title: A meta-analysis of gene expression quantitative trait loci in brain.
abstract: Current catalogs of brain expression quantitative trait loci (eQTL) are incomplete and the findings do not replicate well across studies. All existing cortical eQTL studies are small and emphasize the need for a meta-analysis. We performed a meta-analysis of 424 brain samples across five studies to identify regulatory variants influencing gene expression in human cortex. We identified 3584 genes in autosomes and chromosome X with false discovery rate q<0.05 whose expression was significantly associated with DNA sequence variation. Consistent with previous eQTL studies, local regulatory variants tended to occur symmetrically around transcription start sites and the effect was more evident in studies with large sample sizes. In contrast to random SNPs, we observed that significant eQTLs were more likely to be near 5'-untranslated regions and intersect with regulatory features. Permutation-based enrichment analysis revealed that SNPs associated with schizophrenia and bipolar disorder were enriched among brain eQTLs. Genes with significant eQTL evidence were also strongly associated with diseases from OMIM (Online Mendelian Inheritance in Man) and the NHGRI (National Human Genome Research Institute) genome-wide association study catalog. Surprisingly, we found that a large proportion (28%) of ~1000 autosomal genes encoding proteins needed for mitochondrial structure or function were eQTLs (enrichment P-value=1.3 × 10(-9)), suggesting a potential role for common genetic variation influencing the robustness of energy supply in brain and a possible role in the etiology of some psychiatric disorders. These systematically generated eQTL information should be a valuable resource in determining the functional mechanisms of brain gene expression and the underlying biology of associations with psychiatric disorders.
authors.affiliation: Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
authors.forename: Y
authors.initials: Y
authors.lastname: Kim
authors.affiliation: Department of Psychiatry, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
authors.forename: K
authors.initials: K
authors.lastname: Xia
authors.affiliation: Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
authors.forename: R
authors.initials: R
authors.lastname: Tao
authors.affiliation: Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
authors.forename: P
authors.initials: P
authors.lastname: Giusti-Rodriguez
authors.affiliation: 1] Department of Psychiatry, Virginia Commonwealth University, Richmond, VA, USA [2] Lieber Institute for Brain Development, Johns Hopkins University, Baltimore, MD, USA.
authors.forename: V
authors.initials: V
authors.lastname: Vladimirov
authors.affiliation: Center for Biomarker Research and Personalized Medicine; Virginia Commonwealth University, Richmond, VA, USA.
authors.forename: E
authors.initials: E
authors.lastname: van den Oord
authors.affiliation: 1] Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA [2] Department of Psychiatry, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA [3] Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden.
authors.forename: P F
authors.initials: PF
authors.lastname: Sullivan
mesh_terms: D001714:Bipolar Disorder / Q000235:genetics*; D001921:Brain; D015870:Gene Expression / Q000235:genetics*; D020022:Genetic Predisposition to Disease / Q000235:genetics; D055106:Genome-Wide Association Study; D006801:Humans; D020641:Polymorphism, Single Nucleotide; D040641:Quantitative Trait Loci / Q000235:genetics*; D012559:Schizophrenia / Q000235:genetics*