{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,2,22]],"date-time":"2025-02-22T00:45:24Z","timestamp":1740185124871,"version":"3.37.3"},"reference-count":8,"publisher":"Oxford University Press (OUP)","issue":"9","license":[{"start":{"date-parts":[[2017,12,18]],"date-time":"2017-12-18T00:00:00Z","timestamp":1513555200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/journals\/pages\/about_us\/legal\/notices"}],"funder":[{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","award":["R27 GM46255, R01 MH94293, P50 AG05136, U01 AG049507"],"award-info":[{"award-number":["R27 GM46255, R01 MH94293, P50 AG05136, U01 AG049507"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2018,5,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:sec>\n                  <jats:title>Summary<\/jats:title>\n                  <jats:p>Genome-wide association studies have become common over the last ten years, with a shift towards targeting rare variants, especially in pedigree-data. Despite lower costs, sequencing for rare variants still remains expensive. To have a relatively large sample with acceptable cost, imputation approaches may be used, such as GIGI for pedigree data. GIGI is an imputation method that handles large pedigrees and is particularly good for rare variant imputation. GIGI requires a subset of individuals in a pedigree to be fully sequenced, while other individuals are sequenced only at relevant markers. The imputation will infer the missing genotypes at untyped markers. Running GIGI on large pedigrees for large numbers of markers can be very time consuming. We present GIGI-Quick as a method to efficiently split GIGI\u2019s input, run GIGI in parallel and efficiently merge the output to reduce the runtime with the number of cores. This allows obtaining imputation results faster, and therefore all subsequent association analyses.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Availability and and implementation<\/jats:title>\n                  <jats:p>GIGI-Quick is open source and publicly available via: https:\/\/cse-git.qcri.org\/Imputation\/GIGI-Quick.<\/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\/btx782","type":"journal-article","created":{"date-parts":[[2017,12,15]],"date-time":"2017-12-15T21:33:00Z","timestamp":1513373580000},"page":"1591-1593","source":"Crossref","is-referenced-by-count":1,"title":["GIGI-Quick: a fast approach to impute missing genotypes in genome-wide association family data"],"prefix":"10.1093","volume":"34","author":[{"given":"Khalid","family":"Kunji","sequence":"first","affiliation":[{"name":"Data Analytics Department, Qatar Computing Research Institute, Hamad Bin Khalifa University, Doha, Qatar"}]},{"given":"Ehsan","family":"Ullah","sequence":"additional","affiliation":[{"name":"Data Analytics Department, Qatar Computing Research Institute, Hamad Bin Khalifa University, Doha, Qatar"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8745-9046","authenticated-orcid":false,"suffix":"Jr","given":"Alejandro Q","family":"Nato","sequence":"additional","affiliation":[{"name":"Division of Medical Genetics, Department of Medicine, University of Washington, Seattle, WA, USA"}]},{"given":"Ellen M","family":"Wijsman","sequence":"additional","affiliation":[{"name":"Division of Medical Genetics, Department of Medicine, University of Washington, Seattle, WA, USA"}]},{"given":"Mohamad","family":"Saad","sequence":"additional","affiliation":[{"name":"Data Analytics Department, Qatar Computing Research Institute, Hamad Bin Khalifa University, Doha, Qatar"}]}],"member":"286","published-online":{"date-parts":[[2017,12,18]]},"reference":[{"key":"2023012713032550400_btx782-B1","doi-asserted-by":"crossref","first-page":"97","DOI":"10.1038\/ng786","article-title":"Merlin \u2013 rapid analysis of dense genetic maps using sparse gene flow trees","volume":"30","author":"Abecasis","year":"2002","journal-title":"Nat. Genet"},{"key":"2023012713032550400_btx782-B2","doi-asserted-by":"crossref","first-page":"773","DOI":"10.1038\/nrg2867","article-title":"Statistical analysis strategies for association studies involving rare variants","volume":"11","author":"Bansal","year":"2010","journal-title":"Nat. Rev. Genet"},{"key":"2023012713032550400_btx782-B3","doi-asserted-by":"crossref","first-page":"e51589.","DOI":"10.1371\/journal.pone.0051589","article-title":"Using family-based imputation in genome-wide association studies with large complex pedigrees: The Framingham Heart Study","volume":"7","author":"Chen","year":"2012","journal-title":"PLoS ONE"},{"key":"2023012713032550400_btx782-B4","doi-asserted-by":"crossref","first-page":"504","DOI":"10.1016\/j.ajhg.2013.02.011","article-title":"GIGI: an approach to effective imputation of dense genotypes on large pedigrees","volume":"92","author":"Cheung","year":"2013","journal-title":"Am. J. Hum. Genet"},{"key":"2023012713032550400_btx782-B6","doi-asserted-by":"crossref","first-page":"379","DOI":"10.1038\/nrg3472","article-title":"Meta-analysis methods for genome-wide association studies and beyond","volume":"14","author":"Evangelou","year":"2013","journal-title":"Nat. Rev. Genet"},{"key":"2023012713032550400_btx782-B7","doi-asserted-by":"crossref","first-page":"747","DOI":"10.1038\/nature08494","article-title":"Finding the missing heritability of complex diseases","volume":"461","author":"Manolio","year":"2009","journal-title":"Nature"},{"key":"2023012713032550400_btx782-B8","doi-asserted-by":"crossref","first-page":"86","DOI":"10.1159\/000313555","article-title":"The structure of genetic linkage data: from LIPED to 1M SNPs","volume":"71","author":"Thompson","year":"2011","journal-title":"Hum. Hered"},{"key":"2023012713032550400_btx782-B9","doi-asserted-by":"crossref","first-page":"1555","DOI":"10.1007\/s00439-012-1190-2","article-title":"The role of large pedigrees in an era of high-throughput sequencing","volume":"131","author":"Wijsman","year":"2012","journal-title":"Hum. Genet"}],"container-title":["Bioinformatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article-pdf\/34\/9\/1591\/48916026\/bioinformatics_34_9_1591.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article-pdf\/34\/9\/1591\/48916026\/bioinformatics_34_9_1591.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,1,27]],"date-time":"2023-01-27T13:58:34Z","timestamp":1674827914000},"score":1,"resource":{"primary":{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article\/34\/9\/1591\/4756093"}},"subtitle":[],"editor":[{"given":"Oliver","family":"Stegle","sequence":"additional","affiliation":[]}],"short-title":[],"issued":{"date-parts":[[2017,12,18]]},"references-count":8,"journal-issue":{"issue":"9","published-print":{"date-parts":[[2018,5,1]]}},"URL":"https:\/\/doi.org\/10.1093\/bioinformatics\/btx782","relation":{},"ISSN":["1367-4803","1367-4811"],"issn-type":[{"type":"print","value":"1367-4803"},{"type":"electronic","value":"1367-4811"}],"subject":[],"published-other":{"date-parts":[[2018,5,1]]},"published":{"date-parts":[[2017,12,18]]}}}