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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...
Principles of Pharmacogenetics: Types of Genetic Variants01:27

Principles of Pharmacogenetics: Types of Genetic Variants

The human genome is over 99.9% identical between individuals, yet genetic differences exist at millions of bases. The human genome contains approximately 3 million variant positions per individual, many of which are heterozygous, contributing to genetic diversity and individual traits. Genetic variations include single-nucleotide polymorphisms (SNPs), insertions, deletions, and copy number variations (CNVs).SNPs, the most common variation, involve single-base changes in DNA. These can be...
Mismatch Repair01:20

Mismatch Repair

Organisms are capable of detecting and fixing nucleotide mismatches that occur during DNA replication. This sophisticated process requires identifying the new strand and replacing the erroneous bases with correct nucleotides. Mismatch repair is coordinated by many proteins in both prokaryotes and eukaryotes.
The Mutator Protein Family Plays a Key Role in DNA Mismatch Repair
The human genome has more than 3 billion base pairs of DNA per cell. Prior to cell division, that vast amount of genetic...
Comparing Copy Number Variations and SNPs02:26

Comparing Copy Number Variations and SNPs

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.
Copy number variations or CNVs are the structural variations that cover more than 1kb of DNA sequence. The single nucleotide polymorphism (SNP), on the other hand, is a single nucleotide change or a point mutation that is found in more than 1%...
Pharmacogenetic Phenotypes: Alterations in Pharmacokinetics, Drug Targets and Biologic Milieu01:29

Pharmacogenetic Phenotypes: Alterations in Pharmacokinetics, Drug Targets and Biologic Milieu

Genetic variations significantly influence drug response through pharmacokinetics, receptor interactions, and biologic milieu modifications. Pharmacokinetic alterations impact drug metabolism and clearance, affecting efficacy and toxicity. Variants in drug-metabolizing enzymes, such as CYP2C9 and CYP2C19, alter drug activation and elimination. For example, CYP2C9 loss-of-function variants require lower warfarin doses to prevent excessive bleeding, while CYP2C19 variants reduce clopidogrel...
Genomic Imprinting and Inheritance02:30

Genomic Imprinting and Inheritance

Diploid organisms inherit genetic material through chromosomes from both parents. Copies of the same gene are known as alleles. In most cases, both alleles are simultaneously expressed and allow various cellular processes to function optimally. If one of the alleles is missing or mutated, the expression of the other allele can compensate; however, this is not true for all genes.
The expression of some genes depends on which parent passed the gene to the offspring, through a phenomenon known as...

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Related Experiment Video

Updated: May 8, 2026

Screening for Functional Non-coding Genetic Variants Using Electrophoretic Mobility Shift Assay (EMSA) and DNA-affinity Precipitation Assay (DAPA)
11:35

Screening for Functional Non-coding Genetic Variants Using Electrophoretic Mobility Shift Assay (EMSA) and DNA-affinity Precipitation Assay (DAPA)

Published on: August 21, 2016

Misclassification in binary responses and effect on genome-wide association studies.

Romdhane Rekaya1, Shannon Smith, El Hamidi Hay

  • 1Department of Animal and Dairy Science, University of Georgia, Athens, GA 30602, USA. rrekaya@uga.edu

Poultry Science
|August 21, 2013
PubMed
Summary

Misclassification in binary traits significantly impacts genome-wide association studies. Accounting for misclassification improves model performance and accuracy in identifying genetic associations for binary traits.

Related Experiment Videos

Last Updated: May 8, 2026

Screening for Functional Non-coding Genetic Variants Using Electrophoretic Mobility Shift Assay (EMSA) and DNA-affinity Precipitation Assay (DAPA)
11:35

Screening for Functional Non-coding Genetic Variants Using Electrophoretic Mobility Shift Assay (EMSA) and DNA-affinity Precipitation Assay (DAPA)

Published on: August 21, 2016

Area of Science:

  • Genetics
  • Animal Science
  • Statistical Genetics

Background:

  • Misclassification of dependent variables is a common problem in scientific research, particularly when using indirect markers or treating continuous traits as categorical.
  • This issue can compromise diagnostic accuracy in human medicine and negatively affect selection accuracy and biological mechanism ascertainment in animal science.
  • Genomic markers like single nucleotide polymorphisms (SNPs) offer direct insights into genetic factors but are vulnerable to misclassification errors in dependent variables.

Purpose of the Study:

  • To quantify the impact of misclassification on genome-wide association studies (GWAS) for binary response traits.
  • To evaluate the effectiveness of a latent-threshold model in handling misclassified data in GWAS.
  • To compare the performance of analyses that ignore versus account for misclassification.

Main Methods:

  • A real-data-based simulation was conducted using 2,400 animals genotyped for 50K SNPs.
  • A binary trait with 0.10 heritability and 20% prevalence was simulated, with 1%, 5%, and 10% misclassification rates artificially introduced.
  • Three analytical approaches were used: analysis of true data (M1), analysis of contaminated data ignoring misclassification (M2), and analysis of contaminated data accounting for misclassification (M3) via a latent-threshold model.

Main Results:

  • Ignoring misclassification (M2) led to a significant deterioration in model performance compared to using true data (M1).
  • The latent-threshold model effectively handled misclassification when accounted for (M3), demonstrating a strong capacity to identify and correct miscoded samples in the training set.
  • The method showed robustness and improved validity of results even with substantial misclassification rates.

Conclusions:

  • Misclassification in binary traits poses a significant challenge to the reliability of genome-wide association studies.
  • The proposed latent-threshold model provides an effective and adequate solution for addressing misclassification in GWAS for binary traits.
  • Accounting for misclassification is crucial for accurate genetic analysis and robust conclusions in studies involving binary response variables.