{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,2]],"date-time":"2026-06-02T05:36:01Z","timestamp":1780378561892,"version":"3.54.1"},"reference-count":86,"publisher":"Oxford University Press (OUP)","issue":"2","license":[{"start":{"date-parts":[[2022,2,22]],"date-time":"2022-02-22T00:00:00Z","timestamp":1645488000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/journals\/pages\/open_access\/funder_policies\/chorus\/standard_publication_model"}],"funder":[{"DOI":"10.13039\/501100001809","name":"Natural Science Foundation of China","doi-asserted-by":"publisher","award":["81703321"],"award-info":[{"award-number":["81703321"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100007270","name":"University of Michigan","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100007270","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022,3,10]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Polygenic scores (PGS) are important tools for carrying out genetic prediction of common diseases and disease related complex traits, facilitating the development of precision medicine. Unfortunately, despite the critical importance of PGS and the vast number of PGS methods recently developed, few comprehensive comparison studies have been performed to evaluate the effectiveness of PGS methods. To fill this critical knowledge gap, we performed a comprehensive comparison study on 12 different PGS methods through internal evaluations on 25 quantitative and 25 binary traits within the UK Biobank with sample sizes ranging from 147\u00a0408 to 336\u00a0573, and through external evaluations via 25 cross-study and 112 cross-ancestry analyses on summary statistics from multiple genome-wide association studies with sample sizes ranging from 1415 to 329\u00a0345. We evaluate the prediction accuracy, computational scalability, as well as robustness and transferability of different PGS methods across datasets and\/or genetic ancestries, providing important guidelines for practitioners in choosing PGS methods. Besides method comparison, we present a simple aggregation strategy that combines multiple PGS from different methods to take advantage of their distinct benefits to achieve stable and superior prediction performance. To facilitate future applications of PGS, we also develop a PGS webserver (http:\/\/www.pgs-server.com\/) that allows users to upload summary statistics and choose different PGS methods to fit the data directly. We hope that our results, method and webserver will facilitate the routine application of PGS across different research areas.<\/jats:p>","DOI":"10.1093\/bib\/bbac039","type":"journal-article","created":{"date-parts":[[2022,2,12]],"date-time":"2022-02-12T12:08:26Z","timestamp":1644667706000},"source":"Crossref","is-referenced-by-count":38,"title":["PGS-server: accuracy, robustness and transferability of polygenic score methods for biobank scale studies"],"prefix":"10.1093","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3657-8771","authenticated-orcid":false,"given":"Sheng","family":"Yang","sequence":"first","affiliation":[{"name":"Department of Biostatistics, School of Public Health, Nanjing Medical University, Nanjing, Jiangsu 211166, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4331-7599","authenticated-orcid":false,"given":"Xiang","family":"Zhou","sequence":"additional","affiliation":[{"name":"Department of Biostatistics, School of Public Health, University of Michigan, Ann Arbor, MI 48109, USA"},{"name":"Center for Statistical Genetics, University of Michigan, Ann Arbor, MI 48109, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2022,2,22]]},"reference":[{"key":"2022031506404611700_ref1","doi-asserted-by":"crossref","first-page":"542","DOI":"10.1038\/s41591-020-0785-8","article-title":"Trans-biobank