{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T10:52:49Z","timestamp":1740135169246,"version":"3.37.3"},"reference-count":16,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2021,5,3]],"date-time":"2021-05-03T00:00:00Z","timestamp":1620000000000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2021,5,3]],"date-time":"2021-05-03T00:00:00Z","timestamp":1620000000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","award":["R01 GM053275","R01 HG006139","R01 HG009120","T32 HG002536","K01 DK106116"],"award-info":[{"award-number":["R01 GM053275","R01 HG006139","R01 HG009120","T32 HG002536","K01 DK106116"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000001","name":"National Science Foundation","doi-asserted-by":"publisher","award":["DMS 1264153"],"award-info":[{"award-number":["DMS 1264153"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["BMC Bioinformatics"],"published-print":{"date-parts":[[2021,12]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:sec>\n                <jats:title>Background<\/jats:title>\n                <jats:p>Statistical geneticists employ simulation to estimate the power of proposed studies, test new analysis tools, and evaluate properties of causal models. Although there are existing trait simulators, there is ample room for modernization. For example, most phenotype simulators are limited to Gaussian traits or traits transformable to normality, while ignoring qualitative traits and realistic, non-normal trait distributions. Also, modern computer languages, such as Julia, that accommodate parallelization and cloud-based computing are now mainstream but rarely used in older applications. To meet the challenges of contemporary big studies, it is important for geneticists to adopt new computational tools.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Results<\/jats:title>\n                <jats:p>We present , an open-source Julia package that makes it trivial to quickly simulate phenotypes under a variety of genetic architectures. This package is integrated into our OpenMendel suite for easy downstream analyses. Julia was purpose-built for scientific programming and provides tremendous speed and memory efficiency, easy access to multi-CPU and GPU hardware, and to distributed and cloud-based parallelization.  is designed to encourage flexible trait simulation, including via the standard devices of applied statistics, generalized linear models (GLMs) and generalized linear mixed models (GLMMs).  also accommodates many study designs: unrelateds, sibships, pedigrees, or a mixture of all three. (Of course, for data with pedigrees or cryptic relationships, the simulation process must include the genetic dependencies among the individuals.) We consider an assortment of trait models and study designs to illustrate integrated simulation and analysis pipelines. Step-by-step instructions for these analyses are available in our electronic Jupyter notebooks on Github. These interactive notebooks are ideal for reproducible research.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Conclusion<\/jats:title>\n                <jats:p>The  package has three main advantages. (1) It leverages the computational efficiency and ease of use of Julia to provide extremely fast, straightforward simulation of even the most complex genetic models, including GLMs and GLMMs. (2) It can be operated entirely within, but is not limited to, the integrated analysis pipeline of OpenMendel. And finally (3), by allowing a wider range of more realistic phenotype models,  brings power calculations and diagnostic tools closer to what investigators might see in real-world analyses.<\/jats:p>\n              <\/jats:sec>","DOI":"10.1186\/s12859-021-04086-8","type":"journal-article","created":{"date-parts":[[2021,5,3]],"date-time":"2021-05-03T10:03:10Z","timestamp":1620036190000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Modern simulation utilities for genetic analysis"],"prefix":"10.1186","volume":"22","author":[{"given":"Sarah S.","family":"Ji","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Christopher A.","family":"German","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kenneth","family":"Lange","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Janet S.","family":"Sinsheimer","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hua","family":"Zhou","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jin","family":"Zhou","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1718-0031","authenticated-orcid":false,"given":"Eric M.","family":"Sobel","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2021,5,3]]},"reference":[{"key":"4086_CR1","doi-asserted-by":"publisher","first-page":"1001779","DOI":"10.1371\/journal.pmed.1001779","volume":"12","author":"C Sudlow","year":"2015","unstructured":"Sudlow C, Gallacher J, Allen N, Beral V, Burton P, Danesh J, Downey P, Elliott P, Green J, Landray M, Liu B, Matthews P, Ong G, Pell J, Silman A, Young A, Sprosen T, Peakman T, Collins R. UK Biobank: an open access resource for identifying the causes of a wide range of complex diseases of middle and old age. PLoS Med. 2015;12:1001779. https:\/\/doi.org\/10.1371\/journal.pmed.1001779.","journal-title":"PLoS Med"},{"key":"4086_CR2","unstructured":"UK Biobank: UK biobank data repository. https:\/\/www.ukbiobank.ac.uk."