Identification of candidate genes for prostate cancer-risk SNPs utilizing a normal prostate tissue eQTL data set.
Identification of candidate genes for prostate cancer-risk SNPs utilizing a normal prostate tissue eQTL data set.
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DOI：
10.1038/ncomms9653
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发表时间：
2015-11-27
影响因子：
16.6
通讯作者：
Schaid DJ
中科院分区：
文献类型：
--
作者：
Thibodeau SN;French AJ;McDonnell SK;Cheville J;Middha S;Tillmans L;Riska S;Baheti S;Larson MC;Fogarty Z;Zhang Y;Larson N;Nair A;O'Brien D;Wang L;Schaid DJ
多项研究已经确定了与前列腺癌风险相关的基因，但相关基因还没有得到很好的研究。在这里，我们创建了一个正常的前列腺组织特异性eQTL数据集，并将该数据集应用于先前发现的前列腺癌(PrCa)风险SNPs，以努力识别候选目标基因。通过对471个样本进行基因分型和RNA测序，构建了eQTL数据集。我们重点研究了146个PrCa风险SNP，包括与每个风险SNP连锁不平衡的所有SNP，导致了100个独特的风险区间。我们分析转录本位于风险单核苷酸多态性区间2 Mb(±1 Mb)内的顺式作用关联。在测试的所有SNP基因组合中，41.7%的SNPs在调整样本组织学和14个表达主成分协变量后显示出显著的eQTL信号。在100个PrCa风险区间中，有51个具有显著的eQTL信号，这些信号与88个基因相关。这项研究为研究PrCa遗传风险的生物学机制提供了丰富的资源。单核苷酸多态-SNPs-已被确定为前列腺癌，但这些SNPs是否会改变基因的表达在很大程度上尚不清楚。在这项研究中，作者寻找位于SNPs的2 Mb内的基因，并确定影响基因表达的SNPs，即所谓的表达数量性状基因座。
Multiple studies have identified loci associated with the risk of developing prostate cancer but the associated genes are not well studied. Here we create a normal prostate tissue-specific eQTL data set and apply this data set to previously identified prostate cancer (PrCa)-risk SNPs in an effort to identify candidate target genes. The eQTL data set is constructed by the genotyping and RNA sequencing of 471 samples. We focus on 146 PrCa-risk SNPs, including all SNPs in linkage disequilibrium with each risk SNP, resulting in 100 unique risk intervals. We analyse cis-acting associations where the transcript is located within 2 Mb (±1 Mb) of the risk SNP interval. Of all SNP–gene combinations tested, 41.7% of SNPs demonstrate a significant eQTL signal after adjustment for sample histology and 14 expression principal component covariates. Of the 100 PrCa-risk intervals, 51 have a significant eQTL signal and these are associated with 88 genes. This study provides a rich resource to study biological mechanisms underlying genetic risk to PrCa. Single nucleotide polymorphisms—SNPs—have been identified for prostate cancer but whether these SNPs alter the expression of genes is largely unknown. In this study, the authors search for genes located within 2 Mb of the SNPs and identify SNPs that influence gene expression, so called expression quantitative trait loci.