analysis with 676,000 individuals elucidates the association of polygenic risk scores of complex traits with human lifespan","volume":"26","author":"Sakaue","year":"2020","journal-title":"Nat Med"},{"key":"2022031506404611700_ref2","doi-asserted-by":"crossref","first-page":"133","DOI":"10.15302\/J-QB-021-0238","article-title":"Qiongshi Lu. Polygenic risk scores: effect estimation and model optimization","volume":"9","author":"Zijie Zhao","year":"2021","journal-title":"Quant Biol"},{"key":"2022031506404611700_ref3","doi-asserted-by":"crossref","first-page":"995","DOI":"10.1016\/j.tig.2021.06.004","article-title":"Genetic prediction of complex traits with polygenic scores: a statistical review","volume":"37","author":"Ma","year":"2021","journal-title":"Trends Genet"},{"key":"2022031506404611700_ref4","doi-asserted-by":"crossref","first-page":"187","DOI":"10.1038\/nature14132","article-title":"New genetic loci link adipose and insulin biology to body fat distribution","volume":"518","author":"Shungin","year":"2015","journal-title":"Nature"},{"key":"2022031506404611700_ref5","doi-asserted-by":"crossref","first-page":"748","DOI":"10.1038\/nature08185","article-title":"Common polygenic variation contributes to risk of schizophrenia and bipolar disorder","volume":"460","author":"Purcell","year":"2009","journal-title":"Nature"},{"key":"2022031506404611700_ref6","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1016\/j.ajhg.2017.06.005","article-title":"10 years of GWAS discovery: biology, function, and translation","volume":"101","author":"Visscher","year":"2017","journal-title":"Am J Hum Genetics"},{"key":"2022031506404611700_ref7","doi-asserted-by":"crossref","first-page":"3865","DOI":"10.1038\/s41467-020-17719-y","article-title":"Theoretical and empirical quantification of the accuracy of polygenic scores in ancestry divergent populations","volume":"11","author":"Wang","year":"2020","journal-title":"Nat Commun"},{"key":"2022031506404611700_ref8","doi-asserted-by":"crossref","first-page":"636","DOI":"10.1001\/jama.2019.22241","article-title":"Predictive accuracy of a polygenic risk score\u2013enhanced prediction model vs a clinical risk score for coronary artery disease","volume":"323","author":"Elliott","year":"2020","journal-title":"JAMA"},{"key":"2022031506404611700_ref9","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pmed.1003152","article-title":"Development of a polygenic risk score to improve screening for fracture risk: a genetic risk prediction study","volume":"17","author":"Forgetta","year":"2020","journal-title":"PLoS Med"},{"key":"2022031506404611700_ref10","doi-asserted-by":"crossref","first-page":"616","DOI":"10.1161\/CIRCULATIONAHA.119.043805","article-title":"Predicting benefit from evolocumab therapy in patients with atherosclerotic disease using a genetic risk score","volume":"141","author":"Marston","year":"2020","journal-title":"Circulation"},{"key":"2022031506404611700_ref11","doi-asserted-by":"crossref","first-page":"696","DOI":"10.1016\/S2213-2600(20)30101-6","article-title":"Chronic obstructive pulmonary disease and related phenotypes: polygenic risk scores in population-based and case-control cohorts","volume":"8","author":"Moll","year":"2020","journal-title":"Lancet Respir Med"},{"key":"2022031506404611700_ref12","doi-asserted-by":"crossref","first-page":"155","DOI":"10.1176\/appi.ajp.2019.18060721","article-title":"Polygenic risk score contribution to psychosis prediction in a target population of persons at clinical high risk","volume":"177","author":"Perkins","year":"2020","journal-title":"Am J Psychiatry"},{"key":"2022031506404611700_ref13","doi-asserted-by":"crossref","first-page":"881","DOI":"10.1016\/S2213-2600(19)30144-4","article-title":"Identification of risk loci and a polygenic risk