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Genomic Prediction Including SNP-Specific Variance Predictors
Swedish University of Agricultural Sciences.
Norwegian University of Science and Technology.
Högskolan Dalarna, Akademin Industri och samhälle, Statistik.ORCID-id: 0000-0002-1057-5401
2019 (engelsk)Inngår i: G3: Genes, Genomes, Genetics, ISSN 2160-1836, E-ISSN 2160-1836, Vol. 9, nr 10, s. 3333-3343Artikkel i tidsskrift (Fagfellevurdert) Published
Abstract [en]

The increasing amount of available biological information on the markers can be used to inform the models applied for genomic selection to improve predictions. The objective of this study was to propose a general model for genomic selection using a link function approach within the hierarchical generalized linear model framework (hglm) that can include external information on the markers. These models can be fitted using the well-established hglm package in R. We also present an R package (CodataGS) to fit these models, which is significantly faster than the hglm package. Simulated data was used to validate the proposed model. We tested categorical, continuous and combination models where the external information on the markers was related to 1) the location of the QTLs on the genome with varying degree of uncertainty, 2) the relationship of the markers with the QTLs calculated as the LD between them, and 3) a combination of both. The proposed models showed improved accuracies from 3.8% up to 23.2% compared to the SNP-BLUP method in a simulated population derived from a base population with 100 individuals. Moreover, the proposed categorical model was tested on a dairy cattle dataset for two traits (Milk Yield and Fat Percentage). These results also showed improved accuracy compared to SNP-BLUP, especially for the Fat% trait. The performance of the proposed models depended on the genetic architecture of the trait, as traits that deviate from the infinitesimal model benefited more from the external information. Also, the gain in accuracy depended on the degree of uncertainty of the external information provided to the model. The usefulness of these type of models is expected to increase with time as more accurate information on the markers becomes available.

sted, utgiver, år, opplag, sider
2019. Vol. 9, nr 10, s. 3333-3343
Emneord [en]
BLUP, CodataGS, external information, genomic selection, hglm
HSV kategori
Forskningsprogram
Komplexa system - mikrodataanalys
Identifikatorer
URN: urn:nbn:se:du-30685DOI: 10.1534/g3.119.400381ISI: 000490151100023PubMedID: 31467030Scopus ID: 2-s2.0-85072994135OAI: oai:DiVA.org:du-30685DiVA, id: diva2:1350963
Tilgjengelig fra: 2019-09-12 Laget: 2019-09-12 Sist oppdatert: 2019-10-31bibliografisk kontrollert

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