可待因处方计数的全基因组关联研究-动脉网

A genome-wide Association study of the Count of Codeine prescriptions

Nature 等信源发布 2024-10-01 16:44

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AbstractOpioid prescription records in existing electronic health record (EHR) databases are a potentially useful, high-fidelity data source for opioid use-related risk phenotyping in genetic analyses. Prescriptions for codeine derived from EHR records were used as targeting traits by screening 16 million patient-level medication records.

摘要现有电子健康记录（EHR）数据库中的阿片类药物处方记录是遗传分析中与阿片类药物使用相关的风险表型分析的潜在有用的高保真数据源。通过筛选1600万患者级药物记录，将来自EHR记录的可待因处方用作靶向特征。

Genome-wide association analyses were then conducted to identify genomic loci and candidate genes associated with different count patterns of codeine prescriptions. Both low- and high-prescription counts were captured by developing 8 types of phenotypes with selected ranges of prescription numbers to reflect potentially different levels of opioid risk severity.

然后进行全基因组关联分析，以鉴定与可待因处方的不同计数模式相关的基因组位点和候选基因。通过开发具有选定处方数范围的8种表型来捕获低处方数和高处方数，以反映潜在不同水平的阿片类药物风险严重程度。

We identified one significant locus associated with low-count codeine prescriptions (1, 2 or 3 prescriptions), while up to 7 loci were identified for higher counts (≥ 4, ≥ 5, ≥6, or ≥ 7 prescriptions), with a strong overlap across different thresholds. We identified 9 significant genomic loci with all-count phenotype.

我们确定了一个与低计数可待因处方（1,2或3个处方）相关的重要基因座，而高计数（≥4，≥5，≥6或≥7个处方）最多有7个基因座，不同阈值之间有很强的重叠。我们确定了9个具有全计数表型的重要基因组位点。

Further, using the polygenic risk approach, we identified a significant correlation (Tau = 0.67, p = 0.01) between an externally derived polygenic risk score for opioid use disorder and numbers of codeine prescriptions. As a proof-of-concept study, our research provides a novel and generalizable phenotyping pipeline for the genomic study of opioid-related risk traits..

此外，使用多基因风险方法，我们确定了阿片类药物使用障碍的外部多基因风险评分与可待因处方数量之间的显着相关性（Tau=0.67，p=0.01）。作为概念验证研究，我们的研究为阿片类药物相关风险特征的基因组研究提供了一种新颖且可推广的表型分析管道。。

IntroductionOpioids are among the top 10 most-prescribed prescription medications in the U.S., and about 80% of surgical patients are treated with opioids for acute post-surgical pain1,2.Opioids are also commonly prescribed for patients with moderate or severe chronic pain that is not managed well by non-opioid drugs3.

简介阿片类药物是美国处方最多的前10种处方药之一，约80%的手术患者接受阿片类药物治疗急性手术后疼痛1,2。阿片类药物也常用于中度或重度慢性疼痛患者，非阿片类药物治疗效果不佳3。

Starting in the early 1990s, opioid prescriptions increased significantly for pain management, leading to surges in overdoses, opioid use disorder (OUD), and the so-called “opioid crisis”4,5. While opioid drugs are very effective for controlling pain, they are highly addictive6.Side effects of opioid use include respiratory depression and excessive sedation7.

。虽然阿片类药物对控制疼痛非常有效，但它们很容易上瘾6。阿片类药物的副作用包括呼吸抑制和过度镇静7。

Further, patients who take opioids for longer than 90 days have an increased risk of developing OUD8. In the U.S., up to 3 million people have current or past OUD9. It also has been estimated that 80,816 deaths were related to opioid overdose in the United States in 202110. In recent years, opioid prescription rates have dropped precipitously and most deaths are due to illicit fentanyl, but prescription opioids are still associated with about 12,000 overdose deaths in the U.S.

