ArticleBMC bioinformatics2017
Identifying and mitigating batch effects in whole genome sequencing data.
Article in BMC bioinformatics, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers.
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Who cites it
29 citing papers in PubMed.
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- Quartet DNA reference materials and datasets for comprehensively evaluating germline variant calling performance.Genome biology · 2023Article
- Evolution of the Mutation Spectrum Across a Mammalian Phylogeny.Molecular biology and evolution · 2023Article
- Batch-effect correction with sample remeasurement in highly confounded case-control studies.Nature computational science · 2023Article
- "Evolution of the mutation spectrum across a mammalian phylogeny".bioRxiv : the preprint server for biology · 2023Article
- Cryptic diversity and spatial genetic variation in the coralEvolutionary applications · 2023Article
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- Dual Domestication, Diversity, and Differential Introgression in Old World Cotton Diploids.Genome biology and evolution · 2022Article
- A Fast and Robust Strategy to Remove Variant-Level Artifacts in Alzheimer Disease Sequencing Project Data.Neurology. Genetics · 2022Article
- Rare and de novo variants in 827 congenital diaphragmatic hernia probands implicate LONP1 as candidate risk gene.American journal of human genetics · 2021Article
Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundLarge sample sets of whole genome sequencing with deep coverage are being generated, however assembling datasets from different sources inevitably introduces batch effects. These batch effects are not well understood and can be due to changes in the sequencing protocol or bioinformatics tools used to process the data. No systematic algorithms or heuristics exist to detect and filter batch effects or remove associations impacted by batch effects in whole genome sequencing data.
resultsWe describe key quality metrics, provide a freely available software package to compute them, and demonstrate that identification of batch effects is aided by principal components analysis of these metrics. To mitigate batch effects, we developed new site-specific filters that identified and removed variants that falsely associated with the phenotype due to batch effect. These include filtering based on: a haplotype based genotype correction, a differential genotype quality test, and removing sites with missing genotype rate greater than 30% after setting genotypes with quality scores less than 20 to missing. This method removed 96.1% of unconfirmed genome-wide significant SNP associations and 97.6% of unconfirmed genome-wide significant indel associations. We performed analyses to demonstrate that: 1) These filters impacted variants known to be disease associated as 2 out of 16 confirmed associations in an AMD candidate SNP analysis were filtered, representing a reduction in power of 12.5%, 2) In the absence of batch effects, these filters removed only a small proportion of variants across the genome (type I error rate of 3%), and 3) in an independent dataset, the method removed 90.2% of unconfirmed genome-wide SNP associations and 89.8% of unconfirmed genome-wide indel associations.
conclusionsResearchers currently do not have effective tools to identify and mitigate batch effects in whole genome sequencing data. We developed and validated methods and filters to address this deficiency.
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