Whole genome profiling to detect schizophrenia methylation markers

精神分裂症是一种毁灭性的神经精神疾病。遗传因素与此密切相关。然而，基因对精神分裂症的贡献可能是复杂的，很难仅仅通过考虑序列变异来捕捉。由于几个原因，DNA甲基化研究代表了一个特别有希望的补充。首先，由于甲基化与基因表达直接相关，了解基因甲基化水平可能有助于疾病状态的预测。其次，甲基化研究可以深入了解诸如发病年龄、精神分裂症的偶发性、基因-环境相互作用、亲代影响和性别差异等现象。第三，甲基化位点也是很好的新药靶点，因为它们可以通过药物干预来改变。最后，甲基化标记在稳定的DNA水平上是可获得的，从翻译的角度来看，这意味着它们也可以潜在地用于临床环境，以改善诊断和治疗。甲基化研究历来局限于有限数量的候选基因。然而，最近同时测量数百万个标记的甲基化状态在技术和经济上都是可行的。这与全基因组关联研究（GWAS）的最新进展类似，后者大大加快了疾病变异的发现。为了确定与精神分裂症相关的甲基化位点，我们应用了我们小组开发的统计理论来确定最具成本效益的研究设计。基于这些优化设计计算，我们提出了750例精神分裂症病例和750例对照的全基因组甲基化分析研究。为了消除由于技术和采样错误而导致的错误发现，我们将使用焦磷酸测序在精神分裂症病例和对照的独立样本中跟踪最有希望的位点。我们目前的力量计算表明，我们可能需要对65个地区的800例病例和800例对照病例进行随访。然而，我们开发的统计方法的一个优点是它可以自适应地使用——也就是说，第二焦磷酸测序复制阶段的最佳样本量和标记数量可以根据第一个基于阵列的发现阶段估计的参数来经验地确定。为了提高我们对疾病机制的理解，将进行二级分析，将甲基化区域与临床信息以及已经可用的SNP标记和拷贝数变体呼叫的全基因组面板联系起来。

DESCRIPTION (provided by applicant): PROJECT SUMMARY Schizophrenia is an often devastating neuropsychiatric illness. Genetic factors have been strongly implicated. The genetic contribution to schizophrenia is, however, likely to be complex and difficult to capture by merely considering sequence variation. DNA methylation studies represent a particularly promising complement for several reasons. First, as methylation is directly related to gene expression, knowledge of gene methylation levels may add to the prediction of disease status. Second, methylation studies can provide insight into phenomena such as age of onset, the episodic nature of schizophrenia, gene-environment interactions, parental effects, and sex differences. Third, methylation sites are also excellent new drug targets as they are modifiable by pharmacological interventions. Finally, methylation markers are accessible at the stable DNA level, which from a translational perspective means that they can potentially also be used in clinical settings to improve diagnosis and treatment. Methylation studies have historically been restricted to a limited number of candidate genes. However, it has recently become technically and economically feasible to measure the methylation status of millions of markers simultaneously. This resembles recent developments in genomewide association studies (GWAS), which have accelerated the discovery of disease variants considerably. To identify methylation sites related to schizophrenia, we have applied statistical theory developed by our group to determine the most cost-effective study design. Based on these optimal design calculations, we propose a whole genome methylation profiling study in 750 schizophrenia cases and 750 controls. To eliminate false discoveries due to technical and sampling errors, we will follow up the most promising sites in an independent sample of schizophrenia cases and controls using pyrosequencing. Our current power calculations suggest that we may need to follow up 65 regions in 800 cases and 800 controls. However, one strength of the statistical method we developed is that it can be used adaptively--that is, the optimal sample size and number of markers for the second pyrosequencing replication stage can be empirically determined based on parameters estimated from the first, array-based discovery stage. In order to improve our understanding of the disease mechanisms, secondary analyses will be performed relating the methylation regions to clinical information as well as to a genome-wide panel of already available SNP markers and copy number variant calls. PUBLIC HEALTH RELEVANCE: Schizophrenia is an often devastating neuropsychiatric illness. DNA methylation studies represent a particularly promising complement to current genetic studies that offer great potential to improve the understanding and treatment of the disease.