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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.
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Mass Spectrometry: Complex Analysis01:21

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One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation01:24

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Updated: May 26, 2026

Large-Scale Multi-Omics Genome-Wide Association Studies (Mo-GWAS): Guidelines for Sample Preparation and Normalization
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Published on: July 27, 2021

Mixture model for sub-phenotyping in GWAS.

David Warde-Farley1, Michael Brudno, Quaid Morris

  • 1Genetics and Genome Biology, SickKids Research Institute, Toronto, ON, Canada.

Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing
|December 17, 2011
PubMed
Summary
This summary is machine-generated.

Genome-Wide Association (GWA) studies can identify disease markers, but small effect sizes persist. This research introduces a new model to untangle mixed phenotypes, improving genetic discovery for complex diseases.

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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
  • Computational Biology
  • Biostatistics

Background:

  • Genome-Wide Association (GWA) studies identify genetic variants for complex diseases like Crohn's disease and age-related macular degeneration (AMD).
  • A significant challenge in GWA studies is the small effect size of identified genetic variants, potentially due to factors like rare variants, epistasis, and phenotypic heterogeneity.
  • Phenotypic heterogeneity, where a single phenotype encompasses clinically indistinguishable subtypes, complicates the accurate identification of genetic associations.

Purpose of the Study:

  • To address the challenge of phenotypic heterogeneity in Genome-Wide Association (GWA) studies.
  • To develop a method for identifying distinct genotypic markers from mixed, overlapping phenotypes within GWA data.
  • To improve the precision and power of genetic association studies by accounting for underlying phenotypic variations.

Main Methods:

  • Introduction of a novel generative model designed to handle mixtures of clinically indistinguishable phenotypes.
  • Derivation of an expectation-maximization (EM) algorithm for fitting the proposed generative model to GWA data.
  • Development of a new screening procedure to detect phenotype-specific genetic effects within heterogeneous datasets.

Main Results:

  • The proposed generative model and EM procedure were evaluated on simulated datasets.
  • Preliminary application of the model to a type 2 diabetes dataset demonstrated its potential utility.
  • The methods showed promise in disentangling genetic signals from mixed phenotypic data.

Conclusions:

  • Phenotypic heterogeneity is a critical factor limiting the discovery of genetic variants in GWA studies.
  • The developed generative model and associated EM algorithm offer a robust approach to address phenotypic mixtures.
  • This methodology has the potential to enhance the identification of genetic associations for complex diseases, as suggested by preliminary findings in type 2 diabetes research.