The Michigan Imputation Server implements the whole-genotype imputation workflow using the MapReduce programming model for efficient parallelization of computationally intensive tasks. We use the open source framework Hadoop to implement all workflow steps. Maintenance of the server, including node configuration (for example, amount of parallel tasks, memory for each chunk, and monitoring of all nodes), is achieved using the Cloudera Manager. During cluster initialization, reference panels, genetic maps, and software packages are distributed across all cluster nodes using the Hadoop file system HDFS. The imputation workflow itself consists of two steps: first, we divide the data into non-overlapping chunks (here, chromosome segments of 20 Mb). Second, we run an analysis (here, quality control or phasing and imputation) in parallel across chunks. To avoid edge effects, 5 Mb for phasing and 500 kb for imputation are added to each chunk. Finally, all results are combined to generate an aggregate final output.
