{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,7]],"date-time":"2025-10-07T11:51:46Z","timestamp":1759837906271,"version":"3.37.3"},"reference-count":51,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2018,7,27]],"date-time":"2018-07-27T00:00:00Z","timestamp":1532649600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2018,7,27]],"date-time":"2018-07-27T00:00:00Z","timestamp":1532649600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Sci Rep"],"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Genome-wide association studies depend on accurate ascertainment of patient phenotype. However, phenotyping is difficult, and it is often treated as an afterthought in these studies because of the expense involved. Electronic health records (EHRs) may provide higher fidelity phenotypes for genomic research than other sources such as administrative data. We used whole genome association models to evaluate different EHR and administrative data-based phenotyping methods in a cohort of 16,858 Caucasian subjects for type 1 diabetes mellitus, type 2 diabetes mellitus, coronary artery disease and breast cancer. For each disease, we trained and evaluated polygenic models using three different phenotype definitions: phenotypes derived from billing data, the clinical problem list, or a curated phenotyping algorithm. We observed that for these diseases, the curated phenotype outperformed the problem list, and the problem list outperformed administrative billing data. This suggests that using advanced EHR-derived phenotypes can further increase the power of genome-wide association studies.<\/jats:p>","DOI":"10.1038\/s41598-018-29634-w","type":"journal-article","created":{"date-parts":[[2018,7,23]],"date-time":"2018-07-23T10:49:58Z","timestamp":1532342998000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["Using whole genome scores to compare three clinical phenotyping methods in complex diseases"],"prefix":"10.1038","volume":"8","author":[{"given":"Wenyu","family":"Song","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hailiang","family":"Huang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8825-7158","authenticated-orcid":false,"given":"Cheng-Zhong","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"David W.","family":"Bates","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Adam","family":"Wright","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2018,7,27]]},"reference":[{"key":"29634_CR1","doi-asserted-by":"publisher","first-page":"565","DOI":"10.1038\/ng.608","volume":"42","author":"J Yang","year":"2010","unstructured":"Yang, J. et al. Common SNPs explain a large proportion of the heritability for human height. Nat Genet 42, 565\u2013569, https:\/\/doi.org\/10.1038\/ng.608 (2010).","journal-title":"Nat Genet"},{"key":"29634_CR2","doi-asserted-by":"publisher","first-page":"248","DOI":"10.1038\/nature19806","volume":"538","author":"M Horikoshi","year":"2016","unstructured":"Horikoshi, M. et al. Genome-wide associations for birth weight and correlations with adult disease. Nature 538, 248\u2013252, https:\/\/doi.org\/10.1038\/nature19806 (2016).","journal-title":"Nature"},{"key":"29634_CR3","doi-asserted-by":"publisher","first-page":"173","DOI":"10.1038\/nature22969","volume":"547","author":"H Huang","year":"2017","unstructured":"Huang, H. et al. Fine-mapping inflammatory bowel disease loci to single-variant resolution. Nature 547, 173\u2013178, https:\/\/doi.org\/10.1038\/nature22969 (2017).","journal-title":"Nature"},{"key":"29634_CR4","doi-asserted-by":"publisher","first-page":"1675","DOI":"10.1101\/gr.094615.109","volume":"19","author":"S Murphy","year":"2009","unstructured":"Murphy, S. et al. Instrumenting the health care enterprise for discovery research in the genomic era. Genome Res 19, 1675\u20131681, https:\/\/doi.org\/10.1101\/gr.094615.109 (2009).","journal-title":"Genome