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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
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...
Single Nucleotide Polymorphisms-SNPs01:05

Single Nucleotide Polymorphisms-SNPs

A single nucleotide polymorphism or SNP is a single nucleotide variation at a specific genomic position in a large population. It is the most prevalent type of sequence variation found in the human genome. Point mutations that occur in more than 1% of the population qualify as SNPs. These are present once every 1000 nucleotides on an average in the human genome. Replacement of a purine with another purine (A/G) or a pyrimidine with another pyrimidine (C/T) is known as a transition. In contrast,...
Behavioral Genetics and Its Designs01:23

Behavioral Genetics and Its Designs

Behavior genetics explores how genetic inheritance influences human behavior. It focuses on how genes, passed from parents to offspring, contribute to the development of behavioral traits and tendencies. This branch of genetics seeks to understand the complex interplay between inherited genetic factors and environmental influences in shaping our behaviors.
The primary methodologies used in behavior genetics include family studies, twin studies, and adoption studies, each providing unique...

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Related Experiment Video

Updated: May 11, 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 analysis for multiple continuous secondary phenotypes.

Elizabeth D Schifano1, Lin Li, David C Christiani

  • 1Department of Statistics, University of Connecticut, Storrs, CT 06269, USA. elizabeth.schifano@uconn.edu

American Journal of Human Genetics
|May 7, 2013
PubMed
Summary
This summary is machine-generated.

This study introduces a scaled marginal model for genome-wide association studies (GWASs) to jointly analyze multiple phenotypes, enhancing statistical power for detecting genetic associations, particularly for smoking behaviors.

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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

Related Experiment Videos

Last Updated: May 11, 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

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
  • Biostatistics
  • Epidemiology

Background:

  • Genome-wide association studies (GWASs) increasingly analyze multiple phenotypes to detect pleiotropic effects and improve power.
  • Analyzing multiple secondary phenotypes in case-control studies presents challenges due to differing scales and potential ascertainment bias.

Purpose of the Study:

  • To propose a scaled marginal model for jointly analyzing multiple secondary phenotypes in case-control GWASs.
  • To enhance statistical power compared to single-phenotype or traditional multivariate analyses, especially when phenotypes are correlated.

Main Methods:

  • Developed a scaled marginal model for testing and estimating single-nucleotide polymorphism (SNP) effects on multiple phenotypes.
  • Employed weighted estimating equations to address case-control ascertainment bias and ensure robustness.
  • Utilized a one degree of freedom (1-DF) test for common effect estimation, jointly modeling outcome-specific scales.

Main Results:

  • Simulation studies demonstrated the proposed 1-DF common effect test outperforms alternative methods.
  • The method showed improved statistical power when phenotypes are positively correlated and measure an underlying trait.
  • Application to smoking phenotypes in a lung cancer GWAS identified several SNPs of biological interest.

Conclusions:

  • The scaled marginal model offers a powerful approach for joint analysis of multiple phenotypes in GWASs.
  • The weighted estimating equation method effectively handles ascertainment bias and model misspecification.
  • This approach advances the analysis of complex traits and pleiotropic effects in genetic association studies.