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Related Concept Videos

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,...
Comparing Copy Number Variations and SNPs02:26

Comparing Copy Number Variations and SNPs

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.
Copy number variations or CNVs are the structural variations that cover more than 1kb of DNA sequence. The single nucleotide polymorphism (SNP), on the other hand, is a single nucleotide change or a point mutation that is found in more than 1%...
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...

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Related Experiment Video

Updated: May 18, 2026

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

Quick, "imputation-free" meta-analysis with proxy-SNPs.

Christian Meesters1, Markus Leber, Christine Herold

  • 1German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany.

BMC Bioinformatics
|September 14, 2012
PubMed
Summary

This study introduces YAMAS, a fast meta-analysis software that uses proxy-single nucleotide polymorphisms (SNPs) to analyze genome-wide association studies (GWASes) without imputation, improving efficiency and power for genetic discovery.

Related Experiment Videos

Last Updated: May 18, 2026

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
  • Bioinformatics
  • Statistical Genomics

Background:

  • Meta-analysis (MA) is crucial for pooling genome-wide association studies (GWASes) to enhance statistical power.
  • Imputation is commonly used to harmonize SNP panels across studies, but it is time-consuming.
  • Existing methods often lead to loss of single nucleotide polymorphisms (SNPs) due to differing genotyping chips.

Purpose of the Study:

  • To present a rapid method for MA that avoids SNP forfeiture without relying on imputation.
  • To introduce YAMAS (Yet Another Meta Analysis Software) for efficient cross-GWAS analysis.
  • To enable timely conclusions from GWAS data before complete imputation.

Main Methods:

  • Utilizing reference linkage disequilibrium data (1000 Genomes/HapMap) to identify proxy-SNPs for missing markers.
  • Combining association effect estimates from SNPs and their proxies for MA.
  • Implementing the proxy-SNP algorithm within the YAMAS software.

Main Results:

  • The YAMAS proxy-SNP approach significantly speeds up MA compared to imputation-based methods.
  • The method demonstrates good statistical power and provides valuable preliminary results.
  • Analysis of Type II Diabetes GWAS data showed substantial p-value improvements, outperforming naive MA.

Conclusions:

  • YAMAS offers an efficient and fast MA solution, incorporating proxy-SNPs to mitigate power loss from missing markers.
  • The software simplifies MA by providing a generic parser for diverse GWAS data formats.
  • YAMAS serves as a valuable tool, supplementing the MA process for genetic research.