Genome-wide association studies using large-scale genome and exome sequencing data have become increasingly valuable in identifying associations between genetic variants and disease, transforming basic research and translational medicine. However, this progress has not been equally shared across all people and conditions, in part due to limited resources. Leveraging publicly available sequencing data as external common controls, rather than sequencing new controls for every study, can better allocate resources by augmenting control sample sizes or providing controls where none existed. However, common control studies must be carefully planned and executed as even small differences in sample ascertainment and processing can result in substantial bias. Here, we discuss challenges and opportunities for the robust use of common controls in high-throughput sequencing studies, including study design, quality control and statistical approaches. Thoughtful generation and use of large and valuable genetic sequencing data sets will enable investigation of a broader and more representative set of conditions, environments and genetic ancestries than otherwise possible.

使用大规模基因组和外显子组测序数据的全基因组关联研究在识别遗传变异与疾病之间的关联、改变基础研究和转化医学方面变得越来越有价值。然而，这一进步并未在所有人和条件下平等分享，部分原因是资源有限。利用公开可用的测序数据作为外部通用对照，而不是为每项研究排序新的对照，可以通过增加对照样本量或在不存在的地方提供对照来更好地分配资源。然而，必须仔细计划和执行共同的对照研究，因为即使样本确定和处理中的微小差异也会导致很大的偏差。这里，我们讨论了在高通量测序研究（包括研究设计、质量控制和统计方法）中稳健使用通用对照的挑战和机遇。深思熟虑地生成和使用大量有价值的基因测序数据集，将能够比其他方式调查更广泛、更具代表性的条件、环境和遗传祖先。