Enhancing the usability and performance of structured association mapping algorithms using automation, parallelization, and visualization in the GenAMap software system

To make structured association mapping more accessible to geneticists, we have developed an automatic processing system called Auto-SAM. Auto-SAM enables geneticists to run structured association mapping algorithms automatically, using parallelization. Auto-SAM includes algorithms to discover gene-networks and find population structure. Auto-SAM can also run popular association mapping algorithms, in addition to five structured association mapping algorithms.Auto-SAM is available through GenAMap, a front-end desktop visualization tool. GenAMap and Auto-SAM are implemented in JAVA; binaries for GenAMap can be downloaded from http://sailing.cs.cmu.edu/genamap webcite.High-throughput technology has resulted in an explosion of biological data including gene expression and SNP data for a growing number of organisms. In order to understand the biological/medical implications and mechanistic insights behind such ever-growing amounts of data, biologists have relied on advances in statistical learning and inference technology to give them the tools they need to elucidate relationships between genes, genomic mutations, and phenotypic traits. The need for powerful analytic tools is especially pertinent in genetic association mapping. In a genetic association mapping study, millions of genomic markers, usually single-nucleotide polymorphisms (SNPs), are collected for a cohort of patients. In addition to the vast amount of genomic data, gene expression data for thousands of genes and trait measurement data for hundreds of clinical traits are also collected. The genetics analyst must explore this vast amount of complex, structured data to find SNPs that are associated with genes or traits of interest. Many successful association studies have been performed and provided insight into a variety of human diseases [1-4].Despite the success of association mapping in uncovering SNPs associated with disease, traditional genome wide association studies (GWAS) that look for associations be