next few years we anticipate reference panels of well over 100,000 individuals (200,000 haplotypes). Genotype imputation takes advantage of the fact that all individuals are related, if only slightly, and share short segments of their chromosomes. Imputation algorithms, typically hidden Markov models (Howie, Fuchsberger, Stephens, Marchini, & Abecasis, 2012), take advantage of this relatedness and probabilistically match chromosomes between the array-genotyped study sample and the sequence-based reference panel. When a match is found, the genotypes from the whole-genome-sequenced reference sample are imputed into the array-genotyped study sample. Imputed variants increase power to detect effects over tag SNPs alone, increase the precision with which an associated locus can be defined, and facilitate GWAS meta-analysis across studies by ensuring that all variants are measured or imputed in all studies (Li, Willer, Ding, Scheet, & Abecasis, 2010). Imputation (and the phasing required in order to carry out imputation) is now easier than ever, thanks to web services hosted at various academic institutions including the University of Michigan (https://imputationserver.sph.umich.edu). Users can upload their quality-controlled genotype files. Software on the server implements further quality checks, phases, imputes according to the reference panel of the user's choice, and makes imputed genotypes available for download.
