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Related Concept Videos

Principles of Pharmacogenetics: Types of Genetic Variants01:27

Principles of Pharmacogenetics: Types of Genetic Variants

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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...
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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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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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Pharmacogenetic Phenotypes: Alterations in Pharmacokinetics, Drug Targets and Biologic Milieu01:29

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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...
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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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Genetic Variation01:25

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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.
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Updated: Mar 26, 2026

Screening for Functional Non-coding Genetic Variants Using Electrophoretic Mobility Shift Assay EMSA and DNA-affinity Precipitation Assay DAPA
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Filtering genetic variants and placing informative priors based on putative biological function.

Stefanie Friedrichs1, Dörthe Malzahn2, Elizabeth W Pugh3

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Summary

Functional variant scores and correlation adjustments improve genetic association studies for blood pressure traits. Integrating gene expression data further enhances power in identifying significant genetic markers.

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Area of Science:

  • Genetics
  • Statistical Genetics
  • Bioinformatics

Background:

  • High-density genetic data present a significant multiple testing challenge.
  • Statistical methods are needed to manage this burden and increase power in genetic association studies.

Purpose of the Study:

  • To evaluate the utility of functional variant scores and correlation accounting methods for genetic association analysis.
  • To explore the integration of omics data for improved statistical power.

Main Methods:

  • Utilized functional variant scores (e.g., PolyPhen2, SIFT, RegulomeDB) for filtering and weighting genetic variants.
  • Applied methods to account for correlations between variants, including identity-by-descent mapping.
  • Incorporated gene expression data for false discovery rate (FDR)-controlled weighting of p-values.

Main Results:

  • Significant associations with blood pressure traits were found using functional scores and correlation adjustments.
  • Specific markers (rs218966, rs9836027) linked to hypertension, and rare variants in SNUPN to systolic blood pressure.
  • Variant weighting significantly impacted the power of kernel and burden tests; gene expression data improved power for FDR-controlled weighting.

Conclusions:

  • Functional variant information and accounting for genetic correlations are effective strategies for analyzing high-density genetic data.
  • Integrating gene expression data enhances the power of association tests, particularly for FDR control.
  • These approaches improve the discovery of genetic associations with complex traits like blood pressure.