{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,7,30]],"date-time":"2025-07-30T11:44:39Z","timestamp":1753875879156,"version":"3.41.2"},"reference-count":31,"publisher":"Oxford University Press (OUP)","issue":"6","license":[{"start":{"date-parts":[[2021,8,17]],"date-time":"2021-08-17T00:00:00Z","timestamp":1629158400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"funder":[{"DOI":"10.13039\/100006296","name":"Center for Advanced Study","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100006296","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["2020YFC0848900","2017YFC0907503","2016YFB0201702","31771465","31970634"],"award-info":[{"award-number":["2020YFC0848900","2017YFC0907503","2016YFB0201702","31771465","31970634"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100002367","name":"Chinese Academy of Sciences","doi-asserted-by":"publisher","award":["XDB38030400"],"award-info":[{"award-number":["XDB38030400"]}],"id":[{"id":"10.13039\/501100002367","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021,11,5]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>Genotype imputation is a statistical method for estimating missing genotypes from a denser haplotype reference panel. Existing methods usually performed well on common variants, but they may not be ideal for low-frequency and rare variants. Previous studies showed that the population similarity between study and reference panels is one of the key factors influencing the imputation accuracy. Here, we developed an imputation reference panel reconstruction method (RefRGim) using convolutional neural networks (CNNs), which can generate a study-specified reference panel for each input data based on the genetic similarity of individuals from current study and references. The CNNs were pretrained with single nucleotide polymorphism data from the 1000 Genomes Project. Our evaluations showed that genotype imputation with RefRGim can achieve higher accuracies than original reference panel, especially for low-frequency and rare variants. RefRGim will serve as an efficient reference panel reconstruction method for genotype imputation. RefRGim is freely available via GitHub: https:\/\/github.com\/shishuo16\/RefRGim<\/jats:p>","DOI":"10.1093\/bib\/bbab326","type":"journal-article","created":{"date-parts":[[2021,7,27]],"date-time":"2021-07-27T11:08:46Z","timestamp":1627384126000},"source":"Crossref","is-referenced-by-count":6,"title":["RefRGim: an intelligent reference panel reconstruction method for genotype imputation with convolutional neural networks"],"prefix":"10.1093","volume":"22","author":[{"given":"Shuo","family":"Shi","sequence":"first","affiliation":[{"name":"National Genomics Data Center of Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing, China"}]},{"given":"Qiheng","family":"Qian","sequence":"additional","affiliation":[{"name":"National Genomics Data Center of Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing, China"}]},{"given":"Shuhuan","family":"Yu","sequence":"additional","affiliation":[{"name":"National Genomics Data Center of Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing, China"}]},{"given":"Qi","family":"Wang","sequence":"additional","affiliation":[{"name":"Qujiang culture finance holding (Group) Co., Ltd, Xian, China"}]},{"given":"Jinyue","family":"Wang","sequence":"additional","affiliation":[{"name":"National Genomics Data Center of Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing, China"}]},{"given":"Jingyao","family":"Zeng","sequence":"additional","affiliation":[{"name":"National Genomics Data Center of Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing, China"}]},{"given":"Zhenglin","family":"Du","sequence":"additional","affiliation":[{"name":"National Genomics Data Center of Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8894-3446","authenticated-orcid":false,"given":"Jingfa","family":"Xiao","sequence":"additional","affiliation":[{"name":"National Genomics Data Center of Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing, China"}]}],"member":"286","published-online":{"date-parts":[[2021,8,17]]},"reference":[{"key":"2021110815092698300_ref1","doi-asserted-by":"crossref","first-page":"499","DOI":"10.1038\/nrg2796","article-title":"Genotype imputation for genome-wide association studies","volume":"11","author":"Marchini","year":"2010","journal-title":"Nat Rev Genet"},{"key":"2021110815092698300_ref2","doi-asserted-by":"crossref","first-page":"e1000477","DOI":"10.1371\/journal.pgen.1000477","article-title":"Designing genome-wide association studies: sample size, power, imputation, and the choice of genotyping chip","volume":"5","author":"Spencer","year":"2009","journal-title":"PLoS Genet"},{"key":"2021110815092698300_ref3","doi-asserted-by":"crossref","first-page":"4491","DOI":"10.1093\/hmg\/ddr367","article-title":"Fine-mapping of breast cancer susceptibility loci characterizes genetic risk in African 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