Res"},{"doi-asserted-by":"publisher","unstructured":"Wellcome Trust Case Control, C. Genome-wide association study of 14,000 cases of seven common diseases and 3,000 shared controls. Nature 447, 661\u2013678, https:\/\/doi.org\/10.1038\/nature05911 (2007).","key":"29634_CR5","DOI":"10.1038\/nature05911"},{"key":"29634_CR6","doi-asserted-by":"publisher","first-page":"1236","DOI":"10.1038\/ng.3406","volume":"47","author":"B Bulik-Sullivan","year":"2015","unstructured":"Bulik-Sullivan, B. et al. An atlas of genetic correlations across human diseases and traits. Nat Genet 47, 1236\u20131241, https:\/\/doi.org\/10.1038\/ng.3406 (2015).","journal-title":"Nat Genet"},{"key":"29634_CR7","doi-asserted-by":"publisher","first-page":"417","DOI":"10.1038\/nrg2999","volume":"12","author":"IS Kohane","year":"2011","unstructured":"Kohane, I. S. Using electronic health records to drive discovery in disease genomics. Nat Rev Genet 12, 417\u2013428, https:\/\/doi.org\/10.1038\/nrg2999 (2011).","journal-title":"Nat Rev Genet"},{"key":"29634_CR8","doi-asserted-by":"publisher","DOI":"10.1093\/jamia\/ocx110","author":"G Hripcsak","year":"2017","unstructured":"Hripcsak, G. & Albers, D. J. High-fidelity phenotyping: richness and freedom from bias. J Am Med Inform Assoc. https:\/\/doi.org\/10.1093\/jamia\/ocx110 (2017).","journal-title":"J Am Med Inform Assoc"},{"key":"29634_CR9","doi-asserted-by":"publisher","DOI":"10.1186\/s13073-015-0166-y","volume":"7","author":"WQ Wei","year":"2015","unstructured":"Wei, W. Q. & Denny, J. C. Extracting research-quality phenotypes from electronic health records to support precision medicine. Genome Med 7, 41, https:\/\/doi.org\/10.1186\/s13073-015-0166-y (2015).","journal-title":"Genome Med"},{"key":"29634_CR10","doi-asserted-by":"publisher","DOI":"10.1186\/s40246-014-0023-x","volume":"9","author":"TB Mersha","year":"2015","unstructured":"Mersha, T. B. & Abebe, T. Self-reported race\/ethnicity in the age of genomic research: its potential impact on understanding health disparities. Hum Genomics 9, 1, https:\/\/doi.org\/10.1186\/s40246-014-0023-x (2015).","journal-title":"Hum Genomics"},{"key":"29634_CR11","doi-asserted-by":"publisher","first-page":"682","DOI":"10.2215\/CJN.07650713","volume":"9","author":"ME Grams","year":"2014","unstructured":"Grams, M. E. et al. Performance and limitations of administrative data in the identification of AKI. Clin J Am Soc Nephrol 9, 682\u2013689, https:\/\/doi.org\/10.2215\/CJN.07650713 (2014).","journal-title":"Clin J Am Soc Nephrol"},{"key":"29634_CR12","doi-asserted-by":"publisher","first-page":"547","DOI":"10.1016\/j.annepidem.2010.03.017","volume":"20","author":"T Oksanen","year":"2010","unstructured":"Oksanen, T. et al. Self-report as an indicator of incident disease. Ann Epidemiol 20, 547\u2013554, https:\/\/doi.org\/10.1016\/j.annepidem.2010.03.017 (2010).","journal-title":"Ann Epidemiol"},{"key":"29634_CR13","doi-asserted-by":"publisher","first-page":"687","DOI":"10.1016\/S0895-4356(00)00364-4","volume":"54","author":"H Powell","year":"2001","unstructured":"Powell, H., Lim, L. L. & Heller, R. F. Accuracy of administrative data to assess comorbidity in patients with heart disease. an Australian perspective. J Clin Epidemiol 54, 687\u2013693 (2001).","journal-title":"J Clin Epidemiol"},{"key":"29634_CR14","doi-asserted-by":"publisher","first-page":"859","DOI":"10.1136\/amiajnl-2011-000121","volume":"18","author":"A Wright","year":"2011","unstructured":"Wright, A. et al. A method and knowledge base for automated inference of patient problems from structured data in an electronic medical record. J Am Med Inform Assoc 18, 859\u2013867, https:\/\/doi.org\/10.1136\/amiajnl-2011-000121 (2011).","journal-title":"J Am Med Inform Assoc"},{"key":"29634_CR15","doi-asserted-by":"publisher","first-page":"692","DOI":"10.1007\/s12265-014-9586-0","volume":"7","author":"P