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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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Advances in genomics have profoundly influenced drug discovery by increasing both the speed and accuracy of pharmaceutical development. Pharmacogenomics, which examines how genetic variation influences drug response, facilitates the identification of novel therapeutic targets and enables patient stratification for personalized treatment. These strategies contribute to improved drug efficacy, minimized adverse effects, and more efficient clinical trial design.Mapping genetic differences...
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Human genetics provides a profound framework for understanding the interplay between genetic predispositions and human psychology. At the heart of this discipline lies the study of how genes influence physical traits, behaviors, and susceptibility to diseases. Each person carries a unique genetic code that subtly or significantly shapes their psychological and behavioral landscape.
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Comparing Copy Number Variations and SNPs02:26

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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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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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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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Related Experiment Video

Updated: Apr 28, 2026

Targeted Next-generation Sequencing and Bioinformatics Pipeline to Evaluate Genetic Determinants of Constitutional Disease
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Concordance between direct and imputed APOE genotypes using 1000 Genomes data.

Christopher Oldmeadow1, Elizabeth G Holliday1, Mark McEvoy1

  • 1University of Newcastle and Hunter Medical Research Institute, Newcastle, Australia.

Journal of Alzheimer'S Disease : JAD
|June 7, 2014
PubMed
Summary

Genotyping APOE (Apolipoprotein E) is crucial for aging research. Imputing APOE genotypes from microarray data shows high accuracy, making it a viable alternative when direct measurement is unavailable.

Keywords:
APOEAgreementconcordancedementiagenotyping

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Area of Science:

  • Genetics
  • Genomics
  • Population studies

Background:

  • Large cohorts of older individuals with genome-wide data are increasing.
  • The Apolipoprotein E (APOE) gene is vital for aging and disease research.
  • APOE genotyping is often missing from common microarray platforms.

Purpose of the Study:

  • To evaluate the accuracy of imputing APOE genotypes from microarray data.
  • To assess the feasibility of using imputed APOE data in large-scale genetic studies of aging.

Main Methods:

  • Directly measured APOE genotypes were compared with imputed genotypes.
  • Imputation was performed using microarray data and the 1000 Genomes dataset.
  • The study included a sample of 320 Caucasian individuals.

Main Results:

  • Imputed APOE genotypes showed 90% agreement with directly measured genotypes.
  • Prediction of APOE ε4 carrier status achieved 93% agreement.
  • Kappa values for genotype and ε4 status were 0.81 and 0.84, respectively.
  • Applying stringent thresholds improved agreement to 90-97% (kappa 0.90-0.93).

Conclusions:

  • APOE genotype imputation from microarray data is a reliable method.
  • This approach can be effectively used in large cohorts where direct APOE measurement is lacking.
  • Imputed APOE data can support genetic research in aging populations.