Prediction of human disease genes by human-mouse conserved coexpression analysis.
Prediction of human disease genes by human-mouse conserved coexpression analysis.
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DOI：
10.1371/journal.pcbi.1000043
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发表时间：
2008-03-28
影响因子：
4.3
通讯作者：
Di Cunto F
中科院分区：
文献类型：
--
作者：
Ala U;Piro RM;Grassi E;Damasco C;Silengo L;Oti M;Provero P;Di Cunto F
即使在基因组后时代，与人类遗传疾病相关的基因座中候选基因的鉴定也是一项非常苛刻的任务，因为关键区域通常可能包含数百名位置候选者。由于与类似表型有关的基因倾向于共享非常相似的表达谱，因此高吞吐量基因表达数据可能代表着一个非常重要的资源，以识别测序的最佳候选者。但是，到目前为止，尚未非常成功地使用基因共表达来确定位置候选者的优先级。
我们表明，通过仅集中在人和小鼠中共享相似表达谱的基因上，可以可靠地从大型微阵列数据集中可靠地识别与疾病相关的关系。此外，我们系统地表明，人小鼠保守的共表达与表型相似性图的整合可以有效地鉴定大型基因组区域中的疾病基因。最后，使用这种方法在850个OMIM基因座上以未知分子基础为特征，我们建议对81种遗传疾病的高概率候选。
我们的结果表明，即使在人鼠的系统发育距离处，保守的共表达也代表了预测人类基因之间与疾病相关关系的非常强大的标准。
在基因组后时代生物学研究的最局限性方面之一是能够整合有关基因结构和功能以产生有用的生物学知识的大量数据集的能力。在本报告中，我们采用了一种综合方法来解决与人类遗传疾病相关的基因座中可能候选基因的问题。尽管测序技术最近取得了进展，但从实验角度解决这个问题仍然代表了一项非常苛刻的任务，因为关键区域通常可能包含数百名位置候选者。我们发现，通过仅集中在人和小鼠中共享相似表达谱的基因上，大量的微阵列数据集可用于可靠地识别基因之间的疾病相关关系。此外，我们发现将共表达标准与系统的现象分析相结合可以有效地鉴定大型基因组区域中的疾病基因。在850个OMIM基因座上使用这种方法以未知分子基础为特征，我们提出了81种遗传疾病的高概率候选者。
Even in the post-genomic era, the identification of candidate genes within loci associated with human genetic diseases is a very demanding task, because the critical region may typically contain hundreds of positional candidates. Since genes implicated in similar phenotypes tend to share very similar expression profiles, high throughput gene expression data may represent a very important resource to identify the best candidates for sequencing. However, so far, gene coexpression has not been used very successfully to prioritize positional candidates. We show that it is possible to reliably identify disease-relevant relationships among genes from massive microarray datasets by concentrating only on genes sharing similar expression profiles in both human and mouse. Moreover, we show systematically that the integration of human-mouse conserved coexpression with a phenotype similarity map allows the efficient identification of disease genes in large genomic regions. Finally, using this approach on 850 OMIM loci characterized by an unknown molecular basis, we propose high-probability candidates for 81 genetic diseases. Our results demonstrate that conserved coexpression, even at the human-mouse phylogenetic distance, represents a very strong criterion to predict disease-relevant relationships among human genes. One of the most limiting aspects of biological research in the post-genomic era is the capability to integrate massive datasets on gene structure and function for producing useful biological knowledge. In this report we have applied an integrative approach to address the problem of identifying likely candidate genes within loci associated with human genetic diseases. Despite the recent progress in sequencing technologies, approaching this problem from an experimental perspective still represents a very demanding task, because the critical region may typically contain hundreds of positional candidates. We found that by concentrating only on genes sharing similar expression profiles in both human and mouse, massive microarray datasets can be used to reliably identify disease-relevant relationships among genes. Moreover, we found that integrating the coexpression criterion with systematic phenome analysis allows efficient identification of disease genes in large genomic regions. Using this approach on 850 OMIM loci characterized by unknown molecular basis, we propose high-probability candidates for 81 genetic diseases.
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14.9
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