Starita Lea M, Ahituv Nadav, Dunham Maitreya J, Kitzman Jacob O, Roth Frederick P, Seelig Georg, Shendure Jay, Fowler Douglas M
Department of Genome Sciences, University of Washington, Seattle, WA 98195, USA.
Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, San Francisco, CA 94158, USA; Institute for Human Genetics, University of California, San Francisco, San Francisco, CA 94158, USA.
Am J Hum Genet. 2017 Sep 7;101(3):315-325. doi: 10.1016/j.ajhg.2017.07.014.
Classical genetic approaches for interpreting variants, such as case-control or co-segregation studies, require finding many individuals with each variant. Because the overwhelming majority of variants are present in only a few living humans, this strategy has clear limits. Fully realizing the clinical potential of genetics requires that we accurately infer pathogenicity even for rare or private variation. Many computational approaches to predicting variant effects have been developed, but they can identify only a small fraction of pathogenic variants with the high confidence that is required in the clinic. Experimentally measuring a variant's functional consequences can provide clearer guidance, but individual assays performed only after the discovery of the variant are both time and resource intensive. Here, we discuss how multiplex assays of variant effect (MAVEs) can be used to measure the functional consequences of all possible variants in disease-relevant loci for a variety of molecular and cellular phenotypes. The resulting large-scale functional data can be combined with machine learning and clinical knowledge for the development of "lookup tables" of accurate pathogenicity predictions. A coordinated effort to produce, analyze, and disseminate large-scale functional data generated by multiplex assays could be essential to addressing the variant-interpretation crisis.
用于解释变异的经典遗传学方法，如病例对照研究或共分离研究，需要找到携带每种变异的众多个体。由于绝大多数变异仅存在于少数在世的人类中，这种策略有明显的局限性。要充分实现遗传学的临床潜力，即使对于罕见或个体特有的变异，我们也需要准确推断其致病性。已经开发了许多预测变异效应的计算方法，但它们只能以临床所需的高置信度识别一小部分致病变异。通过实验测量变异的功能后果可以提供更清晰的指导，但仅在发现变异后才进行的个体检测既耗费时间又耗费资源。在这里，我们讨论如何使用变异效应多重检测（MAVEs）来测量疾病相关基因座中所有可能变异对各种分子和细胞表型的功能后果。由此产生的大规模功能数据可以与机器学习和临床知识相结合，以开发准确致病性预测的“查找表”。协调开展工作以产生、分析和传播由多重检测生成的大规模功能数据，对于解决变异解读危机可能至关重要。
