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Genome-wide Association Studies-GWAS

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...

Polygenic Traits

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 Traits

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...

Multiple Allele Traits

Multiple Allele Traits

The Concept of Multiple Allelism

Multiple Allele Traits

Multiple Allele Traits

The Concept of Multiple Allelism

Behavioral Genetics and Its Designs

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: Jun 24, 2026

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

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

Published on: July 27, 2021

Genome-wide association scans for secondary traits using case-control samples.

Genevieve M Monsees¹, Rulla M Tamimi, Peter Kraft

¹Department of Epidemiology, Harvard School of Public Health, Boston, Massachusetts 02115, USA. gmonsees@hsph.harvard.edu

Genetic Epidemiology

|April 15, 2009

Summary

Naïve analyses in genome-wide association studies (GWAS) are generally valid for secondary traits, even with case-control ascertainment. However, caution is advised if both the marker and secondary trait associate with disease risk.

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

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

Published on: June 21, 2018

Related Experiment Videos

Last Updated: Jun 24, 2026

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

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

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

Published on: June 21, 2018

Area of Science:

Genetics
Statistical Genetics
Bioinformatics

Background:

Genome-wide association studies (GWAS) are resource-intensive, often necessitating the analysis of multiple traits from the same subjects.
Case-control designs are common in GWAS, but improper handling of ascertainment can bias secondary trait association estimates.

Purpose of the Study:

To evaluate the impact of case-control ascertainment on marker-secondary trait associations in GWAS.
To compare the performance of naïve analysis methods versus inverse-probability-of-sampling-weighted (IPW) regression.

Main Methods:

Simulations were used to quantify Type I error rates, statistical power, and bias.
Naïve analyses (ignoring or stratifying on case-control status) and IPW regression were compared.

Main Results:

Naïve analyses maintain proper Type I error except when both marker and secondary trait associate with disease risk.
IPW regression offers appropriate Type I error across all scenarios but with reduced statistical power.
Bias in naïve analyses is minimal when markers are independent of disease risk.

Conclusions:

Naïve analyses are often valid and provide nearly unbiased estimates for marker-secondary trait associations in GWAS, especially when markers are not associated with disease.
Careful consideration is needed when both the marker and secondary trait are associated with the primary disease, as demonstrated in a breast cancer example.