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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...
Longitudinal Research02:20

Longitudinal Research

Sometimes we want to see how people change over time, as in studies of human development and lifespan. When we test the same group of individuals repeatedly over an extended period of time, we are conducting longitudinal research. Longitudinal research is a research design in which data-gathering is administered repeatedly over an extended period of time. For example, we may survey a group of individuals about their dietary habits at age 20, retest them a decade later at age 30, and then again...
Longitudinal Studies01:26

Longitudinal Studies

Longitudinal studies are also widely used in other medical and social science fields. For instance, in cardiovascular research, they can monitor patients' health over decades to identify risk factors for heart disease, such as high cholesterol or smoking, and evaluate the long-term effectiveness of preventive measures. Similarly, in mental health studies, researchers might follow individuals from adolescence into adulthood to understand the development and progression of conditions like...

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Related Experiment Video

Updated: May 22, 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

Genome-wide association mapping with longitudinal data.

Nicholas A Furlotte1, Eleazar Eskin, Susana Eyheramendy

  • 1Department of Computer Science, University of California, Los Angeles, California 90095-1596, USA.

Genetic Epidemiology
|May 15, 2012
PubMed
Summary
This summary is machine-generated.

This study introduces a new statistical method for analyzing genetic associations using multiple measurements over time. The approach enhances power and accurately separates genetic and environmental influences on phenotypes.

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Last Updated: May 22, 2026

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

Published on: July 27, 2021

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04:41

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Published on: January 9, 2020

Area of Science:

  • Genetics
  • Statistical Genetics
  • Bioinformatics

Background:

  • Genome-wide association studies (GWAS) typically analyze single time-point phenotype data.
  • Existing methods do not fully leverage longitudinal data for association mapping.
  • Multiple phenotype measurements per individual offer richer information for genetic analysis.

Purpose of the Study:

  • To develop a mixed-model-based approach for association mapping using longitudinal phenotype data.
  • To enhance statistical power for detecting genetic associations compared to traditional methods.
  • To differentiate and quantify genetic, environmental, and residual error contributions to phenotypes.

Main Methods:

  • Proposed a mixed-model-based statistical framework for longitudinal GWAS.
  • Developed an analytical method to calculate statistical power for the proposed model.
  • Implemented a method to predict genetic and environmental contributions to phenotypes.

Main Results:

  • The proposed model significantly increases statistical power compared to single time-point methods.
  • Successfully differentiated genetic, environmental, and residual error components of phenotypes.
  • Demonstrated high accuracy in predicting the proportion of phenotype due to genetics and environment.
  • Showed accurate individual ranking based on predicted genetic and environmental contributions.

Conclusions:

  • Longitudinal data analysis using mixed models improves power in genome-wide association studies.
  • The method provides accurate decomposition of phenotypic variance into genetic and environmental factors.
  • This approach offers a more comprehensive understanding of genotype-phenotype relationships over time.