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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
Multiple Allele Traits01:49

Multiple Allele Traits

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Polygenic Traits01:18

Polygenic Traits

When more than one gene is responsible for a given phenotype, the trait is considered polygenic. Human height is a polygenic trait. Studies have uncovered hundreds of loci that influence height, and there are believed to be many more. Due to the high number of genes involved, as well as environmental and nutritional factors, height varies significantly within a given population. The distribution of height forms a bell-shaped curve, with relatively few individuals in the population at the...
Polygenic Traits01:18

Polygenic Traits

When more than one gene is responsible for a given phenotype, the trait is considered polygenic. Human height is a polygenic trait. Studies have uncovered hundreds of loci that influence height, and there are believed to be many more. Due to the high number of genes involved, as well as environmental and nutritional factors, height varies significantly within a given population. The distribution of height forms a bell-shaped curve, with relatively few individuals in the population at the...
Background and Environment Affect Phenotype02:27

Background and Environment Affect Phenotype

Although the genetic makeup of an organism plays a major role in determining the phenotype, there are also several environmental factors, such as temperature, oxygen availability, presence of mutagens, that can alter an organism’s phenotype.
An example of how genetic background affects phenotype can be seen in horses. The Extension gene in horses is responsible for their coat color. A wild-type gene (EE) produces black pigment in the coat, while a mutant gene (ee) produces red pigment. A...

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Related Experiment Video

Updated: Jul 3, 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

Optimized phenotype definitions boost GWAS power.

Michael Zietz1, Kathleen LaRow Brown2, Undina Gisladottir2

  • 1Department of Computational Biomedicine, Cedars-Sinai Medical Center, Los Angeles, California, United States of America.

Plos Computational Biology
|July 1, 2026
PubMed
Summary
This summary is machine-generated.

Genetics play a role in complex diseases. A new method, MaxGCP, enhances genetic signal analysis in observational data, improving study power for complex disease research.

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Last Updated: Jul 3, 2026

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

  • Genetics and genomics
  • Computational biology
  • Epidemiology

Background:

  • Complex diseases pose significant health challenges, with genetics contributing substantially to disease risk.
  • Observational data offers large-scale, cost-effective insights but is confounded by healthcare and societal factors.
  • Purifying the genetic signal within observational data is crucial for accurate genetic discovery.

Purpose of the Study:

  • Introduce MaxGCP, a novel phenotyping method to isolate the genetic component in observational data.
  • Optimize phenotype definitions to maximize coheritability with complex traits of interest.
  • Enhance the power of genetic studies using large-scale observational datasets.

Main Methods:

  • MaxGCP defines phenotypes to maximize coheritability with the target complex trait.
  • The method exhibits linear computational complexity, enabling scalability with numerous features.
  • MaxGCP does not require manual feature selection, streamlining the phenotyping process.

Main Results:

  • MaxGCP significantly enhances the genetic signal in observational data.
  • In stroke analysis, MaxGCP increased study power by over 13% compared to traditional methods.
  • The method demonstrates improved performance over conventional single-code phenotype definitions.

Conclusions:

  • MaxGCP is an effective tool for improving genetic discovery in observational data.
  • The method offers a phenotype-specific approach to isolate genetic signals.
  • MaxGCP is anticipated to be widely applicable for studying complex diseases in observational datasets.