Genome-wide association identifies candidate genes that influence the human electroencephalogram

• Published in: Proceedings of the National Academy of Sciences - PNAS Vol. 107; no. 19; pp. 8695 - 8700
• Main Authors: Hodgkinson, Colin A, Enoch, Mary-Anne, Srivastava, Vibhuti, Cummins-Oman, Justine S, Ferrier, Cherisse, Iarikova, Polina, Sankararaman, Sriram, Yamini, Goli, Yuan, Qiaoping, Zhou, Zhifeng, Albaugh, Bernard, White, Kenneth V, Shen, Pei-Hong, Goldman, David
• Format: Journal Article
• Language: English
• Published: United States National Academy of Sciences 11.05.2010; National Acad Sciences
• Subjects: Admixtures; alcohol abuse; Alcohol related disorders; Alcoholism; Alcoholism - genetics; Alleles; anxiety; Bayesian analysis; Bayesian theory; Biological Sciences; Biological Transport; Biological variation; brain; Carrier Proteins - genetics; chromosomes; Chromosomes, Human, Pair 1 - genetics; Datasets; Electroencephalography; etiology; European Continental Ancestry Group - genetics; Gene Frequency - genetics; genes; Genes - genetics; Genetic diversity; Genetic loci; Genetic Loci - genetics; Genetic Markers; genetic variation; Genome-Wide Association Study; Genomics; Genotype & phenotype; Golgi Apparatus - metabolism; Heterogeneity; Humans; Medical genetics; Mental disorders; Phenotype; Phenotypes; Polymorphism, Single Nucleotide - genetics; Population genetics; probability; Reproducibility of Results; schizophrenia; transport proteins; United States; variance; Whites; United States
• ISSN: 0027-8424; 1091-6490; 1091-6490
• DOI: 10.1073/pnas.0908134107

Complex psychiatric disorders are resistant to whole-genome analysis due to genetic and etiological heterogeneity. Variation in resting electroencephalogram (EEG) is associated with common, complex psychiatric diseases including alcoholism, schizophrenia, and anxiety disorders, although not diagnostic for any of them. EEG traits for an individual are stable, variable between individuals, and moderately to highly heritable. Such intermediate phenotypes appear to be closer to underlying molecular processes than are clinical symptoms, and represent an alternative approach for the identification of genetic variation that underlies complex psychiatric disorders. We performed a whole-genome association study on alpha (α), beta (β), and theta ({theta}) EEG power in a Native American cohort of 322 individuals to take advantage of the genetic and environmental homogeneity of this population isolate. We identified three genes (SGIP1, ST6GALNAC3, and UGDH) with nominal association to variability of {theta} or α power. SGIP1 was estimated to account for 8.8% of variance in {theta} power, and this association was replicated in US Caucasians, where it accounted for 3.5% of the variance. Bayesian analysis of prior probability of association based upon earlier linkage to chromosome 1 and enrichment for vesicle-related transport proteins indicates that the association of SGIP1 with {theta} power is genuine. We also found association of SGIP1 with alcoholism, an effect that may be mediated via the same brain mechanisms accessed by {theta} EEG, and which also provides validation of the use of EEG as an endophenotype for alcoholism.