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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...
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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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Although Mendel chose seven unrelated traits in peas to study gene segregation, most traits involve multiple gene interactions that create a spectrum of phenotypes. When the interaction of various genes or alleles at different locations influences a phenotype, this is called epistasis. Epistasis often involves one gene masking or interfering with the expression of another (antagonistic epistasis). Epistasis often occurs when different genes are part of the same biochemical pathway. The...
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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...
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Related Experiment Video

Updated: May 20, 2026

Mapping Alzheimer's Disease Variants to Their Target Genes Using Computational Analysis of Chromatin Configuration
04:41

Mapping Alzheimer's Disease Variants to Their Target Genes Using Computational Analysis of Chromatin Configuration

Published on: January 9, 2020

Genotype-phenotype mapping in a post-GWAS world.

Sergey V Nuzhdin1, Maren L Friesen, Lauren M McIntyre

  • 1University of Southern California, Program in Molecular and Computational Biology, Department of Biology, Los Angeles, CA 90089, USA. snuzhdin@usc.edu

Trends in Genetics : TIG
|July 24, 2012
PubMed
Summary
This summary is machine-generated.

This study merges genome-wide association studies (GWAS) with gene regulatory networks (GRNs) using structural equation modeling (SEM). This approach quantifies molecular pathways underlying phenotypic variation in natural populations.

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Last Updated: May 20, 2026

Mapping Alzheimer's Disease Variants to Their Target Genes Using Computational Analysis of Chromatin Configuration
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Large-Scale Multi-Omics Genome-Wide Association Studies (Mo-GWAS): Guidelines for Sample Preparation and Normalization
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A Fast and Quantitative Method for Post-translational Modification and Variant Enabled Mapping of Peptides to Genomes
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A Fast and Quantitative Method for Post-translational Modification and Variant Enabled Mapping of Peptides to Genomes

Published on: May 22, 2018

Area of Science:

  • Genomics
  • Systems Biology
  • Molecular Biology

Background:

  • Translating genomic information into observable traits (phenotypes) is a complex biological challenge.
  • Genome-wide association studies (GWAS) link genetic variations to phenotypes but lack mechanistic insights.
  • Gene regulatory networks (GRNs) explain gene function but are difficult to infer genome-wide.

Purpose of the Study:

  • To integrate GWAS and GRN approaches for a comprehensive understanding of genotype-phenotype relationships.
  • To develop a quantitative framework for analyzing molecular pathways underlying phenotypic variation.
  • To leverage natural genetic variation for elucidating biological mechanisms.

Main Methods:

  • Utilized structural equation modeling (SEM) to merge GWAS and GRN data.
  • Leveraged allele-specific expression from natural variation.
  • Applied SEM to analyze molecular pathways connecting genotype to phenotype.

Main Results:

  • Developed a unified SEM framework to quantitate GRNs.
  • Demonstrated the ability to evaluate GRN consistency across different environments or sexes.
  • Enabled identification of interspecies GRN differences and de novo GRN annotation in non-model organisms.

Conclusions:

  • SEM provides a powerful, integrated approach to bridge the gap between genotype and phenotype.
  • This framework enhances our ability to dissect complex biological pathways and evolutionary differences.
  • The approach is versatile for quantitative GRN analysis and annotation across diverse biological contexts.