},{"key":"4086_CR3","doi-asserted-by":"publisher","first-page":"10","DOI":"10.1186\/s12863-015-0173-4","volume":"16","author":"Z Zhang","year":"2015","unstructured":"Zhang Z, Li X, Ding X, Li J, Zhang Q. GPOPSIM: a simulation tool for whole-genome genetic data. BMC Genet. 2015;16:10. https:\/\/doi.org\/10.1186\/s12863-015-0173-4.","journal-title":"BMC Genet"},{"key":"4086_CR4","doi-asserted-by":"publisher","first-page":"34861","DOI":"10.1371\/journal.pone.0034861","volume":"7","author":"PF O\u2019Reilly","year":"2012","unstructured":"O\u2019Reilly PF, Hoggart CJ, Pomyen Y, Calboli FCF, Elliott P, Jarvelin M-R, Coin LJM. MultiPhen: joint model of multiple phenotypes can increase discovery in GWAS. PLoS ONE. 2012;7:34861. https:\/\/doi.org\/10.1371\/journal.pone.0034861.","journal-title":"PLoS ONE"},{"key":"4086_CR5","doi-asserted-by":"publisher","first-page":"2951","DOI":"10.1093\/bioinformatics\/bty197","volume":"34","author":"HV Meyer","year":"2018","unstructured":"Meyer HV, Birney E. PhenotypeSimulator: a comprehensive framework for simulating multi-trait, multi-locus genotype to phenotype relationships. Bioinformatics. 2018;34:2951\u20136. https:\/\/doi.org\/10.1093\/bioinformatics\/bty197.","journal-title":"Bioinformatics"},{"key":"4086_CR6","doi-asserted-by":"publisher","first-page":"65","DOI":"10.1137\/141000671","volume":"59","author":"J Bezanson","year":"2017","unstructured":"Bezanson J, Edelman A, Karpinski S, Shah VB. Julia: a fresh approach to numerical computing. SIAM Rev. 2017;59:65\u201398. https:\/\/doi.org\/10.1137\/141000671.","journal-title":"SIAM Rev"},{"key":"4086_CR7","unstructured":"Ko S, Zhou H, Zhou J, Won J-H. High-performance statistical computing in the computing environments of the 2020s (preprint). 2020. arxiv:2001.01916."},{"key":"4086_CR8","doi-asserted-by":"publisher","first-page":"61","DOI":"10.1007\/s00439-019-02001-z","volume":"139","author":"H Zhou","year":"2020","unstructured":"Zhou H, Sinsheimer J, Bates D, Chu B, German C, Ji S, Keys K, Kim J, Ko S, Mosher G, Papp J, Sobel E, Zhai J, Zhou J, Lange K. OPENMENDEL: a cooperative programming project for statistical genetics. Hum Genet. 2020;139:61\u201371. https:\/\/doi.org\/10.1007\/s00439-019-02001-z.","journal-title":"Hum Genet"},{"key":"4086_CR9","unstructured":"JuliaComputing: Parallel computing. https:\/\/juliacomputing.com\/industries\/parallel-computing.html."},{"key":"4086_CR10","unstructured":"JuliaComputing: multi-threading. https:\/\/docs.julialang.org\/en\/v1\/base\/multi-threading."},{"key":"4086_CR11","unstructured":"JuliaComputing: distributed computing. https:\/\/docs.julialang.org\/en\/v1\/stdlib\/Distributed."},{"key":"4086_CR12","unstructured":"Zhou H. SnpArrays.jl. https:\/\/openmendel.github.io\/SnpArrays.jl\/stable\/."},{"key":"4086_CR13","doi-asserted-by":"publisher","first-page":"248","DOI":"10.1002\/gepi.22276","volume":"44","author":"CA German","year":"2020","unstructured":"German CA, Sinsheimer JS, Klimentidis YC, Zhou H, Zhou JJ. Ordered multinomial regression for genetic association analysis of ordinal phenotypes at Biobank scale. 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New York: Springer; 2002.","edition":"2"},{"key":"4086_CR16","unstructured":"IGSR: international genome sample resource. https:\/\/www.internationalgenome.org."}],"container-title":["BMC Bioinformatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12859-021-04086-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s12859-021-04086-8\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12859-021-04086-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,5,3]],"date-time":"2021-05-03T10:10:10Z","timestamp":1620036610000},"score":1,"resource":{"primary":{"URL":"https:\/\/bmcbioinformatics.biomedcentral.com\/articles\/10.1186\/s12859-021-04086-8"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,5,3]]},"references-count":16,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2021,12]]}},"alternative-id":["4086"],"URL":"https:\/\/doi.org\/10.1186\/s12859-021-04086-8","relation":{},"ISSN":["1471-2105"],"issn-type":[{"type":"electronic","value":"1471-2105"}],"subject":[],"published":{"date-parts":[[2021,5,3]]},"assertion":[{"value":"15 July 2020","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"17 March 2021","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"3 May 2021","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"Since all data used in this study were either simulated or anonymous, this is Not Applicable.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"Since all data used in this study were either simulated or anonymous, this is Not Applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"The authors declare that they have no competing interests.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"228"}}