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Related Concept Videos

Genome-wide Association Studies-GWAS01:11

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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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Unusual results are those that have a very low chance of occurring. Unusual results can be identified using probabilities and the range rule of thumb. In problems involving probability, unusual results can be observed in 2 instances – an unusually high number of successes or an unusually low number of successes.
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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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Spearman's rank correlation test, also known as Spearman's rho, is a nonparametric method for assessing the strength and direction of association between two variables. This test is particularly valuable when the data distribution is unknown or when the assumption of normality does not hold. Named after the English psychologist and statistician Dr. Charles Edward Spearman, it serves as the nonparametric counterpart to Pearson's correlation coefficient.
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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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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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Related Experiment Video

Updated: Jan 2, 2026

Rare Event Detection Using Error-corrected DNA and RNA Sequencing
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A permutation method for detecting trend correlations in rare variant association studies.

Lifeng Liu1, Pengfei Wang2, Jingbo Meng2

  • 1School of Mathematical Sciences, Heilongjiang University, Harbin150080, China.

Genetics Research
|December 14, 2019
PubMed
Summary

This study introduces OV-RV, a new method for detecting disease-related rare variants when both genotypes and phenotypes are ordinal. OV-RV is valid and efficient, controlling type I error and increasing power in association studies.

Keywords:
contingency tablesordinal variablesrare variantsγ-statistic

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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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Area of Science:

  • Genetics
  • Statistical genetics
  • Bioinformatics

Background:

  • Complex diseases are heritable, but common variants explain limited phenotypic variance.
  • Rare variants are increasingly recognized as contributors to the 'missing heritability' of complex diseases.
  • Existing methods primarily focus on dichotomous, continuous, or ordinal traits, with limited options for ordinal genotypes and phenotypes.

Purpose of the Study:

  • To introduce OV-RV, a novel statistical method for identifying disease-related rare variants.
  • To address the challenge of association analysis when both genetic and phenotypic data are ordinal.
  • To provide a robust method for rare variant association studies.

Main Methods:

  • The study proposes a method based on the gamma-statistic (γ-statistic), named OV-RV.
  • It employs Fisher's permutation approach for robust estimation of the γ-statistic's asymptotic distribution.
  • Extensive simulations were conducted to evaluate the method's performance.

Main Results:

  • OV-RV demonstrates validity and efficiency in simulations.
  • The method controls type I error rates close to the pre-specified significance level.
  • OV-RV achieves greater statistical power compared to existing approaches at the same significance level.
  • The method was successfully applied to rare variant association studies of diastolic blood pressure.

Conclusions:

  • OV-RV is a valid and efficient tool for rare variant association studies with ordinal genotypes and phenotypes.
  • The permutation approach provides a reliable estimation of the γ-statistic's distribution.
  • This method enhances the ability to detect disease-related rare variants in genetic studies.