Common genetic variation in candidate genes and susceptibility to subtypes of breast cancer.
Common genetic variation in candidate genes and susceptibility to subtypes of breast cancer.
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DOI：
10.1158/1055-9965.epi-08-0704
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发表时间：
2009-01
期刊：
影响因子：
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通讯作者：
Pharoah PD
中科院分区：
文献类型：
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作者：
Mavaddat N;Dunning AM;Ponder BA;Easton DF;Pharoah PD
关联研究已被广泛用于寻找乳腺癌的常见低外显性易感等位基因。然而，乳腺癌是一种异质性疾病，有人建议通过限制对特定亚型的分析来识别额外的易感等位基因。我们使用了一项大型候选基因关联研究中120个候选基因中710个SNPs的数据，该研究涉及4470例病例和4560名对照，以比较“总体”乳腺癌分析和基于乳腺癌主要临床病理特征(分期、分级、形态和激素受体状态)的亚组分析的结果。在总体效应分析中，单核苷酸多态(SNP)无显著意义。亚组分析导致了SNPs等级的大幅重新排序，通过测试统计的大小进行评估，并在子组中检测到一些对总体影响不显著的关联，经多次测试调整后的名义水平为5%。CCND1SNP rs3212879与雌激素受体阴性的肿瘤类型之间最显著的关联(p=0.001)，没有达到全基因组的显著水平。这些结果表明，使用总体效应分析中遗漏的子组分析来检测关联是可能的。如果我们发现的关联可以在独立研究中复制，它们可能会为乳腺癌的疾病机制提供重要的见解。
Association studies have been widely used to search for common low penetrance susceptibility alleles to breast cancer in general. However, breast cancer is a heterogeneous disease and it has been suggested that it may be possible to identify additional susceptibility alleles by restricting analyses to particular subtypes. We used data on 710 SNPs in 120 candidate genes from a large candidate-gene association study of up to 4470 cases and 4560 controls to compare the results of analyses of “overall” breast cancer with sub-group analyses based on the major clinico-pathological characteristics of breast cancer (stage, grade, morphology and hormone receptor status). No single nucleotide polymorphism (SNP) was highly significant in overall-effects analysis. Subgroup analysis resulted in substantial reordering of ranks of SNPs, as assessed by the magnitude of the test statistics and some associations that were not significant for an overall effect were detected in sub-groups at a nominal 5% level adjusted for multiple testing. The most significant association, of CCND1 SNP rs3212879 with estrogen receptor negative tumour types (p = 0.001), did not reach genome-wide significance levels. These results demonstrate that it may be possible to detect associations using subgroup analysis that are missed in overall-effects analysis. If the associations we found can be replicated in independent studies they may provide important insights into disease mechanisms in breast cancer.