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Development of a tissue augmented Bayesian model for expression quantitative trait loci analysis.
- Source :
-
Mathematical biosciences and engineering : MBE [Math Biosci Eng] 2019 Sep 26; Vol. 17 (1), pp. 122-143. - Publication Year :
- 2019
-
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.
- Subjects :
- Alleles
Animals
Area Under Curve
Female
Gene Expression Profiling
Gene Expression Regulation
Genotype
Liver metabolism
Male
Mice
Mice, Inbred C57BL
Mice, Inbred DBA
Models, Genetic
Oligonucleotide Array Sequence Analysis
ROC Curve
Reproducibility of Results
Sample Size
Software
Bayes Theorem
Polymorphism, Single Nucleotide
Quantitative Trait Loci
Subjects
Details
- Language :
- English
- ISSN :
- 1551-0018
- Volume :
- 17
- Issue :
- 1
- Database :
- MEDLINE
- Journal :
- Mathematical biosciences and engineering : MBE
- Publication Type :
- Academic Journal
- Accession number :
- 31731343
- Full Text :
- https://doi.org/10.3934/mbe.2020007