Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

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...
Analysis of Population Pharmacokinetic Data01:12

Analysis of Population Pharmacokinetic Data

Analysis of population pharmacokinetic data involves studying the behavior of drugs within diverse populations to understand their pharmacokinetic parameters. Traditional pharmacokinetic methods typically involve collecting samples from a few individuals and estimating these parameters. While these methods are commonly used, they have limitations in capturing the variability in drug response among individuals or heterogeneous populations. Population pharmacokinetics is employed to address these...
Model-Independent Approaches for Pharmacokinetic Data: Noncompartmental Analysis00:59

Model-Independent Approaches for Pharmacokinetic Data: Noncompartmental Analysis

Noncompartmental analyses offer an alternative method for describing drug pharmacokinetics without relying on a specific compartmental model. In this approach, the drug's pharmacokinetics are assumed to be linear, with the terminal phase log-linear. This assumption allows for simplified analysis and interpretation of the drug's behavior in the body.
One important characteristic of noncompartmental analyses is that drug exposure increases proportionally with increasing doses. This relationship...
Analysis Methods of Pharmacokinetic Data: Model and Model-Independent Approaches01:14

Analysis Methods of Pharmacokinetic Data: Model and Model-Independent Approaches

Drug disposition in the body is a complex process and can be studied using two major approaches: the model and the model-independent approaches.
The model approach uses mathematical models to describe changes in drug concentration over time. Pharmacokinetic models help characterize drug behavior in patients, predict drug concentration in the body fluids, calculate optimum dosage regimens, and evaluate the risk of toxicity. However, ensuring that the model fits the experimental data accurately...
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...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Ovarian cancer tumor immune profiles associated with intrauterine device and oral contraceptive use.

British journal of cancer·2026
Same author

Spatial Clustering of Recently Activated Cytotoxic Lymphocytes Improves Association with Overall Survival in Women with High-Grade Serous Ovarian Cancer.

Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology·2026
Same author

Spatial Clustering of Recently Activated Cytotoxic Lymphocytes Improves Association with Overall Survival in Women with High-Grade Serous Ovarian Cancer.

Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology·2026
Same author

FastPCA: An R package for fast singular value decomposition.

Journal of open source software·2026
Same author

scSpatialSIM: a simulator of spatial single-cell molecular data.

SoftwareX·2026
Same author

A repeat expansion in GOLGA8A is a major risk factor for atypical frontotemporal lobar degeneration with ubiquitin-positive inclusions.

Nature genetics·2026

Related Experiment Video

Updated: May 21, 2026

Large-Scale Multi-Omics Genome-Wide Association Studies (Mo-GWAS): Guidelines for Sample Preparation and Normalization
08:27

Large-Scale Multi-Omics Genome-Wide Association Studies (Mo-GWAS): Guidelines for Sample Preparation and Normalization

Published on: July 27, 2021

Simultaneous analysis of multiple data types in pharmacogenomic studies using weighted sparse canonical correlation

Prabhakar Chalise1, Anthony Batzler, Ryan Abo

  • 1Biostatistics Department, University of Kansas Medical Center, Kansas City, Kansas, USA.

Omics : a Journal of Integrative Biology
|June 28, 2012
PubMed
Summary

Genetic variation influences drug response. New integrative methods using SNP and mRNA data, including weighted sparse canonical correlation analysis (SCCA), can uncover novel pharmacogenomic insights.

More Related Videos

CorrelationCalculator and Filigree: Tools for Data-Driven Network Analysis of Metabolomics Data
07:11

CorrelationCalculator and Filigree: Tools for Data-Driven Network Analysis of Metabolomics Data

Published on: November 10, 2023

Related Experiment Videos

Last Updated: May 21, 2026

Large-Scale Multi-Omics Genome-Wide Association Studies (Mo-GWAS): Guidelines for Sample Preparation and Normalization
08:27

Large-Scale Multi-Omics Genome-Wide Association Studies (Mo-GWAS): Guidelines for Sample Preparation and Normalization

Published on: July 27, 2021

CorrelationCalculator and Filigree: Tools for Data-Driven Network Analysis of Metabolomics Data
07:11

CorrelationCalculator and Filigree: Tools for Data-Driven Network Analysis of Metabolomics Data

Published on: November 10, 2023

Area of Science:

  • Pharmacogenomics
  • Bioinformatics
  • Genetics

Background:

  • Drug response varies due to genetic, environmental, and demographic factors.
  • Understanding genetic influences on drug response is crucial for personalized medicine.

Purpose of the Study:

  • To present and evaluate two integrative analytic approaches for pharmacogenomic studies.
  • To compare standard and a novel weighted sparse canonical correlation analysis (SCCA) using SNP and mRNA expression data.

Main Methods:

  • Utilized genome-wide single nucleotide polymorphism (SNP) and mRNA expression data from the same subjects.
  • Applied a step-wise integrative approach and sparse canonical correlation analysis (SCCA).
  • Introduced and tested a novel weighted SCCA modification.

Main Results:

  • Integrative analyses revealed limited overlap in detected genes, suggesting distinct biological mechanisms.
  • The proposed weighted SCCA demonstrated superior performance over unweighted SCCA in identifying genomic feature-phenotype associations.
  • The study was validated using simulated data and a gemcitabine pharmacogenomic dataset.

Conclusions:

  • Integrative genomic analyses, particularly weighted SCCA, offer valuable insights into pharmacogenomics.
  • Further development of integrative methods is essential for advancing our understanding of genomic variation and drug response.