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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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Genetic Profiling and Genome-Scale Dropout Screening to Identify Therapeutic Targets in Mouse Models of Malignant Peripheral Nerve Sheath Tumor

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A quality control algorithm for filtering SNPs in genome-wide association studies.

Monnat Pongpanich¹, Patrick F Sullivan, Jung-Ying Tzeng

¹Bioinformatics Research Center, North Carolina State University, Raleigh, NC 27695-7566, USA.

Bioinformatics (Oxford, England)

|May 27, 2010

Summary

This study introduces a new algorithm for quality control in genome-wide association studies. It improves the identification of low-quality single nucleotide polymorphisms (SNPs) by considering multiple features simultaneously, reducing false associations while retaining true findings.

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Detection of Rare Genomic Variants from Pooled Sequencing Using SPLINTER

Detection of Rare Genomic Variants from Pooled Sequencing Using SPLINTER

Published on: June 23, 2012

Area of Science:

Genetics
Bioinformatics
Statistical genomics

Background:

Quality control (QC) filtering of single nucleotide polymorphisms (SNPs) is crucial for accurate genome-wide association studies (GWAS).
Current expert-guided SNP QC methods rely on arbitrary thresholds and do not holistically assess all QC variables.
Limitations include potential for false findings and suboptimal removal of low-quality SNPs.

Purpose of the Study:

To develop and evaluate a novel algorithm for SNP quality control in GWAS.
To improve the identification of low-quality SNPs by integrating multiple QC features.
To reduce false associations and retain true genetic associations in GWAS datasets.

Main Methods:

The proposed algorithm utilizes principal component analysis and clustering analysis to identify low-quality SNPs.
It minimizes arbitrary cutoff values and considers QC features collectively, offering conditional thresholds.
Applied to Wellcome Trust Case Control Consortium and Genetic Association Information Network studies.

Main Results:

The algorithm demonstrated comparable or reduced SNP exclusion rates versus expert filters.
It achieved similar or lower inflation factors (lambda) for test statistics.
The method significantly reduced false associations while successfully retaining all true associations.

Conclusions:

The proposed PCA and clustering-based algorithm offers a more robust approach to SNP quality control in GWAS.
It effectively balances the exclusion of low-quality SNPs with the preservation of true genetic signals.
This method enhances the reliability and accuracy of GWAS findings.