A framework for integrating directed and undirected annotations to build explanatory models of cis-eQTL data

A longstanding goal of regulatory genetics is to understand how variants in genome sequences lead to changes in gene expression. Here we present a method named Bayesian Annotation Guided eQTL Analysis (BAGEA), a variational Bayes framework to model cis-eQTLs using directed and undirected genomic ann...

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Bibliographic Details
Published in:PLoS computational biology 2020-06, Vol.16 (6), p.e1007770-e1007770
Main Authors: Lamparter, David, Bhatnagar, Rajat, Hebestreit, Katja, Belgard, T. Grant, Zhang, Alice, Hanson-Smith, Victor, Zhang, Weixiong, Pique-Regi, Roger
Format: Article
Language:English
Subjects:
Annotations
Bayesian analysis
Binding sites
Biology and Life Sciences
DNA sequencing
Epigenetics
Gene expression
Gene sequencing
Genes
Genetics
Genomes
Genomics
Genotype & phenotype
Identification and classification
Mathematical models
Medicine and Health Sciences
Methods
Neural networks
Physical Sciences
Quantitative trait loci
Research and Analysis Methods
Software
Statistical analysis
Variance
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Summary:A longstanding goal of regulatory genetics is to understand how variants in genome sequences lead to changes in gene expression. Here we present a method named Bayesian Annotation Guided eQTL Analysis (BAGEA), a variational Bayes framework to model cis-eQTLs using directed and undirected genomic annotations. We used BAGEA to integrate directed genomic annotations with eQTL summary statistics from tissues of various origins. This analysis revealed epigenetic marks that are relevant for gene expression in different tissues and cell types. We estimated the predictive power of the models that were fitted based on directed genomic annotations. This analysis showed that, depending on the underlying eQTL data used, the directed genomic annotations could predict up to 1.5% of the variance observed in the expression of genes with top nominal eQTL association p-values < 10.sup.-7 . For genes with estimated effect sizes in the top 25% quantile, up to 5% of the expression variance could be predicted. Based on our results, we recommend the use of BAGEA for the analysis of cis-eQTL data to reveal annotations relevant to expression biology.
ISSN:1553-7358
1553-734X
1553-7358
DOI:10.1371/journal.pcbi.1007770