Evidence map›Paper›PMID 28738841›Full record

ArticleBMC bioinformatics2017

Identifying and mitigating batch effects in whole genome sequencing data.

Jennifer A Tom, Jens Reeder, William F Forrest, Robert R Graham, Julie Hunkapiller, Timothy W Behrens, Tushar R Bhangale

Abstract read
In one paragraph

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.

0numbers the graph read from it
0cells of the map it votes in
29citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

29 citing papers in PubMed.

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  15. "Evolution of the mutation spectrum across a mammalian phylogeny".bioRxiv : the preprint server for biology · 2023
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Jennifer A TomBioinformatics and Computational Biology Department, Genentech Inc, 1 DNA Way, South San Francisco, CA, 94080, USA. tom.jennifer@gene.com.ORCID http://orcid.org/0000-0003-3060-6751
Jens ReederBioinformatics and Computational Biology Department, Genentech Inc, 1 DNA Way, South San Francisco, CA, 94080, USA.
William F ForrestBioinformatics and Computational Biology Department, Genentech Inc, 1 DNA Way, South San Francisco, CA, 94080, USA.
Robert R GrahamHuman Genetics Department, Genentech Inc, 1 DNA Way, South San Francisco, CA, 94080, USA.
Julie HunkapillerHuman Genetics Department, Genentech Inc, 1 DNA Way, South San Francisco, CA, 94080, USA.
Timothy W BehrensHuman Genetics Department, Genentech Inc, 1 DNA Way, South San Francisco, CA, 94080, USA.
Tushar R BhangaleBioinformatics and Computational Biology Department, Genentech Inc, 1 DNA Way, South San Francisco, CA, 94080, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Indexed as

Genome-Wide Association StudyGenotypeHigh-Throughput Nucleotide SequencingHumansMacular DegenerationPhenotypePolymorphism, Single NucleotidePrincipal Component AnalysisSequence Analysis, DNASoftwareBatch effectsGenome-wide association studiesGenotypingWhole genome sequencing

Identifiers

PMID28738841
PMCPMC5525370

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.