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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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P-value is one of the most crucial concepts in statistics.
P-value stands for the probability value.  P-value is the probability that, if the null hypothesis is true, the results from another randomly selected sample will be as extreme or more extreme as the results obtained from the given sample.
A large P-value calculated from the data indicates to  not reject the null hypothesis. But a higher P-value does not mean that the null hypothesis is true. The smaller the P-value, the more...
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The process of hypothesis testing based on the P-value method includes calculating the P- value using the sample data and interpreting it.
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Diploid organisms have two alleles of each gene, one from each parent, in their somatic cells. Therefore, each individual contributes two alleles to the gene pool of the population. The gene pool of a population is the sum of every allele of all genes within that population and has some degree of variation. Genetic variation is typically expressed as a relative frequency, which is the percentage of the total population that has a given allele, genotype or phenotype.
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The Bonferroni test is a statistical test named after Carlo Emilio Bonferroni, an Italian mathematician best known for Bonferroni inequalities. This statistical test is a type of multiple comparison test to determine which means are different than the rest. Bonferroni test can minimize the Type 1 error by reducing the significance level alpha, which otherwise increases with sample pairs.
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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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Related Experiment Video

Updated: Mar 9, 2026

Large-Scale Multi-Omics Genome-Wide Association Studies Mo-GWAS: Guidelines for Sample Preparation and Normalization
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Efficient and Powerful Method for Combining P-Values in Genome-Wide Association Studies.

Natalia Vilor-Tejedor1, Juan R Gonzalez1, M Luz Calle2

  • 1Center for Research in Environmental Epidemiology, Universitat Pompeu Fabra and CIBER Epidemiología y Salud Pública, C/Doctor Aiguader 88, Barcelona, Spain.

IEEE/ACM Transactions on Computational Biology and Bioinformatics
|January 6, 2017
PubMed
Summary

Genome-wide Association Studies (GWAS) often miss disease heritability. A new method, globalEVT, uses extreme value theory for efficient gene set analysis (GSA), improving power in genetic studies.

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Area of Science:

  • Genetics
  • Bioinformatics
  • Computational Biology

Background:

  • Genome-wide Association Studies (GWAS) identify genetic variants like single nucleotide polymorphisms (SNPs) linked to disease risk.
  • Current GWAS methods explain only a small fraction of common disease heritability.
  • Gene Set Analysis (GSA) offers improved power but often requires extensive computation.

Purpose of the Study:

  • To introduce globalEVT, a novel, computationally efficient GSA method.
  • To enhance the power of genetic association studies by addressing limitations of single-SNP analysis.
  • To explore new susceptibility genes for complex diseases like Attention-deficit/hyperactivity disorder (ADHD).

Main Methods:

  • Developed globalEVT, a GSA approach utilizing extreme value theory to compute gene-level p-values.
  • Reduced computational demands compared to existing GSA methods.
  • Incorporated flexible inheritance models and accounted for SNP correlations.

Main Results:

  • globalEVT demonstrated significantly reduced computational requirements.
  • The method showed improved power in simulation studies.
  • Analysis of ADHD data identified potential susceptibility genes, including those related to Cyclophilin A.

Conclusions:

  • globalEVT provides an efficient and powerful alternative for GSA in GWAS.
  • The approach facilitates the discovery of novel genes implicated in disease development.
  • Gene set analysis is crucial for uncovering the genetic architecture of complex disorders.