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A pilot genome-wide association study of early-onset breast cancer.

机译:早期乳腺癌全基因组关联试验研究。

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High-density oligonucleotide microarrays containing a large number of single nucleotide polymorphisms (SNPs) have enabled genome-wide association (GWA) analysis to become a reality. We used the early access Affymetrix Mendel Nsp 250K chips in a GWA case-control pilot study to identify genomic regions associated with breast cancer. We included 30 randomly sampled incident invasive breast cancer cases aged <45 years without deleterious mutations in the BRCA1 or BRCA2 genes, and 30 population controls individually matched on age, ethnicity and geographical area. The overall genotype call rate was 97.13+/-1.33% for controls and 97.48+/-1.42% for cases. Comparison was made between cases and controls for 203,477 genotyped SNPs using (a) unconditional logistic regression (ULR), (b) conditional logistic regression (CLR) models with adjustment for the matched pairs, (c) allelic tests for single marker tests and (d) haplotype trend regression (HTR). Genomic control and EIGENSTRAT methods were used for correctionof population stratification in appropriate models. We demonstrate the similarity and dissimilarity of results from different statistical analyses. We found several possible significant regions harboring biologically meaningful known candidate genes, such as genes encoding fibroblast growth factor, transforming growth factor, epidermal growth factor, and estrogen synthesis enzymes to be associated with early-onset breast cancer. In single marker analysis, none of the SNPs were statistically significant after correction for multiple testing. However, haplotype association tests, using 90730 tag-SNPs, suggested two regions in GLG1 and UGT1 genes retaining significance even after Bonferroni correction. Nevertheless, without systematic replication, findings from this pilot study, especially the associations of breast cancer in relation to specific SNPs, should be interpreted with great caution.
机译:包含大量单核苷酸多态性(SNP)的高密度寡核苷酸微阵列已使全基因组关联(GWA)分析成为现实。我们在GWA病例对照试验研究中使用了可早期使用的Affymetrix Mendel Nsp 250K芯片,以鉴定与乳腺癌相关的基因组区域。我们纳入了30例年龄小于45岁且未在BRCA1或BRCA2基因中造成有害突变的侵袭性乳腺癌病例,并根据年龄,种族和地理区域分别匹配了30个人群对照。对照的总体基因型检出率为97.13 +/- 1.33%,病例为97.48 +/- 1.42%。使用(a)无条件logistic回归(ULR),(b)有条件logistic回归(CLR)模型,对匹配对进行了调整,(c)单标记检验的等位基因检验和(a)对203,477个基因型SNP的病例和对照进行了比较d)单倍型趋势回归(HTR)。在适当的模型中,使用基因组控制和EIGENSTRAT方法校正种群分层。我们证明了不同统计分析结果的相似性和不相似性。我们发现了几个可能具有重要生物学意义的已知候选基因的重要区域,例如,编码成纤维细胞生长因子,转化生长因子,表皮生长因子和雌激素合成酶的基因与早发性乳腺癌有关。在单标记分析中,校正多个测试后,所有SNP均无统计学意义。但是,使用90730标签-SNP进行的单倍型关联测试表明,即使在Bonferroni校正后,GLG1和UGT1基因中的两个区域仍具有重要意义。然而,如果没有系统的复制,则应非常谨慎地解释该试验研究的结果,尤其是与特定SNP相关的乳腺癌的关联。

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