Breast Cancer (BC) is associated with inherited gene mutations. High throughput genotyping of BC samples has led to the identification and characterization of biomarkers for the diagnosis of BC. The most common genetic variants studied are SNPs (Single Nucleotide Polymorphisms) that determine susceptibility to an array of diseases thus serving as a potential tool for identifying the underlying causes of breast carcinogenesis. SNP genotyping employing the Agena MassARRAY offers a robust, sensitive, cost-effective method to assess multiple SNPs and samples simultaneously. In this present study, we analyzed 15 SNPs of 14 genes in 550 samples (150 cases and 400 controls). We identified four SNPs of genes TCF21, SLC19A1, DCC, and ERCC1 showing significant association with BC in the population under study. The SNPs were rs12190287 (TCF21) having OR 1.713 (1.08–2.716 at 95% CI) p-value 0.022 (dominant), rs1051266 (SLC19A1) having OR 3.461 (2.136–5.609 at 95% CI) p-value 0.000000466 (dominant), rs2229080 (DCC) having OR 0.6867 (0.5123–0.9205 at 95% CI) p-value 0.0116 (allelic) and rs2298881 (ERCC1) having OR 0.669 (0.46–0.973 at 95% CI), p-value 0.035 (additive) respectively. The in-silico analysis was further used to fortify the above findings. It is further anticipated that the variants should be evaluated in other population groups that may aid in understanding the genetic complexity and bridge the missing heritability.

中文翻译：

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基于MassARRAY的印度北部人群乳腺癌单核苷酸多态性分析。

乳腺癌（BC）与遗传基因突变有关。BC样品的高通量基因分型已导致对BC诊断的生物标记物进行鉴定和表征。研究的最常见的遗传变异是SNP（单核苷酸多态性），可确定对多种疾病的易感性，因此可作为识别乳腺癌致癌根本原因的潜在工具。采用Agena MassARRAY进行SNP基因分型提供了一种健壮，灵敏，经济高效的方法，可同时评估多个SNP和样品。在本研究中，我们分析了550个样本（150个病例和400个对照）中14个基因的15个SNP。我们鉴定了基因TCF21，SLC19A1，DCC和ERCC1的四个SNP，它们与研究人群中的BC显着相关。SNP是具有OR 1.的rs12190287（TCF21）。713（在95％CI时为1.08–2.716）p值0.022（显性），rs1051266（SLC19A1）具有OR 3.461（在95％CI时为2.136–5.609）p值0.000000466（显性），rs2229080（DCC）具有OR 0.6867（ 95％CI时为0.5123–0.9205）p值0.0116（等位基因）和rs2298881（ERCC1）具有OR 0.669（95％CI时为0.46-0.973），p值分别为0.035（加法）。进一步使用计算机模拟分析来强化上述发现。进一步预期应该在其他人群中评估这些变体，这可能有助于理解遗传复杂性并弥补缺失的遗传力。973在95％CI时），p值分别为0.035（加法）。进一步使用计算机模拟分析来强化上述发现。进一步预期应该在其他人群中评估这些变体，这可能有助于理解遗传复杂性并弥补缺失的遗传力。973在95％CI时），p值分别为0.035（加法）。进一步使用计算机模拟分析来强化上述发现。进一步预期应该在其他人群中评估变异体，这可能有助于理解遗传复杂性并弥补缺失的遗传力。