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[{"key": "dc.contributor.advisor", "value": "Taskinen, Sara", "language": "", "element": "contributor", "qualifier": "advisor", "schema": "dc"}, {"key": "dc.contributor.advisor", "value": "Nordhausen, Klaus", "language": "", "element": "contributor", "qualifier": "advisor", "schema": "dc"}, {"key": "dc.contributor.advisor", "value": "Lidauer, Martin", "language": "", "element": "contributor", "qualifier": "advisor", "schema": "dc"}, {"key": "dc.contributor.advisor", "value": "Strand\u00e9n, Ismo", "language": "", "element": "contributor", "qualifier": "advisor", "schema": "dc"}, {"key": "dc.contributor.advisor", "value": "Ylitalo, Anna-Kaisa", "language": "", "element": "contributor", "qualifier": "advisor", "schema": "dc"}, {"key": "dc.contributor.author", "value": "Heikkil\u00e4, Antero", "language": "", "element": "contributor", "qualifier": "author", "schema": "dc"}, {"key": "dc.date.accessioned", "value": "2024-10-24T06:43:30Z", "language": null, "element": "date", "qualifier": "accessioned", "schema": "dc"}, {"key": "dc.date.available", "value": "2024-10-24T06:43:30Z", "language": null, "element": "date", "qualifier": "available", "schema": "dc"}, {"key": "dc.date.issued", "value": "2024", "language": "", "element": "date", "qualifier": "issued", "schema": "dc"}, {"key": "dc.identifier.uri", "value": "https://jyx.jyu.fi/handle/123456789/97660", "language": null, "element": "identifier", "qualifier": "uri", "schema": "dc"}, {"key": "dc.description.abstract", "value": "Jalostusarvoilla ilmaistaan yksil\u00f6n geneettist\u00e4 hyvyytt\u00e4 jalostettavan ominaisuuden suhteen verrattuna muihin yksil\u00f6ihin jalostettavassa populaatiossa. Seuraavan sukupolven vanhemmiksi valitaan tyypillisesti yksil\u00f6t, joiden\njalostusarvojen ennusteet ovat suurimmat toivoen, ett\u00e4 heid\u00e4n j\u00e4lkel\u00e4isill\u00e4\u00e4nkin olisi hyv\u00e4t ominaisuudet jalostettavan ominaisuuden suhteen. G-BLUP\n(genomic best linear unbiased prediction) -menetelm\u00e4 on laajasti k\u00e4yt\u00f6ss\u00e4\nel\u00e4in- ja kasvinjalostuksessa. Siin\u00e4 jalostusarvojen ennustamiseen k\u00e4ytet\u00e4\u00e4n\nyksil\u00f6ilt\u00e4 ker\u00e4tty\u00e4 geneettist\u00e4 tietoa. Jotta jalostusarvojen ennusteet olisivat\nmahdollisimman hyvi\u00e4 ja tarkkoja, on t\u00e4rke\u00e4\u00e4, ett\u00e4 populaation sukulaisuussuhteet tiedet\u00e4\u00e4n. Erityisesti el\u00e4inpopulaatioissa on tavallisesti tiedossa populaation sukupuu, jonka avulla jalostusarvojen ennustamiseen k\u00e4ytetyiss\u00e4\nmenetelmiss\u00e4, kuten BLUP- ja G-BLUP -menetelm\u00e4ss\u00e4, voidaan muodostaa\nniiss\u00e4 tarvittava sukulaisuusmatriisi. Nykyisin, kun yksil\u00f6iden genotyypitt\u00e4misen hinta on laskenut, on entist\u00e4 yleisemp\u00e4\u00e4 muodostaa sukulaisuusmatriisi hy\u00f6dynt\u00e4en yksil\u00f6ilt\u00e4 ker\u00e4tty\u00e4 tietoa snipeist\u00e4 (SNP), eli yhden nukleotidin polymorfismeista. Snipit ovat edustava otos genomia, ja kuvaavat siin\u00e4\nolevaa geneettist\u00e4 vaihtelua.\nTilastollisena mallina jalostuksessa k\u00e4ytet\u00e4\u00e4n tavallisesti lineaariseen sekamalliin pohjautuvaa mallia. Siin\u00e4 yksil\u00f6iden fenotyyppisi\u00e4 havaintoja selitet\u00e4\u00e4n joukolla kiinteit\u00e4 tekij\u00f6it\u00e4, kuten ik\u00e4\u00e4, sukupuolta ja painoa, ja satunnaistekij\u00f6it\u00e4. Satunnaistekij\u00f6in\u00e4 mallissa ovat erityisesti yksil\u00f6iden jalostusarvot, joten ratkaisemalla satunnaistekij\u00f6iden ennusteet saadaan ennusteet jalostusarvoille. Kasvinjalostuksessa k\u00e4ytett\u00e4v\u00e4ss\u00e4 hybridimallissa satunnaistekij\u00f6it\u00e4 on usein kolme: risteytyksen molempien vanhempien sek\u00e4\nitse risteytyksen satunnaisvaikutus fenotyyppiseen havaintoon. T\u00e4ss\u00e4 tutkielmassa hybridimalli sovitetaan k\u00e4ytt\u00e4en G-BLUP -menetelm\u00e4\u00e4.