Krishnamoorthy","year":"2014","unstructured":"Krishnamoorthy, P., Gupta, D., Chatterjee, S., Huston, J. & Ryan, J. J. A review of the role of electronic health record in genomic research. J Cardiovasc Transl Res 7, 692\u2013700, https:\/\/doi.org\/10.1007\/s12265-014-9586-0 (2014).","journal-title":"J Cardiovasc Transl Res"},{"key":"29634_CR16","doi-asserted-by":"publisher","first-page":"h1885","DOI":"10.1136\/bmj.h1885","volume":"350","author":"KP Liao","year":"2015","unstructured":"Liao, K. P. et al. Development of phenotype algorithms using electronic medical records and incorporating natural language processing. BMJ 350, h1885, https:\/\/doi.org\/10.1136\/bmj.h1885 (2015).","journal-title":"BMJ"},{"key":"29634_CR17","doi-asserted-by":"publisher","first-page":"761","DOI":"10.1038\/gim.2013.72","volume":"15","author":"O Gottesman","year":"2013","unstructured":"Gottesman, O. et al. The Electronic Medical Records and Genomics (eMERGE) Network: past, present, and future. Genet Med 15, 761\u2013771, https:\/\/doi.org\/10.1038\/gim.2013.72 (2013).","journal-title":". Genet Med"},{"doi-asserted-by":"publisher","unstructured":"Gainer, V. S. et al. The Biobank Portal for Partners Personalized Medicine: A Query Tool for Working with Consented Biobank Samples, Genotypes, and Phenotypes Using i2b2. J Pers Med 6, https:\/\/doi.org\/10.3390\/jpm6010011 (2016).","key":"29634_CR18","DOI":"10.3390\/jpm6010011"},{"key":"29634_CR19","doi-asserted-by":"publisher","first-page":"e20","DOI":"10.1093\/jamia\/ocv130","volume":"23","author":"WQ Wei","year":"2016","unstructured":"Wei, W. Q. et al. Combining billing codes, clinical notes, and medications from electronic health records provides superior phenotyping performance. J Am Med Inform Assoc 23, e20\u201327, https:\/\/doi.org\/10.1093\/jamia\/ocv130 (2016).","journal-title":"J Am Med Inform Assoc"},{"key":"29634_CR20","doi-asserted-by":"publisher","DOI":"10.1038\/s41398-018-0133-7","volume":"8","author":"CY Chen","year":"2018","unstructured":"Chen, C. Y. et al. Genetic validation of bipolar disorder identified by automated phenotyping using electronic health records. Transl Psychiatry 8, 86, https:\/\/doi.org\/10.1038\/s41398-018-0133-7 (2018).","journal-title":"Transl Psychiatry"},{"key":"29634_CR21","doi-asserted-by":"publisher","first-page":"891","DOI":"10.1016\/j.jbi.2010.09.009","volume":"43","author":"A Wright","year":"2010","unstructured":"Wright, A., Chen, E. S. & Maloney, F. L. An automated technique for identifying associations between medications, laboratory results and problems. J Biomed Inform 43, 891\u2013901, https:\/\/doi.org\/10.1016\/j.jbi.2010.09.009 (2010).","journal-title":"J Biomed Inform"},{"key":"29634_CR22","doi-asserted-by":"publisher","DOI":"10.1186\/1472-6947-11-36","volume":"11","author":"A Wright","year":"2011","unstructured":"Wright, A., Maloney, F. L. & Feblowitz, J. C. Clinician attitudes toward and use of electronic problem lists: a thematic analysis. BMC Med Inform Decis Mak 11, 36, https:\/\/doi.org\/10.1186\/1472-6947-11-36 (2011).","journal-title":"BMC Med Inform Decis Mak"},{"key":"29634_CR23","doi-asserted-by":"publisher","DOI":"10.1093\/jamia\/ocx111","author":"S Yu","year":"2017","unstructured":"Yu, S. et al. Enabling phenotypic big data with PheNorm. J Am Med Inform Assoc. https:\/\/doi.org\/10.1093\/jamia\/ocx111 (2017).","journal-title":"J Am Med Inform Assoc"},{"key":"29634_CR24","doi-asserted-by":"publisher","first-page":"560","DOI":"10.1016\/j.ajhg.2010.03.003","volume":"86","author":"MD Ritchie","year":"2010","unstructured":"Ritchie, M. D. et al. Robust replication of genotype-phenotype associations across multiple diseases in an electronic medical record. Am J Hum Genet 86, 560\u2013572, https:\/\/doi.org\/10.1016\/j.ajhg.2010.03.003 (2010).","journal-title":"Am J Hum