LabWAS: Novel findings and study design recommendations from a meta-analysis of clinical labs in two independent biobanks.
LabWAS: Novel findings and study design recommendations from a meta-analysis of clinical labs in two independent biobanks.
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DOI：
10.1371/journal.pgen.1009077
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发表时间：
2020-11
期刊：
影响因子：
4.5
通讯作者：
Zawistowski M
中科院分区：
文献类型：
--
作者：
Goldstein JA;Weinstock JS;Bastarache LA;Larach DB;Fritsche LG;Schmidt EM;Brummett CM;Kheterpal S;Abecasis GR;Denny JC;Zawistowski M
从电子健康记录（EHR）中提取的表型在遗传研究中越来越普遍。EHR包含数百个不同的临床实验室测试结果，提供了诊断之外的健康数据。这样的实验室数据很复杂，缺乏普遍存在的编码方案，使其比诊断数据更具挑战性。在这里，我们描述了第一个大规模的跨健康系统全基因组关联研究（GWAS）的EHR为基础的定量实验室衍生表型。我们对来自范德比尔特大学卫生系统的BioVU队列和来自密歇根医学的密歇根基因组学倡议（MGI）队列之间匹配的70个实验室性状进行了荟萃分析。我们显示了这些性状的已知关联的高度复制，验证了基于EHR的测量作为遗传分析的高质量表型。值得注意的是，我们的分析提供了699个以前的GWAS协会在46个不同的性状的第一次复制。我们发现了22个不同性状的31个具有全基因组意义的新关联，包括两个基于实验室的性状的首次报道的关联。我们在一个独立的BioVU样本中复制了22个新的关联。所有关联检验的汇总统计量都是免费提供的，以使其他研究人员受益。最后，我们在BioVU和MGI中进行了镜像分析，以评估EHR实验室特征的竞争分析实践。我们发现，使用所有可用的实验室测量的平均值提供了一个强大的汇总值，但在某些情况下，替代汇总可以提高功率。本研究为跨卫生系统GWAS提供了一个原理验证，并为未来定量EHR实验室特征的研究提供了一个框架。电子健康记录（EHR）已经成为一个丰富的数据源，用于推导遗传关联研究中使用的表型。EHR在大型卫生系统队列中提供了广泛的临床数据，并易于纳入大规模荟萃分析。EHR中丰富的可用数据带来了独特的技术挑战，特别是缺乏更常用疾病诊断代码结构的纵向临床实验室测量。文献中有预防战略，但尚不清楚这些战略在卫生系统中的可移植性。在这项研究中，我们对两个大型卫生系统的70个临床实验室特征进行了原理验证荟萃分析：来自范德比尔特大学的BioVU和密歇根医学的密歇根基因组学倡议。尽管在两个卫生系统中匹配实验室存在挑战，但我们观察到已知遗传变异的复制率很高。此外，我们确定了31个新的关联，其中22个在独立的BioVU队列中重复，表明未来荟萃分析的潜力。最后，我们探讨了各种分析策略的影响，寻找我们两个队列之间的一致影响，以确定未来EHR衍生实验室性状遗传分析的最佳策略。
Phenotypes extracted from Electronic Health Records (EHRs) are increasingly prevalent in genetic studies. EHRs contain hundreds of distinct clinical laboratory test results, providing a trove of health data beyond diagnoses. Such lab data is complex and lacks a ubiquitous coding scheme, making it more challenging than diagnosis data. Here we describe the first large-scale cross-health system genome-wide association study (GWAS) of EHR-based quantitative laboratory-derived phenotypes. We meta-analyzed 70 lab traits matched between the BioVU cohort from the Vanderbilt University Health System and the Michigan Genomics Initiative (MGI) cohort from Michigan Medicine. We show high replication of known association for these traits, validating EHR-based measurements as high-quality phenotypes for genetic analysis. Notably, our analysis provides the first replication for 699 previous GWAS associations across 46 different traits. We discovered 31 novel associations at genome-wide significance for 22 distinct traits, including the first reported associations for two lab-based traits. We replicated 22 of these novel associations in an independent tranche of BioVU samples. The summary statistics for all association tests are freely available to benefit other researchers. Finally, we