\nTutkielman varsinaisena tavoitteena oli selvitt\u00e4\u00e4, miten ennustevirhevariansseja (PEV) approksimoivat menetelm\u00e4t toimivat hybridimallin kanssa. Ennustevirhevarianssilla mitataan sit\u00e4, kuinka l\u00e4hell\u00e4 jalostusarvon ennuste on\ntodellista jalostusarvoa. Approksimoivat menetelm\u00e4t perustuvat mallin simuloimiseen Monte Carlo -menetelm\u00e4ll\u00e4. Approksimoivien menetelmien toimivuutta tutkittiin kolmen geneettisen ryhm\u00e4n v\u00e4lill\u00e4, jotka olivat risteytyksen vanhempaiskasvit ja risteytys itse, jonka lis\u00e4ksi tutkittiin, miten menetelm\u00e4t toimivat tilanteessa, joissa geneettisi\u00e4 variansseja ja j\u00e4\u00e4nn\u00f6svarianssia\nmuutettiin, ja tilanteessa, jossa analyysiin otettiin mukaan vain puolet havainnoista. Tutkielmaan otettiin mukaan nelj\u00e4 tunnettua menetelm\u00e4\u00e4, joita\nkutsutaan nimill\u00e4 PEV1, PEV2, PEV3 ja NF2. Menetelm\u00e4t perustuvat mallin simuloimiseen ja niiss\u00e4 verrataan simuloidun jalostusarvon ja simuloidun\ndatan perusteella saadun jalostusarvon estimaatin v\u00e4list\u00e4 eroa. T\u00e4m\u00e4 tutkielma osoitti, ett\u00e4 kaikki (tutkittavat) ennustevirhevarianssia approksimoivat menetelm\u00e4t toimivat asymptoottisesti Monte Carlo -n\u00e4ytteiden m\u00e4\u00e4r\u00e4n\nkasvaessa my\u00f6s hybridimallin kanssa. Tutkielmassa kuitenkin selvisi, ett\u00e4 menetelmien v\u00e4lill\u00e4 on my\u00f6s eroja. Parhaimmiksi havaittiin menetelm\u00e4t PEV3\nja NF2. Sen sijaan erityisesti menetelm\u00e4 PEV2 toimi huonosti tilanteessa,\njossa ennustevirhevarianssin vaihteluv\u00e4li oli pieni.", "language": "fi", "element": "description", "qualifier": "abstract", "schema": "dc"}, {"key": "dc.description.abstract", "value": "Genomic best linear unbiased prediction (GBLUP) is a method widely used\nin animal and plant breeding. It uses individuals\u2019 genomic information to estimate breeding values. Breeding values are an essential part of animal and\nplant breeding, and they tell the genetic merit of an individual compared\nto the others. Using estimated breeding values (EBVs), breeders can select\nthe best individuals to be the ancestors of the next generation. To estimate\nbreeding values accurately, relationship information from the breeding population should be used. A relationship matrix is constructed using either\npedigree or genetic information. In GBLUP, the relationships of a population are presented in a genomic relationship matrix, which is constructed\nusing the individuals\u2019 genetic information. The genomic information is usually based on single nucleotide polymorphisms (SNPs), which tell the variant\nof a gene an individual carries.\nA linear mixed model is a typical choice for estimating breeding values. Individual breeding values are treated as random effects in the linear mixed\nmodel. Using Henderson\u2019s mixed model equations (MMEs) makes it possible\nto obtain the estimates for the fixed and random effects simultaneously. A\nhybrid model in plant breeding is a linear mixed model in which phenotypic\nobservations are explained by both maternal and paternal effects separately\nand a cross effect. A cross is a plant that emerges when two plants reproduce.