Department of Human Genetics, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA.
Medical Scientist Training Program, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA.
Hum Mol Genet. 2022 Oct 20;31(R1):R84-R96. doi: 10.1093/hmg/ddac194.
Linkage disequilibrium and the incomplete regulatory annotation of the noncoding genome complicates the identification of functional noncoding genetic variants and their causal association with disease. Current computational methods for variant prioritization have limited predictive value, necessitating the application of highly parallelized experimental assays to efficiently identify functional noncoding variation. Here, we summarize two distinct approaches, massively parallel reporter assays and CRISPR-based pooled screens and describe their flexible implementation to characterize human noncoding genetic variation at unprecedented scale. Each approach provides unique advantages and limitations, highlighting the importance of multimodal methodological integration. These multiplexed assays of variant effects are undoubtedly poised to play a key role in the experimental characterization of noncoding genetic risk, informing our understanding of the underlying mechanisms of disease-associated loci and the development of more robust predictive classification algorithms.
连锁不平衡和非编码基因组的不完全调控注释使得功能非编码遗传变异的鉴定及其与疾病的因果关系变得复杂。目前用于变异优先级划分的计算方法预测价值有限，因此需要应用高度并行化的实验检测来有效地鉴定功能非编码变异。在这里，我们总结了两种不同的方法，即大规模平行报告基因检测和基于 CRISPR 的池式筛选，并描述了它们灵活的实施方式，以空前的规模来描述人类非编码遗传变异。每种方法都有独特的优点和局限性，突出了多模态方法整合的重要性。这些变异效应的多重检测无疑将在非编码遗传风险的实验表征中发挥关键作用，使我们能够深入了解疾病相关基因座的潜在机制，并开发更稳健的预测分类算法。
