Development of a tissue augmented Bayesian model for expression quantitative trait loci analysis
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 igno...
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| Format: | Article |
| Language: | English |
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AIMS Press
2020-01-01
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| Series: | Mathematical Biosciences and Engineering |
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| Online Access: | https://www.aimspress.com/article/doi/10.3934/mbe.2020007?viewType=HTML |
| _version_ | 1831672574985109504 |
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| author | Yonghua Zhuang Kristen Wade Laura M. Saba Katerina Kechris |
| author_facet | Yonghua Zhuang Kristen Wade Laura M. Saba Katerina Kechris |
| author_sort | Yonghua Zhuang |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-19T23:55:13Z |
| format | Article |
| id | doaj.art-236d8c29c4284bdb83cd2f1d59c4ac94 |
| institution | Directory Open Access Journal |
| issn | 1551-0018 |
| language | English |
| last_indexed | 2024-12-19T23:55:13Z |
| publishDate | 2020-01-01 |
| publisher | AIMS Press |
| record_format | Article |
| series | Mathematical Biosciences and Engineering |
| spelling | doaj.art-236d8c29c4284bdb83cd2f1d59c4ac942022-12-21T20:01:01ZengAIMS PressMathematical Biosciences and Engineering1551-00182020-01-0117112214310.3934/mbe.2020007Development of a tissue augmented Bayesian model for expression quantitative trait loci analysisYonghua Zhuang0Kristen Wade1Laura M. Saba2Katerina Kechris31. 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, USA2. Human Medical Genetics and Genomics Program, School of Medicine, University of Colorado Denver Anschutz Medical Campus, 80045, Aurora, USA3. Department of Pharmaceutical Sciences, Skaggs School of Pharmacy and Pharmaceutical Sciences, University of Colorado Denver Anschutz Medical Campus, 80045, Aurora, USA1. 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, USAExpression 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.https://www.aimspress.com/article/doi/10.3934/mbe.2020007?viewType=HTMLeqtlbayesian modelallele-specific expression |
| spellingShingle | Yonghua Zhuang Kristen Wade Laura M. Saba Katerina Kechris Development of a tissue augmented Bayesian model for expression quantitative trait loci analysis Mathematical Biosciences and Engineering eqtl bayesian model allele-specific expression |
| title | Development of a tissue augmented Bayesian model for expression quantitative trait loci analysis |
| title_full | Development of a tissue augmented Bayesian model for expression quantitative trait loci analysis |
| title_fullStr | Development of a tissue augmented Bayesian model for expression quantitative trait loci analysis |
| title_full_unstemmed | Development of a tissue augmented Bayesian model for expression quantitative trait loci analysis |
| title_short | Development of a tissue augmented Bayesian model for expression quantitative trait loci analysis |
| title_sort | development of a tissue augmented bayesian model for expression quantitative trait loci analysis |
| topic | eqtl bayesian model allele-specific expression |
| url | https://www.aimspress.com/article/doi/10.3934/mbe.2020007?viewType=HTML |
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