RT Journal Article SR Electronic T1 Speeding up eQTL scans in the BXD population using GPUs JF bioRxiv FD Cold Spring Harbor Laboratory SP 2020.06.22.153742 DO 10.1101/2020.06.22.153742 A1 Trotter, Chelsea A1 Kim, Hyeonju A1 Farage, Gregory A1 Prins, Pjotr A1 Williams, Robert W. A1 Broman, Karl W. A1 Sen, Ĺšaunak YR 2020 UL http://biorxiv.org/content/early/2020/06/22/2020.06.22.153742.abstract AB The BXD recombinant inbred strains of mice are an important reference population for systems biology and genetics that have been full sequenced and deeply phenotyped. To facilitate inter-active use of genotype-phenotype relations using many massive omics data sets for this and other segregating populations, we have developed new algorithms and code that enables near-real time whole genome QTL scans for up to 1 million traits. By using easily parallelizable operations including matrix multiplication, vectorized operations, and element-wise operations, we have decreased run-time to a few seconds for large transcriptome data sets. Our code is ideal for interactive web services, such as GeneNetwork.org. We used parallelization of different CPU threads as well as GPUs. We found that the speed advantage of GPUs is dependent on problem size and shape (number of cases, number of genotypes, number of traits). Our results provide a path for speeding up eQTL scans using linear mixed models (LMMs). Our implementation is in the Julia programming language.Competing Interest StatementThe authors have declared no competing interest.