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Bioinformatics. 2019 Aug 1. pii: btz545. doi: 10.1093/bioinformatics/btz545. [Epub ahead of print]

Bayesian Estimation of Genetic Regulatory Effects in High-throughput Reporter Assays.

Author information

1
Duke Center for Statistical Genetics and Genomics, Duke University, Durham, NC, USA.
2
Division of Integrative Genomics, Department of Biostatistics & Bioinformatics, Duke University Medical School, Durham, NC, USA.
3
Center for Genomic and Computational Biology, Duke University Medical School, Durham, NC, USA.
4
Program in Computational Biology & Bioinformatics, Duke University, Durham, NC, USA.
5
Department of Biostatistics, Yale University, New Haven, CT, USA.
6
Masters Program in Biostatistics, Department of Biostatistics and Bioinformatics, Duke University Medical School, Durham, NC, USA.
7
University Program in Genetics and Genomics, Duke University, Durham, NC, USA.
8
Department of Biostatistics and Bioinformatics, Duke University, Durham, NC, USA.
9
Division of Endocrinology, Metabolism & Molecular Medicine, Northwestern University Feinberg School of Medicine, Chicago, Illinois, USA.
10
Division of Biostatistics, Department of Preventive Medicine, Northwestern University Feinberg School of Medicine,Chicago, Illinois, USA.

Abstract

MOTIVATION:

High-throughput reporter assays dramatically improve our ability to assign function to noncoding genetic variants, by measuring allelic effects on gene expression in the controlled setting of a reporter gene. Unlike genetic association tests, such assays are not confounded by linkage disequilibrium when loci are independently assayed. These methods can thus improve the identification of causal disease mutations. While work continues on improving experimental aspects of these assays, less effort has gone into developing methods for assessing the statistical significance of assay results, particularly in the case of rare variants captured from patient DNA.

RESULTS:

We describe a Bayesian hierarchical model, called Bayesian Inference of Regulatory Differences (BIRD), which integrates prior information and explicitly accounts for variability between experimental replicates. The model produces substantially more accurate predictions than existing methods when allele frequencies are low, which is of clear advantage in the search for disease-causing variants in DNA captured from patient cohorts. Using the model, we demonstrate a clear tradeoff between variant sequencing coverage and numbers of biological replicates, and we show that the use of additional biological replicates decreases variance in estimates of effect size, due to the properties of the Poisson-binomial distribution. We also provide a power and sample size calculator, which facilitates decision making in experimental design parameters.

AVAILABILITY:

The software is freely available from www.geneprediction.org/bird. The experimental design web tool can be accessed at http://67.159.92.22:8080.

SUPPLEMENTARY INFORMATION:

Supplementary information is available online.

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