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Related Concept Videos

Genome-wide Association Studies-GWAS01:11

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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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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...
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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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Updated: Mar 31, 2026

Large-Scale Multi-Omics Genome-Wide Association Studies Mo-GWAS: Guidelines for Sample Preparation and Normalization
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An Adaptive Association Test for Multiple Phenotypes with GWAS Summary Statistics.

Junghi Kim1, Yun Bai1, Wei Pan1

  • 1Division of Biostatistics, University of Minnesota, Minneapolis, Minnesota, United States of America.

Genetic Epidemiology
|October 24, 2015
PubMed
Summary

This study introduces a new adaptive test for analyzing genome-wide association study (GWAS) summary statistics, enabling the detection of single marker-multiple phenotype associations without individual data. The method shows potential power gains over existing approaches.

Keywords:
GEEadaptive sum of powered score testmeta analysismultivariate traitstatistical power

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Area of Science:

  • Genetics
  • Statistical Genetics
  • Bioinformatics

Background:

  • Genome-wide association studies (GWAS) often generate summary statistics, which are more accessible than individual-level data.
  • Analyzing associations between single genetic markers and multiple correlated phenotypes using only summary statistics is a growing challenge.
  • Existing methods may not fully leverage the information available in GWAS summary statistics for multi-phenotype analyses.

Purpose of the Study:

  • To develop and evaluate a powerful adaptive test for single marker-multiple phenotype association analysis using GWAS summary statistics.
  • To compare the performance of the proposed test against existing methods.
  • To extend the methodology for meta-analysis of multiple GWAS datasets.

Main Methods:

  • Development of a novel adaptive statistical test designed for GWAS summary data.
  • Comparative performance evaluation through realistic simulation studies.
  • Application to real-world GWAS datasets, including blood lipid and anthropometric traits.

Main Results:

  • The proposed adaptive test demonstrates superior or comparable power to existing methods in simulation studies.
  • Successful application to meta-analyzed GWAS data for complex traits.
  • The method effectively handles correlated phenotypes using summary-level data.

Conclusions:

  • The novel adaptive test provides a powerful and practical tool for multi-phenotype association analysis in GWAS.
  • This approach is valuable for researchers working with accessible GWAS summary statistics.
  • The method offers a complementary or more powerful alternative to current analytical techniques.