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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...
Single Nucleotide Polymorphisms-SNPs01:05

Single Nucleotide Polymorphisms-SNPs

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,...
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%...
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...
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...
Incomplete Dominance01:43

Incomplete Dominance

Gregor Mendel's work (1822 - 1884) was primarily focused on pea plants. Through his initial experiments, he determined that every gene in a diploid cell has two variants called alleles inherited from each parent. He suggested that amongst these two alleles, one allele is dominant in character and the other recessive. The combination of alleles determines the phenotype of a gene in an organism.

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Updated: Jun 23, 2026

Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry
05:53

Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry

Published on: June 21, 2018

Parametric model-based statistics for possible genotyping errors and sample stratification in sibling-pair SNP data.

Michael Korostishevsky1, Ida Malkin, Tim Spector

  • 1Human Population Biology Research Unit, Department of Anatomy and Anthropology, Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel.

Genetic Epidemiology
|May 21, 2009
PubMed
Summary
This summary is machine-generated.

This study introduces a new method to detect genotyping errors and sample stratification in sibling pair data. The approach effectively identifies errors in heterozygotes, crucial for accurate genetic association studies.

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Infinium Assay for Large-scale SNP Genotyping Applications
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Infinium Assay for Large-scale SNP Genotyping Applications

Published on: November 19, 2013

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Last Updated: Jun 23, 2026

Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry
05:53

Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry

Published on: June 21, 2018

Infinium Assay for Large-scale SNP Genotyping Applications
13:33

Infinium Assay for Large-scale SNP Genotyping Applications

Published on: November 19, 2013

Area of Science:

  • Genetics
  • Bioinformatics
  • Statistical Genetics

Background:

  • Genotyping errors and sample stratification complicate genetic analyses like association and linkage studies.
  • Apparent Mendelian incompatibilities in sib-pairs are difficult to interpret due to unknown parental genotypes and confounding factors.
  • Accurate estimation of these factors is essential before conducting genetic analyses.

Purpose of the Study:

  • To develop and validate a method for the combined analysis of genotyping errors and sample stratification in single nucleotide polymorphism (SNP) data from sib-pairs.
  • To improve the reliability of genetic association and linkage analyses by accounting for potential data inaccuracies.

Main Methods:

  • Developed parametric models incorporating genotyping error and sample stratification.
  • Utilized likelihood ratio statistics for combined analysis of sib-pair SNP data.
  • Simulated data using Monte Carlo methods to assess the power and efficiency of the proposed approach.
  • Implemented models for genotyping errors (heterozygotes/homozygotes) and stratification (population admixture/intermarriage).

Main Results:

  • The proposed method demonstrates high efficiency in detecting genotyping errors, particularly in heterozygotes.
  • Monte Carlo simulations confirmed the method's power in identifying both genotyping errors and sample stratification.
  • The approach successfully distinguishes between errors arising from genotyping inaccuracies and those due to population structure.

Conclusions:

  • The developed method provides a robust framework for simultaneously addressing genotyping errors and sample stratification in sib-pair studies.
  • Accurate identification of genotyping errors, especially in heterozygotes, is critical for reliable genetic analysis.
  • The method offers a valuable tool for enhancing the quality and interpretability of genetic data in population studies.