title: DAIRRy-BLUP: a high-performance computing approach to genomic prediction.
abstract: In genomic prediction, common analysis methods rely on a linear mixed-model framework to estimate SNP marker effects and breeding values of animals or plants. Ridge regression-best linear unbiased prediction (RR-BLUP) is based on the assumptions that SNP marker effects are normally distributed, are uncorrelated, and have equal variances. We propose DAIRRy-BLUP, a parallel, Distributed-memory RR-BLUP implementation, based on single-trait observations ( Y: ), that uses the Average Information algorithm for restricted maximum-likelihood estimation of the variance components. The goal of DAIRRy-BLUP is to enable the analysis of large-scale data sets to provide more accurate estimates of marker effects and breeding values. A distributed-memory framework is required since the dimensionality of the problem, determined by the number of SNP markers, can become too large to be analyzed by a single computing node. Initial results show that DAIRRy-BLUP enables the analysis of very large-scale data sets (up to 1,000,000 individuals and 360,000 SNPs) and indicate that increasing the number of phenotypic and genotypic records has a more significant effect on the prediction accuracy than increasing the density of SNP arrays.
authors.affiliation: Research Unit Knowledge-based Systems KERMIT, Department of Mathematical Modelling, Statistics and Bioinformatics, Ghent University, B-9000 Ghent, Belgium arne.deconinck@ugent.be.
authors.forename: Arne
authors.initials: A
authors.lastname: De Coninck
authors.affiliation: IBCN, Internet Based Communication Networks and Services Research Unit Department of Information Technology, Ghent University-iMinds, B-9000 Ghent, Belgium.
authors.forename: Jan
authors.initials: J
authors.lastname: Fostier
authors.affiliation: Progeno, B-9052 Zwijnaarde, Belgium.
authors.forename: Steven
authors.initials: S
authors.lastname: Maenhout
authors.affiliation: Research Unit Knowledge-based Systems KERMIT, Department of Mathematical Modelling, Statistics and Bioinformatics, Ghent University, B-9000 Ghent, Belgium.
authors.forename: Bernard
authors.initials: B
authors.lastname: De Baets
mesh_terms: D000465:Algorithms; D000818:Animals; D001947:Breeding; D023281:Genomics / Q000379:methods*; D005838:Genotype; D020641:Polymorphism, Single Nucleotide / Q000235:genetics; D040641:Quantitative Trait Loci / Q000235:genetics
keywords: distributed-memory architecture; genomic prediction; high-performance computing; simulated data; variance component estimation
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