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      Replication of association between schizophrenia and ZNF804A in the Irish Case Control Study of Schizophrenia (ICCSS) sample

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          Abstract

          A recent genomewide association study reported association between schizophrenia and the ZNF804A gene on chromosome 2q32.1. We attempted to replicate these findings in our Irish Case-Control Study of Schizophrenia (ICCSS) sample (N=1021 cases, 626 controls). Following consultation with the original investigators we genotyped 3 of the most promising SNPs from the Cardiff study. We replicate association with rs1344706 (trend test one tailed p=0.0113 with the previously associated A allele) in ZNF804A. We detect no evidence of association with rs6490121 in NOS1 (one tailed p=0.21), and only a trend with rs9922369 in RGRIP1L (one tailed p=0.0515). Based on these results, we completed genotyping of 11 additional LD-tagging SNPs in ZNF804A. Of 12 SNPs genotyped, 11 pass QC criteria and 4 are nominally associated, with our most significant evidence of association at rs7597593 (p=0.0013) followed by rs1344706. We observe no evidence of differential association in ZNF804A based on family history or sex of case. The associated SNP rs1344706 lies in ~30 bp of conserved mammalian sequence and the associated A allele is predicted to maintain binding sites for the brain-expressed transcription factors MYT1L and POU3F1/OCT-6. In controls, expression is significantly increased from the A allele of rs1344706 compared to the C allele. Expression is increased in schizophrenic cases compared to controls, but this difference does not achieve statistical significance. This study replicates the original reported association of ZNF804A with schizophrenia and suggests that there is a consistent link between the the A allele of rs1344706, increased expression of ZNF804A and risk for schizophrenia.

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          A genome-wide association study identifies novel risk loci for type 2 diabetes.

          Type 2 diabetes mellitus results from the interaction of environmental factors with a combination of genetic variants, most of which were hitherto unknown. A systematic search for these variants was recently made possible by the development of high-density arrays that permit the genotyping of hundreds of thousands of polymorphisms. We tested 392,935 single-nucleotide polymorphisms in a French case-control cohort. Markers with the most significant difference in genotype frequencies between cases of type 2 diabetes and controls were fast-tracked for testing in a second cohort. This identified four loci containing variants that confer type 2 diabetes risk, in addition to confirming the known association with the TCF7L2 gene. These loci include a non-synonymous polymorphism in the zinc transporter SLC30A8, which is expressed exclusively in insulin-producing beta-cells, and two linkage disequilibrium blocks that contain genes potentially involved in beta-cell development or function (IDE-KIF11-HHEX and EXT2-ALX4). These associations explain a substantial portion of disease risk and constitute proof of principle for the genome-wide approach to the elucidation of complex genetic traits.
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            Efficiency and power in genetic association studies.

            We investigated selection and analysis of tag SNPs for genome-wide association studies by specifically examining the relationship between investment in genotyping and statistical power. Do pairwise or multimarker methods maximize efficiency and power? To what extent is power compromised when tags are selected from an incomplete resource such as HapMap? We addressed these questions using genotype data from the HapMap ENCODE project, association studies simulated under a realistic disease model, and empirical correction for multiple hypothesis testing. We demonstrate a haplotype-based tagging method that uniformly outperforms single-marker tests and methods for prioritization that markedly increase tagging efficiency. Examining all observed haplotypes for association, rather than just those that are proxies for known SNPs, increases power to detect rare causal alleles, at the cost of reduced power to detect common causal alleles. Power is robust to the completeness of the reference panel from which tags are selected. These findings have implications for prioritizing tag SNPs and interpreting association studies.
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              The future of genetic studies of complex human diseases.

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                Author and article information

                Journal
                9607835
                20545
                Mol Psychiatry
                Molecular psychiatry
                1359-4184
                1476-5578
                25 September 2009
                20 October 2009
                January 2010
                1 July 2010
                : 15
                : 1
                : 29-37
                Affiliations
                [1 ] Department of Psychiatry, Virginia Commonwealth University, Richmond, VA, USA
                [2 ] Department of Human Genetics, Virginia Commonwealth University, Richmond, VA, USA
                [3 ] Virginia Institute for Psychiatric and Behavioral Genetics, Virginia Commonwealth University, Richmond, VA, USA
                [4 ] Department of Psychiatry, Georgetown University School of Medicine, Washington DC, USA
                [5 ] Mental Health Service Line, Washington VA Medical Center, Washington DC, USA
                [6 ] Department of Psychiatry, Queens University, Belfast, UK
                [7 ] The Health Research Board, Dublin, Ireland
                Author notes
                Address correspondence to: Brien Riley, PO Box 980424, Virginia Commonwealth University, Richmond VA, 23298-0424, USA, tel: 804 828 8083, fax: 804 828 1471, bpriley@ 123456vcu.edu
                Article
                nihpa146480
                10.1038/mp.2009.109
                2797562
                19844207
                0ceb3c00-520c-46c3-8b0b-66000feafd52

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                History
                Funding
                Funded by: National Institute of Mental Health : NIMH
                Award ID: R01 MH041953-17 ||MH
                Funded by: National Institute of Mental Health : NIMH
                Award ID: R01 MH041953-16 ||MH
                Funded by: National Institute of Mental Health : NIMH
                Award ID: R01 MH041953-15 ||MH
                Funded by: National Institute of Mental Health : NIMH
                Award ID: R01 MH041953-14 ||MH
                Funded by: National Institute of Mental Health : NIMH
                Award ID: R01 MH041953-13 ||MH
                Funded by: National Institute of Mental Health : NIMH
                Award ID: R01 MH041953-12 ||MH
                Funded by: National Institute of Mental Health : NIMH
                Award ID: R01 MH041953-11 ||MH
                Funded by: National Institute of Mental Health : NIMH
                Award ID: R01 MH041953-10 ||MH
                Funded by: National Institute of Mental Health : NIMH
                Award ID: R01 MH041953-09A2 ||MH
                Funded by: National Institute of Mental Health : NIMH
                Award ID: R01 MH041953-08S1 ||MH
                Categories
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                Molecular medicine
                Molecular medicine

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