score for lung cancer: a large-scale prospective cohort study in Chinese populations","volume":"7","author":"Dai","year":"2019","journal-title":"Lancet Respir Med"},{"key":"2022031506404611700_ref14","first-page":"408\u201312","article-title":"The role of polygenic risk scores in breast cancer risk assessment","volume":"174","author":"Cases in Precision Medicine","journal-title":"Ann Intern Med"},{"key":"2022031506404611700_ref15","doi-asserted-by":"crossref","first-page":"418","DOI":"10.1016\/j.ajhg.2020.07.002","article-title":"Combined utility of 25 disease and risk factor polygenic risk scores for stratifying risk of all-cause mortality","volume":"107","author":"Meisner","year":"2020","journal-title":"Am J Hum Genet"},{"key":"2022031506404611700_ref16","doi-asserted-by":"crossref","first-page":"1219","DOI":"10.1038\/s41588-018-0183-z","article-title":"Genome-wide polygenic scores for common diseases identify individuals with risk equivalent to monogenic mutations","volume":"50","author":"Khera","year":"2018","journal-title":"Nat Genet"},{"key":"2022031506404611700_ref17","doi-asserted-by":"crossref","first-page":"432","DOI":"10.1016\/j.ajhg.2020.07.006","article-title":"Genome-wide modeling of polygenic risk score in colorectal cancer risk","volume":"107","author":"Thomas","year":"2020","journal-title":"Am J Hum Genet"},{"key":"2022031506404611700_ref18","article-title":"Liver-specific polygenic risk score is more strongly associated than genome-wide score with Alzheimer\u2019s disease diagnosis in a case-control analysis","author":"Panyard","year":"2021","journal-title":"medRxiv"},{"key":"2022031506404611700_ref19","doi-asserted-by":"crossref","first-page":"773","DOI":"10.1002\/sim.8445","article-title":"The emerging landscape of health research based on biobanks linked to electronic health records: existing resources, statistical challenges, and potential opportunities","volume":"39","author":"Beesley","year":"2020","journal-title":"Stat Med"},{"key":"2022031506404611700_ref20","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pmed.1001779","article-title":"UK biobank: an open access resource for identifying the causes of a wide range of complex diseases of middle and old age","volume":"12","author":"Sudlow","year":"2015","journal-title":"PLoS Med"},{"key":"2022031506404611700_ref21","doi-asserted-by":"crossref","first-page":"S2","DOI":"10.1016\/j.je.2016.12.005","article-title":"Overview of the BioBank Japan project: study design and profile","volume":"27","author":"Nagai","year":"2017","journal-title":"J Epidemiol"},{"key":"2022031506404611700_ref22","doi-asserted-by":"crossref","first-page":"1652","DOI":"10.1093\/ije\/dyr120","article-title":"China Kadoorie Biobank of 0.5 million people: survey methods, baseline characteristics and long-term follow-up","volume":"40","author":"Chen","year":"2011","journal-title":"Int J Epidemiol"},{"key":"2022031506404611700_ref23","doi-asserted-by":"crossref","first-page":"323","DOI":"10.1038\/s41586-019-1457-z","article-title":"Exome sequencing of Finnish isolates enhances rare-variant association power","volume":"572","author":"Locke","year":"2019","journal-title":"Nature"},{"key":"2022031506404611700_ref24","doi-asserted-by":"crossref","first-page":"668","DOI":"10.1056\/NEJMsr1809937","article-title":"The \u201cAll of Us\u201d Research Program","volume":"381","year":"2019","journal-title":"N Engl J Med"},{"key":"2022031506404611700_ref25","doi-asserted-by":"crossref","first-page":"91","DOI":"10.1186\/s13073-014-0091-5","article-title":"Trans-ethnic genome-wide association studies: advantages and challenges of mapping in diverse populations","volume":"6","author":"Li","year":"2014","journal-title":"Genome Med"},{"key":"2022031506404611700_ref26","doi-asserted-by":"crossref","first-page":"1198","DOI":"10.1016\/j.cell.2020.06.045","article-title":"Trans-ethnic