High performance solutions for big-data GWAS
We consider mixed-models based genome-wide association studies of large scale. We address GWAS with only a single trait and with many traits. GWAS with arbitrarily large populations, and of arbitrarily many SNPs and traits are enabled. Distributed memory architectures, such as Cloud, clusters, and supercomputers are used. Scalability with respect to problem size and resources is demonstrated.
mixed-models genome-wide association study distributed memory generalized least squares big data omics