{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,24]],"date-time":"2026-03-24T21:57:13Z","timestamp":1774389433537,"version":"3.50.1"},"reference-count":4,"publisher":"Oxford University Press (OUP)","issue":"16","license":[{"start":{"date-parts":[[2020,6,17]],"date-time":"2020-06-17T00:00:00Z","timestamp":1592352000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2020,8,15]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:sec>\n                  <jats:title>Summary<\/jats:title>\n                  <jats:p>Summary statistics from a meta-analysis of genome-wide association studies (meta-GWAS) can be used for many follow-up analyses. One valuable application is the creation of polygenic scores. However, if polygenic scores are calculated in a validation cohort that was part of the meta-GWAS consortium, this cohort is not independent and analyses will therefore yield inflated results. The R package \u2018MetaSubtract\u2019 was developed to subtract the results of the validation cohort from meta-GWAS summary statistics analytically. The statistical formulas for a meta-analysis were inverted to compute corrected summary statistics of a meta-GWAS leaving one (or more) cohort(s) out. These formulas have been implemented in MetaSubtract for different meta-analyses methods (fixed effects inverse variance or square root sample size weighted z-score) accounting for no, single or double genomic control correction. Results obtained by MetaSubtract correlate very well to those calculated using the traditional way, i.e. by performing a meta-analysis leaving out the validation cohort. In conclusion, MetaSubtract allows researchers to compute meta-GWAS summary statistics that are independent of the GWAS results of the validation cohort without requiring access to the cohort level GWAS results of the corresponding meta-GWAS consortium.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Availability and implementation<\/jats:title>\n                  <jats:p>https:\/\/cran.r-project.org\/web\/packages\/MetaSubtract.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Supplementary information<\/jats:title>\n                  <jats:p>Supplementary data are available at Bioinformatics online.<\/jats:p>\n               <\/jats:sec>","DOI":"10.1093\/bioinformatics\/btaa570","type":"journal-article","created":{"date-parts":[[2020,6,10]],"date-time":"2020-06-10T11:31:50Z","timestamp":1591788710000},"page":"4521-4522","source":"Crossref","is-referenced-by-count":53,"title":["Metasubtract: an R-package to analytically produce leave-one-out meta-analysis GWAS summary statistics"],"prefix":"10.1093","volume":"36","author":[{"given":"Ilja M","family":"Nolte","sequence":"first","affiliation":[]}],"member":"286","published-online":{"date-parts":[[2020,7,21]]},"reference":[{"key":"2023062213534372500_btaa570-B1","doi-asserted-by":"crossref","first-page":"15805","DOI":"10.1038\/ncomms15805","article-title":"Genetic loci associated with heart rate variability and their effects on cardiac disease risk","volume":"8","author":"Nolte","year":"2017","journal-title":"Nat. Commun"},{"key":"2023062213534372500_btaa570-B2","volume-title":"R: A Language and Environment for Statistical Computing","year":"2012"},{"key":"2023062213534372500_btaa570-B3","doi-asserted-by":"crossref","first-page":"2190","DOI":"10.1093\/bioinformatics\/btq340","article-title":"METAL: fast and efficient meta-analysis of genomewide association scans","volume":"26","author":"Willer","year":"2010","journal-title":"Bioinformatics"},{"key":"2023062213534372500_btaa570-B4","doi-asserted-by":"crossref","first-page":"507","DOI":"10.1038\/nrg3457","article-title":"Pitfalls of predicting complex traits from SNPs","volume":"14","author":"Wray","year":"2013","journal-title":"Nat. Rev. Genet"}],"container-title":["Bioinformatics"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/academic.oup.com\/bioinformatics\/advance-article-pdf\/doi\/10.1093\/bioinformatics\/btaa570\/33519346\/btaa570.pdf","content-type":"application\/pdf","content-version":"am","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article-pdf\/36\/16\/4521\/50676354\/btaa570.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article-pdf\/36\/16\/4521\/50676354\/btaa570.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,6,22]],"date-time":"2023-06-22T20:51:04Z","timestamp":1687467064000},"score":1,"resource":{"primary":{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article\/36\/16\/4521\/5858976"}},"subtitle":[],"editor":[{"given":"Russell","family":"Schwartz","sequence":"additional","affiliation":[]}],"short-title":[],"issued":{"date-parts":[[2020,7,21]]},"references-count":4,"journal-issue":{"issue":"16","published-print":{"date-parts":[[2020,8,15]]}},"URL":"https:\/\/doi.org\/10.1093\/bioinformatics\/btaa570","relation":{},"ISSN":["1367-4803","1367-4811"],"issn-type":[{"value":"1367-4803","type":"print"},{"value":"1367-4811","type":"electronic"}],"subject":[],"published-other":{"date-parts":[[2020,8,15]]},"published":{"date-parts":[[2020,7,21]]}}}