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

ABSTRACT: 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 (alpha), beta (beta), 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 alpha power. SGIP1 was estimated to account for 8.8% of variance in 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.

SUBMITTER: Hodgkinson CA

PROVIDER: S-EPMC2889314 | biostudies-literature | 2010 May

REPOSITORIES: biostudies-literature

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

Hodgkinson Colin A CA Enoch Mary-Anne MA Srivastava Vibhuti V Cummins-Oman Justine S JS Ferrier Cherisse C Iarikova Polina P Sankararaman Sriram S Yamini Goli G Yuan Qiaoping Q Zhou Zhifeng Z Albaugh Bernard B White Kenneth V KV Shen Pei-Hong PH Goldman David D

Proceedings of the National Academy of Sciences of the United States of America 20100426 19

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 ...[more]

PMID: 20421487

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Project description:To date, nine Parkinson disease (PD) genome-wide association studies in North American, European and Asian populations have been published. The majority of studies have confirmed the association of the previously identified genetic risk factors, SNCA and MAPT, and two studies have identified three new PD susceptibility loci/genes (PARK16, BST1 and HLA-DRB5). In a recent meta-analysis of datasets from five of the published PD GWAS an additional 6 novel candidate genes (SYT11, ACMSD, STK39, MCCC1/LAMP3, GAK and CCDC62/HIP1R) were identified. Collectively the associations identified in these GWAS account for only a small proportion of the estimated total heritability of PD suggesting that an 'unknown' component of the genetic architecture of PD remains to be identified.We applied a GWAS approach to a relatively homogeneous Ashkenazi Jewish (AJ) population from New York to search for both 'rare' and 'common' genetic variants that confer risk of PD by examining any SNPs with allele frequencies exceeding 2%. We have focused on a genetic isolate, the AJ population, as a discovery dataset since this cohort has a higher sharing of genetic background and historically experienced a significant bottleneck. We also conducted a replication study using two publicly available datasets from dbGaP. The joint analysis dataset had a combined sample size of 2,050 cases and 1,836 controls.We identified the top 57 SNPs showing the strongest evidence of association in the AJ dataset (p < 9.9 × 10(-5)). Six SNPs located within gene regions had positive signals in at least one other independent dbGaP dataset: LOC100505836 (Chr3p24), LOC153328/SLC25A48 (Chr5q31.1), UNC13B (9p13.3), SLCO3A1(15q26.1), WNT3(17q21.3) and NSF (17q21.3). We also replicated published associations for the gene regions SNCA (Chr4q21; rs3775442, p = 0.037), PARK16 (Chr1q32.1; rs823114 (NUCKS1), p = 6.12 × 10(-4)), BST1 (Chr4p15; rs12502586, p = 0.027), STK39 (Chr2q24.3; rs3754775, p = 0.005), and LAMP3 (Chr3; rs12493050, p = 0.005) in addition to the two most common PD susceptibility genes in the AJ population LRRK2 (Chr12q12; rs34637584, p = 1.56 × 10(-4)) and GBA (Chr1q21; rs2990245, p = 0.015).We have demonstrated the utility of the AJ dataset in PD candidate gene and SNP discovery both by replication in dbGaP datasets with a larger sample size and by replicating association of previously identified PD susceptibility genes. Our GWAS study has identified candidate gene regions for PD that are implicated in neuronal signalling and the dopamine pathway.

Dataset Information

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

Publications

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

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