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Related Concept Videos

Genome-wide Association Studies-GWAS01:11

Genome-wide Association Studies-GWAS

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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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Pleiotropy is the phenomenon in which a single gene impacts multiple, seemingly unrelated phenotypic traits. For example, defects in the SOX10 gene cause Waardenburg Syndrome Type 4, or WS4, which can cause defects in pigmentation, hearing impairments, and an absence of intestinal contractions necessary for elimination. This diversity of phenotypes results from the expression pattern of SOX10 in early embryonic and fetal development. SOX10 is found in neural crest cells that form melanocytes,...
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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,...
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Although Mendel chose seven unrelated traits in peas to study gene segregation, most traits involve multiple gene interactions that create a spectrum of phenotypes. When the interaction of various genes or alleles at different locations influences a phenotype, this is called epistasis. Epistasis often involves one gene masking or interfering with the expression of another (antagonistic epistasis). Epistasis often occurs when different genes are part of the same biochemical pathway. The...
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Related Experiment Video

Updated: Feb 21, 2026

Large-Scale Multi-Omics Genome-Wide Association Studies Mo-GWAS: Guidelines for Sample Preparation and Normalization
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Testing Genetic Pleiotropy with GWAS Summary Statistics for Marginal and Conditional Analyses.

Yangqing Deng1, Wei Pan2

  • 1Division of Biostatistics, University of Minnesota, Minneapolis, Minnesota 55455.

Genetics
|October 4, 2017
PubMed
Summary

This study introduces a new method to test genetic pleiotropy using only genome-wide association study (GWAS) summary statistics. The approach enables both marginal and conditional analyses, overcoming limitations of existing methods for genetic variant discovery.

Keywords:
GEEWald testlikelihood ratio testmultiple-trait association testingstructural equation modelsunion-intersection test

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Area of Science:

  • Statistical Genetics
  • Genomics
  • Bioinformatics

Background:

  • Genetic pleiotropy, where one genetic variant affects multiple traits, is of increasing research interest.
  • Current methods for testing pleiotropy often require individual-level data, which is not always available from large genome-wide association studies (GWAS).
  • Existing tests for marginal pleiotropy cannot differentiate direct from indirect genetic effects when traits are correlated.

Purpose of the Study:

  • To develop a novel statistical procedure for testing genetic pleiotropy using only GWAS summary statistics.
  • To enable both marginal and conditional pleiotropy analyses, addressing limitations of current methods.
  • To provide a robust method applicable to large-scale genetic datasets for identifying pleiotropic variants.

Main Methods:

  • Developed a new pleiotropy testing procedure based on union-intersection testing methods and GWAS summary statistics.
  • Incorporated conditional analysis by adjusting for correlated traits to identify direct genetic effects.
  • Utilized both likelihood ratio tests and generalized estimating equations under a working independence model for robust statistical inference.

Main Results:

  • The new method successfully performs both marginal and conditional pleiotropy analyses using only summary statistics.
  • Demonstrated the distinction between marginal and conditional pleiotropy effects using simulated and real genetic data.
  • Validated the approach on large lipid GWAS summary datasets (N≈100,000 and N≈189,000).

Conclusions:

  • The proposed method offers a powerful and flexible tool for genetic pleiotropy testing in the era of large-scale GWAS.
  • It overcomes data accessibility limitations and enhances the ability to distinguish direct genetic influences on multiple traits.
  • This approach can aid in identifying novel therapeutic targets by accurately characterizing pleiotropic genetic variants.