Cardiovascular Institute, Department of Medicine (K.M.), Department of Genetics (K.M.), and Department of Pathology and Laboratory Medicine (F.S.L.), Perelman School of Medicine at the University of Pennsylvania, Philadelphia. Department of Pediatrics (D.B.), Cardiovascular Institute (D.B., T.Q.), and Department of Medicine (T.Q.), Stanford University, CA. Edward Mallinckrodt Department of Pediatrics, Washington University School of Medicine, St. Louis, MO (F.S.C.). St. Louis Children's Hospital, MO (F.S.C.). Pediatric Genomics Discovery Program, Department of Pediatrics and Genetics, Yale University School of Medicine, New Haven, CT (M.K.K.). Division of Cardiology, Department of Medicine, University of Washington, Seattle (S.L.). Department of Cardiovascular Sciences, University of South Florida Morsani College of Medicine, Tampa, FL (T.V.M.). Department of Pediatrics (I.P.M.), Department of Pathology (I.P.M.), and Department of Human Genetics (I.P.M.), The University of Chicago, IL. Division of Hematology/ Oncology, Boston Children's Hospital, MA (V.G.S.). Department of Pediatric Oncology, Dana-Farber Cancer Institute (V.G.S.) and Channing Division of Network Medicine, Brigham and Women's Hospital (E.K.S., X.Z.), Harvard Medical School, Boston. Broad Institute of MIT and Harvard, Cambridge, MA (V.G.S.). University of Colorado, Aurora (D.A.S.). Division of Cardiovascular Sciences, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD (A.A.K.H., X.-z.J.L.).
Circ Genom Precis Med. 2018 Apr;11(4):e002178. doi: 10.1161/CIRCGEN.118.002178.
The National Institutes of Health have made substantial investments in genomic studies and technologies to identify DNA sequence variants associated with human disease phenotypes. The National Heart, Lung, and Blood Institute has been at the forefront of these commitments to ascertain genetic variation associated with heart, lung, blood, and sleep diseases and related clinical traits. Genome-wide association studies, exome- and genome-sequencing studies, and exome-genotyping studies of the National Heart, Lung, and Blood Institute-funded epidemiological and clinical case-control studies are identifying large numbers of genetic variants associated with heart, lung, blood, and sleep phenotypes. However, investigators face challenges in identification of genomic variants that are functionally disruptive among the myriad of computationally implicated variants. Studies to define mechanisms of genetic disruption encoded by computationally identified genomic variants require reproducible, adaptable, and inexpensive methods to screen candidate variant and gene function. High-throughput strategies will permit a tiered variant discovery and genetic mechanism approach that begins with rapid functional screening of a large number of computationally implicated variants and genes for discovery of those that merit mechanistic investigation. As such, improved variant-to-gene and gene-to-function screens-and adequate support for such studies-are critical to accelerating the translation of genomic findings. In this White Paper, we outline the variety of novel technologies, assays, and model systems that are making such screens faster, cheaper, and more accurate, referencing published work and ongoing work supported by the National Heart, Lung, and Blood Institute's R21/R33 Functional Assays to Screen Genomic Hits program. We discuss priorities that can accelerate the impressive but incomplete progress represented by big data genomic research.
美国国立卫生研究院（NIH）在基因组研究和技术方面投入了大量资金，以鉴定与人类疾病表型相关的 DNA 序列变异。美国国立心肺血液研究所（NHLBI）一直处于这些承诺的前沿，以确定与心脏、肺、血液和睡眠疾病以及相关临床特征相关的遗传变异。全基因组关联研究、外显子组和基因组测序研究以及 NHLBI 资助的流行病学和临床病例对照研究的外显子组基因分型研究正在确定与心脏、肺、血液和睡眠表型相关的大量遗传变异。然而，研究人员在确定具有功能破坏性的基因组变异方面面临挑战，而这些变异在众多计算上涉及的变异中。定义由计算确定的基因组变异所编码的遗传破坏机制的研究需要可重复、可适应和廉价的方法来筛选候选变体和基因功能。高通量策略将允许分层的变体发现和遗传机制方法，该方法从快速功能筛选大量计算上涉及的变体和基因开始，以发现那些值得进行机制研究的变体。因此，改进的变体到基因和基因到功能筛选——以及对这些研究的充分支持——对于加速基因组发现的转化至关重要。在本白皮书中，我们概述了各种新型技术、检测方法和模型系统，这些技术、检测方法和模型系统使这些筛选更快、更便宜、更准确，并参考了已发表的工作和 NHLBI 的 R21/R33 功能检测以筛选基因组命中计划的正在进行的工作。我们讨论了可以加速大数据基因组研究所代表的令人印象深刻但不完整进展的优先事项。
