Regional heritability advanced complex trait analysis for GPU and traditional parallel architectures

Motivation: Quantification of the contribution of genetic variation to phenotypic variation for complex traits becomes increasingly compu-tationally demanding with increasing numbers of single-nucleotide polymorphisms and individuals. To meet the challenges in making feasible large-scale studies, we present the REgional heritability advanced complex trait analysis software. Adapted from advanced complex trait analysis (and, in turn, genome-wide complex trait ana-lysis), it is tailored to exploit the parallelism present in modern trad-itional and graphics processing unit (GPU)-accelerated machines, from workstations to supercomputers. Results:We adapt the genetic relationship matrix estimation algorithm to remove limitations on memory, allowing the analysis of large data-sets. We build on this to develop a version of the code able to efficiently exploit GPU-accelerated systems for both the genetic relationship matrix and REstricted maximum likelihood (REML) parts of the analysis, offering substantial speedup over the traditional central processing unit version. We develop the ability to analyze multiple small regions of the genome across multiple compute nodes in parallel, following the ‘re-gional heritability ’ approach. We demonstrate the new software using 1024 GPUs in parallel on one of the world’s fastest supercomputers