Bayesian model combining linkage and linkage disequilibrium analysis for low density-based genomic selection in animal breeding
| Autor: | Marcos Deon Vilela de Resende, Elcer Albenis Zamora Jerez, Fabyano Fonseca e Silva, Simone Eliza Facioni Guimarães, Paulo Sávio Lopes, José Marcelo Soriano Viana, Rodrigo Oliveira de Lima, Camila Ferreira Azevedo, Moysés Nascimento |
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| Přispěvatelé: | Fabyano Fonseca Silva, UFV, Elcer Albenis Zamora Jerez, UFV, MARCOS DEON VILELA DE RESENDE, CNPF, José Marcelo Soriano Viana, UFV, Camila Ferreira Azevedo, UFV, Paulo Sávio Lopes, UFV, Moysés Nascimento, UFV, Rodrigo Oliveira de Lima, UFV, Simone Eliza Facioni Guimarães, UFV. |
| Rok vydání: | 2017 |
| Předmět: |
0301 basic medicine
Linkage (software) Linkage disequilibrium lcsh:Veterinary medicine Animal breeding General Veterinary Computational biology Biology Bayesian inference Genetic architecture Term (time) 03 medical and health sciences 030104 developmental biology Low density lcsh:SF600-1100 Genome prediction genotypic IBD matrix SNP marker Animal Science and Zoology genome prediction Genotypic IBD matrix Genomic selection |
| Zdroj: | LOCUS Repositório Institucional da UFV Universidade Federal de Viçosa (UFV) instacron:UFV Repositório Institucional da EMBRAPA (Repository Open Access to Scientific Information from EMBRAPA-Alice) Empresa Brasileira de Pesquisa Agropecuária (Embrapa) instacron:EMBRAPA Journal of Applied Animal Research, Vol 46, Iss 1, Pp 873-878 (2018) |
| ISSN: | 0974-1844 0971-2119 |
| DOI: | 10.1080/09712119.2017.1415903 |
| Popis: | We combined linkage (LA) and linkage disequilibrium (LDA) analyses (emerging the term ?LALDA?) for genomic selection (GS) purposes. The models were fitted to a simulated dataset and to a real data of feed conversion ratio in pigs. Firstly, the significant QTLs (quantitative trait locus) were identified through LA-based mixed models considering the QTL-genotypes as random effects by means of genotypic identity by descent matrix. This matrix was calculated at the positions of significant QTLs (based on LA) allowing to include the QTL-genotype effects additionally to SNP (single nucleotide polymorphism) markers (based on LDA) and additive polygenic effects in several GS models (Bayesian Ridge Regression ? BRR; Bayes A ? BA; Bayes B ? BB; Bayes C ? BC and Bayesian LASSO ? BL). These models combing all mentioned effects were denominated LALDA. Goodness-of-fit and predictive ability analyses were performed to evaluate the efficiency of these models. For the real data, although slightly, the superiority of the LALDA models was verified in comparison to traditional LDA models for GS. For the simulated dataset, the models presented similar results. For both LDA and LALDA frameworks, BA showed the best fitting through Deviance Information Criterion and higher predictive ability in the simulated and real datasets. Made available in DSpace on 2018-07-26T01:02:59Z (GMT). No. of bitstreams: 1 2018M.DeonJAAQRBayesian.pdf: 253306 bytes, checksum: 21b0ffae73773cb12eec83f7ba086154 (MD5) Previous issue date: 2018-07-25 |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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