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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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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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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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Expected Frequencies in Goodness-of-Fit Tests01:19

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A goodness-of-fit test is conducted to determine whether the observed frequency values are statistically similar to the frequencies expected for the dataset. Suppose the expected frequencies for a dataset are equal such as when predicting the frequency of any number appearing when casting a die. In that case, the expected frequency is the ratio of the total number of observations (n) to the number of categories (k).
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A population is composed of members of the same species that simultaneously live and interact in the same area. When individuals in a population breed, they pass down their genes to their offspring. Many of these genes are polymorphic, meaning that they occur in multiple variants. Such variations of a gene are referred to as alleles. The collective set of all the alleles within a population is known as the gene pool.
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Determining the Likelihood of Variant Pathogenicity Using Amino Acid-level Signal-to-Noise Analysis of Genetic Variation
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CCAFE: Estimating case and control allele frequencies from GWAS summary statistics.

Hayley R Stoneman1, Hugo Lemus Gomez2, Adelle Price2

  • 1Department of Biomedical Informatics, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA; Human Medical Genetics and Genomics Program, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA.

HGG Advances
|April 22, 2026
PubMed
Summary
This summary is machine-generated.

Researchers developed methods to calculate case and control allele frequencies (AFs) from genome-wide association study (GWAS) summary statistics. This enhances the utility of genetic data for various downstream analyses.

Keywords:
GWASR packageallele frequenciesstatistical geneticssummary statistics

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Area of Science:

  • Genetics
  • Bioinformatics
  • Statistical Genetics

Background:

  • Genetic summary statistics are valuable for understanding trait architecture but often lack complete data.
  • Case and control allele frequencies (AFs) are crucial for many post-GWAS analyses but are not always published.

Purpose of the Study:

  • To present methods and software for deriving case and control AFs from GWAS summary statistics.
  • To improve the reusability of publicly available genetic data.

Main Methods:

  • Developed methods to calculate case and control AFs using odds ratios, sample sizes, and either total AF or standard error (SE).
  • Implemented a bias adjustment using gnomAD AFs when SE is used, as SE can underestimate AFs with covariates.
  • Created the R package CCAFE (Case-Control Allele Frequency Estimation).

Main Results:

  • Derivations using total AF were highly accurate in simulations and real data.
  • Derivations using SE underestimated AFs when covariates were included in GWAS.
  • Bias adjustment using gnomAD AFs improved accuracy when using SE.

Conclusions:

  • The developed methods and CCAFE package enhance the utility of GWAS summary statistics.
  • These tools promote data reusability in genetic research.
  • Accurate allele frequency estimation is critical for advancing genetic studies.