ドーパミン関連遺伝子の効果のグラフィックベイズ分析

基于贝叶斯分层模型，提出了对复杂统计模型中的参数进行更精确推断的估计方法。例如，一种方法是用作谎言分类器的分层判别分析。另一个例子是贝叶斯分析，估计行为遗传学的遗传决定性指数，另一方面，我们也进行了研究，从生物学基础上寻找行为的遗传决定因素。也就是说，为了研究动物行为与dompanine功能之间的关系，我们对C elegance的多巴胺受体基因进行了cDNA克隆。结果表明，预测的F15，K09，C52和T14的多巴胺受体基因从秀丽隐杆线虫被鉴定。克隆cDNA揭示剪接变体的存在。我们用GFP融合技术分析了每个受体的表达模式，发现它们在包括所有多巴胺能神经元在内的几个神经元中表达。

We proposed the estimation procedures to make more precise inference about the parameters involved in complex statistical models, based on Baysian hierarchical modeling. For example, one method is hierarchical discriminant analysis which serves as a lie classifier. Another example is the Bayesian analysis to estimate the heredity determinacy index for Behavioral Genetics.On the other hand, we also conducted research to find the heredity determinant on behavior from the biological basis. That is, in order to examine the relationship between animal behavior and the function of dompanine, we performed cDNA cloning of the dopamine receptor genes of C elegance. As a result, the predicted dopamine receptor genes of F15, K09, C52 and T14 from C.elegans was identified. Cloning the cDNA revealed the presence of splice variants. We analyzed expression pattern of each receptor by using GFP-fusion technique and found that they are expressed in several neurons including all dopaminergic neurons.