此外，服用阿片类药物超过90天的患者患OUD8的风险增加。在美国，多达300万人目前或过去拥有OUD9。据估计，202110年，美国有80816人死亡与阿片类药物过量有关。近年来，阿片类药物处方率急剧下降，大多数死亡是由于非法芬太尼引起的，但在美国，处方阿片类药物仍与约12000例过量死亡有关。

each year11. Additionally, opioid-related adverse drug events (ORADEs) can cause harmful patient outcomes, including inpatient costs, readmissions, and mortality12.Genome-wide association studies (GWAS) have suggested that both OUD and opioid-related patient responses have strong genetic underpinnings13,14,15,16,17,18,19.

。此外，与阿片类药物相关的不良药物事件（ORADEs）可导致有害的患者结局，包括住院费用，再入院率和死亡率12。全基因组关联研究（GWAS）表明，OUD和阿片类药物相关的患者反应都有很强的遗传基础13,14,15,16,17,18,19。

GWAS have identified significant genomic loci and related genes that can affect efficacy, metabolism, and adverse effects of opioids, which can in turn cause heterogeneous individual responses to drugs, including both pain levels and development of addiction20,21,22. This is particularly relevant to codeine, i.

GWAS已经确定了可能影响阿片类药物疗效，代谢和不良反应的重要基因组位点和相关基因，这反过来又可能导致个体对药物的异质反应，包括疼痛程度和成瘾的发展20,21,22。这与可待因特别相关。

(1)

Three low-count prescription phenotypes: patients with 1, 2 or 3 codeine prescriptions.

三种低计数处方表型：服用1、2或3种可待因处方的患者。

(2)

Four high-count prescription phenotypes: patients with 4 or more, 5 or more, 6 or more, or 7 or more codeine prescriptions. For both low- and high-count prescription groups, the control group was defined as patients with no opioid prescriptions.

四种高计数处方表型：具有4种或更多，5种或更多，6种或更多或7种或更多可待因处方的患者。对于低剂量和高剂量处方组，对照组被定义为没有阿片类药物处方的患者。

(3)

All-count prescription phenotype: codeine prescription count was coded as integers and winsorized at 8 prescriptions to reduce the influence of outliers.

所有计数处方表型：可待因处方计数被编码为整数，并在8个处方中获胜，以减少异常值的影响。

Genotyping data and quality controlGenotyping was performed by the MGB Biobank team. Prior to imputation, standard GWAS quality control procedures were carried out. These included: (1) sample-level QC. samples with discrepant reported and predicted sex or high missing rates were excluded; (2) Variant-level QC.

基因分型数据和质量控制基因分型由MGB生物库团队进行。在插补之前，执行了标准GWAS质量控制程序。。报告和预测性别不一致或缺失率高的样本被排除在外；（2）变体级别QC。

variants with invalid alleles, allele mismatch with the reference panel, SNPs not found within the reference panel and duplicated, monomorphic variants, indels (insertion and deletions), and variants with low call rate (less than 90%) were excluded. Imputation was performed using the Michigan Imputation Server with 1000 Genomes panel and haplotype phasing was performed using SHAPEIT34,35,36.Post-imputation quality control was conducted to select high-quality SNPs and control for population stratification and family structure.

排除了具有无效等位基因的变体，与参考面板不匹配的等位基因，在参考面板中未发现的SNP以及重复的单态变体，插入缺失（插入和缺失）和呼叫率低（低于90%）的变体。使用具有1000个基因组面板的密歇根插补服务器进行插补，并使用SHAPEIT34,35,36进行单倍型定相。进行插补后质量控制以选择高质量的SNP并控制种群分层和家庭结构。

The relatedness of the cohort was detected by pairwise IBD estimation filtered by pi-hat (1 for 100% identical by descent [IBD], 0.5 for 50%, 0.25 for 25%) using PLINK to estimate the probability of sharing 0, 1, or 2 alleles IBD for any two individuals from the study population. Only autosomal biallelic SNPs with minor allele frequencies (MAF) of at least 1%, an info score above 0.8 and call rates above 98% were retained, which led to ~ 5 million SNPs.

