Genotype imputation and variability in polygenic risk score estimation.
Genotype imputation and variability in polygenic risk score estimation.
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DOI：
10.1186/s13073-020-00801-x
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发表时间：
2020-11-23
期刊：
影响因子：
12.3
通讯作者：
Torkamani A
中科院分区：
文献类型：
--
作者：
Chen SF;Dias R;Evans D;Salfati EL;Liu S;Wineinger NE;Torkamani A
多基因风险分数(PRS)是个体对一种疾病或特征的遗传风险的汇总。这些分数是在研究和商业环境中产生的，以研究如何使用它们来指导医疗决策。PRSS应该随着遗传知识库的改进而更新；然而，没有关于它们的生成或更新的指导方针。在这里，我们通过在他们的世代中使用的一种常见的计算过程--基因归算来描述在PRS计算中引入的可变性。我们使用3种不同的预分相工具(Beagle、Eagle、SHAPEIT)和2种不同的补偿工具(Beagle、Minimac4)，相对于基于WGS的黄金标准，评估了在进行基因定位时的PRS变异性。评估了跨越不同疾病架构和PR生成方法的14种不同的PRSS。我们发现，在个体水平上，基因归因可以在计算的PRS中引入可变性，而不会对潜在的遗传模型进行任何改变。在不同的算法中，由基因分配引入的可变性程度不同，其中带有随机元素的预分相算法引入的分数可变性程度最大。在大多数情况下，由于归罪引起的PRS变异性很小(<； 5%的等级变化)，并且不影响对分数的解释。在提供更多信息的prs分布的尾部，prs百分位数的波动也减少了。然而，在极少数情况下，个体水平上的PRS不稳定会导致单独的PrS计算与基于全基因组序列的黄金标准分数有很大不同。我们的研究突出了将群体遗传学工具应用于个体水平的遗传分析中的一些挑战，包括返回结果。罕见的个体水平变异性事件被总体水平高度的总分重复性所掩盖。为了避免数据包络分析结果在更新过程中出现波动，我们建议使用确定性的推算过程或随机推算过程的多次迭代的平均值来生成和传递包络分析结果。网上版载有补充材料，可在10.1186/s13073-020-00801-x查阅。
Polygenic risk scores (PRSs) are a summarization of an individual’s genetic risk for a disease or trait. These scores are being generated in research and commercial settings to study how they may be used to guide healthcare decisions. PRSs should be updated as genetic knowledgebases improve; however, no guidelines exist for their generation or updating. Here, we characterize the variability introduced in PRS calculation by a common computational process used in their generation—genotype imputation. We evaluated PRS variability when performing genotype imputation using 3 different pre-phasing tools (Beagle, Eagle, SHAPEIT) and 2 different imputation tools (Beagle, Minimac4), relative to a WGS-based gold standard. Fourteen different PRSs spanning different disease architectures and PRS generation approaches were evaluated. We find that genotype imputation can introduce variability in calculated PRSs at the individual level without any change to the underlying genetic model. The degree of variability introduced by genotype imputation differs across algorithms, where pre-phasing algorithms with stochastic elements introduce the greatest degree of score variability. In most cases, PRS variability due to imputation is minor (< 5 percentile rank change) and does not influence the interpretation of the score. PRS percentile fluctuations are also reduced in the more informative tails of the PRS distribution. However, in rare instances, PRS instability at the individual level can result in singular PRS calculations that differ substantially from a whole genome sequence-based gold standard score. Our study highlights some challenges in applying population genetics tools to individual-level genetic analysis including return of results. Rare individual-level variability events are masked by a high degree of overall score reproducibility at the population level. In order to avoid PRS result fluctuations during updates, we suggest that deterministic imputation processes or the average of multiple iterations of stochastic imputation processes be used to generate and deliver PRS results. The online version contains supplementary material available at 10.1186/s13073-020-00801-x.
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影响因子：
30.8
作者：
Das, Sayantan;Forer, Lukas;Schoenherr, Sebastian;Sidore, Carlo;Locke, Adam E.;Kwong, Alan;Vrieze, Scott I.;Chew, Emily Y.;Levy, Shawn;McGue, Matt;Schlessinger, David;Stambolian, Dwight;Loh, Po-Ru;Iacono, William G.;Swaroop, Anand;Scott, Laura J.;Cucca, Francesco;Kronenberg, Florian;Boehnke, Michael;Abecasis, Goncalo R.;Fuchsberger, Christian
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