Gene Expression and Genetic Variation Data Implicate PCLO in Bipolar Disorder

• Published in: Biological psychiatry (1969) Vol. 69; no. 4; pp. 353 - 359
• Main Authors: Choi, Kwang H, Higgs, Brandon W, Wendland, Jens R, Song, Jonathan, McMahon, Francis J, Webster, Maree J
• Format: Journal Article
• Language: English
• Published: New York, NY Elsevier Inc 15.02.2011; Elsevier
• Subjects: Adult and adolescent clinical studies; Alleles; Allelic expression; Biological and medical sciences; Bipolar Disorder - genetics; Bipolar disorders; Cytoskeletal Proteins - genetics; Databases, Genetic; expression quantitative trait loci (eQTL); Female; functional genomics; Gene Expression; Gene Frequency; Genetic Association Studies; Genetic Predisposition to Disease; genetic variants; Genetic Variation; Genotype; Haplotypes; Humans; Male; Medical sciences; Mood disorders; Neuropeptides - genetics; Oligonucleotide Array Sequence Analysis; Polymorphism, Single Nucleotide; Psychiatry; Psychology. Psychoanalysis. Psychiatry; Psychopathology. Psychiatry; Reverse Transcriptase Polymerase Chain Reaction; risk factors; expression quantitative trait loci (eQTL); genetic variants; risk factors; functional genomics; Allelic expression; Mood disorder; Genetic variability; Genomics; Genetic variant; Bipolar disorder; Gene expression; Genetic determinism; Quantitative trait loci; Risk factor; Genetics
• ISSN: 0006-3223; 1873-2402
• DOI: 10.1016/j.biopsych.2010.09.042

Background Genetic variation may contribute to differential gene expression in the brain of individuals with psychiatric disorders. To test this hypothesis, we identified genes that were differentially expressed in individuals with bipolar disorder, along with nearby single nucleotide polymorphisms (SNPs) that were associated with expression of the same genes. We then tested these SNPs for association with bipolar disorder in large case-control samples. Methods We used the Stanley Genomics Database to extract gene expression and SNP microarray data from individuals with bipolar disorder ( n = 40) and unaffected controls ( n = 43). We identified 367 genes that were differentially expressed in the prefrontal cortex of cases vs. controls (fold change > 1.3 and FDR q-value < .05) and 45 nearby SNPs that were associated with expression of those same genes (FDR q-value < .05). We tested these SNPs for association with bipolar disorder in a meta-analysis of genome-wide association studies (GWAS) including 4,936 cases and 6,654 healthy controls. Results We identified 45 SNPs that were associated with expression of differentially expressed genes, including HBS1L (15 SNPs), HLA-DPB1 (15 SNPs), AMFR (8 SNPs), PCLO (2 SNPs) and WDR41 (2 SNPs). Of these, one SNP (rs13438494), in an intron of the piccolo ( PCLO ) gene, was significantly associated with bipolar disorder (FDR adjusted p < .05) in the meta-analysis of GWAS. Conclusions These results support the previous findings implicating PCLO in mood disorders and demonstrate the utility of combining gene expression and genetic variation data to improve our understanding of the genetic contribution to bipolar disorder.