Department of Biomedical Data Science, Stanford University, 1265 Welch Road, Stanford, CA 94305, USA.
Pac Symp Biocomput. 2022;27:385-396.
Precision medicine faces many challenges, including the gap of knowledge between disease genetics and pharmacogenomics (PGx). Disease genetics interprets the pathogenicity of genetic variants for diagnostic purposes, while PGx investigates the genetic influences on drug responses. Ideally, the quality of health care would be improved from the point of disease diagnosis to drug prescribing if PGx is integrated with disease genetics in clinical care. However, PGx genes or variants are usually not reported as a secondary finding even if they are included in a clinical genetic test for diagnostic purposes. This happens even though the detection of PGx variants can provide valuable drug prescribing recommendations. One underlying reason is the lack of systematic classification of the knowledge overlap between PGx and disease genetics. Here, we address this issue by analyzing gene and genetic variant annotations from multiple expert-curated knowledge databases, including PharmGKB, CPIC, ClinGen and ClinVar. We further classified genes based on the strength of evidence supporting a gene's pathogenic role or PGx effect as well as the level of clinical actionability of a gene. Twenty-six genes were found to have pathogenic variation associated with germline diseases as well as strong evidence for a PGx association. These genes were classified into four sub-categories based on the distinct connection between the gene's pathogenic role and PGx effect. Moreover, we have also found thirteen RYR1 genetic variants that were annotated as pathogenic and at the same time whose PGx effect was supported by a preponderance of evidence and given drug prescribing recommendations. Overall, we identified a nontrivial number of gene and genetic variant overlaps between disease genetics and PGx, which laid out a foundation for combining PGx and disease genetics to improve clinical care from disease diagnoses to drug prescribing and adherence.
精准医学面临诸多挑战，其中包括疾病遗传学与药物基因组学（PGx）之间的知识鸿沟。疾病遗传学用于诊断目的，解释遗传变异的致病性，而 PGx 则研究遗传对药物反应的影响。如果将 PGx 与临床护理中的疾病遗传学相结合，理想情况下，从疾病诊断到药物处方，医疗保健的质量将得到提高。然而，即使 PGx 基因或变体包含在用于诊断目的的临床遗传测试中，通常也不会将其作为次要发现报告。即使检测到 PGx 变体可以提供有价值的药物处方建议，也会出现这种情况。一个潜在的原因是缺乏对 PGx 和疾病遗传学之间知识重叠的系统分类。在这里，我们通过分析来自多个专家策划知识数据库（包括 PharmGKB、CPIC、ClinGen 和 ClinVar）的基因和遗传变体注释来解决这个问题。我们还根据支持基因致病性作用或 PGx 效应的证据强度以及基因临床可操作性水平对基因进行分类。发现有 26 个基因与种系疾病相关的致病性变异以及与 PGx 关联的强有力证据。这些基因根据基因致病性作用与 PGx 效应之间的不同联系分为四个亚类。此外，我们还发现了 13 个 RYR1 遗传变体，这些变体被注释为致病性，同时其 PGx 效应得到了大量证据的支持，并给出了药物处方建议。总体而言，我们确定了疾病遗传学和 PGx 之间存在大量的基因和遗传变体重叠，为将 PGx 和疾病遗传学结合起来，从疾病诊断到药物处方和用药依从性，改善临床护理奠定了基础。