Genet"},{"key":"29634_CR25","doi-asserted-by":"publisher","first-page":"e147","DOI":"10.1136\/amiajnl-2012-000896","volume":"20","author":"KM Newton","year":"2013","unstructured":"Newton, K. M. et al. Validation of electronic medical record-based phenotyping algorithms: results and lessons learned from the eMERGE network. J Am Med Inform Assoc 20, e147\u2013154, https:\/\/doi.org\/10.1136\/amiajnl-2012-000896 (2013).","journal-title":"J Am Med Inform Assoc"},{"key":"29634_CR26","doi-asserted-by":"publisher","first-page":"747","DOI":"10.1038\/nature08494","volume":"461","author":"TA Manolio","year":"2009","unstructured":"Manolio, T. A. et al. Finding the missing heritability of complex diseases. Nature 461, 747\u2013753, https:\/\/doi.org\/10.1038\/nature08494 (2009).","journal-title":"Nature"},{"key":"29634_CR27","doi-asserted-by":"publisher","first-page":"748","DOI":"10.1038\/nature08185","volume":"460","author":"C International Schizophrenia","year":"2009","unstructured":"International Schizophrenia, C. et al. Common polygenic variation contributes to risk of schizophrenia and bipolar disorder. Nature 460, 748\u2013752, https:\/\/doi.org\/10.1038\/nature08185 (2009).","journal-title":"Nature"},{"key":"29634_CR28","doi-asserted-by":"publisher","first-page":"506","DOI":"10.1002\/gepi.20600","volume":"35","author":"MJ Machiela","year":"2011","unstructured":"Machiela, M. J. et al. Evaluation of polygenic risk scores for predicting breast and prostate cancer risk. Genet Epidemiol 35, 506\u2013514, https:\/\/doi.org\/10.1002\/gepi.20600 (2011).","journal-title":"Genet Epidemiol"},{"key":"29634_CR29","doi-asserted-by":"publisher","first-page":"322","DOI":"10.1038\/gim.2016.103","volume":"19","author":"K Lall","year":"2017","unstructured":"Lall, K., Magi, R., Morris, A., Metspalu, A. & Fischer, K. Personalized risk prediction for type 2 diabetes: the potential of genetic risk scores. Genet Med 19, 322\u2013329, https:\/\/doi.org\/10.1038\/gim.2016.103 (2017).","journal-title":"Genet Med"},{"key":"29634_CR30","doi-asserted-by":"publisher","DOI":"10.1186\/s12920-015-0157-2","volume":"9","author":"V Potenciano","year":"2016","unstructured":"Potenciano, V., Abad-Grau, M. M., Alcina, A. & Matesanz, F. A comparison of genomic profiles of complex diseases under different models. BMC Med Genomics 9, 3, https:\/\/doi.org\/10.1186\/s12920-015-0157-2 (2016).","journal-title":"BMC Med Genomics"},{"key":"29634_CR31","doi-asserted-by":"publisher","first-page":"392","DOI":"10.1038\/nrg.2016.27","volume":"17","author":"N Chatterjee","year":"2016","unstructured":"Chatterjee, N., Shi, J. & Garcia-Closas, M. Developing and evaluating polygenic risk prediction models for stratified disease prevention. Nat Rev Genet 17, 392\u2013406, https:\/\/doi.org\/10.1038\/nrg.2016.27 (2016).","journal-title":"Nat Rev Genet"},{"key":"29634_CR32","doi-asserted-by":"publisher","first-page":"e1000864","DOI":"10.1371\/journal.pgen.1000864","volume":"6","author":"NR Wray","year":"2010","unstructured":"Wray, N. R., Yang, J., Goddard, M. E. & Visscher, P. M. The genetic interpretation of area under the ROC curve in genomic profiling. PLoS Genet 6, e1000864, https:\/\/doi.org\/10.1371\/journal.pgen.1000864 (2010).","journal-title":"PLoS Genet"},{"key":"29634_CR33","doi-asserted-by":"publisher","first-page":"986","DOI":"10.1038\/ng.3865","volume":"49","author":"D Speed","year":"2017","unstructured":"Speed, D. et al. Reevaluation of SNP heritability in complex human traits. Nat Genet 49, 986\u2013992, https:\/\/doi.org\/10.1038\/ng.3865 (2017).","journal-title":"Nat Genet"},{"key":"29634_CR34","doi-asserted-by":"publisher","first-page":"823","DOI":"10.1016\/j.jacc.2016.11.056","volume":"69","author":"TR Webb","year":"2017","unstructured":"Webb, T. R. et al. Systematic Evaluation of Pleiotropy Identifies 6 Further Loci Associated With Coronary Artery Disease. J Am Coll Cardiol 69, 823\u2013836, https:\/\/doi.org\/10.1016\/j.jacc.2016.11.056 (2017).","journal-title":"J Am Coll Cardiol"},{"key":"29634_CR35","doi-asserted-by":"publisher","first-page":"234","DOI":"10.1038\/ng.2897","volume":"46","author":"DIG Replication","year":"2014","unstructured":"Replication, D. I. G. et al. Genome-wide trans-ancestry meta-analysis provides insight into the genetic architecture of type 2 diabetes susceptibility. Nat Genet 46, 234\u2013244, https:\/\/doi.org\/10.1038\/ng.2897 (2014).","journal-title":"Nat Genet"},{"key":"29634_CR36","doi-asserted-by":"publisher","first-page":"92","DOI":"10.1038\/nature24284","volume":"551","author":"K Michailidou","year":"2017","unstructured":"Michailidou, K. et al. Association analysis identifies 65 new breast cancer risk loci. Nature 551, 92\u201394, https:\/\/doi.org\/10.1038\/nature24284 (2017).","journal-title":"Nature"},{"key":"29634_CR37","doi-asserted-by":"publisher","DOI":"10.1186\/s12883-017-0864-8","volume":"17","author":"HH Dasenbrock","year":"2017","unstructured":"Dasenbrock, H. H. et al. Validation of an International Classification of Disease, Ninth Revision coding algorithm to identify decompressive craniectomy for stroke. BMC Neurol 17, 121, https:\/\/doi.org\/10.1186\/s12883-017-0864-8 (2017).","journal-title":"BMC Neurol"},{"key":"29634_CR38","doi-asserted-by":"publisher","first-page":"41","DOI":"10.1038\/nature18642","volume":"536","author":"C Fuchsberger","year":"2016","unstructured":"Fuchsberger, C. et al. The genetic architecture of type 2 diabetes. Nature 536, 41\u201347, https:\/\/doi.org\/10.1038\/nature18642 (2016).","journal-title":"Nature"},{"doi-asserted-by":"publisher","unstructured":"International Consortium for Blood Pressure Genome-Wide Association, S. et al. Genetic variants in novel pathways influence blood pressure and cardiovascular disease risk. Nature 478, 103\u2013109, https:\/\/doi.org\/10.1038\/nature10405 (2011).","key":"29634_CR39","DOI":"10.1038\/nature10405"},{"key":"29634_CR40","doi-asserted-by":"publisher","first-page":"1432","DOI":"10.1101\/gr.190603.115","volume":"25","author":"WL Lowe Jr","year":"2015","unstructured":"Lowe, W. L. Jr & Reddy, T. E. Genomic approaches for understanding the genetics of complex disease. Genome Res 25, 1432\u20131441, https:\/\/doi.org\/10.1101\/gr.190603.115 (2015).","journal-title":"Genome Res"},{"key":"29634_CR41","doi-asserted-by":"publisher","DOI":"10.1186\/1746-4811-9-29","volume":"9","author":"A Korte","year":"2013","unstructured":"Korte, A. & Farlow, A. The advantages and limitations of trait analysis with GWAS: a review. Plant Methods 9, 29, https:\/\/doi.org\/10.1186\/1746-4811-9-29 (2013).","journal-title":"Plant Methods"},{"key":"29634_CR42","doi-asserted-by":"crossref","first-page":"141","DOI":"10.24095\/hpcdp.30.4.06","volume":"30","author":"K Tu","year":"2010","unstructured":"Tu, K., Mitiku, T., Guo, H., Lee, D. S. & Tu, J. V. Myocardial infarction and the validation of physician billing and hospitalization data using electronic medical records. Chronic Dis Can 30, 141\u2013146 (2010).","journal-title":"Chronic Dis Can"},{"key":"29634_CR43","doi-asserted-by":"publisher","first-page":"e225","DOI":"10.1016\/S0828-282X(10)70412-8","volume":"26","author":"K Tu","year":"2010","unstructured":"Tu, K., Mitiku, T., Lee, D. S., Guo, H. & Tu, J. V. Validation of physician billing and hospitalization data to identify patients with ischemic heart disease using data from the Electronic Medical Record Administrative data Linked Database (EMRALD). Can J Cardiol 26, e225\u2013228 (2010).","journal-title":"Can J Cardiol"},{"key":"29634_CR44","doi-asserted-by":"publisher","first-page":"564","DOI":"10.1111\/j.1475-6773.2005.00482.x","volume":"41","author":"EF Kern","year":"2006","unstructured":"Kern, E. F. et al. Failure of ICD-9-CM codes to identify patients with comorbid chronic kidney disease in diabetes. Health Serv Res 41, 