Combining epidemiologic designs to model genetic risks for psychiatric disorders

结合流行病学设计来模拟精神疾病的遗传风险 现在人们普遍认识到，遗传易感性构成精神疾病，如精神分裂症（SZ）和双极障碍（BP）的主要危险因素。然而，在阐明复杂遗传结构的流行病学方法方面，或者在基因型水平的突变模式与环境和其他因素结合联合收割机以产生特定行为表型的方式方面，我们落后了。其中一个障碍是很难将不同遗传流行病学研究设计的信息结合起来，例如，在同时分析中同时利用家族研究和病例对照研究。因此，很难构建模型，充分考虑到给定基因发挥作用的遗传和流行病学背景。我们已经开发了一个强大的统计方法（基于“PPL”框架），并建立了一个专门的计算平台（KELVIN）的遗传结构建模。我们现在建议将KELVIN应用于NIH建立的存储库中包含的数据，利用公开可用的数据集，以制定更全面的SZ和BP遗传结构模型。具体来说，我们将利用大量的多重家系与微卫星基因组扫描数据由NIMH人类遗传学倡议（HGI）组装;和广泛的病例对照样本与全基因组SNP数据由遗传协会信息网络（GAIN）组装。通过将我们的方法用于将遗传流行病学研究设计中的信息结合到已经在公共领域的数据中，我们可以利用早期的公共数据。 投资进行强大的建模遗传风险因素的主要精神疾病。

Combining epidemiologic designs to model genetic risks for psychiatric disorders It is now widely recognized that genetic predispositions constitute major risk factors for psychiatric disorders such as schizophrenia (SZ) and biopolar disorder (BP). Yet we lag behind in epidemiologic methods for illuminating complex genetic architecture, or the ways in which mutational patterns at the genotypic level combine with environmental and other factors to produce specific behavioral phenotypes. One obstacle is the difficulty of combining information across different genetic epidemiologic study designs, for instance, exploiting both family studies and case-control studies in simultaneous analyses. As a result, it is difficult to construct models which adequately allow for the genetic and epidemiologic background in which given genes function. We have developed a powerful statistical approach (based on the “PPL” framework) and built a specialized computational platform (KELVIN) for modeling genetic architecture. We now propose to apply KELVIN to data contained in repositories established by the NIH, leveraging publicly available data sets in order to formulate more comprehensive models of the genetic architecture of SZ and BP. Specifically, we will make use of the large collection of multiplex pedigrees with microsatellite genome scan data assembled by the NIMH Human Genetics Initiative (HGI); and the extensive case-control samples with genome-wide SNP data assembled by the Genetic Association Information Network (GAIN). By applying our methods for combining information across genetic epidemiologic study designs to data already in the public domain, we can leverage earlier public investments to perform powerful modeling of genetic risk factors for major psychiatric disorders.