and ancestry-specific blood-cell genetics in 746,667 individuals from 5 global populations","volume":"182","author":"Chen","year":"2020","journal-title":"Cell"},{"key":"2022031506404611700_ref27","doi-asserted-by":"crossref","first-page":"679","DOI":"10.1016\/j.ajhg.2020.03.013","article-title":"Accurate and scalable construction of polygenic scores in large biobank data sets","volume":"106","author":"Yang","year":"2020","journal-title":"Am J Hum Genet"},{"key":"2022031506404611700_ref28","doi-asserted-by":"crossref","first-page":"3328","DOI":"10.1038\/s41467-019-11112-0","article-title":"Analysis of polygenic risk score usage and performance in diverse human populations","volume":"10","author":"Duncan","year":"2019","journal-title":"Nat Commun"},{"key":"2022031506404611700_ref29","doi-asserted-by":"crossref","first-page":"632","DOI":"10.1016\/j.ajhg.2021.03.002","article-title":"A unified framework for cross-population trait prediction by leveraging the genetic correlation of polygenic traits","volume":"108","author":"Cai","year":"2021","journal-title":"Am J Hum Genet"},{"key":"2022031506404611700_ref30","doi-asserted-by":"crossref","first-page":"2759","DOI":"10.1038\/s41596-020-0353-1","article-title":"Tutorial: a guide to performing polygenic risk score analyses","volume":"15","author":"Choi","year":"2020","journal-title":"Nat Protoc"},{"key":"2022031506404611700_ref31","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pgen.1003264","article-title":"Polygenic Modeling with Bayesian sparse linear mixed models","volume":"9","author":"Zhou","year":"2013","journal-title":"PLoS Genet"},{"key":"2022031506404611700_ref32","doi-asserted-by":"crossref","first-page":"257","DOI":"10.1186\/s13059-021-02479-9","article-title":"PUMAS: fine-tuning polygenic risk scores with GWAS summary statistics","volume":"22","author":"Zhao","year":"2021","journal-title":"Genome Biol"},{"key":"2022031506404611700_ref33","first-page":"5424\u201331","article-title":"LDpred2: better, faster, stronger","volume":"36","author":"Priv\u00e9","year":"2020","journal-title":"Bioinformatics"},{"key":"2022031506404611700_ref34","doi-asserted-by":"crossref","first-page":"0016","DOI":"10.1038\/s41562-016-0016","article-title":"Genetic evidence of assortative mating in humans","volume":"1","author":"Robinson","year":"2017","journal-title":"Nat Hum Behav"},{"key":"2022031506404611700_ref35","doi-asserted-by":"crossref","first-page":"469","DOI":"10.1002\/gepi.22050","article-title":"Polygenic scores via penalized regression on summary statistics","volume":"41","author":"Mak","year":"2017","journal-title":"Genet Epidemiol"},{"key":"2022031506404611700_ref36","doi-asserted-by":"crossref","first-page":"456","DOI":"10.1038\/s41467-017-00470-2","article-title":"Non-parametric genetic prediction of complex traits with latent Dirichlet process regression models","volume":"8","author":"Zeng","year":"2017","journal-title":"Nat Commun"},{"key":"2022031506404611700_ref37","doi-asserted-by":"crossref","first-page":"1776","DOI":"10.1038\/s41467-019-09718-5","article-title":"Polygenic prediction via Bayesian regression and continuous shrinkage priors","volume":"10","author":"Ge","year":"2019","journal-title":"Nat Commun"},{"key":"2022031506404611700_ref38","doi-asserted-by":"crossref","first-page":"5086","DOI":"10.1038\/s41467-019-12653-0","article-title":"Improved polygenic prediction by Bayesian multiple regression on summary statistics","volume":"10","author":"Lloyd-Jones","year":"2019","journal-title":"Nat Commun"},{"key":"2022031506404611700_ref39","doi-asserted-by":"crossref","first-page":"1213","DOI":"10.1016\/j.ajhg.2019.11.001","article-title":"Making the most of clumping and thresholding for polygenic