Benchmarking Association Analyses of Continuous Exposures with RNA-seq in Observational Studies

Sofer, T.; Kurniansyah, N.; Aguet, F.; Ardlie, K.; Durda, P.; Nickerson, D. A.; Smith, J. D.; Liu, Y.; Gharib, S. A.; Redline, S.; Rich, S. S.; Rotter, J. I.; Taylor, K.

doi:10.1101/2021.02.12.430989

bioRxiv · 10.1101/2021.02.12.430989

Benchmarking Association Analyses of Continuous Exposures with RNA-seq in Observational Studies

Sofer, T.·Kurniansyah, N.·Aguet, F.·Ardlie, K.·Durda, P.·Nickerson, D. A.·Smith, J. D.·Liu, Y.·Gharib, S. A.·Redline, S.·Rich, S. S.·Rotter, J. I.·Taylor, K.

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Abstract

Large datasets of hundreds to thousands of individuals measuring RNA-seq in observational studies are becoming available. Many popular software packages for analysis of RNA-seq data were constructed to study differences in expression signatures in an experimental design with well-defined conditions (exposures). In contrast, observational studies may have varying levels of confounding of the transcript-exposure associations; further, exposure measures may vary from discrete (exposed, yes/no) to continuous (levels of exposure), with non-normal distributions of exposure. We compare popular software for gene expression - DESeq2, edgeR, and limma - as well as linear regression-based analyses for studying the association of continuous exposures with RNA-seq. We developed a computation pipeline that includes transformation, filtering, and generation of empirical null distribution of association p-values, and we apply the pipeline to compute empirical p-values with multiple testing correction. We employ a resampling approach that allows for assessment of false positive detection across methods, power comparison, and the computation of quantile empirical p-values. The results suggest that linear regression methods are substantially faster with better control of false detections than other methods, even with the resampling method to compute empirical p-values. We provide the proposed pipeline with fast algorithms in R.

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Sofer, T., Kurniansyah, N., Aguet, F., Ardlie, K., Durda, P., Nickerson, D. A., Smith, J. D., Liu, Y., Gharib, S. A., Redline, S., Rich, S. S., Rotter, J. I., Taylor, K.. 2021-02-13. Benchmarking Association Analyses of Continuous Exposures with RNA-seq in Observational Studies. https://doi.org/10.1101/2021.02.12.430989

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