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BIMBAM 0.99

BIMBAM is a software for Bayesian Imputation Based Association Mapping.

 

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BIMBAM 0.99 facilities

Vendor
Vendor`s Webhttp://stephenslab.uchicago.edu
Stephens Lab webshot
OSWindows 98, Windows 2000, Windows XP, Windows Vista, Windows 7
Limitationsnot specified
Actualizedmore than year ago
Downloads697
LocalizedEnglish
LicenseOpen source

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BIMBAM manufacturer description

BIMBAM 0.99 implements methods for ``Bayesian IMputation-Based Association Mapping". It is suitable for single-SNP analyses of large studies (e.g. genome scans) and multi-SNP analyses of smaller studies (candidate regions or genes).

The software is written by Yongtao Guan, based on work from Scheet and Stephens (2006) and Servin and Stephens (2007). New findings regarding practical issues of imputation based association mappings are reported in Guan and Stephens (2008). Please send bug reports and requests for help to bimbam_help@googlegroups.com. Before sending a request check out http://groups.google.com/group/bimbam_help to see if someone else has already asked the same question. If you use BIMBAM Mathematics for imputation, please cite Guan and Stephens (2008) and Scheet and Stephens (2006). If you use BIMBAM download for association mapping, please cite Servin and Stephens (2007). If you use it for both imputation and association mapping, please cite all three papers.

To briefly explain the rationale for imputation-based methods, consider the ``tag-SNP" design for association studies, where SNPs are first identified (e.g. by resequencing) in a panel of individuals, and then a subset of these SNPs (``tags") are typed in the study sample. The imputation-based approach exploits the fact that tag SNPs are often good predictors for the other (non-tag) SNPs, to first ``impute" the genotypes of all individuals at all non-tag SNPs, and then assesses the strength of the association between the imputed genotypes and the phenotype. The idea is that this both improves power to detect associations, and interpretability of results (by assessing which SNPs, both tag and non-tag, are the best candidates for causally affecting the phenotype).

Imputation-based methods are also extremely helpful in combining data from multiple studies that have typed different SNPs in the same region (e.g. genome-wide scans using different genotyping platforms). Here, the idea is to use known patterns of correlation among the two sets of markers (e.g. from the HapMap data) to impute genotypes at all markers in all individuals, allowing the data from both studies to be used when assessing correlation between phenotype and each marker.
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Free download BIMBAM 0.99
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BIMBAM categories

mcmc, fastphase, bimbam

What is new in 0.99 changeinfo log

Support new input file formats, including inputting posterior genotype distributions and mean genotypes as input genotype files via gmode option. Support input files that are quantitative variables rather than genotypes, also via gmode option. This allows one use BIMBAM to do Bayesian variable selection using more general covariates (eg using gene expression values to predict phenotype). New data management functions, excluding SNPs according to missing proportion, minor allele frequency, whether SNPs have entries in position file. New options for writing mean genotypes and genotype distributions, so that one can choose to write cohort SNPs only, or write both cohort and panel SNPs.

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