scores","volume":"105","author":"Priv\u00e9","year":"2019","journal-title":"Am J Hum Genet"},{"key":"2022031506404611700_ref40","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pgen.1009021","article-title":"Evaluation of polygenic prediction methodology within a reference-standardized framework","volume":"17","author":"Pain","year":"2021","journal-title":"PLoS Genet"},{"key":"2022031506404611700_ref41","doi-asserted-by":"crossref","DOI":"10.2139\/ssrn.3808292","article-title":"A systematic framework for assessing the clinical impact of polygenic risk scores","volume-title":"medRxiv","author":"Kulm","year":"2021"},{"key":"2022031506404611700_ref42","doi-asserted-by":"crossref","first-page":"611","DOI":"10.1016\/j.biopsych.2021.04.018","article-title":"A comparison of ten polygenic score methods for psychiatric disorders applied across multiple cohorts","volume":"90","author":"Ni","year":"2021","journal-title":"Biol Psychiatry"},{"key":"2022031506404611700_ref43","doi-asserted-by":"crossref","first-page":"635","DOI":"10.1016\/j.ajhg.2017.03.004","article-title":"Human demographic history impacts genetic risk prediction across diverse populations","volume":"100","author":"Martin","year":"2017","journal-title":"Am J Hum Genet"},{"key":"2022031506404611700_ref44","doi-asserted-by":"crossref","first-page":"2781","DOI":"10.1093\/bioinformatics\/bty185","article-title":"Efficient analysis of large-scale genome-wide data with two R packages: bigstatsr and bigsnpr","volume":"34","author":"Priv\u00e9","year":"2018","journal-title":"Bioinformatics"},{"key":"2022031506404611700_ref45","doi-asserted-by":"crossref","first-page":"46","DOI":"10.1016\/j.ajhg.2020.05.004","article-title":"Non-parametric polygenic risk prediction via partitioned GWAS summary statistics","volume":"107","author":"Chun","year":"2020","journal-title":"Am J Hum Genet"},{"key":"2022031506404611700_ref46","doi-asserted-by":"crossref","first-page":"283","DOI":"10.1093\/bioinformatics\/btv546","article-title":"Approximately independent linkage disequilibrium blocks in human populations","volume":"32","author":"Berisa","year":"2015","journal-title":"Bioinformatics"},{"key":"2022031506404611700_ref47","doi-asserted-by":"crossref","first-page":"1158","DOI":"10.1214\/10-AOAS338","article-title":"Using linear predictors to impute allele frequencies from summary or pooled genotype data","volume":"4","author":"Wen","year":"2010","journal-title":"Ann Appl Stat"},{"key":"2022031506404611700_ref48","doi-asserted-by":"crossref","first-page":"821","DOI":"10.1038\/ng.2310","article-title":"Genome-wide efficient mixed-model analysis for association studies","volume":"44","author":"Zhou","year":"2012","journal-title":"Nat Genet"},{"key":"2022031506404611700_ref49","doi-asserted-by":"crossref","DOI":"10.1186\/s13742-015-0047-8","article-title":"Second-generation PLINK: rising to the challenge of larger and richer datasets","volume":"4","author":"Chang","year":"2015","journal-title":"GigaScience"},{"key":"2022031506404611700_ref50","first-page":"507","article-title":"Frequency distribution of the values of the correlation coefficient in samples from an indefinitely large population","volume":"10","author":"Fisher","year":"1915","journal-title":"Biometrika"},{"key":"2022031506404611700_ref51","doi-asserted-by":"crossref","first-page":"4393","DOI":"10.1038\/s41467-019-12276-5","article-title":"Characterizing rare and low-frequency height-associated variants in the Japanese population","volume":"10","author":"Akiyama","year":"2019","journal-title":"Nat Commun"},{"key":"2022031506404611700_ref52","doi-asserted-by":"crossref","first-page":"390","DOI":"10.1038\/s41588-018-0047-6","article-title":"Genetic analysis of quantitative traits in the Japanese population links cell types to complex