Reclassification of genetic-based risk predictions as GWAS data accumulate.
Reclassification of genetic-based risk predictions as GWAS data accumulate.
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DOI：
10.1186/s13073-016-0272-5
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发表时间：
2016-02-17
期刊：
影响因子：
12.3
通讯作者：
Kraft P
中科院分区：
文献类型：
--
作者：
Krier J;Barfield R;Green RC;Kraft P
近年来，基于常见遗传变异的疾病风险评估得到了广泛的关注和使用。遗传风险概况的临床效用取决于已识别位点的数量和效应大小，以及发现其他位点时预测风险的稳定性。随着时间的推移，个体风险分类的变化将破坏风险预测的常见遗传变异的有效性。在本分析中，我们根据过去和预期的未来 GWAS 数据对遗传风险的重新分类进行了量化。我们通过 NHGRI GWAS 目录和最近的大规模全基因组关联研究 (GWAS) 确定了与疾病相关的 SNP。我们根据乘法优势比模型，使用 2007 年、2009 年、2011 年和 2013 年四个时间点的累积 GWAS 识别的 SNP，计算了 100,000 名个体的模拟队列的基因组风险。每个时间点的个体被分类为较高风险（群体调整优势 >2）、平均风险（0.5 和 2 之间）和较低风险（<0.5），并进行比较乳腺癌 (BrCa)、前列腺癌 (PrCa)、2 型糖尿病 (T2D) 和心血管心脏病 (CHD) 时间点之间的分类。我们使用未发现的 SNP 的预期数量来估计未来的重新分类。从 2007 年到 2013 年，所有四种表型都发生了风险重新分类。在最近的时间间隔（2011-2013 年），风险重新分类的程度从 CHD 的 16.3% 到 PrCa 的 24.4% 不等。许多在早期时间点被分类为较高风险的个人随后被重新分类为较低风险类别。从 2011 年到 2013 年，这种向下风险重新分类的程度从 T2D 的 24.9% 到 CHD 的 55% 不等。随着更多 SNP 的发现，被归类为高风险的个体百分比也随之增加，从 2007 年到 2013 年，CHD 增加了 5%，PrCa 增加了 9%。当我们根据当前样本量的两倍对预期 SNP 的发现进行建模时，重新分类继续发生。根据常见遗传变异进行的风险估计显示出很大的重新分类率。识别与疾病相关的 SNP 有助于识别高风险个体的临床相关任务。然而，我们在最初被分类为较高风险但后来被分类为平均风险或较低风险的个体中展示的大量重新分类表明，目前在根据许多复杂疾病的常见遗传变异做出临床决策时需要谨慎。本文的在线版本 (doi:10.1186/s13073-016-0272-5) 包含补充材料，可供授权用户使用。
Disease risk assessments based on common genetic variation have gained widespread attention and use in recent years. The clinical utility of genetic risk profiles depends on the number and effect size of identified loci, and how stable the predicted risks are as additional loci are discovered. Changes in risk classification for individuals over time would undermine the validity of common genetic variation for risk prediction. In this analysis, we quantified reclassification of genetic risk based on past and anticipated future GWAS data. We identified disease-associated SNPs via the NHGRI GWAS catalog and recent large scale genome-wide association study (GWAS). We calculated the genomic risk for a simulated cohort of 100,000 individuals based on a multiplicative odds ratio model using cumulative GWAS-identified SNPs at four time points: 2007, 2009, 2011, and 2013. Individuals were classified as Higher Risk (population adjusted odds >2), Average Risk (between 0.5 and 2), and Lower Risk (<0.5) for each time point and we compared classifications between time points for breast cancer (BrCa), prostate cancer (PrCa), diabetes mellitus type 2 (T2D), and cardiovascular heart disease (CHD). We estimated future reclassification using the anticipated number of undiscovered SNPs. Risk reclassification occurred for all four phenotypes from 2007 to 2013. During the most recent interval (2011-2013), the degree of risk reclassification ranged from 16.3 % for CHD to 24.4 % for PrCa. Many individuals classified as Higher Risk at earlier time points were subsequently reclassified into a lower risk category. From 2011 to 2013, the degree of such downward risk reclassification ranged from 24.9 % for T2D to 55 % for CHD. The percent of individuals classified as Higher Risk increased as more SNPs were discovered, ranging from an increase of 5 % for CHD to 9 % for PrCa from 2007 to 2013. Reclassification continued to occur when we modeled the discovery of anticipated SNPs based on doubling current sample size. Risk estimates from common genetic variation show large reclassification rates. Identifying disease-associated SNPs facilitates the clinically relevant task of identifying higher-risk individuals. However, the large amount of reclassification that we demonstrated in individuals initially classified as Higher Risk but later as Average Risk or Lower Risk, suggests that caution is currently warranted in basing clinical decisions on common genetic variation for many complex diseases. The online version of this article (doi:10.1186/s13073-016-0272-5) contains supplementary material, which is available to authorized users.