Evaluating the frequency and the impact of pharmacogenetic alleles in an ancestrally diverse Biobank population.
Evaluating the frequency and the impact of pharmacogenetic alleles in an ancestrally diverse Biobank population.
复制标题
DOI：
10.1186/s12967-022-03745-5
复制
发表时间：
2022-11-28
影响因子：
7.4
通讯作者：
Tuteja, Sony
中科院分区：
文献类型：
--
作者：
Verma, Shefali S.;Keat, Karl;Li, Binglan;Hoffecker, Glenda;Risman, Marjorie;Sangkuhl, Katrin;Whirl-Carrillo, Michelle;Dudek, Scott;Verma, Anurag;Klein, Teri E.;Ritchie, Marylyn D.;Tuteja, Sony
药物基因组学（PGx）旨在利用患者的遗传数据，以实现更安全，更有效的药物处方。临床药物遗传学实施联盟（CPIC）为影响72种药物的24种基因提供了强有力的证据指南。尽管有强有力的证据将PGx等位基因与药物反应联系起来，但在标准临床实践中，可操作的药物遗传学结果的实施和返回给患者方面存在很大的差距。在这项研究中，我们评估了在不同的卫生系统中遗传指导药物处方的机会，并确定了祖先多样性生物库人群中可操作的PGx等位基因的频率。对Penn Medicine电子健康记录（EHR）的回顾性分析，其中包括2012年至2020年期间的约330万患者，提供了Penn Medicine卫生系统中基于基因型处方指南（“CPIC A级或B级”）的药物处方趋势的快照。Penn Medicine BioBank（PMBB）由43，359名参与者组成，他们的EHR与全基因组SNP阵列和全外显子组测序（WES）数据相关。我们使用药物基因组学临床注释工具（PharmCAT）来注释来自PMBB变体调用格式（VCF）文件的PGx等位基因，并鉴定具有可操作的PGx等位基因的样品。我们确定了约316.000例根据CPIC A级或B级指南处方至少2种药物的独特患者。PMBB中的遗传分析确定，98.9%的参与者携带一个或多个PGx可操作等位基因，建议进行治疗调整。在将遗传数据与EHR的处方数据联系起来后，14.2%的参与者（n = 6157）被开了可能受其基因型影响的药物（如PharmCAT报告所示）。例如，856名携带CYP 2C 19功能降低等位基因的受试者接受了氯吡格雷治疗，使他们发生主要不良心血管事件的风险增加。当我们按遗传祖先分层时，我们发现PGx等位基因频率和临床负担存在差异。PMBB中亚洲血统的氯吡格雷使用者的CYP 2C 19可操作等位基因的发生率显著高于欧洲血统的氯吡格雷使用者（p < 0.0001，OR = 3.68）。临床上可行的PGx等位基因在我们的卫生系统中非常普遍，许多患者的处方药物可能受到PGx等位基因的影响。这些结果说明了先发制人的基因分型的潜在效用定制的药物和实施PGx到常规的临床护理。在线版本包含补充材料，可通过10.1186/s12967-022-03745-5获得。
Pharmacogenomics (PGx) aims to utilize a patient’s genetic data to enable safer and more effective prescribing of medications. The Clinical Pharmacogenetics Implementation Consortium (CPIC) provides guidelines with strong evidence for 24 genes that affect 72 medications. Despite strong evidence linking PGx alleles to drug response, there is a large gap in the implementation and return of actionable pharmacogenetic findings to patients in standard clinical practice. In this study, we evaluated opportunities for genetically guided medication prescribing in a diverse health system and determined the frequencies of actionable PGx alleles in an ancestrally diverse biobank population. A retrospective analysis of the Penn Medicine electronic health records (EHRs), which includes ~ 3.3 million patients between 2012 and 2020, provides a snapshot of the trends in prescriptions for drugs with genotype-based prescribing guidelines (‘CPIC level A or B’) in the Penn Medicine health system. The Penn Medicine BioBank (PMBB) consists of a diverse group of 43,359 participants whose EHRs are linked to genome-wide SNP array and whole exome sequencing (WES) data. We used the Pharmacogenomics Clinical Annotation Tool (PharmCAT), to annotate PGx alleles from PMBB variant call format (VCF) files and identify samples with actionable PGx alleles. We identified ~ 316.000 unique patients that were prescribed at least 2 drugs with CPIC Level A or B guidelines. Genetic analysis in PMBB identified that 98.9% of participants carry one or more PGx actionable alleles where treatment modification would be recommended. After linking the genetic data with prescription data from the EHR, 14.2% of participants (n = 6157) were prescribed medications that could be impacted by their genotype (as indicated by their PharmCAT report). For example, 856 participants received clopidogrel who carried CYP2C19 reduced function alleles, placing them at increased risk for major adverse cardiovascular events. When we stratified by genetic ancestry, we found disparities in PGx allele frequencies and clinical burden. Clopidogrel users of Asian ancestry in PMBB had significantly higher rates of CYP2C19 actionable alleles than European ancestry users of clopidrogrel (p < 0.0001, OR = 3.68). Clinically actionable PGx alleles are highly prevalent in our health system and many patients were prescribed medications that could be affected by PGx alleles. These results illustrate the potential utility of preemptive genotyping for tailoring of medications and implementation of PGx into routine clinical care. The online version contains supplementary material available at 10.1186/s12967-022-03745-5.
登录
查看更多内容
DOI：
10.1056/nejmsr1809937
发表时间：
2019-08-15
期刊：
The New England journal of medicine
影响因子：
--
作者：
All of Us Research Program Investigators;Denny JC;Rutter JL;Goldstein DB;Philippakis A;Smoller JW;Jenkins G;Dishman E
通讯作者：
Dishman E
影响因子：
6.7
作者：
Truong, Tien M.;Apfelbaum, Jeffrey;O'Donnell, Peter H.
通讯作者：
O'Donnell, Peter H.
影响因子：
--
作者：
David SP;Singh L;Pruitt J;Hensing A;Hulick P;Meltzer DO;O'Donnell PH;Dunnenberger HM
通讯作者：
Dunnenberger HM
影响因子：
64.8
作者：
Van Hout CV;Tachmazidou I;Backman JD;Hoffman JD;Liu D;Pandey AK;Gonzaga-Jauregui C;Khalid S;Ye B;Banerjee N;Li AH;O'Dushlaine C;Marcketta A;Staples J;Schurmann C;Hawes A;Maxwell E;Barnard L;Lopez A;Penn J;Habegger L;Blumenfeld AL;Bai X;O'Keeffe S;Yadav A;Praveen K;Jones M;Salerno WJ;Chung WK;Surakka I;Willer CJ;Hveem K;Leader JB;Carey DJ;Ledbetter DH;Geisinger-Regeneron DiscovEHR Collaboration;Cardon L;Yancopoulos GD;Economides A;Coppola G;Shuldiner AR;Balasubramanian S;Cantor M;Regeneron Genetics Center;Nelson MR;Whittaker J;Reid JG;Marchini J;Overton JD;Scott RA;Abecasis GR;Yerges-Armstrong L;Baras A
通讯作者：
Baras A
DOI：
10.1002/cpz1.226
发表时间：
2021-08
期刊：
Current protocols
影响因子：
--
作者：
Gong L;Whirl-Carrillo M;Klein TE
通讯作者：
Klein TE