RNA-Seq optimization with eQTL gold standards
Open accessAbstract
Background: RNA-Sequencing (RNA-Seq) experiments have been optimized for library preparation, mapping, and gene expression estimation. These methods, however, have revealed weaknesses in the next stages of analysis of differential expression, with results sensitive to systematic sample stratification or, in more extreme cases, to outliers. Further, a method to assess normalization and adjustment measures imposed on the data is lacking. Results: To address these issues, we utilize previously published eQTLs as a novel gold standard at the center of a framework that integrates DNA genotypes and RNA-Seq data to optimize analysis and aid in the understanding of genetic variation and gene expression. After detecting sample contamination and sequencing outliers in RNA-Seq data, a set of previously published brain eQTLs was used to determine if sample outlier removal was appropriate. Improved replication of known eQTLs supported removal of these samples in downstream analyses. eQTL replication was further employed to assess normalization methods, covariate inclusion, and gene annotation. This method was validated in an independent RNA-Seq blood data set from the GTEx project and a tissue-appropriate set of eQTLs. eQTL replication in both data sets highlights the necessity of accounting for unknown covariates in RNA-Seq data analysis. Conclusion: As each RNA-Seq experiment is unique with its own experiment-specific limitations, we offer an easily-implementable method that uses the replication of known eQTLs to guide each step in one’s data analysis pipeline. In the two data sets presented herein, we highlight not only the necessity of careful outlier detection but also the need to account for unknown covariates in RNA-Seq experiments.
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We assessed summarized values on a per-gene basis, removing gene estimates for samples whose gene expression values were more than three standard deviations from the mean expression of each gene, as these outliers are artefactual in origin.
“We assessed summarized values on a per-gene basis, removing gene estimates for samples whose gene expression values were more than three standard deviations (s.d.) from the mean expression of each gene (per-gene outlier), as these outliers are artefactual in origin16.”
From Transcriptome analysis reveals dysregulation of innate immune response genes and neuronal activity-dependent genes in autism (2014) · cites this paper as [16]
All 104 samples were within three s.d. of the mean of the first six principal components, which together explained ~55% of the variance.
“All 104 samples were within three s.d. of the mean of the first six principal components, which together explained ~55% of the variance16.”
From Transcriptome analysis reveals dysregulation of innate immune response genes and neuronal activity-dependent genes in autism (2014) · cites this paper as [16]
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Additional file 4 of A common regulatory haplotype doubles lactoferrin concentration in milk
Lopdell, T., Trevarton, A. J., Moody, J., Prowse-Wilkins, C., Knowles, S., Tiplady, K., Chamberlain, A. J., Goddard, M. E., Spelman, R. J., Lehnert, K., et al. (2024) · Figshare · doi:10.6084/m9.figshare.26699132
Additional file 1 of A common regulatory haplotype doubles lactoferrin concentration in milk
Lopdell, T., Trevarton, A. J., Moody, J., Prowse-Wilkins, C., Knowles, S., Tiplady, K., Chamberlain, A. J., Goddard, M. E., Spelman, R. J., Lehnert, K., et al. (2024) · Figshare · doi:10.6084/m9.figshare.25506641
Prowse-Wilkins, C., Lopdell, T., Xiang, R., Vander-Jagt, C. J., Littlejohn, M. D., Chamberlain, A. J., & Goddard, M. E. (2023) · Figshare · doi:10.6084/m9.figshare.22616741
Additional file 2 of A common regulatory haplotype doubles lactoferrin concentration in milk
Lopdell, T., Trevarton, A. J., Moody, J., Prowse-Wilkins, C., Knowles, S., Tiplady, K., Chamberlain, A. J., Goddard, M. E., Spelman, R. J., Lehnert, K., et al. (2024) · Figshare · doi:10.6084/m9.figshare.25506742
Prowse-Wilkins, C., Lopdell, T., Xiang, R., Vander-Jagt, C. J., Littlejohn, M. D., Chamberlain, A. J., & Goddard, M. E. (2023) · Figshare · doi:10.6084/m9.figshare.22616747
Prowse-Wilkins, C., Lopdell, T., Xiang, R., Vander-Jagt, C. J., Littlejohn, M. D., Chamberlain, A. J., & Goddard, M. E. (2023) · Figshare · doi:10.6084/m9.figshare.22616744
Additional file 3 of A common regulatory haplotype doubles lactoferrin concentration in milk
Lopdell, T., Trevarton, A. J., Moody, J., Prowse-Wilkins, C., Knowles, S., Tiplady, K., Chamberlain, A. J., Goddard, M. E., Spelman, R. J., Lehnert, K., et al. (2024) · Figshare · doi:10.6084/m9.figshare.26699129
- Authors
- Ellis, S. E., Gupta, S., Ashar, F. N., Bader, J. S., West, A. B., & Arking, D. E.
- Affiliations
- Johns Hopkins University (US)
- University of Alabama (US)
- Corresponding author
- Arking, Dan E
- Venue
- BMC Genomics
- Published
- 2013-12
- Volume
- 14
- Issue
- 1
- Pages
- 892-892
- ISSN
- 1471-2164 (ISSN-L)
- PMC
- PMC3890578
- OpenAlex
- W2025063820
- Cited by
- 30
- References
- 35
- Citations by year
- 2024: 4 · 2023: 6 · 2022: 1 · 2020: 1 · 2019: 2 · 2018: 1 · 2017: 7 · 2016: 4 · 2015: 2 · 2014: 2
- FWCI
- 1.19
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- cc-by
- Locations
- BMC Genomics (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- eScholarship (California Digital Library) (submittedVersion)
- eScholarship (California Digital Library) (submittedVersion, cc-by)
- Funders
- Simons Foundation (137603)
- Van Andel Research Institute (10ST1035)
- University of Miami (MH090936, MH090951, MH090948, DA006227, MH090941)
- Harvard University (MH090948)
- University of Chicago (MH090951)
- Johns Hopkins University
- Broad Institute (HHSN268201000029C)
- Simons Foundation Autism Research Initiative
- Université de Genève (MH090941)
- National Institutes of Health (10XS170, #HHSN261200800001E, HHSN268201000029C, 10XS171, 10ST1035)
- National Heart, Lung, and Blood Institute (HHSN268201000029C, HHSN261200800001E)
- National Institute of Mental Health (R24MH068855, HHSN261200800001E, MH-090941, R01MH090948, MH090948, R01MH090941, MH090951, MH09037, R01MH090951)
- National Institute on Drug Abuse (DA006227, R01DA006227)
- National Human Genome Research Institute (HHSN261200800001E)
- NIH Office of the Director (HHSN261200800001E)
- National Cancer Institute (HHSN261200800001E), HHSN268201000029C)
- National Institute of Neurological Disorders and Stroke
- Common Fund
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene expression and cancer classification
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Phylogenetic Studies
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Associations and Epidemiology
- Keywords
- RNA-seq, expression quantitative trait loci, sample outlier detection, sample contamination, normalization methods, covariate adjustment, genome annotation, differential gene expression, gene expression estimation
- MeSH terms
- Blood, Brain, Chromosome Mapping, Humans, Reference Standards, Reproducibility of Results, Sequence Analysis, RNA, Quantitative Trait Loci, Genotyping Techniques
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