Development of a tissue augmented Bayesian model for expression quantitative trait loci analysis

Yonghua Zhuang; Kristen Wade; Laura M. Saba; Katerina Kechris; Yonghua Zhuang; Kristen Wade; Laura M. Saba; Katerina Kechris

doi:10.3934/mbe.2020007

Mathematical Biosciences and Engineering

2020, Volume 17, Issue 1: 122-143. doi: 10.3934/mbe.2020007

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Development of a tissue augmented Bayesian model for expression quantitative trait loci analysis

1.
Department of Biostatistics and Informatics, Colorado School of Public Health, University of Colorado Denver Anschutz Medical Campus, Mail Stop B119, 13001 E. 17th Place, Aurora, 80045, USA
2.
Human Medical Genetics and Genomics Program, School of Medicine, University of Colorado Denver Anschutz Medical Campus, 80045, Aurora, USA
3.
Department of Pharmaceutical Sciences, Skaggs School of Pharmacy and Pharmaceutical Sciences, University of Colorado Denver Anschutz Medical Campus, 80045, Aurora, USA

Received: 08 March 2019 Accepted: 16 September 2019 Published: 26 September 2019

Expression quantitative trait loci (eQTL) analyses detect genetic variants (SNPs) associated with RNA expression levels of genes. The conventional eQTL analysis is to perform individual tests for each gene-SNP pair using simple linear regression and to perform the test on each tissue separately ignoring the extensive information known about RNA expression in other tissue(s). Although Bayesian models have been recently developed to improve eQTL prediction on multiple tissues, they are often based on uninformative priors or treat all tissues equally. In this study, we develop a novel tissue augmented Bayesian model for eQTL analysis (TA-eQTL), which takes prior eQTL information from a different tissue into account to better predict eQTL for another tissue. We demonstrate that our modified Bayesian model has comparable performance to several existing methods in terms of sensitivity and specificity using allele-specific expression (ASE) as the gold standard. Furthermore, the tissue augmented Bayesian model improves the power and accuracy for local-eQTL prediction especially when the sample size is small. In summary, TA-eQTL's performance is comparable to existing methods but has additional flexibility to evaluate data from different platforms, can focus prediction on one tissue using only summary statistics from the secondary tissue(s), and provides a closed form solution for estimation.
- eQTL,
- Bayesian model,
- allele-specific expression
Citation: Yonghua Zhuang, Kristen Wade, Laura M. Saba, Katerina Kechris. Development of a tissue augmented Bayesian model for expression quantitative trait loci analysis[J]. Mathematical Biosciences and Engineering, 2020, 17(1): 122-143. doi: 10.3934/mbe.2020007

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Abstract

Expression quantitative trait loci (eQTL) analyses detect genetic variants (SNPs) associated with RNA expression levels of genes. The conventional eQTL analysis is to perform individual tests for each gene-SNP pair using simple linear regression and to perform the test on each tissue separately ignoring the extensive information known about RNA expression in other tissue(s). Although Bayesian models have been recently developed to improve eQTL prediction on multiple tissues, they are often based on uninformative priors or treat all tissues equally. In this study, we develop a novel tissue augmented Bayesian model for eQTL analysis (TA-eQTL), which takes prior eQTL information from a different tissue into account to better predict eQTL for another tissue. We demonstrate that our modified Bayesian model has comparable performance to several existing methods in terms of sensitivity and specificity using allele-specific expression (ASE) as the gold standard. Furthermore, the tissue augmented Bayesian model improves the power and accuracy for local-eQTL prediction especially when the sample size is small. In summary, TA-eQTL's performance is comparable to existing methods but has additional flexibility to evaluate data from different platforms, can focus prediction on one tissue using only summary statistics from the secondary tissue(s), and provides a closed form solution for estimation.

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