564\u2013580, https:\/\/doi.org\/10.1111\/j.1475-6773.2005.00482.x (2006).","journal-title":"Health Serv Res"},{"key":"29634_CR45","doi-asserted-by":"publisher","first-page":"191","DOI":"10.2217\/14622416.10.2.191","volume":"10","author":"E Zeggini","year":"2009","unstructured":"Zeggini, E. & Ioannidis, J. P. Meta-analysis in genome-wide association studies. Pharmacogenomics 10, 191\u2013201, https:\/\/doi.org\/10.2217\/14622416.10.2.191 (2009).","journal-title":"Pharmacogenomics"},{"key":"29634_CR46","doi-asserted-by":"publisher","first-page":"e841","DOI":"10.1371\/journal.pone.0000841","volume":"2","author":"JP Ioannidis","year":"2007","unstructured":"Ioannidis, J. P., Patsopoulos, N. A. & Evangelou, E. Heterogeneity in meta-analyses of genome-wide association investigations. PLoS One 2, e841, https:\/\/doi.org\/10.1371\/journal.pone.0000841 (2007).","journal-title":"PLoS One"},{"key":"29634_CR47","doi-asserted-by":"publisher","first-page":"379","DOI":"10.1073\/pnas.1705859115","volume":"115","author":"G Jun","year":"2018","unstructured":"Jun, G. et al. Evaluating the contribution of rare variants to type 2 diabetes and related traits using pedigrees. Proc Natl Acad Sci USA 115, 379\u2013384, https:\/\/doi.org\/10.1073\/pnas.1705859115 (2018).","journal-title":"Proc Natl Acad Sci USA"},{"doi-asserted-by":"publisher","unstructured":"Turner, S. et al. Quality control procedures for genome-wide association studies. Curr Protoc Hum Genet Chapter 1, Unit1 19, https:\/\/doi.org\/10.1002\/0471142905.hg0119s68 (2011).","key":"29634_CR48","DOI":"10.1002\/0471142905.hg0119s68"},{"key":"29634_CR49","doi-asserted-by":"publisher","first-page":"904","DOI":"10.1038\/ng1847","volume":"38","author":"AL Price","year":"2006","unstructured":"Price, A. L. et al. Principal components analysis corrects for stratification in genome-wide association studies. Nat Genet 38, 904\u2013909, https:\/\/doi.org\/10.1038\/ng1847 (2006).","journal-title":"Nat Genet"},{"key":"29634_CR50","doi-asserted-by":"publisher","first-page":"997","DOI":"10.1111\/j.0006-341X.1999.00997.x","volume":"55","author":"B Devlin","year":"1999","unstructured":"Devlin, B. & Roeder, K. Genomic control for association studies. Biometrics 55, 997\u20131004 (1999).","journal-title":"Biometrics"},{"key":"29634_CR51","doi-asserted-by":"publisher","first-page":"559","DOI":"10.1086\/519795","volume":"81","author":"S Purcell","year":"2007","unstructured":"Purcell, S. et al. PLINK: a tool set for whole-genome association and population-based linkage analyses. Am J Hum Genet 81, 559\u2013575, https:\/\/doi.org\/10.1086\/519795 (2007).","journal-title":"Am J Hum Genet"}],"container-title":["Scientific Reports"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.nature.com\/articles\/s41598-018-29634-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41598-018-29634-w","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41598-018-29634-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,12,21]],"date-time":"2022-12-21T07:50:00Z","timestamp":1671609000000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.nature.com\/articles\/s41598-018-29634-w"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,7,27]]},"references-count":51,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2018,12]]}},"alternative-id":["29634"],"URL":"https:\/\/doi.org\/10.1038\/s41598-018-29634-w","relation":{},"ISSN":["2045-2322"],"issn-type":[{"type":"electronic","value":"2045-2322"}],"subject":[],"published":{"date-parts":[[2018,7,27]]},"assertion":[{"value":"20 March 2018","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"16 July 2018","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"27 July 2018","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"The authors declare no competing interests.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing Interests"}}],"article-number":"11360"}}