human diseases","volume":"50","author":"Kanai","year":"2018","journal-title":"Nat Genet"},{"key":"2022031506404611700_ref53","doi-asserted-by":"crossref","first-page":"1458","DOI":"10.1038\/ng.3951","article-title":"Genome-wide association study identifies 112 new loci for body mass index in the Japanese population","volume":"49","author":"Akiyama","year":"2017","journal-title":"Nat Genet"},{"key":"2022031506404611700_ref54","doi-asserted-by":"crossref","first-page":"1977","DOI":"10.1038\/s41467-018-04398-z","article-title":"Elucidating the genetic architecture of reproductive ageing in the Japanese population","volume":"9","author":"Horikoshi","year":"2018","journal-title":"Nat Commun"},{"key":"2022031506404611700_ref55","doi-asserted-by":"crossref","first-page":"i185","DOI":"10.1093\/bioinformatics\/btu273","article-title":"GRASP: analysis of genotype\u2013phenotype results from 1390 genome-wide association studies and corresponding open access database","volume":"30","author":"Leslie","year":"2014","journal-title":"Bioinformatics"},{"key":"2022031506404611700_ref56","doi-asserted-by":"crossref","first-page":"1339","DOI":"10.1038\/s41588-019-0481-0","article-title":"A global overview of pleiotropy and genetic architecture in complex traits","volume":"51","author":"Watanabe","year":"2019","journal-title":"Nat Genet"},{"key":"2022031506404611700_ref57","doi-asserted-by":"crossref","first-page":"1173","DOI":"10.1038\/ng.3097","article-title":"Defining the role of common variation in the genomic and biological architecture of adult human height","volume":"46","author":"Wood","year":"2014","journal-title":"Nat Genet"},{"key":"2022031506404611700_ref58","doi-asserted-by":"crossref","first-page":"88","DOI":"10.1016\/j.ajhg.2017.12.005","article-title":"Life-course genome-wide association study meta-analysis of total body BMD and assessment of age-specific effects","volume":"102","author":"Medina-Gomez","year":"2018","journal-title":"Am J Hum Genet"},{"key":"2022031506404611700_ref59","doi-asserted-by":"crossref","first-page":"1274","DOI":"10.1038\/ng.2797","article-title":"Discovery and refinement of loci associated with lipid levels","volume":"45","author":"Willer","year":"2013","journal-title":"Nat Genet"},{"key":"2022031506404611700_ref60","doi-asserted-by":"crossref","first-page":"56","DOI":"10.1038\/ng.291","article-title":"Common variants at 30 loci contribute to polygenic dyslipidemia","volume":"41","author":"Kathiresan","year":"2009","journal-title":"Nat Genet"},{"key":"2022031506404611700_ref61","doi-asserted-by":"crossref","first-page":"11122","DOI":"10.1038\/ncomms11122","article-title":"Genome-wide study for circulating metabolites identifies 62 loci and reveals novel systemic effects of LPA","volume":"7","author":"Kettunen","year":"2016","journal-title":"Nat Commun"},{"key":"2022031506404611700_ref62","doi-asserted-by":"crossref","first-page":"745","DOI":"10.1016\/j.ajhg.2009.10.005","article-title":"Sequence variants in three loci influence monocyte counts and erythrocyte volume","volume":"85","author":"Ferreira","year":"2009","journal-title":"Am J Hum Genet"},{"key":"2022031506404611700_ref63","doi-asserted-by":"crossref","first-page":"197","DOI":"10.1038\/nature14177","article-title":"Genetic studies of body mass index yield new insights for obesity biology","volume":"518","author":"Locke","year":"2015","journal-title":"Nature"},{"key":"2022031506404611700_ref64","doi-asserted-by":"crossref","first-page":"92","DOI":"10.1038\/nature13545","article-title":"Parent-of-origin-specific allelic associations among 106 genomic loci for age at menarche","volume":"514","author":"Perry","year":"2014","journal-title":"Nature"},{"key":"2022031506404611700_ref65","doi-asserted-by":"crossref","first-page":"834","DOI":"10.1038\/ng.3841","article-title":"Genomic