{"data":{"id":"10.6084/m9.figshare.7751780.v3","type":"dois","attributes":{"doi":"10.6084/m9.figshare.7751780.v3","prefix":"10.6084","suffix":"m9.figshare.7751780.v3","identifiers":[],"alternateIdentifiers":[],"creators":[{"name":"Grazyella Yoshida","affiliation":[],"nameIdentifiers":[]},{"name":"Lhorente, Jean Paul","nameType":"Personal","givenName":"Jean Paul","familyName":"Lhorente","affiliation":[],"nameIdentifiers":[]},{"name":"Correa, Katharina","nameType":"Personal","givenName":"Katharina","familyName":"Correa","affiliation":[],"nameIdentifiers":[]},{"name":"Yáñez, José Manuel","nameType":"Personal","givenName":"José Manuel","familyName":"Yáñez","affiliation":[],"nameIdentifiers":[]}],"titles":[{"title":"Genome-wide association study and cost-efficient genomic predictions for growth and fillet yield in Nile tilapia (Oreochromis niloticus)"}],"publisher":"figshare","container":{},"publicationYear":2019,"subjects":[{"subject":"70401 Aquaculture","subjectScheme":"FOR"},{"subject":"FOS: Agriculture, forestry and fisheries","schemeUri":"http://www.oecd.org/science/inno/38235147.pdf","subjectScheme":"Fields of Science and Technology (FOS)"},{"subject":"FOS: Agriculture, forestry and fisheries","subjectScheme":"Fields of Science and Technology (FOS)"}],"contributors":[],"dates":[{"date":"2019-08-08","dateType":"Created"},{"date":"2019-08-08","dateType":"Updated"},{"date":"2019","dateType":"Issued"}],"language":null,"types":{"ris":"DATA","bibtex":"misc","citeproc":"dataset","schemaOrg":"Dataset","resourceType":"Dataset","resourceTypeGeneral":"Dataset"},"relatedIdentifiers":[{"relationType":"IsPreviousVersionOf","relatedIdentifier":"10.6084/m9.figshare.7751780","relatedIdentifierType":"DOI"}],"relatedItems":[],"sizes":["108873182 Bytes"],"formats":[],"version":null,"rightsList":[{"rights":"Creative Commons Attribution 4.0 International","rightsUri":"https://creativecommons.org/licenses/by/4.0/legalcode","schemeUri":"https://spdx.org/licenses/","rightsIdentifier":"cc-by-4.0","rightsIdentifierScheme":"SPDX"}],"descriptions":[{"description":"Dataset to GWAS, genomic prediction and imputation analysis for the paper \"\u003cb\u003eGenome-wide association study and cost-efficient genomic predictions for growth and fillet yield in Nile tilapia (\u003ci\u003eOreochromis niloticus\u003c/i\u003e)\". \u003c/b\u003eFurthermore, supplemental figures are avaliable \u003cb\u003e(\u003c/b\u003e\u003cb\u003ehttps://doi.org/10.1534/g3.119.400116).\u003c/b\u003e\u003cb\u003e\u003cbr\u003e\u003c/b\u003e\u003cb\u003e\u003cbr\u003e\u003c/b\u003e\u003cb\u003eFigure S1.\u003c/b\u003e Correlations between observed and imputed genotypes for each SNP for imputation from low-density (LD3K) to high-density (HD) panel in Nile tilapia using parents (n = 108) and 20% of offspring (n = 226) genotyped with the HD panel as the reference set and 80% of offspring (n = 904) as the validation set. The black line indicates the mean imputation accuracy. \u003cb\u003eFigure S2.\u003c/b\u003e Correlations between observed and imputed genotypes for each SNP for imputation from low-density (LD1K) to high-density (HD) in Nile tilapia using parents (n = 108) and 20% of the offspring (n = 226) genotyped with the HD panel as the reference set and 80% of the offspring (n = 904) as the validation set. The black line indicates the mean imputation accuracy. \u003cb\u003eFigure S3.\u003c/b\u003e Correlations between observed and imputed genotypes for each SNP for imputation from low-density (LD0.5K) to high-density (HD) in Nile tilapia using parents (n = 108) and 20% of offspring (n = 226) genotyped with the HD panel as the reference set and 80% of the offspring (n = 904) as the validation set. The black line indicates the mean imputation accuracy. \u003cb\u003eFigure S4.\u003c/b\u003e Genotyping cost reduction (%) compared to scenario A, where all animals are genotyped using a HD panel.\u003cb\u003e\u003c/b\u003e","descriptionType":"Abstract"}],"geoLocations":[],"fundingReferences":[],"xml":"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","url":"https://figshare.com/articles/Tilapia_GWAS_GS_Imputation/7751780/3","contentUrl":null,"metadataVersion":5,"schemaVersion":"http://datacite.org/schema/kernel-4","source":"mds","isActive":true,"state":"findable","reason":null,"viewCount":0,"viewsOverTime":[],"downloadCount":0,"downloadsOverTime":[],"referenceCount":0,"citationCount":0,"citationsOverTime":[],"partCount":0,"partOfCount":0,"versionCount":0,"versionOfCount":0,"created":"2019-08-08T16:10:53.000Z","registered":"2019-08-08T16:10:53.000Z","published":"2019","updated":"2020-08-27T17:41:33.000Z"},"relationships":{"client":{"data":{"id":"figshare.ars","type":"clients"}},"provider":{"data":{"id":"otjm","type":"providers"}},"media":{"data":{"id":"10.6084/m9.figshare.7751780.v3","type":"media"}},"references":{"data":[]},"citations":{"data":[]},"parts":{"data":[]},"partOf":{"data":[]},"versions":{"data":[]},"versionOf":{"data":[]}}}}