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Updated: Feb 26, 2026

Rare Event Detection Using Error-corrected DNA and RNA Sequencing

Rare Event Detection Using Error-corrected DNA and RNA Sequencing

Published on: August 3, 2018

Identifying and mitigating batch effects in whole genome sequencing data.

Jennifer A Tom¹, Jens Reeder², William F Forrest²

¹Bioinformatics and Computational Biology Department, Genentech Inc, 1 DNA Way, South San Francisco, CA, 94080, USA. tom.jennifer@gene.com.

BMC Bioinformatics

|July 26, 2017

Summary

This summary is machine-generated.

Researchers can now identify and remove batch effects in whole genome sequencing data using new quality metrics and filters. This software package aids in detecting and mitigating data inconsistencies for more accurate genetic association studies.

Keywords:

Batch effects Genome-wide association studies Genotyping Whole genome sequencing

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Last Updated: Feb 26, 2026

Rare Event Detection Using Error-corrected DNA and RNA Sequencing

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Published on: August 3, 2018

Tick Microbiome Characterization by Next-Generation 16S rRNA Amplicon Sequencing

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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:

Genomics
Bioinformatics
Statistical Genetics

Background:

Large-scale whole genome sequencing (WGS) projects generate deep coverage datasets.
Integrating data from diverse sources introduces batch effects, stemming from variations in sequencing protocols or bioinformatics pipelines.
Current methods lack systematic approaches to detect, filter, or correct for batch effects in WGS data.

Purpose of the Study:

To develop and validate methods for identifying and mitigating batch effects in WGS data.
To provide a software package for computing key quality metrics to aid in batch effect detection.
To implement novel filters to remove variants falsely associated with phenotypes due to batch effects.

Main Methods:

Development of key quality metrics for assessing WGS data.
Utilizing principal components analysis (PCA) on quality metrics to identify batch effects.
Implementation of site-specific filters including haplotype-based genotype correction, differential genotype quality testing, and missing genotype rate thresholds.

Main Results:

The developed filters effectively removed 96.1% of unconfirmed significant SNP associations and 97.6% of indel associations.
In an AMD candidate SNP analysis, filters reduced power by 12.5% while removing 2 of 16 confirmed associations.
In independent datasets, the method demonstrated high efficacy, removing over 90% of unconfirmed associations with a low type I error rate of 3%.

Conclusions:

Effective tools for identifying and mitigating batch effects in WGS data were lacking.
The developed methods and filters successfully address this deficiency.
The validated approach enhances the reliability of WGS data analysis.