analyses identify hundreds of variants associated with age at menarche and support a role for puberty timing in cancer risk","volume":"49","author":"Day","year":"2017","journal-title":"Nat Genet"},{"key":"2022031506404611700_ref66","doi-asserted-by":"crossref","first-page":"481","DOI":"10.1038\/s41588-018-0321-7","article-title":"New genetic signals for lung function highlight pathways and chronic obstructive pulmonary disease associations across multiple ancestries","volume":"51","author":"Shrine","year":"2019","journal-title":"Nat Genet"},{"key":"2022031506404611700_ref67","doi-asserted-by":"crossref","first-page":"804","DOI":"10.1038\/s41588-019-0403-1","article-title":"Maternal and fetal genetic effects on birth weight and their relevance to cardio-metabolic risk factors","volume":"51","author":"Warrington","year":"2019","journal-title":"Nat Genet"},{"key":"2022031506404611700_ref68","doi-asserted-by":"crossref","first-page":"76","DOI":"10.1038\/ng.2477","article-title":"New loci associated with birth weight identify genetic links between intrauterine growth and adult height and metabolism","volume":"45","author":"Horikoshi","year":"2013","journal-title":"Nat Genet"},{"key":"2022031506404611700_ref69","article-title":"Alternative global\u2013local shrinkage priors using hypergeometric\u2013beta mixtures","author":"Polson","year":"2009\u201314","journal-title":"Tech Rep"},{"key":"2022031506404611700_ref70","doi-asserted-by":"crossref","first-page":"203","DOI":"10.1038\/s41586-018-0579-z","article-title":"The UK Biobank resource with deep phenotyping and genomic data","volume":"562","author":"Bycroft","year":"2018","journal-title":"Nature"},{"key":"2022031506404611700_ref71","doi-asserted-by":"crossref","first-page":"584","DOI":"10.1038\/s41588-019-0379-x","article-title":"Clinical use of current polygenic risk scores may exacerbate health disparities","volume":"51","author":"Martin","year":"2019","journal-title":"Nat Genet"},{"key":"2022031506404611700_ref72","doi-asserted-by":"crossref","first-page":"245","DOI":"10.1038\/ng.3506","article-title":"Integrative approaches for large-scale transcriptome-wide association studies","volume":"48","author":"Gusev","year":"2016","journal-title":"Nat Genet"},{"key":"2022031506404611700_ref73","doi-asserted-by":"crossref","first-page":"1229","DOI":"10.1093\/ije\/dyab031","article-title":"Selection into shift work is influenced by educational attainment and body mass index: a Mendelian randomization study in the UK Biobank","volume":"50","author":"Daghlas","year":"2021","journal-title":"Int J Epidemiol"},{"key":"2022031506404611700_ref74","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pgen.1009141","article-title":"A fast and scalable framework for large-scale and ultrahigh-dimensional sparse regression with application to the UK Biobank","volume":"16","author":"Qian","year":"2020","journal-title":"PLoS Genet"},{"key":"2022031506404611700_ref75","doi-asserted-by":"crossref","first-page":"1","DOI":"10.18637\/jss.v033.i01","article-title":"Regularization paths for generalized linear models via coordinate descent","volume":"33","author":"Friedman","year":"2010","journal-title":"J Stat Softw"},{"key":"2022031506404611700_ref76","doi-asserted-by":"crossref","first-page":"70","DOI":"10.1002\/bimj.200900028","article-title":"L1 penalized estimation in the cox proportional hazards model","volume":"52","author":"Goeman","year":"2010","journal-title":"Biom J"},{"key":"2022031506404611700_ref77","doi-asserted-by":"crossref","first-page":"659","DOI":"10.1111\/j.1467-9868.2007.00607.x","article-title":"L1-regularization path algorithm for generalized linear models","volume":"69","author":"Park","year":"2007","journal-title":"J R Stat Soc Series B Stat