Related Experiment Video
Updated: Aug 16, 2026
14:45
Enumeration of Major Peripheral Blood Leukocyte Populations for Multicenter Clinical Trials Using a Whole Blood Phenotyping Assay
Published on: September 16, 2012
PHENOME-WIDE INTERACTION STUDY (PheWIS) IN AIDS CLINICAL TRIALS GROUP DATA (ACTG)
Shefali S Verma1, Alex T Frase, Anurag Verma
1Center for System Genomics, The Pennsylvania State University, University Park, PA 16802, USA;
Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing
|January 19, 2016
Summary
This study developed a pipeline to find genetic interactions influencing multiple traits by prioritizing variants using biological data. The method successfully identified significant genetic associations for fasting blood glucose and high-density lipoprotein levels.
Area of Science:
- Genetics and Genomics
- Bioinformatics
- Computational Biology
Background:
- Genome-wide association studies (GWAS) identify single nucleotide polymorphisms (SNPs) linked to phenotypes but face challenges in prioritizing findings.
- Genetic interactions (epistasis) are crucial for understanding complex traits, yet identifying them in large datasets remains difficult.
- Existing methods struggle to effectively select variants for gene-gene interaction analysis within high-throughput genomic data.
Purpose of the Study:
- To propose and validate a computational pipeline for identifying genetic variant interactions associated with multiple phenotypes.
- To prioritize variants for interaction analysis using prior association results and biological knowledge.
- To apply the pipeline to AIDS Clinical Trials Group (ACTG) data for discovery and replication of epistatic interactions.
Main Methods:
- Developed a pipeline integrating results from Genome-Wide Association Studies (GWAS) and Phenome-Wide Association Studies (PheWAS).
- Utilized the Roadmap Epigenome project for functional annotation to filter variants based on chromatin states.
- Performed pairwise interaction analysis using linear regression on two independent ACTG datasets.
Main Results:
- Identified 50,798 significant associations (p < 0.01) replicating across 26 phenotypes.
- Discovered 2,176 SNP associations for fasting blood glucose, with 212 unique SNPs reaching Bonferroni significance.
- Found 9,970 interactions for high-density lipoprotein (HDL) and fasting blood glucose, reaching FDR significance (total 12,146 associations).
Conclusions:
- The proposed variant prioritization method effectively identifies epistatic interactions for genome-wide and phenome-wide analyses.
- This Phenome-wide Interaction study (PheWIS) approach can generate hypotheses for complex trait and drug response studies.
- The method is applicable to clinical trial data to link non-linear genetic combinations to patient outcomes and drug therapies.
More Related Videos
09:26In Vitro Assay to Evaluate the Impact of Immunoregulatory Pathways on HIV-specific CD4 T Cell Effector Function
Published on: October 16, 2013
09:38Generalized Psychophysiological Interaction (PPI) Analysis of Memory Related Connectivity in Individuals at Genetic Risk for Alzheimer's Disease
Published on: November 14, 2017
Related Concept Videos
01:11Genome-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...
GWAS does not require the identification of the target gene involved in...
01:16Clinical Trials
Clinical trials are prospective experimental studies conducted on humans to determine the safety and efficacy of treatments, drugs, diet methods, and medical devices. Using statistics in clinical trials enables researchers to derive reasonable and accurate conclusions from the collected data, allowing them to make wise decisions in uncertain situations. In medical research, statistical methods are crucial for preventing errors and bias.
There are four phases in a clinical trial. A phase one...
There are four phases in a clinical trial. A phase one...
01:11Clinical Trials: Overview
Clinical development focuses on how the drug will interact with the human body and encompasses four key phases of clinical trials, each serving a specific purpose in assessing the safety and effectiveness of new drugs. These phases overlap and build upon one another. Phase I involves a small group of healthy volunteers (typically 20-80 individuals) or, in cases where significant toxicity is expected, patients with the targeted disease, such as cancer or AIDS. The volunteers are tested for...
01:12Analysis 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...
01:29Pharmacogenomics: 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...