GVCBLUP: a computer package for genomic prediction and variance component estimation of additive and dominance effects

Background Dominance effect may play an important role in genetic variation of complex traits. Full featured and easy-to-use computing tools for genomic prediction and variance component estimation of additive and dominance effects using genome-wide single nucleotide polymorphism (SNP) markers are n...

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Vydané v:BMC bioinformatics Ročník 15; číslo 1; s. 270
Hlavní autori: Wang, Chunkao, Prakapenka, Dzianis, Wang, Shengwen, Pulugurta, Sujata, Runesha, Hakizumwami Birali, Da, Yang
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: London BioMed Central 09.08.2014
BioMed Central Ltd
Springer Nature B.V
Predmet:
Algorithms
Analysis
Animals
Bioinformatics
Biomedical and Life Sciences
Breeding
C plus plus
Colleges & universities
Comparative genomics
Computational Biology/Bioinformatics
Computer Appl. in Life Sciences
Estimates
Genetic aspects
Genetic diversity
Genetic effects
Genetic Markers - genetics
Genomes
Genomics
Genomics - methods
Genotype
Genotype & phenotype
Life Sciences
Linear algebra
Linux
Microarrays
Operating systems
Phenotype
Polymorphism, Single Nucleotide
Programming languages
Single nucleotide polymorphisms
Software
User interface
Heritability
BLUP
GVCBLUP
Genomic selection
Variance component
ISSN:1471-2105, 1471-2105
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Shrnutí:Background Dominance effect may play an important role in genetic variation of complex traits. Full featured and easy-to-use computing tools for genomic prediction and variance component estimation of additive and dominance effects using genome-wide single nucleotide polymorphism (SNP) markers are necessary to understand dominance contribution to a complex trait and to utilize dominance for selecting individuals with favorable genetic potential. Results The GVCBLUP package is a shared memory parallel computing tool for genomic prediction and variance component estimation of additive and dominance effects using genome-wide SNP markers. This package currently has three main programs (GREML_CE, GREML_QM, and GCORRMX) and a graphical user interface (GUI) that integrates the three main programs with an existing program for the graphical viewing of SNP additive and dominance effects (GVCeasy). The GREML_CE and GREML_QM programs offer complementary computing advantages with identical results for genomic prediction of breeding values, dominance deviations and genotypic values, and for genomic estimation of additive and dominance variances and heritabilities using a combination of expectation-maximization (EM) algorithm and average information restricted maximum likelihood (AI-REML) algorithm. GREML_CE is designed for large numbers of SNP markers and GREML_QM for large numbers of individuals. Test results showed that GREML_CE could analyze 50,000 individuals with 400 K SNP markers and GREML_QM could analyze 100,000 individuals with 50K SNP markers. GCORRMX calculates genomic additive and dominance relationship matrices using SNP markers. GVCeasy is the GUI for GVCBLUP integrated with an existing software tool for the graphical viewing of SNP effects and a function for editing the parameter files for the three main programs. Conclusion The GVCBLUP package is a powerful and versatile computing tool for assessing the type and magnitude of genetic effects affecting a phenotype by estimating whole-genome additive and dominance heritabilities, for genomic prediction of breeding values, dominance deviations and genotypic values, for calculating genomic relationships, and for research and education in genomic prediction and estimation.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
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ISSN:1471-2105
1471-2105
DOI:10.1186/1471-2105-15-270