PT - JOURNAL ARTICLE
AU - Coninck, Arne De
AU - Fostier, Jan
AU - Maenhout, Steven
AU - De Baets, Bernard
TI - DAIRRy-BLUP: A High-Performance Computing Approach to Genomic Prediction
DP - 2014 Jul 01
TA - Genetics
PG - 813--822
VI - 197
IP - 3
4099 - http://www.genetics.org/content/197/3/813.short
4100 - http://www.genetics.org/content/197/3/813.full
SO - Genetics2014 Jul 01; 197
AB - 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 regressionâ€“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.