Research Article: Landscape of Conditional eQTL in Dorsolateral Prefrontal Cortex and Co-localization with Schizophrenia GWAS

Date Published: May 24, 2018

Publisher: Elsevier

Author(s): Amanda Dobbyn, Laura M. Huckins, James Boocock, Laura G. Sloofman, Benjamin S. Glicksberg, Claudia Giambartolomei, Gabriel E. Hoffman, Thanneer M. Perumal, Kiran Girdhar, Yan Jiang, Towfique Raj, Douglas M. Ruderfer, Robin S. Kramer, Dalila Pinto, Schahram Akbarian, Panos Roussos, Enrico Domenici, Bernie Devlin, Pamela Sklar, Eli A. Stahl, Solveig K. Sieberts.


Causal genes and variants within genome-wide association study (GWAS) loci can be identified by integrating GWAS statistics with expression quantitative trait loci (eQTL) and determining which variants underlie both GWAS and eQTL signals. Most analyses, however, consider only the marginal eQTL signal, rather than dissect this signal into multiple conditionally independent signals for each gene. Here we show that analyzing conditional eQTL signatures, which could be important under specific cellular or temporal contexts, leads to improved fine mapping of GWAS associations. Using genotypes and gene expression levels from post-mortem human brain samples (n = 467) reported by the CommonMind Consortium (CMC), we find that conditional eQTL are widespread; 63% of genes with primary eQTL also have conditional eQTL. In addition, genomic features associated with conditional eQTL are consistent with context-specific (e.g., tissue-, cell type-, or developmental time point-specific) regulation of gene expression. Integrating the 2014 Psychiatric Genomics Consortium schizophrenia (SCZ) GWAS and CMC primary and conditional eQTL data reveals 40 loci with strong evidence for co-localization (posterior probability > 0.8), including six loci with co-localization of conditional eQTL. Our co-localization analyses support previously reported genes, identify novel genes associated with schizophrenia risk, and provide specific hypotheses for their functional follow-up.

Partial Text

Significant advances in understanding the genetic architecture of schizophrenia (MIM: 181500) have occurred within the last 10 years. However, for common variants identified in genome-wide association studies (GWASs), the success in locus identification is not yet matched by an understanding of their underlying basic mechanism or effect on pathophysiology. Expression quantitative trait loci (eQTL), which are responsible for a significant proportion of variation in gene expression, could serve as a link between the numerous non-coding genetic associations that have been identified in GWASs and susceptibility to common diseases directly through their association with gene expression regulation.1, 2, 3, 4 Accordingly, results from eQTL mapping studies have been successfully utilized to identify genes and causal variants from GWASs for various complex phenotypes, including asthma (MIM: 600807), body mass index (MIM: 601665), celiac disease (MIM: 212750), and Crohn disease (MIM: 266600).5, 6, 7, 8

We utilized genotype and expression data from 467 human post-mortem brain samples from the DLPFC to conduct eQTL mapping analyses, to characterize both primary and conditional eQTL. We then identified co-localization between SCZ GWAS and eQTL association signals, comprising both primary and conditional eQTL. Our principal findings include four major observations. First, we detect that conditional eQTL are widespread in the brain tissue samples we investigated. In 63% of genes with at least one eQTL, we found multiple statistically independent eQTL (representing 8,136 genes). In addition, conditional eQTL make substantial contributions to regulatory genetic variation, as there is a strong association between eQTL number and gene expression cis-SNP-heritability. This demonstrates that genetic variation affecting RNA abundance is incompletely characterized by focusing on only one primary eQTL per gene, which is the case currently for most eQTL studies.

CMC leadership: Pamela Sklar, Joseph Buxbaum (Icahn School of Medicine at Mount Sinai), Bernie Devlin, David Lewis (University of Pittsburgh), Raquel Gur, Chang-Gyu Hahn (University of Pennsylvania), Keisuke Hirai, Hiroyoshi Toyoshiba (Takeda Pharmaceuticals Company Limited), Enrico Domenici, Laurent Essioux (F. Hoffmann-La Roche Ltd), Lara Mangravite, Mette Peters (Sage Bionetworks), Thomas Lehner, and Barbara Lipska (NIMH). Additional members of CMC: A. Ercument Cicek, Cong Lu, Kathryn Roeder, Lu Xie (Carnegie Mellon Univ.); Konrad Talbot (Cedars-Sinai Medical Center); Scott E. Hemby (High Point Univ.); Laurent Essioux (Hoffmann-La Roche); Andrew Browne, Andrew Chess, Aaron Topol, Alexander Charney, Amanda Dobbyn, Ben Readhead, Bin Zhang, Dalila Pinto, David A. Bennett, David H. Kavanagh, Douglas M. Ruderfer, Eli A. Stahl, Eric E. Schadt, Gabriel E. Hoffman, Hardik R. Shah, Jun Zhu, Jessica S. Johnson, John F. Fullard, Joel T. Dudley, Kiran Girdhar, Kristen J. Brennand, Laura G. Sloofman, Laura M. Huckins, Menachem Fromer, Milind C. Mahajan, Panos Roussos, Schahram Akbarian, Shaun M. Purcell, Tymor Hamamsy, Towfique Raj, Vahram Haroutunian, Ying-Chih Wang, Zeynep H. Gümüş (Mount Sinai School of Med.); Geetha Senthil, Robin Kramer (NIMH); Benjamin A. Logsdon, Jonathan M.J. Derry, Kristen K. Dang, Solveig K. Sieberts, Thanneer M. Perumal (Sage Bionetworks); Roberto Visintainer (Univ. Trento, Italy); Leslie A. Shinobu (Takeda); Patrick F. Sullivan (Univ. North Carolina); and Lambertus L. Klei (Univ. Pittsburgh School of Med.).




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