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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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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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Sequencing of the human genome has opened up several best-kept secrets of the genome. Scientists have identified thousands of genome variations that exist within a population. These variations can be a single nucleotide or a larger chromosomal variation.
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Updated: Nov 1, 2025

Screening for Functional Non-coding Genetic Variants Using Electrophoretic Mobility Shift Assay EMSA and DNA-affinity Precipitation Assay DAPA
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Mapping pleiotropic loci using a fast-sequential testing algorithm.

Fernando M Aguate1, Ana I Vazquez2, Tony R Merriman3

  • 1Department of Epidemiology & Biostatistics, IQ - Institute for Quantitative Health Science and Engineering, Michigan State University, East Lansing, MI, USA. matiasfe@msu.edu.

European Journal of Human Genetics : EJHG
|June 19, 2021
PubMed
Summary
This summary is machine-generated.

We developed a new scalable method to identify genes affecting multiple traits, crucial for understanding disease causes and predicting risk. This approach successfully mapped pleiotropic loci for metabolic syndrome using large-scale biobank data.

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Area of Science:

  • Genetics and Genomics
  • Bioinformatics
  • Statistical Genetics

Background:

  • Pleiotropy, where genes influence multiple traits, creates genetic correlations and contributes to complex diseases.
  • Identifying pleiotropic variants enhances understanding of gene function, disease etiology, and disease risk prediction.
  • Current sequential testing methods for pleiotropy mapping lack scalability for large datasets and millions of SNPs.

Purpose of the Study:

  • To develop and validate a scalable sequential testing method and software for pleiotropy mapping in large biobank-sized datasets.
  • To identify genomic regions with pleiotropic effects on metabolic syndrome-related traits.
  • To advance the biological understanding of gene action and disease etiology for complex traits.

Main Methods:

  • Developed a novel sequential testing approach designed for high-throughput analysis of millions of SNPs.
  • Implemented the method in user-friendly software capable of handling biobank-scale data.
  • Conducted a whole-genome scan for pleiotropic effects on seven metabolic syndrome traits using UK-Biobank data (n~300K).

Main Results:

  • Simulations confirmed the method's power and adequate type-I error rate control.
  • Identified 170, 44, and 18 genomic regions with SNPs affecting at least two, three, and four of the seven traits, respectively.
  • Results were validated against the GWAS Catalog and GTEx data, confirming known loci and discovering novel pleiotropic associations.

Conclusions:

  • The developed sequential testing method and software effectively address the scalability limitations of previous approaches.
  • Abundant pleiotropy was observed for metabolic syndrome traits, highlighting complex genetic architectures.
  • The findings provide new insights into the genetic links between metabolic syndrome components and suggest plausible biological pathways.