Inferring gene transcriptional modulatory relations: a genetical genomics approach
Inferring gene transcriptional modulatory relations: a genetical genomics approach
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DOI：
10.1093/hmg/ddi124
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发表时间：
2005-05-01
影响因子：
3.5
通讯作者：
Cui, Y
中科院分区：
文献类型：
--
作者：
Li, HQ;Lu, L;Cui, Y
贝叶斯网络模型是一种很有前途的方法来定义和评估不同组织和细胞类型在不同实验条件下的基因表达回路。通过限制基因间潜在相互作用的数量，并在评估数十亿网络的后验概率之前定义因果关系，可以提高这种方法的能力和实用性。一种新开发的遗传基因组学方法，结合转录组分析与复杂的性状分析，现在提供了强大的网络架构的约束。该方法检测染色体间隔负责的mRNA表达的差异，使用数量性状基因座（OTL）定位。我们已经开发了一种有效的贝叶斯方法，利用遗传基因组学方法，将计算工作集中在最合理的基因调控网络。我们利用一个密集的标记图谱的遗传参考群体（GRP），由32 BXD品系的小鼠杂交两个祖株-C57 BL/6 J和DBA/2 J。这些祖细胞在1130万个已知的单核苷酸多态性（SNP）上存在差异，所有这些都可以用来估计基因包含在GRP内分离的功能多态性的概率。我们构建了66个候选网络，包括位于209个统计学显著的反式作用QTL区域的所有候选调节基因。区分两种祖菌株的SNP被用于进一步筛选候选调节剂的列表。然后使用贝叶斯网络来识别最能解释微阵列数据的遗传调节关系。
Bayesian network modeling is a promising approach to define and evaluate gene expression circuits in diverse tissues and cell types under different experimental conditions. The power and practicality of this approach can be improved by restricting the number of potential interactions among genes and by defining causal relations before evaluating posterior probabilities for billions of networks. A newly developed genetical genomics method that combines transcriptome profiling with complex trait analysis now provides strong constraints on network architecture. This method detects those chromosomal intervals responsible for differences in mRNA expression using quantitative trait locus (OTL) mapping. We have developed an efficient Bayesian approach that exploits the genetical genomics method to focus computational effort on the most plausible gene modulatory networks. We exploit a dense marker map for a genetic reference population (GRP) that consists of 32 BXD strains of mice made by intercrossing two progenitor strains - C57BL/6J and DBA/2J. These progenitors differ at 11.3 million known single nucleotide polymorphisms (SNPs), all of which can be exploited to estimate the probability that a gene contains functional polymorphisms that segregate within the GRP. We constructed 66 candidate networks that include all the candidate modulator genes located in the 209 statistically significant trans-acting QTL regions. SNPs that distinguish between the two progenitor strains were used to further winnow the list of candidate modulators. Bayesian network was then used to identify the genetic modulatory relations that best explain the microarray data.