BIMBAM (Bayesian IMputation-Based Association Mapping)  video
Description Description: BIMBAM is a program for the analysis of association studies, designed to allow un-typed variants to be more effectively and directly tested for association with a phenotype. The idea is to combine knowledge on patterns of correlation among SNPs (e.g., from the International HapMap project or resequencing data in a candidate region of interest) with genotype data at tag SNPs collected on a phenotyped study sample, to estimate (“impute”) unmeasured genotypes, and then assess association between the phenotype and these estimated genotypes. Compared with standard single-SNP tests, this approach results in increased power to detect association, even in cases in which the causal variant is typed, with the greatest gain occurring when multiple causal variants are present. It also provides more interpretable explanations for observed associations, including assessing, for each SNP, the strength of the evidence that it (rather than another correlated SNP) is causal.
Authors Bertrand Servin and Matthew Stephens
References Servin, B and Stephens, M (2007). Imputation-based analysis of association studies: candidate genes and quantitative traits. PLoS Genetics. 2007 Jul 27; 3(7):e114. Epub 2007 May 30.