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Related Concept Videos

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.
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...
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...
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...

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Related Experiment Video

Updated: Jun 17, 2026

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

Infinium Assay for Large-scale SNP Genotyping Applications

Published on: November 19, 2013

Assessment of genotype imputation methods.

Joanna M Biernacka1, Rui Tang, Jia Li

  • 1Department of Health Sciences Research, Mayo Clinic, 200 First Street Southwest, Rochester, MN 55905 USA. biernacka.joanna@mayo.edu.

BMC Proceedings
|December 19, 2009
PubMed
Summary

Comparing genotype imputation methods for rheumatoid arthritis research, MACH and IMPUTE showed lower error rates than PLINK and fastPHASE. Association tests using imputed data performed well but didn't fully replicate complete data findings.

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Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry
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Published on: June 21, 2018

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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

Area of Science:

  • Genetics and Bioinformatics
  • Computational Biology
  • Statistical Genetics

Background:

  • Genotype imputation is crucial for analyzing genomic data, especially when dealing with untyped or missing markers.
  • Accurate imputation is essential for robust genetic association studies, including those for complex diseases like rheumatoid arthritis.

Purpose of the Study:

  • To compare the performance of four genotype imputation methods: IMPUTE, MACH, PLINK, and fastPHASE.
  • To evaluate imputation error rates and the effectiveness of association tests using imputed genotypes.
  • To assess imputation performance in scenarios involving completely untyped markers and combining datasets with different marker sets.

Main Methods:

  • Utilized the Genetic Analysis Workshop 16 rheumatoid arthritis case-control dataset.
  • Compared IMPUTE, MACH, PLINK, and fastPHASE for imputing genotypes at untyped and missing markers.
  • Assessed imputation error rates and the performance of association tests on imputed data.

Main Results:

  • All imputation methods performed better for single-nucleotide polymorphisms (SNPs) in high linkage disequilibrium with genotyped SNPs.
  • MACH and IMPUTE demonstrated lower imputation error rates compared to fastPHASE and PLINK.
  • Association tests using MACH (allele dosage) and IMPUTE (posterior probabilities) yielded results closest to complete data analyses.

Conclusions:

  • MACH and IMPUTE are superior genotype imputation methods regarding accuracy and association test performance.
  • While imputation methods improve analysis with incomplete data, they do not fully replicate the statistical power of complete genotype data for strongly associated SNPs.
  • Careful consideration of imputation method choice is vital for reliable genetic association studies in rheumatoid arthritis and other complex diseases.