使用PLINK通过pi hat过滤的成对IBD估计来检测队列的相关性（1代表100%相同的血统[IBD]，0.5代表50%，0.25代表25%），以估计来自研究人群的任何两个个体共享0,1或2个等位基因IBD的概率。仅保留次要等位基因频率（MAF）至少为1%，信息得分高于0.8且呼叫率高于98%的常染色体双等位基因SNP，这导致约500万个SNP。

A principal components analysis was applied in a linkage-disequilibrium-pruned set of genotyped SNPs to characterize population structure within samples from included individuals.Genome-wide association and gene-level analysisWe used PLINK 2.0 to conduct the genome-wide association analysis for each codeine prescription phenotype, using linear regression for continuous phenotypes and logistic regression for binary phenotypes37.

主成分分析应用于连锁不平衡修剪的基因型SNP集，以表征来自所包括个体的样本中的种群结构。。

All association analyses were adjusted for age, sex and the top 5 .

所有关联分析均针对年龄，性别和前5名进行了调整。

Data availability

数据可用性

The clinical/genetic datasets generated and analyzed during the current study are not publicly available due to hospital IRB regulation and patient privacy. The genetic summary statistics are available from the corresponding author upon request.

由于医院IRB法规和患者隐私，当前研究期间生成和分析的临床/遗传数据集无法公开获得。遗传摘要统计数据可应要求从通讯作者处获得。

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Download referencesAcknowledgementsThis study was supported by National Institute on Drug Abuse (NIDA) 1K01DA059572-01.The authors would like to acknowledge contributions of The Mass General Brigham (MGB) Biobank for providing genomic data and health information data, and The MGB Biobank Team for providing all the technique support.We gratefully acknowledge all the studies and databases that made GWAS summary data available: Psychiatric Genomics Consortium, the Pan-UKB project and UK Biobank.Psychiatric Genomics Consortium: https://pgc.unc.edu/for-researchers/download-results/Pan-UKB team: https://pan.ukbb.broadinstitute.org.

下载参考文献致谢本研究得到了美国国家药物滥用研究所（NIDA）1K01DA059572-01的支持。作者要感谢马萨诸塞州布莱根将军（MGB）生物库为提供基因组数据和健康信息数据所做的贡献，以及MGB生物库团队为提供所有技术支持。我们非常感谢所有提供GWAS摘要数据的研究和数据库：精神病学基因组学联盟，泛UKB项目和英国生物库。精神病学基因组学协会：https://pgc.unc.edu/for-researchers/download-results/Pan-UKB团队：https://pan.ukbb.broadinstitute.org.

2020.UK Biobank: https://biobank.ctsu.ox.ac.uk/crystal/exinfo.cgi? src=accessing_data_guide.Author informationAuthors and AffiliationsDepartment of Medicine, Brigham and Women’s Hospital, Boston, MA, USAWenyu Song & David W. BatesStanley Center for Psychiatric Research, The Broad Institute of MIT and Harvard, Cambridge, MA, USAWenyu Song, Max Lam & Ruize LiuAnalytic and Translational Genetics Unit, Department of Medicine, Massachusetts General Hospital, Boston, MA, USARuize LiuNorth Region, Institute of Mental Health, Singapore, SingaporeMax LamDepartment of Emergency Medicine, Brigham and Women’s Hospital, Boston, MA, USAScott G.