performed mirrored analyses in BioVU and MGI to assess competing analytic practices for EHR lab traits. We find that using the mean of all available lab measurements provides a robust summary value, but alternate summarizations can improve power in certain circumstances. This study provides a proof-of-principle for cross health system GWAS and is a framework for future studies of quantitative EHR lab traits. Electronic Health Records (EHRs) have emerged as an abundant data source for deriving phenotypes used in genetic association studies. EHRs provide a broad range of clinical data in large health system cohorts and are readily incorporated into large-scale meta-analyses. The abundance of available data in EHRs introduces unique technical challenges, particularly longitudinal clinical lab measurements which lack the structure of more commonly used disease diagnosis codes. Conflicting strategies exist in the literature and it is not clear how portable these strategies are across health systems. In this study we performed a proof-of-principle meta-analysis of 70 clinical lab traits in two large-scale health systems: BioVU from Vanderbilt University and the Michigan Genomics Initiative from Michigan Medicine. Despite the challenges of matching labs across the two health systems, we observed a high replication rate for known genetic variants. Further, we identified 31 novel associations, 22 of which replicated in an independent BioVU cohort, indicating the potential for future meta-analyses. Finally, we explored the impact of various analytic strategies, looking for consistent effects between our two cohorts, to determine optimal strategies for future genetic analysis of EHR-derived lab traits.
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影响因子：
30.8
作者：
DIAbetes Genetics Replication And Meta-analysis (DIAGRAM) Consortium;Asian Genetic Epidemiology Network Type 2 Diabetes (AGEN-T2D) Consortium;South Asian Type 2 Diabetes (SAT2D) Consortium;Mexican American Type 2 Diabetes (MAT2D) Consortium;Type 2 Diabetes Genetic Exploration by Nex-generation sequencing in muylti-Ethnic Samples (T2D-GENES) Consortium;Mahajan A;Go MJ;Zhang W;Below JE;Gaulton KJ;Ferreira T;Horikoshi M;Johnson AD;Ng MC;Prokopenko I;Saleheen D;Wang X;Zeggini E;Abecasis GR;Adair LS;Almgren P;Atalay M;Aung T;Baldassarre D;Balkau B;Bao Y;Barnett AH;Barroso I;Basit A;Been LF;Beilby J;Bell GI;Benediktsson R;Bergman RN;Boehm BO;Boerwinkle E;Bonnycastle LL;Burtt N;Cai Q;Campbell H;Carey J;Cauchi S;Caulfield M;Chan JC;Chang LC;Chang TJ;Chang YC;Charpentier G;Chen CH;Chen H;Chen YT;Chia KS;Chidambaram M;Chines PS;Cho NH;Cho YM;Chuang LM;Collins FS;Cornelis MC;Couper DJ;Crenshaw AT;van Dam RM;Danesh J;Das D;de Faire U;Dedoussis G;Deloukas P;Dimas AS;Dina C;Doney AS;Donnelly PJ;Dorkhan M;van Duijn C;Dupuis J;Edkins