Methodology"},{"key":"2022031506404611700_ref78","doi-asserted-by":"crossref","first-page":"1775","DOI":"10.1093\/bioinformatics\/btp322","article-title":"Gradient lasso for cox proportional hazards model","volume":"25","author":"Sohn","year":"2009","journal-title":"Bioinformatics"},{"key":"2022031506404611700_ref79","doi-asserted-by":"crossref","first-page":"787","DOI":"10.1038\/s41588-021-00847-6","article-title":"Genome-wide survival study identifies a novel synaptic locus and polygenic score for cognitive progression in Parkinson\u2019s disease","volume":"53","author":"Liu","year":"2021","journal-title":"Nat Genet"},{"key":"2022031506404611700_ref80","doi-asserted-by":"crossref","first-page":"kxaa038","DOI":"10.1093\/biostatistics\/kxaa038","article-title":"Fast Lasso method for large-scale and ultrahigh-dimensional cox model with applications to UK Biobank","author":"Li","year":"2020","journal-title":"Biostatistics"},{"key":"2022031506404611700_ref81","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pcbi.1005589","article-title":"Leveraging functional annotations in genetic risk prediction for human complex diseases","volume":"13","author":"Hu","year":"2017","journal-title":"PLoS Comput Biol"},{"key":"2022031506404611700_ref82","doi-asserted-by":"crossref","first-page":"6052","DOI":"10.1038\/s41467-021-25171-9","article-title":"Incorporating functional priors improves polygenic prediction accuracy in UK Biobank and 23andMe data sets","volume":"12","author":"M\u00e1rquez-Luna","year":"2021","journal-title":"Nat Commun"},{"key":"2022031506404611700_ref83","doi-asserted-by":"crossref","first-page":"407","DOI":"10.1038\/nmeth.2848","article-title":"Efficient multivariate linear mixed model algorithms for genome-wide association studies","volume":"11","author":"Zhou","year":"2014","journal-title":"Nat Methods"},{"key":"2022031506404611700_ref84","doi-asserted-by":"crossref","first-page":"283","DOI":"10.1016\/j.ajhg.2014.12.006","article-title":"Joint analysis of psychiatric disorders increases accuracy of risk prediction for schizophrenia, bipolar disorder, and major depressive disorder","volume":"96","author":"Maier","year":"2015","journal-title":"Am J Hum Genet"},{"key":"2022031506404611700_ref85","doi-asserted-by":"crossref","first-page":"989","DOI":"10.1038\/s41467-017-02769-6","article-title":"Improving genetic prediction by leveraging genetic correlations among human diseases and traits","volume":"9","author":"Maier","year":"2018","journal-title":"Nat Commun"},{"key":"2022031506404611700_ref86","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pgen.1006836","article-title":"Joint modeling of genetically correlated diseases and functional annotations increases accuracy of polygenic risk prediction","volume":"13","author":"Hu","year":"2017","journal-title":"PLoS Genet"}],"container-title":["Briefings in Bioinformatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/academic.oup.com\/bib\/article-pdf\/23\/2\/bbac039\/42806392\/bbac039.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/bib\/article-pdf\/23\/2\/bbac039\/42806392\/bbac039.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,9,18]],"date-time":"2024-09-18T11:26:09Z","timestamp":1726658769000},"score":1,"resource":{"primary":{"URL":"https:\/\/academic.oup.com\/bib\/article\/doi\/10.1093\/bib\/bbac039\/6534383"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,2,22]]},"references-count":86,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2022,3,10]]}},"URL":"https:\/\/doi.org\/10.1093\/bib\/bbac039","relation":{},"ISSN":["1467-5463","1477-4054"],"issn-type":[{"value":"1467-5463","type":"print"},{"value":"1477-4054","type":"electronic"}],"subject":[],"published-other":{"date-parts":[[2022,3]]},"published":{"date-parts":[[2022,2,22]]},"article-number":"bbac039"}}