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Related Concept Videos

Genome-wide Association Studies-GWAS01:11

Genome-wide Association Studies-GWAS

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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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In a dataset, the 5-number summary includes the minimum data value, the data value of the first quartile, the median data value or data value of the second quartile, the data value of the third quartile, and the maximum data value. These 5 data values can be visualized as a box and whisker plot.
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The deviations show how spread out the data are about the mean. A positive deviation occurs when the data value exceeds the mean, whereas a negative deviation occurs when the data value is less than the mean. If the deviations are added, the sum is always zero. So one cannot simply add the deviations to get the data spread. By squaring the deviations, the numbers are made positive; thus, their sum will also be positive.
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Once data is collected from both the experimental and the control groups, a statistical analysis is conducted to find out if there are meaningful differences between the two groups. A statistical analysis determines how likely any difference found is due to chance (and thus not meaningful). In psychology, group differences are considered meaningful, or significant, if the odds that these differences occurred by chance alone are 5 percent or less. Stated another way, if we repeated this...
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Related Experiment Video

Updated: Feb 11, 2026

A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types
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VarExp: estimating variance explained by genome-wide GxE summary statistics.

Vincent Laville1, Amy R Bentley2, Florian Privé1,3

  • 1Groupe de Génétique Statistique, Département de Génomes and Génétique, C3BI, Institut Pasteur, Paris, France.

Bioinformatics (Oxford, England)
|May 5, 2018
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Summary

This study introduces the VarExp R package for estimating phenotypic variance explained by genetic factors using only summary statistics. This method simplifies analysis for genome-wide association studies and gene-environment interactions, even from meta-analyses.

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Area of Science:

  • Genetics
  • Statistical Genetics
  • Bioinformatics

Background:

  • Genome-wide association studies (GWAS) and gene-environment (GxE) interactions are crucial for understanding human traits and diseases.
  • Quantifying the contribution of genetic variants is typically done using individual-level data, which is difficult with meta-analysis results.

Purpose of the Study:

  • To present the 'VarExp' R package for estimating the percentage of phenotypic variance explained.
  • To enable variance estimation using only summary statistics from GWAS and GxE interaction studies.

Main Methods:

  • Development of the 'VarExp' R package.
  • Integration of recent methodological advancements for variance estimation.
  • Implementation allows evaluation of marginal genetic effects, GxE interactions, and joint effects.

Main Results:

  • The 'VarExp' package enables phenotypic variance estimation from summary statistics, overcoming limitations of meta-analyses.
  • It supports various models, including marginal genetic effects, GxE interactions, and combined effects.
  • No external data upload is required, simplifying the user workflow.

Conclusions:

  • The 'VarExp' R package provides a powerful and accessible tool for estimating phenotypic variance explained.
  • It facilitates the analysis of complex genetic architectures, including gene-environment interactions, from large-scale meta-analyses.
  • This package advances the field by enabling robust genetic variance estimation without individual-level data.