Dataset Information

GWA study data mining and independent replication identify cardiomyopathy-associated 5 (CMYA5) as a risk gene for schizophrenia.

ABSTRACT: We conducted data-mining analyses using the Clinical Antipsychotic Trials of Intervention Effectiveness (CATIE) and molecular genetics of schizophrenia genome-wide association study supported by the genetic association information network (MGS-GAIN) schizophrenia data sets and performed bioinformatic prioritization for all the markers with P-values ?0.05 in both data sets. In this process, we found that in the CMYA5 gene, there were two non-synonymous markers, rs3828611 and rs10043986, showing nominal significance in both the CATIE and MGS-GAIN samples. In a combined analysis of both the CATIE and MGS-GAIN samples, rs4704591 was identified as the most significant marker in the gene. Linkage disequilibrium analyses indicated that these markers were in low LD (3?828?611-rs10043986, r(2)=0.008; rs10043986-rs4704591, r(2)=0.204). In addition, CMYA5 was reported to be physically interacting with the DTNBP1 gene, a promising candidate for schizophrenia, suggesting that CMYA5 may be involved in the same biological pathway and process. On the basis of this information, we performed replication studies for these three single-nucleotide polymorphisms. The rs3828611 was found to have conflicting results in our Irish samples and was dropped out without further investigation. The other two markers were verified in 23 other independent data sets. In a meta-analysis of all 23 replication samples (family samples, 912 families with 4160 subjects; case-control samples, 11?380 cases and 15?021 controls), we found that both markers are significantly associated with schizophrenia (rs10043986, odds ratio (OR)=1.11, 95% confidence interval (CI)=1.04-1.18, P=8.2 × 10(-4) and rs4704591, OR=1.07, 95% CI=1.03-1.11, P=3.0 × 10(-4)). The results were also significant for the 22 Caucasian replication samples (rs10043986, OR=1.11, 95% CI=1.03-1.17, P=0.0026 and rs4704591, OR=1.07, 95% CI=1.02-1.11, P=0.0015). Furthermore, haplotype conditioned analyses indicated that the association signals observed at these two markers are independent. On the basis of these results, we concluded that CMYA5 is associated with schizophrenia and further investigation of the gene is warranted.

SUBMITTER: Chen X

PROVIDER: S-EPMC3443634 | biostudies-literature | 2011 Nov

REPOSITORIES: biostudies-literature

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GWA study data mining and independent replication identify cardiomyopathy-associated 5 (CMYA5) as a risk gene for schizophrenia.

Chen X X Lee G G Maher B S BS Fanous A H AH Chen J J Zhao Z Z Guo A A van den Oord E E Sullivan P F PF Shi J J Levinson D F DF Gejman P V PV Sanders A A Duan J J Owen M J MJ Craddock N J NJ O'Donovan M C MC Blackman J J Lewis D D Kirov G K GK Qin W W Schwab S S Wildenauer D D Chowdari K K Nimgaonkar V V Straub R E RE Weinberger D R DR O'Neill F A FA Walsh D D Bronstein M M Darvasi A A Lencz T T Malhotra A K AK Rujescu D D Giegling I I Werge T T Hansen T T Ingason A A Nöethen M M MM Rietschel M M Cichon S S Djurovic S S Andreassen O A OA Cantor R M RM Ophoff R R Corvin A A Morris D W DW Gill M M Pato C N CN Pato M T MT Macedo A A Gurling H M D HM McQuillin A A Pimm J J Hultman C C Lichtenstein P P Sklar P P Purcell S M SM Scolnick E E St Clair D D Blackwood D H R DH Kendler K S KS

Molecular psychiatry 20100914 11

We conducted data-mining analyses using the Clinical Antipsychotic Trials of Intervention Effectiveness (CATIE) and molecular genetics of schizophrenia genome-wide association study supported by the genetic association information network (MGS-GAIN) schizophrenia data sets and performed bioinformatic prioritization for all the markers with P-values ≤0.05 in both data sets. In this process, we found that in the CMYA5 gene, there were two non-synonymous markers, rs3828611 and rs10043986, showing n ...[more]

PMID: 20838396

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Project description:BackgroundThe amount of available and potentially significant data describing study subjects is ever growing with the introduction and integration of different registries and data banks. The single specific attribute of these data are not always necessary; more often, membership to a specific group (e.g. diet, social 'bubble', living area) is enough to build a successful machine learning or data mining model without overfitting it. Therefore, in this article we propose an approach to building taxonomies using clustering to replace detailed data from large heterogenous data sets from different sources, while improving interpretability. We used the GISTAR study data base that holds exhaustive self-assessment questionnaire data to demonstrate this approach in the task of differentiating between H. pylori positive and negative study participants, and assessing their potential risk factors. We have compared the results of taxonomy-based classification to the results of classification using raw data.ResultsEvaluation of our approach was carried out using 6 classification algorithms that induce rule-based or tree-based classifiers. The taxonomy-based classification results show no significant loss in information, with similar and up to 2.5% better classification accuracy. Information held by 10 and more attributes can be replaced by one attribute demonstrating membership to a cluster in a hierarchy at a specific cut. The clusters created this way can be easily interpreted by researchers (doctors, epidemiologists) and describe the co-occurring features in the group, which is significant for the specific task.ConclusionsWhile there are always features and measurements that must be used in data analysis as they are, the use of taxonomies for the description of study subjects in parallel allows using membership to specific naturally occurring groups and their impact on an outcome. This can decrease the risk of overfitting (picking attributes and values specific to the training set without explaining the underlying conditions), improve the accuracy of the models, and improve privacy protection of study participants by decreasing the amount of specific information used to identify the individual.

Dataset Information

GWA study data mining and independent replication identify cardiomyopathy-associated 5 (CMYA5) as a risk gene for schizophrenia.

Publications

GWA study data mining and independent replication identify cardiomyopathy-associated 5 (CMYA5) as a risk gene for schizophrenia.

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