Clinical laboratory test-wide association scan of polygenic scores identifies biomarkers of complex disease.
Clinical laboratory test-wide association scan of polygenic scores identifies biomarkers of complex disease.
复制标题
DOI：
10.1186/s13073-020-00820-8
复制
发表时间：
2021-01-13
期刊：
影响因子：
12.3
通讯作者：
Davis LK
中科院分区：
文献类型：
--
作者：
Dennis JK;Sealock JM;Straub P;Lee YH;Hucks D;Actkins K;Faucon A;Feng YA;Ge T;Goleva SB;Niarchou M;Singh K;Morley T;Smoller JW;Ruderfer DM;Mosley JD;Chen G;Davis LK
临床实验室（实验室）测试在临床实践中用于诊断，治疗和监测疾病状况。检测结果存储在电子健康记录（EHR）中，越来越多的EHR与患者DNA相关联，为查询复杂疾病的遗传风险与大量人群中收集的定量生理测量之间的关系提供了前所未有的机会。从范德比尔特大学医学中心（VUMC）的EHR系统中提取了总共3075个定量实验室测试，并根据我们的QualityLab协议进行了人群水平分析。使用遗传力和遗传相关性分析将从BioVU提取的实验室值与先前的群体研究进行比较。然后，我们测试了生物标志物和复杂疾病的多基因风险评分与从EHR提取的疾病生物标志物相关的假设。在概念验证分析中，我们关注脂质和冠状动脉疾病（CAD）。我们清理了从EHR中提取的实验室特征，对315项遗传实验室测试中的脂质和CAD多基因风险评分进行了实验室范围的关联扫描（LabWAS），然后在马萨诸塞州General Brigham Biobank中复制了管道和分析。脂质值的遗传性估计值（用QualityLab清洗后）与以前的报告相当，脂质的多基因评分与LabWAS中的参考脂质密切相关。CAD多基因评分的LabWAS概括了典型心脏病生物标志物特征，包括欧洲和非洲裔人群中HDL降低，用药前LDL增加，甘油三酯，血糖和糖化血红蛋白（HgbA 1C）。值得注意的是，即使在调整了心血管疾病的存在之后，这些关联中的许多仍然存在，并且在MGBB中得到了复制。多基因风险评分可用于在大规模基于EHR的基因组分析中识别复杂疾病的生物标志物，为发现新的生物标志物和更深入地了解症状前个体的疾病轨迹提供了新的途径。我们提出了两种方法和相关的软件，QualityLab和LabWAS，来大规模地清洁和分析EHR实验室，并进行实验室范围的关联扫描。在线版本包含补充材料，可通过10.1186/s13073-020-00820-8获取。
Clinical laboratory (lab) tests are used in clinical practice to diagnose, treat, and monitor disease conditions. Test results are stored in electronic health records (EHRs), and a growing number of EHRs are linked to patient DNA, offering unprecedented opportunities to query relationships between genetic risk for complex disease and quantitative physiological measurements collected on large populations. A total of 3075 quantitative lab tests were extracted from Vanderbilt University Medical Center’s (VUMC) EHR system and cleaned for population-level analysis according to our QualityLab protocol. Lab values extracted from BioVU were compared with previous population studies using heritability and genetic correlation analyses. We then tested the hypothesis that polygenic risk scores for biomarkers and complex disease are associated with biomarkers of disease extracted from the EHR. In a proof of concept analyses, we focused on lipids and coronary artery disease (CAD). We cleaned lab traits extracted from the EHR performed lab-wide association scans (LabWAS) of the lipids and CAD polygenic risk scores across 315 heritable lab tests then replicated the pipeline and analyses in the Massachusetts General Brigham Biobank. Heritability estimates of lipid values (after cleaning with QualityLab) were comparable to previous reports and polygenic scores for lipids were strongly associated with their referent lipid in a LabWAS. LabWAS of the polygenic score for CAD recapitulated canonical heart disease biomarker profiles including decreased HDL, increased pre-medication LDL, triglycerides, blood glucose, and glycated hemoglobin (HgbA1C) in European and African descent populations. Notably, many of these associations remained even after adjusting for the presence of cardiovascular disease and were replicated in the MGBB. Polygenic risk scores can be used to identify biomarkers of complex disease in large-scale EHR-based genomic analyses, providing new avenues for discovery of novel biomarkers and deeper understanding of disease trajectories in pre-symptomatic individuals. We present two methods and associated software, QualityLab and LabWAS, to clean and analyze EHR labs at scale and perform a Lab-Wide Association Scan. The online version contains supplementary material available at 10.1186/s13073-020-00820-8.
登录
查看更多内容
影响因子：
30.8
作者：
Das, Sayantan;Forer, Lukas;Schoenherr, Sebastian;Sidore, Carlo;Locke, Adam E.;Kwong, Alan;Vrieze, Scott I.;Chew, Emily Y.;Levy, Shawn;McGue, Matt;Schlessinger, David;Stambolian, Dwight;Loh, Po-Ru;Iacono, William G.;Swaroop, Anand;Scott, Laura J.;Cucca, Francesco;Kronenberg, Florian;Boehnke, Michael;Abecasis, Goncalo R.;Fuchsberger, Christian
通讯作者：
Fuchsberger, Christian
影响因子：
30.8
作者：
Bulik-Sullivan, Brendan K.;Loh, Po-Ru;Finucane, Hilary K.;Ripke, Stephan;Yang, Jian;Patterson, Nick;Daly, Mark J.;Price, Alkes L.;Neale, Benjamin M.
通讯作者：
Neale, Benjamin M.
影响因子：
46.9
作者：
通讯作者：
--
DOI：
10.1146/annurev-genom-090314-024956
发表时间：
2016-08-31
影响因子：
8.7
作者：
Denny JC;Bastarache L;Roden DM
通讯作者：
Roden DM
影响因子：
4.5
作者：
Pendergrass SA;Brown-Gentry K;Dudek S;Frase A;Torstenson ES;Goodloe R;Ambite JL;Avery CL;Buyske S;Bůžková P;Deelman E;Fesinmeyer MD;Haiman CA;Heiss G;Hindorff LA;Hsu CN;Jackson RD;Kooperberg C;Le Marchand L;Lin Y;Matise TC;Monroe KR;Moreland L;Park SL;Reiner A;Wallace R;Wilkens LR;Crawford DC;Ritchie MD
通讯作者：
Ritchie MD