Mixed model methods for genomic prediction and variance component estimation of additive and dominance effects using SNP markers.

We established a genomic model of quantitative trait with genomic additive and dominance relationships that parallels the traditional quantitative genetics model, which partitions a genotypic value as breeding value plus dominance deviation and calculates additive and dominance relationships using p...

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Autores principales:	Yang Da, Chunkao Wang, Shengwen Wang, Guo Hu
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2014
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/ff3e6669eb6646d9b2870577c4ecf837
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spelling	oai:doaj.org-article:ff3e6669eb6646d9b2870577c4ecf8372021-11-18T08:34:45ZMixed model methods for genomic prediction and variance component estimation of additive and dominance effects using SNP markers.1932-620310.1371/journal.pone.0087666https://doaj.org/article/ff3e6669eb6646d9b2870577c4ecf8372014-01-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/24498162/pdf/?tool=EBIhttps://doaj.org/toc/1932-6203We established a genomic model of quantitative trait with genomic additive and dominance relationships that parallels the traditional quantitative genetics model, which partitions a genotypic value as breeding value plus dominance deviation and calculates additive and dominance relationships using pedigree information. Based on this genomic model, two sets of computationally complementary but mathematically identical mixed model methods were developed for genomic best linear unbiased prediction (GBLUP) and genomic restricted maximum likelihood estimation (GREML) of additive and dominance effects using SNP markers. These two sets are referred to as the CE and QM sets, where the CE set was designed for large numbers of markers and the QM set was designed for large numbers of individuals. GBLUP and associated accuracy formulations for individuals in training and validation data sets were derived for breeding values, dominance deviations and genotypic values. Simulation study showed that GREML and GBLUP generally were able to capture small additive and dominance effects that each accounted for 0.00005-0.0003 of the phenotypic variance and GREML was able to differentiate true additive and dominance heritability levels. GBLUP of the total genetic value as the summation of additive and dominance effects had higher prediction accuracy than either additive or dominance GBLUP, causal variants had the highest accuracy of GREML and GBLUP, and predicted accuracies were in agreement with observed accuracies. Genomic additive and dominance relationship matrices using SNP markers were consistent with theoretical expectations. The GREML and GBLUP methods can be an effective tool for assessing the type and magnitude of genetic effects affecting a phenotype and for predicting the total genetic value at the whole genome level.Yang DaChunkao WangShengwen WangGuo HuPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 9, Iss 1, p e87666 (2014)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Yang Da Chunkao Wang Shengwen Wang Guo Hu Mixed model methods for genomic prediction and variance component estimation of additive and dominance effects using SNP markers.
description	We established a genomic model of quantitative trait with genomic additive and dominance relationships that parallels the traditional quantitative genetics model, which partitions a genotypic value as breeding value plus dominance deviation and calculates additive and dominance relationships using pedigree information. Based on this genomic model, two sets of computationally complementary but mathematically identical mixed model methods were developed for genomic best linear unbiased prediction (GBLUP) and genomic restricted maximum likelihood estimation (GREML) of additive and dominance effects using SNP markers. These two sets are referred to as the CE and QM sets, where the CE set was designed for large numbers of markers and the QM set was designed for large numbers of individuals. GBLUP and associated accuracy formulations for individuals in training and validation data sets were derived for breeding values, dominance deviations and genotypic values. Simulation study showed that GREML and GBLUP generally were able to capture small additive and dominance effects that each accounted for 0.00005-0.0003 of the phenotypic variance and GREML was able to differentiate true additive and dominance heritability levels. GBLUP of the total genetic value as the summation of additive and dominance effects had higher prediction accuracy than either additive or dominance GBLUP, causal variants had the highest accuracy of GREML and GBLUP, and predicted accuracies were in agreement with observed accuracies. Genomic additive and dominance relationship matrices using SNP markers were consistent with theoretical expectations. The GREML and GBLUP methods can be an effective tool for assessing the type and magnitude of genetic effects affecting a phenotype and for predicting the total genetic value at the whole genome level.
format	article
author	Yang Da Chunkao Wang Shengwen Wang Guo Hu
author_facet	Yang Da Chunkao Wang Shengwen Wang Guo Hu
author_sort	Yang Da
title	Mixed model methods for genomic prediction and variance component estimation of additive and dominance effects using SNP markers.
title_short	Mixed model methods for genomic prediction and variance component estimation of additive and dominance effects using SNP markers.
title_full	Mixed model methods for genomic prediction and variance component estimation of additive and dominance effects using SNP markers.
title_fullStr	Mixed model methods for genomic prediction and variance component estimation of additive and dominance effects using SNP markers.
title_full_unstemmed	Mixed model methods for genomic prediction and variance component estimation of additive and dominance effects using SNP markers.
title_sort	mixed model methods for genomic prediction and variance component estimation of additive and dominance effects using snp markers.
publisher	Public Library of Science (PLoS)
publishDate	2014
url	https://doaj.org/article/ff3e6669eb6646d9b2870577c4ecf837
work_keys_str_mv	AT yangda mixedmodelmethodsforgenomicpredictionandvariancecomponentestimationofadditiveanddominanceeffectsusingsnpmarkers AT chunkaowang mixedmodelmethodsforgenomicpredictionandvariancecomponentestimationofadditiveanddominanceeffectsusingsnpmarkers AT shengwenwang mixedmodelmethodsforgenomicpredictionandvariancecomponentestimationofadditiveanddominanceeffectsusingsnpmarkers AT guohu mixedmodelmethodsforgenomicpredictionandvariancecomponentestimationofadditiveanddominanceeffectsusingsnpmarkers
_version_	1718421660803006464

Mixed model methods for genomic prediction and variance component estimation of additive and dominance effects using SNP markers.

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