It is important to note that genotype imputation is far more powerful than other common forms of imputation in the behavioral sciences. Typically, imputation techniques in the behavioral sciences, like multiple imputation or full information maximum likelihood, are meant to allow more accurate estimation of sampling error and statistical effects, but in the end one has no more information about a variable or dataset than was initially present. In genotype imputation, one obtains vast amounts of additional genetic information by leveraging a reference panel of additional individuals whose genomes are extensively measured through whole genome sequencing. The largest reference panel as of this writing contains 33,000 individuals and nearly 45 million variants with minor allele count ≥ 5 (Haplotype Reference Consortium, http://haplotype-reference-consortium.org); through genotype imputation this resource provides vast additional information about the genotypes in one's own dataset. In the 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
