Imputing with BC|SNPmax

Using reference haplotypes from the same population, for example Hapmap or the 1000 Genomes Project (1), it is possible to impute (2) genotypes at markers you do not have in your original data. Imputing increases power to detect associations, as imputed SNPs may be better surrogates for the causal variant, i.e., denser data is likely to contain a better tag-SNP for the corresponding ancestral haplotype, or the causal variant may even be one of the imputed SNPs. Imputing can also be used to combine genome-wide data made on different platforms and reliably solve strand-orientation issues when combining datasets.

BC|SNPmax data management and analysis platform now includes workflows to easily impute more genotypes. BC|SNPmax queue system and automated job segmentation enables computationally effective parallel imputation of genotypes using IMPUTE V2(3) or MaCH(5), utilizing a large number of calculation nodes. The imputed genotypes can be saved back into the database and further analyzed, using for example PLINK(4), SNPTEST(6),MaCH2QTL(7), Eigenstrat(8) and ProbABEL(9) , for which BC|SNPmax provides user-friendly web browser interfaces and result delivery work-flows.

Imputing as a service

BC Platforms also offers imputing as a service, please contact us for more information.

References

(1) The 1000 Genomes Project
(2) Marchini J. et al. 2007, A new multipoint method for genome-wide association studies by imputation of genotypes, Nature Genetics 39:906-13
(3) IMPUTE home page
(4) Purcell S. et al. 2007, PLINK: a tool set for whole-genome association and population-based linkage analysis, American Journal of Human Genetics 81:559-75
(5), (7) MaCH home page
(6) SNPTEST home page
(8) Price A. et al. 2006, Principal components analysis corrects for stratification in genome-wide association studies, Nature Genetics 38:904-9
(9) ProbABEL home page