained the nominal level under the null hypothesis.
  • The test showed performance comparable to the permutation test in simulations and real data analyses.

Conclusions:

  • PCA-BCIT is a statistically valid and powerful method for evaluating gene-disease associations involving multiple SNPs.
  • The findings support the use of PCA-BCIT as a robust tool in genetic association studies.