Variation in Predictive Ability of Common Genetic Variants by Established Strata The Example of Breast Cancer and Age
Variation in Predictive Ability of Common Genetic Variants by Established Strata The Example of Breast Cancer and Age
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DOI：
10.1097/ede.0000000000000195
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发表时间：
2015-01-01
期刊：
影响因子：
5.4
通讯作者：
Kraft, Peter
中科院分区：
文献类型：
--
作者：
Aschard, Hugues;Zaitlen, Noah;Kraft, Peter
背景：最近对乳腺癌和常见遗传标记的研究未能确定普遍的基因-基因和基因-环境相互作用。理论上的考虑还表明，在一般人群中，适度的相互作用对风险歧视的贡献可能很小。然而，常见的乳腺癌风险标志物的临床效用可能仍然不同的分层定义的已知的风险因素，如age.Methods：我们研究了年龄特异性的每等位基因的优势比15个常见的单核苷酸多态性（SNP），发现与乳腺癌在1142例乳腺癌和1145控制护士的健康研究。我们计算了结合这些SNP的风险模型的年龄特异性区分能力。然后，我们进行了模拟研究，以探索假设的潜在遗传模型如何拟合观察到的结果。结果：尽管所有个体SNP与年龄的相互作用都是适度的，但我们发现年龄和由风险总和定义的遗传风险评分之间存在负相互作用效应等位基因（P = 0.04）。我们还观察到，随着年龄的增长，通过曲线下面积（AUC）测量，SNP的辨别能力下降（P = 0.04）。模拟研究揭示了模型，其中AUC可以通过由风险因子定义的分层而不同，而不存在相互作用;然而，我们的研究表明，观察到的AUC差异可以通过SNP的年龄特异性效应来解释。确定改变多种遗传变异影响的风险因素有助于解释多因素疾病的遗传结构，并确定可以从基因筛查中获益。
Background: Recent studies of breast cancer and common genetic markers have failed to identify pervasive gene-gene and gene-environment interactions. Theoretical considerations also suggest that the contribution of modest interactions to risk discrimination in the general population is likely small. However, the clinical utility of common breast cancer risk markers may nonetheless differ across strata defined by known risk factors, such as age.Methods: We examined the age-specific per-allele odds ratios of 15 common single nucleotide polymorphisms (SNPs) found to be associated with breast cancer in 1142 breast cancer cases and 1145 controls from the Nurses' Health Study. We calculated the age-specific discriminatory ability of risk models incorporating these SNPs. We then conducted simulation studies to explore how hypothetical underlying genetic models may fit the observed results.Results: Although all individual SNP-by-age interactions were modest, we found a negative interaction effect between age and a genetic risk score defined by the sum of risk alleles (P = 0.04). We also observed a decrease in discriminatory ability, as measured by the area under the curve (AUC), of the SNPs with age (P = 0.04). Simulation studies revealed models where the AUC can differ by strata defined by a risk factor without the presence of interactions; however, our study suggests that the observed differences in AUC are explained by the age-specific effect of the SNPs.Conclusion: The identification of risk factors that alter the effect of multiple genetic variants can help to explain the genetic architecture of multifactorial diseases and identify subgroups of persons who may benefit from genetic screening.