2020.UK生物库：https://biobank.ctsu.ox.ac.uk/crystal/exinfo.cgi?src=访问\u data\u指南。作者信息作者和附属机构马萨诸塞州波士顿布莱根妇女医院医学系，USAWenyu Song＆David W.BatesStanley精神病学研究中心，麻省理工学院和哈佛大学布罗德研究所，马萨诸塞州剑桥，USAWenyu Song，Max Lam＆Ruize LIU马萨诸塞州波士顿总医院医学系分析和转化遗传学系，USARUZE LiuNorth地区，新加坡精神卫生研究所，新加坡Max Lam急诊医学系，马萨诸塞州波士顿布莱根妇女医院，USAScott G。

WeinerDepartment of Medicine, Beth Israel Deaconess Medical Center, Boston, MA, USAKenneth J. MukamalDepartment of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, USAAdam WrightDepartment of Anesthesiology, The Ohio State University Wexner Medical Center, Columbus, OH, USARichard D.

韦纳医学系，贝斯以色列女执事医学中心，波士顿，马萨诸塞州，美国肯尼斯·J·穆卡马尔生物医学信息学系，范德比尔特大学医学中心，纳什维尔，田纳西州，美国亚当·赖特俄亥俄州立大学韦克斯纳医学中心麻醉学系，哥伦布，俄亥俄州，美国理查德·D。

UrmanDivision of Clinical Pharmacology and Toxicology, Geneva University Hospitals and Faculty of Medicine, Geneva, SwitzerlandAurélien SimonaHarvard Medical School, Boston, MA, USAWenyu Song, Scott.

日内瓦大学医院和医学院临床药理学和毒理学研究所，日内瓦，瑞士Daurélien SimonaHarvard医学院，马萨诸塞州波士顿，USAWenyu Song，斯科特。

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PubMed Google ScholarContributionsWS initiated the study and developed the study cohort. WS, ML and RL designed and conducted data analysis. DB, KM, SW, RU and AS provided important clinical opinions. DB and AW were involved in study supervision. All authors are participated in manuscript development and are accountable for integrity of this work.Corresponding authorCorrespondence to.

PubMed Google ScholarContributionsWS启动了这项研究并开发了研究队列。WS，ML和RL设计并进行了数据分析。DB，KM，SW，RU和AS提供了重要的临床意见。DB和AW参与了研究监督。所有作者都参与了稿件的开发，并对这项工作的完整性负责。相应的作者回复。

Wenyu Song.Ethics declarations

。道德宣言

Competing interests

相互竞争的利益

Richard D. Urman received consulting fees from AcelRx and has received funding from National Institutes of Health.

Richard D.Urman从AcelRx获得了咨询费，并获得了美国国立卫生研究院的资助。

Ethics approval

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This project was reviewed and approved by the Mass General Brigham (MGB) Human Research Committee. Due to the retrospective nature of the study, the MGB Institutional Review Board waived the need of obtaining informed consent. All methods were carried out in accordance with relevant guidelines and regulations..

该项目已由马萨诸塞州布莱根将军（MGB）人类研究委员会审查和批准。由于该研究具有回顾性，MGB机构审查委员会放弃了获得知情同意的需要。所有方法均按照相关指南和规定进行。。

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Reprints and permissionsAbout this articleCite this articleSong, W., Lam, M., Liu, R. et al. A genome-wide Association study of the Count of Codeine prescriptions.

转载和许可本文引用本文Song，W.，Lam，M.，Liu，R。等人对可待因处方计数的全基因组关联研究。

Sci Rep 14, 22780 (2024). https://doi.org/10.1038/s41598-024-73925-4Download citationReceived: 21 March 2024Accepted: 23 September 2024Published: 01 October 2024DOI: https://doi.org/10.1038/s41598-024-73925-4Share this articleAnyone you share the following link with will be able to read this content:Get shareable linkSorry, a shareable link is not currently available for this article.Copy to clipboard.

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KeywordsMedication use phenotypeElectronic health recordGenome-wide association studyOpioid use disorderPolygenic risk scoreOpioid prescription phenotype

关键词药物使用表型电子健康记录全基因组关联研究类药物使用障碍多基因风险评分类药物处方表型

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