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首页> 外文期刊>Journal of Clinical Bioinformatics >Improved branch and bound algorithm for detecting SNP-SNP interactions in breast cancer
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Improved branch and bound algorithm for detecting SNP-SNP interactions in breast cancer

机译：检测乳腺癌中SNP-SNP相互作用的改进的分支定界算法

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Background Single nucleotide polymorphisms (SNPs) in genes derived from distinct pathways are associated with a breast cancer risk. Identifying possible SNP-SNP interactions in genome-wide case–control studies is an important task when investigating genetic factors that influence common complex traits; the effects of SNP-SNP interaction need to be characterized. Furthermore, observations of the complex interplay (interactions) between SNPs for high-dimensional combinations are still computationally and methodologically challenging. An improved branch and bound algorithm with feature selection (IBBFS) is introduced to identify SNP combinations with a maximal difference of allele frequencies between the case and control groups in breast cancer, i.e., the high/low risk combinations of SNPs. Results A total of 220 real case and 334 real control breast cancer data are used to test IBBFS and identify significant SNP combinations. We used the odds ratio (OR) as a quantitative measure to estimate the associated cancer risk of multiple SNP combinations to identify the complex biological relationships underlying the progression of breast cancer, i.e., the most likely SNP combinations. Experimental results show the estimated odds ratio of the best SNP combination with genotypes is significantly smaller than 1 (between 0.165 and 0.657) for specific SNP combinations of the tested SNPs in the low risk groups. In the high risk groups, predicted SNP combinations with genotypes are significantly greater than 1 (between 2.384 and 6.167) for specific SNP combinations of the tested SNPs. Conclusions This study proposes an effective high-speed method to analyze SNP-SNP interactions in breast cancer association studies. A number of important SNPs are found to be significant for the high/low risk group. They can thus be considered a potential predictor for breast cancer association.
机译：背景技术从不同途径衍生的基因中的单核苷酸多态性（SNP）与患乳腺癌的风险有关。在调查影响共同复杂性状的遗传因素时，在全基因组病例对照研究中鉴定可能的SNP-SNP相互作用是一项重要任务。 SNP-SNP相互作用的影响需要表征。此外，对于高维组合，SNP之间复杂的相互作用（相互作用）的观察仍在计算和方法上具有挑战性。引入了一种改进的带有特征选择的分支定界算法（IBBFS），以识别乳腺癌病例和对照组之间等位基因频率差异最大的SNP组合，即SNP的高/低风险组合。结果总共使用了220个实际病例和334个实际对照乳腺癌数据来测试IBBFS并确定重要的SNP组合。我们使用比值比（OR）作为定量方法来估计多个SNP组合的相关癌症风险，以识别潜在的乳腺癌进展（即最可能的SNP组合）的复杂生物学关系。实验结果表明，对于低风险组中被测SNP的特定SNP组合，具有基因型的最佳SNP组合的估计比值比明显小于1（在0.165和0.657之间）。在高风险组中，对于测试的SNP的特定SNP组合，具有基因型的预测SNP组合显着大于1（在2.384和6.167之间）。结论本研究提出了一种有效的高速方法，用于分析乳腺癌关联研究中的SNP-SNP相互作用。已发现许多重要的SNP对高/低风险组很重要。因此，它们可以被认为是乳腺癌关联的潜在预测因子。

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