S;Elliott P;Emilsson V;Erbel R;Eriksson JG;Escobedo J;Esko T;Eury E;Florez JC;Fontanillas P;Forouhi NG;Forsen T;Fox C;Fraser RM;Frayling TM;Froguel P;Frossard P;Gao Y;Gertow K;Gieger C;Gigante B;Grallert H;Grant GB;Grrop LC;Groves CJ;Grundberg E;Guiducci C;Hamsten A;Han BG;Hara K;Hassanali N;Hattersley AT;Hayward C;Hedman AK;Herder C;Hofman A;Holmen OL;Hovingh K;Hreidarsson AB;Hu C;Hu FB;Hui J;Humphries SE;Hunt SE;Hunter DJ;Hveem K;Hydrie ZI;Ikegami H;Illig T;Ingelsson E;Islam M;Isomaa B;Jackson AU;Jafar T;James A;Jia W;Jöckel KH;Jonsson A;Jowett JB;Kadowaki T;Kang HM;Kanoni S;Kao WH;Kathiresan S;Kato N;Katulanda P;Keinanen-Kiukaanniemi KM;Kelly AM;Khan H;Khaw KT;Khor CC;Kim HL;Kim S;Kim YJ;Kinnunen L;Klopp N;Kong A;Korpi-Hyövälti E;Kowlessur S;Kraft P;Kravic J;Kristensen MM;Krithika S;Kumar A;Kumate J;Kuusisto J;Kwak SH;Laakso M;Lagou V;Lakka TA;Langenberg C;Langford C;Lawrence R;Leander K;Lee JM;Lee NR;Li M;Li X;Li Y;Liang J;Liju S;Lim WY;Lind L;Lindgren CM;Lindholm E;Liu CT;Liu JJ;Lobbens S;Long J;Loos RJ;Lu W;Luan J;Lyssenko V;Ma RC;Maeda S;Mägi R;Männisto S;Matthews DR;Meigs JB;Melander O;Metspalu A;Meyer J;Mirza G;Mihailov E;Moebus S;Mohan V;Mohlke KL;Morris AD;Mühleisen TW;Müller-Nurasyid M;Musk B;Nakamura J;Nakashima E;Navarro P;Ng PK;Nica AC;Nilsson PM;Njølstad I;Nöthen MM;Ohnaka K;Ong TH;Owen KR;Palmer CN;Pankow JS;Park KS;Parkin M;Pechlivanis S;Pedersen NL;Peltonen L;Perry JR;Peters A;Pinidiyapathirage JM;Platou CG;Potter S;Price JF;Qi L;Radha V;Rallidis L;Rasheed A;Rathman W;Rauramaa R;Raychaudhuri S;Rayner NW;Rees SD;Rehnberg E;Ripatti S;Robertson N;Roden M;Rossin EJ;Rudan I;Rybin D;Saaristo TE;Salomaa V;Saltevo J;Samuel M;Sanghera DK;Saramies J;Scott J;Scott LJ;Scott RA;Segrè AV;Sehmi J;Sennblad B;Shah N;Shah S;Shera AS;Shu XO;Shuldiner AR;Sigurđsson G;Sijbrands E;Silveira A;Sim X;Sivapalaratnam S;Small KS;So WY;Stančáková A;Stefansson K;Steinbach G;Steinthorsdottir V;Stirrups K;Strawbridge RJ;Stringham HM;Sun Q;Suo C;Syvänen AC;Takayanagi R;Takeuchi F;Tay WT;Teslovich TM;Thorand B;Thorleifsson G;Thorsteinsdottir U;Tikkanen E;Trakalo J;Tremoli E;Trip MD;Tsai FJ;Tuomi T;Tuomilehto J;Uitterlinden AG;Valladares-Salgado A;Vedantam S;Veglia F;Voight BF;Wang C;Wareham NJ;Wennauer R;Wickremasinghe AR;Wilsgaard T;Wilson JF;Wiltshire S;Winckler W;Wong TY;Wood AR;Wu JY;Wu Y;Yamamoto K;Yamauchi T;Yang M;Yengo L;Yokota M;Young R;Zabaneh D;Zhang F;Zhang R;Zheng W;Zimmet PZ;Altshuler D;Bowden DW;Cho YS;Cox NJ;Cruz M;Hanis CL;Kooner J;Lee JY;Seielstad M;Teo YY;Boehnke M;Parra EJ;Chambers JC;Tai ES;McCarthy MI;Morris AP
通讯作者：
Morris AP
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：
14.9
作者：
Buniello, Annalisa;MacArthur, Jacqueline A. L.;Parkinson, Helen
通讯作者：
Parkinson, Helen
影响因子：
64.5
作者：
Astle, William J.;Elding, Heather;Soranzo, Nicole
通讯作者：
Soranzo, Nicole