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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...
Chi-square Analysis02:46

Chi-square Analysis

The chi-square test is a statistical hypothesis test. It is used to check whether there is a significant difference between an expected value and an observed value. In the context of genetics, it enables us to either accept or reject a hypothesis, based on how much the observed values deviate from the expected values.
The chi-square test was developed by Pearson in 1990.
The first step of performing a Chi-square analysis is to establish a null hypothesis, which assumes that there is no real...
Variability: Analysis01:11

Variability: Analysis

Measures of variability are statistical metrics that reveal the dispersion pattern within a dataset. They are pivotal in biostatistics, providing insights into the heterogeneity within health and biological data. Variability signifies the degree to which data points diverge from one another, helping researchers understand the potential range of values and associated uncertainty within the data.
The range is a simple measure of variability, indicating the difference between the highest and...
Statistical Methods to Analyze Parametric Data: ANOVA01:12

Statistical Methods to Analyze Parametric Data: ANOVA

Analysis of Variance, or ANOVA, is a powerful statistical technique used to analyze parametric data, primarily in research and experimental studies. It's designed to compare the means of two or more groups, assisting researchers in identifying any significant differences between these group means. There are two main types of ANOVA based on the complexity of the analysis: one-way and two-way.
One-way ANOVA is applied when a single independent variable or factor is scrutinized. It compares the...
Genetic Variation01:25

Genetic Variation

Genetic variation is the diversity in DNA sequences found among individuals of the same species. This diversity is crucial for a species' survival because it helps organisms adapt to environmental changes. Genetic variation begins with fertilization, where an egg and sperm cell merge. Each of these cells carries 23 chromosomes, up to 46 in the fertilized egg. Chromosomes are long DNA strands that contain genes, the basic units of heredity.
Genes exist in different versions called alleles, which...
Finding Critical Values for Chi-Square01:18

Finding Critical Values for Chi-Square

Consider a curve representing sample data drawn randomly from a normally distributed population. One must construct confidence intervals to estimate or to test a claim regarding the population standard deviation. For example, a 95% confidence interval covers 95% of the area under the curve, and the remaining 5% is equally distributed on either side of the curve. To achieve such confidence intervals, one must determine the critical values. The critical values are simply the values separating the...

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Related Experiment Video

Updated: May 12, 2026

Large-Scale Multi-Omics Genome-Wide Association Studies (Mo-GWAS): Guidelines for Sample Preparation and Normalization
08:27

Large-Scale Multi-Omics Genome-Wide Association Studies (Mo-GWAS): Guidelines for Sample Preparation and Normalization

Published on: July 27, 2021

Using volcano plots and regularized-chi statistics in genetic association studies.

Wentian Li1, Jan Freudenberg1, Young Ju Suh2

  • 1The Robert S. Boas Center for Genomics and Human Genetics, The Feinstein Institute for Medical Research, North Shore LIJ Health System, 350 Community Drive, Manhasset, NY 11030, USA.

Computational Biology and Chemistry
|April 23, 2013
PubMed
Summary

Identifying causal disease variants requires ranking genetic associations. A new "regularized-chi" method using volcano plots prioritizes variants more effectively than traditional measures, especially rare ones.

Keywords:
Genetic association analysisRare variantsRegularized-chiSNPsType-2 diabetesVolcano plot

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Three Differential Expression Analysis Methods for RNA Sequencing: limma, EdgeR, DESeq2
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Three Differential Expression Analysis Methods for RNA Sequencing: limma, EdgeR, DESeq2

Published on: September 18, 2021

Related Experiment Videos

Last Updated: May 12, 2026

Large-Scale Multi-Omics Genome-Wide Association Studies (Mo-GWAS): Guidelines for Sample Preparation and Normalization
08:27

Large-Scale Multi-Omics Genome-Wide Association Studies (Mo-GWAS): Guidelines for Sample Preparation and Normalization

Published on: July 27, 2021

Three Differential Expression Analysis Methods for RNA Sequencing: limma, EdgeR, DESeq2
10:10

Three Differential Expression Analysis Methods for RNA Sequencing: limma, EdgeR, DESeq2

Published on: September 18, 2021

Area of Science:

  • Genetics
  • Bioinformatics
  • Statistical Genetics

Background:

  • Identifying causal disease variants from large genomic datasets is challenging.
  • Traditional genetic association measures like odds-ratio (OR) and p-value can yield different variant rankings.
  • Prioritizing candidate variants for experimental validation is crucial but complex.

Purpose of the Study:

  • To adapt the volcano plot methodology from gene expression analysis for genetic association studies.
  • To develop a novel method integrating OR and chi-square statistics for variant prioritization.
  • To improve the identification of functionally significant disease variants, particularly rare ones.

Main Methods:

  • Transferred volcano plot methodology to genetic association studies, plotting OR against Pearson's chi-square statistic.
  • Developed a "regularized-chi" filtering method using a smooth curve for combined OR and chi-square thresholds.
  • Compared "regularized-chi" performance against standard thresholds for variant selection.

Main Results:

  • Volcano plots provide visual inspection of genetic association measures (OR and chi-square).
  • The "regularized-chi" method offers an intuitive approach to filter associated markers.
  • "Regularized-chi" prioritizes variants with lower minor-allele frequencies more effectively than the standard chi-square test.

Conclusions:

  • Volcano plots and the "regularized-chi" method enhance the prioritization of candidate disease variants.
  • This approach is particularly beneficial for identifying rare variants with potentially strong functional effects.
  • The "regularized-chi" method offers a more nuanced and effective strategy for genetic association studies.