Genetic variation in putative regulatory loci controlling gene expression in breast cancer
Genetic variation in putative regulatory loci controlling gene expression in breast cancer
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DOI：
10.1073/pnas.0601893103
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发表时间：
2006-05-16
影响因子：
11.1
通讯作者：
Borresen-Dale, Anne-Lise
中科院分区：
文献类型：
--
作者：
Kristensen, Vessela N.;Edvardsen, Hege;Borresen-Dale, Anne-Lise
在50名无关的乳腺癌患者中，分析了候选单核苷酸多态性（SNP）与肿瘤mRNA转录物的100个全基因组库的关联。从203个活性氧途径的候选基因中选择SNPs。我们描述了一个通用的统计框架，用于同时分析同一队列的基因表达数据和SNP基因型数据，这揭示了SNP和转录物子集之间的显着关联，揭示了潜在的生物学。我们在EGF、IL1A、MAPK8、XPC、SOD2和ALOX12中鉴定了与大量转录本的表达模式相关的SNP，表明这些基因中存在调节性SNP。SNPs被发现在总共115个基因中起反式作用。这115个基因中的43个SNP被发现在顺式和反式中起作用。最后，确定了与一组转录本（双簇）共享许多共同关联的SNP子集。与同一组SNP或单个SNP显著相关的转录物子集在基因本体论和途径分析中显示出功能一致性，并在其他独立数据集中共表达，这表明许多观察到的关联在相同的功能途径内。据我们所知，这篇文章是第一个研究相关的单核苷酸多态性基因型数据在生殖细胞与体细胞基因表达数据在乳腺肿瘤。它提供了进一步的基因型表达相关性研究癌症数据集的统计框架。
Candidate single-nucleotide polymorphisms (SNPs) were analyzed for associations to an unselected whole genome pool of tumor mRNA transcripts in 50 unrelated patients with breast cancer. SNPs were selected from 203 candidate genes of the reactive oxygen species pathway. We describe a general statistical framework for the simultaneous analysis of gene expression data and SNP genotype data measured for the same cohort, which revealed significant associations between subsets of SNPs and transcripts, shedding light on the underlying biology. We identified SNPs in EGF, IL1A, MAPK8, XPC, SOD2, and ALOX12 that are associated with the expression patterns of a significant number of transcripts, indicating the presence of regulatory SNPs in these genes. SNPs were found to act in trans in a total of 115 genes. SNPs in 43 of these 115 genes were found to act both in cis and in trans. Finally, subsets of SNPs that share significantly many common associations with a set of transcripts (biclusters) were identified. The subsets of transcripts that are significantly associated with the same set of SNPs or to a single SNP were shown to be functionally coherent in Gene Ontology and pathway analyses and coexpressed in other independent data sets, suggesting that many of the observed associations are within the same functional pathways. To our knowledge, this article is the first study to correlate SNP genotype data in the germ line with somatic gene expression data in breast tumors. It provides the statistical framework for further genotype expression correlation studies in cancer data sets.