\nThis thesis shows how a hybrid model is fitted using a GBLUP model.\nWhen the number of individuals is large, the use of exact solving methods becomes computationally infeasible, making the use of iterative solving\nmethods for solving the MME and approximate methods for obtaining prediction error variances (PEVs) necessary. The behaviour of four methods for\napproximating PEVs was studied using a hybrid model. The methods are\ncalled PEV1, PEV2, PEV3, and NF2, and they are widely used methods\nto approximate the exact PEV of a model. PEV measures the accuracy of an EBV. These methods, which are based on Monte Carlo (MC) sampling\nof the model, were compared across different genetic groups and situations.\nThe results indicate that the methods PEV3 and NF2 work better than the\nmethods PEV1 and PEV2. Especially the method PEV2 behaved poorly\nwhen the distribution of the exact PEV values was narrow. Overall, the\nthesis demonstrates that all the methods work in a hybrid model framework\nwhen the MC sample size is large enough.", "language": "en", "element": "description", "qualifier": "abstract", "schema": "dc"}, {"key": "dc.description.provenance", "value": "Submitted by Paivi Vuorio (paelvuor@jyu.fi) on 2024-10-24T06:43:30Z\nNo. of bitstreams: 0", "language": "en", "element": "description", "qualifier": "provenance", "schema": "dc"}, {"key": "dc.description.provenance", "value": "Made available in DSpace on 2024-10-24T06:43:30Z (GMT). No. of bitstreams: 0\n Previous issue date: 2024", "language": "en", "element": "description", "qualifier": "provenance", "schema": "dc"}, {"key": "dc.format.extent", "value": "100", "language": "", "element": "format", "qualifier": "extent", "schema": "dc"}, {"key": "dc.language.iso", "value": "eng", "language": null, "element": "language", "qualifier": "iso", "schema": "dc"}, {"key": "dc.rights", "value": "In Copyright", "language": "en", "element": "rights", "qualifier": null, "schema": "dc"}, {"key": "dc.subject.other", "value": "prediction error variance", "language": "", "element": "subject", "qualifier": "other", "schema": "dc"}, {"key": "dc.subject.other", "value": "estimated breeding value", "language": "", "element": "subject", "qualifier": "other", "schema": "dc"}, {"key": "dc.subject.other", "value": "GBLUP", "language": "", "element": "subject", "qualifier": "other", "schema": "dc"}, {"key": "dc.subject.other", "value": "linear mixed model", "language": "", "element": "subject", "qualifier": "other", "schema": "dc"}, {"key": "dc.subject.other", "value": "Monte Carlo sampling", "language": "", "element": "subject", "qualifier": "other", "schema": "dc"}, {"key": "dc.subject.other", "value": "ennustevirhevarianssi", "language": "", "element": "subject", "qualifier": "other", "schema": "dc"}, {"key": "dc.subject.other", "value": "jalostusarvon ennuste", "language": "", "element": "subject", "qualifier": "other", "schema": "dc"}, {"key": "dc.subject.other", "value": "G-BLUP", "language": "", "element": "subject", "qualifier": "other", "schema": "dc"}, {"key": "dc.subject.other", "value": "lineaarinen sekamalli", "language": "", "element": "subject", "qualifier": "other", "schema": "dc"}, {"key": "dc.subject.other", "value": "Monte Carlo -simulointi", "language": "", "element": "subject", "qualifier": "other", "schema": "dc"}, {"key": "dc.title", "value": "Estimating