دورية أكاديمية
أعرض في EDS

Genomic Prediction Including SNP-Specific Variance Predictors.

التفاصيل البيبلوغرافية
العنوان: Genomic Prediction Including SNP-Specific Variance Predictors.
المؤلفون: Mouresan EF; Department of Animal Breeding and Genetics, Swedish University of Agricultural Sciences, Sweden, 75007, elena.flavia.mouresan@slu.se., Selle M; Department of Mathematical Sciences, Norwegian University of Science and Technology, Norway, 7491, and., Rönnegård L; Department of Animal Breeding and Genetics, Swedish University of Agricultural Sciences, Sweden, 75007.; School of Technology and Business Studies, Dalarna University, Sweden, 79188.
المصدر: G3 (Bethesda, Md.) [G3 (Bethesda)] 2019 Oct 07; Vol. 9 (10), pp. 3333-3343. Date of Electronic Publication: 2019 Oct 07.
نوع المنشور: Journal Article; Research Support, Non-U.S. Gov't
اللغة: English
بيانات الدورية: Publisher: Oxford University Press Country of Publication: England NLM ID: 101566598 Publication Model: Electronic Cited Medium: Internet ISSN: 2160-1836 (Electronic) Linking ISSN: 21601836 NLM ISO Abbreviation: G3 (Bethesda) Subsets: MEDLINE
أسماء مطبوعة: Publication: 2021- : [Oxford] : Oxford University Press
Original Publication: Bethesda, MD : Genetics Society of America, 2011-
مواضيع طبية MeSH: Genome* , Polymorphism, Single Nucleotide*, Computational Biology/*methods , Genomics/*methods, Algorithms ; Animals ; Cattle ; Evolution, Molecular ; Genetics, Population ; Models, Genetic ; Quantitative Trait Loci
مستخلص: 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 were 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 QTL on the genome with varying degree of uncertainty, 2) the relationship of the markers with the QTL 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.
(Copyright © 2019 Mouresan et al.)
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فهرسة مساهمة: Keywords: BLUP; CodataGS; GenPred; Genomic Prediction; Shared Data Resources; external information; hglm
تواريخ الأحداث: Date Created: 20190831 Date Completed: 20200312 Latest Revision: 20200312
رمز التحديث: 20221213
مُعرف محوري في PubMed: PMC6778789
DOI: 10.1534/g3.119.400381
PMID: 31467030
قاعدة البيانات: MEDLINE
الوصف
تدمد:2160-1836
DOI:10.1534/g3.119.400381