@article{8b9ad0db584240c0ad9f3fa7825c08c6,
title = "RNA-Seq optimization with eQTL gold standards",
abstract = "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.",
keywords = "Blood, Brain, EQTL, GTEx, LCL, RNA-Seq",
author = "Ellis, {Shannon E.} and Simone Gupta and Ashar, {Foram N.} and Bader, {Joel S.} and West, {Andrew B.} and Arking, {Dan E.}",
note = "Funding Information: Tissue was provided by the NICHD Brain and Tissue Bank for Developmental Disorders at the University of Maryland, the Autism Tissue Program (ATP), and the Harvard Brain Tissue Resource Center (supported in part by PHS grant R24 MH068855). We thank J. Pickett, C. K. Hare, and E. Xiu from the ATP for tissue coordination. Sequencing was completed by the Genetic Resources Core Facility (GRCF) at Johns Hopkins. SG, JSB, ABW and DEA acknowledge funding from the Simons Foundation (SFARI 137603 to DEA). The Genotype-Tissue Expression (GTEx) Project was supported by the Common Fund of the Office of the Director of the National Institutes of Health (commonfund.nih.gov/GTEx). Additional funds were provided by the NCI, NHGRI, NHLBI, NIDA, NIMH, and NINDS. Donors were enrolled at Biospecimen Source Sites funded by NCI\SAIC-Frederick, Inc. (SAIC-F) subcontracts to the National Disease Research Interchange (10XS170), Roswell Park Cancer Institute (10XS171), and Science Care, Inc. (X10S172). The Laboratory, Data Analysis, and Coordinating Center (LDACC) was funded through a contract (HHSN268201000029C) to The Broad Institute, Inc. Biorepository operations were funded through an SAIC-F subcontract to Van Andel Research Institute (10ST1035). Additional data repository and project management were provided by SAIC-F (HHSN261200800001E). The Brain Bank was supported by a supplement to University of Miami grant DA006227. Statistical Methods development grants were made to the University of Geneva (MH090941), the University of Chicago (MH090951 & MH09037), the University of North Carolina - Chapel Hill (MH090936) and to Harvard University (MH090948). The datasets used for the analyses described in this manuscript were obtained from dbGaP at http://www.ncbi.nlm.nih. gov/gap through dbGaP accession number phs000424.GTEx.v3.p1.",
year = "2013",
month = dec,
day = "17",
doi = "10.1186/1471-2164-14-892",
language = "English (US)",
volume = "14",
journal = "BMC genomics",
issn = "1471-2164",
publisher = "BioMed Central",
number = "1",
}