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Related Concept Videos

Pharmacogenomics: Identification of New Drug Targets01:29

Pharmacogenomics: Identification of New Drug Targets

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...
Pharmacogenetics of Drug Metabolism: Overview01:27

Pharmacogenetics of Drug Metabolism: Overview

Genetic polymorphism in drug metabolism is crucial to the inter-individual variability observed in drug responses. Drug metabolism primarily involves the chemical modification of drugs and other xenobiotics to enhance their elimination by increasing their polarity. Two main classes of enzymes mediate this biotransformation process: Phase I enzymes, primarily cytochrome P450s, catalyze oxidation and reduction reactions, while other enzymes, such as esterases, mediate hydrolysis, and Phase II...
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...
Pharmacogenetics and Pharmacogenomics: Overview01:29

Pharmacogenetics and Pharmacogenomics: Overview

Pharmacogenetics and pharmacogenomics examine how genetic factors influence an individual's response to drugs. While pharmacogenetics focuses on the impact of specific genetic variants on drug effects, pharmacogenomics takes a broader approach, studying how genetic variation across populations contributes to differences in drug responses. These fields aim to explain why individuals may experience varying levels of efficacy or adverse reactions to the same medication.Variability in drug...
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...
Dosage Regimens: Partial Pharmacokinetic Parameters01:01

Dosage Regimens: Partial Pharmacokinetic Parameters

It is not uncommon for complete drug pharmacokinetic profiles to remain elusive in pharmacokinetics. This necessitates certain educated assumptions by pharmacokineticists to determine appropriate dosage regimens without comprehensive pharmacokinetic data from animal or human studies. One prevalent assumption is setting the bioavailability factor, denoted as F, to 1 or 100%. This assumption caters to the scenario where a drug doesn't achieve full systemic absorption, resulting in the patient...

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Related Experiment Video

Updated: Jul 11, 2026

Determining the Likelihood of Variant Pathogenicity Using Amino Acid-level Signal-to-Noise Analysis of Genetic Variation
07:15

Determining the Likelihood of Variant Pathogenicity Using Amino Acid-level Signal-to-Noise Analysis of Genetic Variation

Published on: January 16, 2019

Estimating a positive false discovery rate for variable selection in pharmacogenetic studies.

Lang Li1, Siu Hui, Gene Pennello

  • 1Department of Medicine, Division of Biostatistics, Indiana University, Indianapolis, Indiana 46202, USA. lali@inpui.edu

Journal of Biopharmaceutical Statistics
|September 22, 2007
PubMed
Summary

This study introduces a positive false discovery rate (pFDR) estimate for stepwise variable selection, improving prediction accuracy by accounting for false positives. The method enhances the power to identify true predictors, especially when many are relevant.

Related Experiment Videos

Last Updated: Jul 11, 2026

Determining the Likelihood of Variant Pathogenicity Using Amino Acid-level Signal-to-Noise Analysis of Genetic Variation
07:15

Determining the Likelihood of Variant Pathogenicity Using Amino Acid-level Signal-to-Noise Analysis of Genetic Variation

Published on: January 16, 2019

Area of Science:

  • Statistical methodology
  • Bioinformatics
  • Genomics

Background:

  • Predictive modeling relies on selecting relevant variables.
  • Traditional methods often overlook false positives in selected predictors.
  • Accurate variable selection is crucial for reliable outcome prediction.

Purpose of the Study:

  • To develop a positive false discovery rate (pFDR) estimate for stepwise variable selection.
  • To address the issue of unacknowledged false positives in predictor selection.
  • To enhance the statistical rigor of variable selection procedures.

Main Methods:

  • Proposed a novel positive false discovery rate (pFDR) estimation method.
  • Applied the method to a conventional stepwise forward variable selection procedure.
  • Introduced two perspectives for viewing the variable selection process: overall and individual tests.

Main Results:

  • The developed pFDR estimate effectively accounts for false positives.
  • The overall test demonstrated increased power in selecting non-null predictors as their proportion grew.
  • The methodology was validated using a pharmacogenetics dataset.

Conclusions:

  • The proposed pFDR estimation enhances the reliability of variable selection in predictive modeling.
  • The method offers improved power for identifying true predictors, particularly in high-dimensional data.
  • This approach provides a more robust statistical framework for outcome prediction.