prediction error variances of a plant breeding hybrid model using Monte Carlo sampling", "language": "", "element": "title", "qualifier": null, "schema": "dc"}, {"key": "dc.type", "value": "master thesis", "language": null, "element": "type", "qualifier": null, "schema": "dc"}, {"key": "dc.identifier.urn", "value": "URN:NBN:fi:jyu-202410246517", "language": null, "element": "identifier", "qualifier": "urn", "schema": "dc"}, {"key": "dc.type.ontasot", "value": "Master\u2019s thesis", "language": "en", "element": "type", "qualifier": "ontasot", "schema": "dc"}, {"key": "dc.type.ontasot", "value": "Pro gradu -tutkielma", "language": "fi", "element": "type", "qualifier": "ontasot", "schema": "dc"}, {"key": "dc.contributor.faculty", "value": "Matemaattis-luonnontieteellinen tiedekunta", "language": "fi", "element": "contributor", "qualifier": "faculty", "schema": "dc"}, {"key": "dc.contributor.faculty", "value": "Faculty of Sciences", "language": "en", "element": "contributor", "qualifier": "faculty", "schema": "dc"}, {"key": "dc.contributor.department", "value": "Matematiikan ja tilastotieteen laitos", "language": "fi", "element": "contributor", "qualifier": "department", "schema": "dc"}, {"key": "dc.contributor.department", "value": "Department of Mathematics and Statistics", "language": "en", "element": "contributor", "qualifier": "department", "schema": "dc"}, {"key": "dc.contributor.organization", "value": "Jyv\u00e4skyl\u00e4n yliopisto", "language": "fi", "element": "contributor", "qualifier": "organization", "schema": "dc"}, {"key": "dc.contributor.organization", "value": "University of Jyv\u00e4skyl\u00e4", "language": "en", "element": "contributor", "qualifier": "organization", "schema": "dc"}, {"key": "dc.subject.discipline", "value": "Tilastotiede", "language": "fi", "element": "subject", "qualifier": "discipline", "schema": "dc"}, {"key": "dc.subject.discipline", "value": "Statistics", "language": "en", "element": "subject", "qualifier": "discipline", "schema": "dc"}, {"key": "yvv.contractresearch.funding", "value": "0", "language": "", "element": "contractresearch", "qualifier": "funding", "schema": "yvv"}, {"key": "dc.type.coar", "value": "http://purl.org/coar/resource_type/c_bdcc", "language": null, "element": "type", "qualifier": "coar", "schema": "dc"}, {"key": "dc.rights.accesslevel", "value": "openAccess", "language": null, "element": "rights", "qualifier": "accesslevel", "schema": "dc"}, {"key": "dc.type.publication", "value": "masterThesis", "language": null, "element": "type", "qualifier": "publication", "schema": "dc"}, {"key": "dc.subject.oppiainekoodi", "value": "4043", "language": "", "element": "subject", "qualifier": "oppiainekoodi", "schema": "dc"}, {"key": "dc.subject.yso", "value": "kasvinjalostus", "language": null, "element": "subject", "qualifier": "yso", "schema": "dc"}, {"key": "dc.subject.yso", "value": "Monte Carlo -menetelm\u00e4t", "language": null, "element": "subject", "qualifier": "yso", "schema": "dc"}, {"key": "dc.subject.yso", "value": "plant breeding", "language": null, "element": "subject", "qualifier": "yso", "schema": "dc"}, {"key": "dc.subject.yso", "value": "Monte Carlo methods", "language": null, "element": "subject", "qualifier": "yso", "schema": "dc"}, {"key": "dc.rights.url", "value": "https://rightsstatements.org/page/InC/1.0/", "language": null, "element": "rights", "qualifier": "url", "schema": "dc"}]
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