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Genome-wide Association Studies-GWAS

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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Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry

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A Novel Statistical Method to Diagnose, Quantify and Correct Batch Effects in Genomic Studies.

Gift Nyamundanda^1,2, Pawan Poudel¹, Yatish Patil^1,2

¹Division of Molecular Pathology, The Institute of Cancer Research, London, United Kingdom.

Scientific Reports

|September 9, 2017

Summary

Batch effects in large-scale genomic data can obscure biological signals. A new probabilistic method, findBATCH, effectively identifies and corrects these effects, improving data integration for cancer research.

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Area of Science:

Genomics and Bioinformatics
Statistical Genetics
Computational Biology

Background:

Large-scale genome projects generate complex datasets from diverse sources, increasing the risk of batch effects.
Existing batch effect evaluation methods, such as principal component analysis (PCA), can be insufficient and may inadvertently remove biological signals.
Accurate identification and correction of batch effects are crucial for reliable genomic data integration and analysis.

Purpose of the Study:

To develop a novel statistical method for evaluating batch effects in large-scale genomic data.
To introduce a new batch correction approach that outperforms traditional PCA-based methods.
To provide an accessible R package for batch effect exploration and correction.

Main Methods:

Proposed a new method, finding batch effect (findBATCH), based on probabilistic principal component and covariates analysis (PPCCA) for batch effect evaluation.
Developed a complementary method, correcting batch effect (correctBATCH), within the same PPCCA framework for data correction.
Utilized gene expression data from breast and colorectal cancer studies to demonstrate the methods' efficacy.

Main Results:

The findBATCH method effectively identifies batch effects, overcoming limitations of visual inspection-based PCA.
The correctBATCH approach demonstrates superior performance compared to traditional PCA-based correction methods.
Successful merging of gene expression data from multiple studies was achieved by diagnosing and correcting batch effects while preserving biological variations.

Conclusions:

The proposed PPCCA-based findBATCH and correctBATCH methods offer a robust solution for evaluating and correcting batch effects in genomic data.
These novel methods enhance the reliability of integrating multi-platform, multi-study genomic datasets.
The exploBATCH R package provides researchers with practical tools for managing batch effects in their analyses.