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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...
Multiple Allele Traits01:49

Multiple Allele Traits

The Concept of Multiple Allelism

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Related Experiment Video

Updated: Jul 6, 2026

Application of Unsupervised Multi-Omic Factor Analysis to Uncover Patterns of Variation and Molecular Processes Linked to Cardiovascular Disease
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Application of Unsupervised Multi-Omic Factor Analysis to Uncover Patterns of Variation and Molecular Processes Linked to Cardiovascular Disease

Published on: September 20, 2024

Associations among multiple markers and complex disease: models, algorithms, and applications.

Themistocles L Assimes1, Adam B Olshen, Balasubramanian Narasimhan

  • 1Department of Medicine, Division of Cardiovascular Medicine, Stanford University, Stanford, CA 94305-5406, USA.

Advances in Genetics
|March 25, 2008
PubMed
Summary

Researchers aim to predict genetically complex diseases like hypertension by analyzing gene effects and environmental interactions. Understanding genotype-phenotype associations is key to disease prediction, even with current technological limitations.

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Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry
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Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry

Published on: June 21, 2018

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Last Updated: Jul 6, 2026

Application of Unsupervised Multi-Omic Factor Analysis to Uncover Patterns of Variation and Molecular Processes Linked to Cardiovascular Disease
08:51

Application of Unsupervised Multi-Omic Factor Analysis to Uncover Patterns of Variation and Molecular Processes Linked to Cardiovascular Disease

Published on: September 20, 2024

Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry
05:53

Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry

Published on: June 21, 2018

Area of Science:

  • Genetics
  • Genomic Medicine
  • Biostatistics

Background:

  • Understanding the genetic basis of complex diseases is crucial for effective prediction and management.
  • Current genotype data lacks strand-specific information for heterozygous individuals, posing a challenge for accurate prediction.
  • Complex diseases result from intricate interactions between genes, environment, and demographics.

Purpose of the Study:

  • To develop methods for predicting genetically complex diseases by analyzing gene effects and interactions.
  • To investigate associations between genetic factors, demographic, and environmental features in disease prediction.
  • To improve phenotype prediction accuracy for conditions like hypertension and myocardial infarction.

Main Methods:

  • Utilizing collaborative, long-term research to analyze gene main and epistatic effects.
  • Integrating demographic and environmental factors with genetic data for predictive modeling.
  • Developing novel approaches for phenotype prediction, distinct from linkage analysis or haplotype frequency determination.

Main Results:

  • Established methods for analyzing gene-environment interactions in complex disease prediction.
  • Demonstrated the challenges posed by unphased genotypic data in heterozygous states.
  • Advanced the understanding of genetic contributions to phenotypes such as hypertension and insulin resistance.

Conclusions:

  • Accurate prediction of genetically complex diseases requires integrating diverse data sources, including genetic, environmental, and demographic factors.
  • Overcoming limitations in current genotyping technology is essential for enhancing disease prediction models.
  • The developed approaches offer a pathway towards more precise phenotype prediction within specific timeframes.