HIBLUP: an integration of statistical models on the BLUP framework for efficient genetic evaluation using big genomic data.

Both human diseases and agricultural traits can be predicted by incorporating phenotypic observations and a relationship matrix among individuals in a linear mixed model. Due to the great demand for processing massive data of genotyped individuals, the existing algorithms that require several repetitions of inverse computing on increasingly big dense matrices (e.g. the relationship matrix and the coefficient matrix of mixed model equations) have encountered a bottleneck. Here, we presented a software tool named 'HIBLUP' to address the challenges. Powered by our advanced algorithms (e.g. HE + PCG), elaborate design and efficient programming, HIBLUP can successfully avoid the inverse computing for any big matrix and compute fastest under the lowest memory, which makes it very promising for genetic evaluation using big genomic data.