WELBIO, GIGA-R Medical Genomics-BIO3, University of Liège, Liege, Belgium.
Department of Human Genetics, University of Leuven, Leuven, Belgium.
Hum Genet. 2019 Apr;138(4):293-305. doi: 10.1007/s00439-019-01987-w. Epub 2019 Mar 6.
The understanding that differences in biological epistasis may impact disease risk, diagnosis, or disease management stands in wide contrast to the unavailability of widely accepted large-scale epistasis analysis protocols. Several choices in the analysis workflow will impact false-positive and false-negative rates. One of these choices relates to the exploitation of particular modelling or testing strategies. The strengths and limitations of these need to be well understood, as well as the contexts in which these hold. This will contribute to determining the potentially complementary value of epistasis detection workflows and is expected to increase replication success with biological relevance. In this contribution, we take a recently introduced regression-based epistasis detection tool as a leading example to review the key elements that need to be considered to fully appreciate the value of analytical epistasis detection performance assessments. We point out unresolved hurdles and give our perspectives towards overcoming these.
人们已经认识到，生物上位性的差异可能会影响疾病的风险、诊断或疾病管理，但目前还没有广泛接受的大规模上位性分析协议。分析工作流程中的一些选择会影响假阳性和假阴性率。其中一个选择涉及到特定建模或测试策略的利用。需要充分了解这些策略的优缺点，以及它们适用的情况。这将有助于确定上位性检测工作流程的潜在互补价值，并有望提高具有生物学相关性的复制成功率。在本贡献中，我们以最近引入的基于回归的上位性检测工具为例，回顾了需要考虑的关键因素，以充分了解分析上位性检测性能评估的价值。我们指出了尚未解决的障碍，并提出了克服这些障碍的观点。
