Epigenomic mapping and effect sizes of noncoding variants associated with psychotropic drug response.
OBJECTIVE
To provide insight into potential regulatory mechanisms of gene expression underlying addiction, analgesia, psychotropic drug response and adverse drug events, genome-wide association studies searching for variants associated with these phenotypes has been undertaken with limited success. We undertook analysis of these results with the aim of applying epigenetic knowledge to aid variant discovery and interpretation.


METHODS
We applied conditional imputation to results from 26 genome-wide association studies and three candidate gene-association studies. The analysis workflow included data from chromatin conformation capture, chromatin state annotation, DNase I hypersensitivity, hypomethylation, anatomical localization and biochronicity. We also made use of chromatin state data from the epigenome roadmap, transcription factor-binding data, spatial maps from published Hi-C datasets and 'guilt by association' methods.


RESULTS
We identified 31 pharmacoepigenomic SNPs from a total of 2024 variants in linkage disequilibrium with lead SNPs, of which only 6% were coding variants. Interrogation of chromatin state using our workflow and the epigenome roadmap showed agreement on 34 of 35 tissue assignments to regulatory elements including enhancers and promoters. Loop boundary domains were inferred by association with CTCF (CCCTC-binding factor) and cohesin, suggesting proximity to topologically associating domain boundaries and enhancer clusters. Spatial interactions between enhancer-promoter pairs detected both known and previously unknown mechanisms. Addiction and analgesia SNPs were common in relevant populations and exhibited large effect sizes, whereas a SNP located in the promoter of the SLC1A2 gene exhibited a moderate effect size for lithium response in bipolar disorder in patients of European ancestry. SNPs associated with drug-induced organ injury were rare but exhibited the largest effect sizes, consistent with the published literature.


CONCLUSIONS
This work demonstrates that an in silico bioinformatics-based approach using integrative analysis of a diversity of molecular and morphological data types can discover pharmacoepigenomic variants that are suitable candidates for further validation in cell lines, animal models and human clinical trials.
Department of Computational Medicine & Bioinformatics, University of Michigan Medical School, 1301 Catherine Road, Ann Arbor, MI 48109, USA.|Pharmacogenomic Science, Assurex Health, Inc., Mason, OH, USA.
Gerald A
GA
Higgins
Department of Computational Medicine & Bioinformatics, University of Michigan Medical School, 1301 Catherine Road, Ann Arbor, MI 48109, USA.
Ari
A
Allyn-Feuer
Department of Computational Medicine & Bioinformatics, University of Michigan Medical School, 1301 Catherine Road, Ann Arbor, MI 48109, USA.|Department of Psychiatry, University of Michigan Medical School, Ann Arbor, MI, USA.
Brian D
BD
Athey
D018797:Cell Cycle Proteins / Q000235:genetics; D002843:Chromatin / Q000235:genetics; D002868:Chromosomal Proteins, Non-Histone / Q000235:genetics; D002874:Chromosome Mapping / Q000379:methods*; D019295:Computational Biology; D003198:Computer Simulation; D019175:DNA Methylation; D003850:Deoxyribonuclease I / Q000235:genetics; D057890:Epigenomics / Q000379:methods*; D014644:Genetic Variation; D055106:Genome-Wide Association Study; D006801:Humans; D020641:Polymorphism, Single Nucleotide; D011619:Psychotropic Drugs / Q000494:pharmacology*; D014157:Transcription Factors / Q000235:genetics; D044465:White People; D000097722:Cohesins
D018797:Cell Cycle Proteins; D002843:Chromatin; D002868:Chromosomal Proteins, Non-Histone; D003850:Deoxyribonuclease I; D011619:Psychotropic Drugs; D014157:Transcription Factors; D000097722:Cohesins
chromatin spatial interactions; pharmacodynamics; pharmacogenomics; pharmacokinetics