Abstract

One of the primary challenges in making sense the dramatic increase in human genotype data is finding suitable phenotype information for correlational analyses. While the price of genotyping has fallen dramatically and promises to continue to decrease, the cost of generating the phenotypes necessary to take advantage of this data has held steady or even increased. Until recently, human phenotype data was primarily derived from assays or measurements made in clinical or research laboratories. However, laboratory phenotyping is expensive and low-throughput. Recently, a variety of promising alternatives have arisen that can provide important new information at greatly reduced costs. However, the nature, extent and complexity of the data produced involve significant new computational challenges.