Closed ttbek closed 1 year ago
Hello,
All the imputations were performed on either Michigan Imputation Server or TOPMed Imputation Server. I believe they implemented minimac4 version 1.0.0, released 2.14.2018.
Best, Quan
On Jan 19, 2023, at 5:53 AM, ttbek @.**@.>> wrote:
You don't often get email from @.**@.>. Learn why this is importanthttps://aka.ms/LearnAboutSenderIdentification
Which exact version of Minimac 4 was used for training and testing in the associated paper?
That is: https://doi.org/10.1016/j.ajhg.2022.09.009 Sun, Q., Yang, Y., Rosen, J. D., Jiang, M. Z., Chen, J., Liu, W., Wen, J., Raffield, L. M., Pace, R. G., Zhou, Y. H., Wright, F. A., Blackman, S. M., Bamshad, M. J., Gibson, R. L., Cutting, G. R., Knowles, M. R., Schrider, D. R., Fuchsberger, C., & Li, Y. (2022). MagicalRsq: Machine-learning-based genotype imputation quality calibration. American journal of human genetics, 109(11), 1986–1997. https://doi.org/10.1016/j.ajhg.2022.09.009
— Reply to this email directly, view it on GitHubhttps://github.com/quansun98/MagicalRsq/issues/1, or unsubscribehttps://github.com/notifications/unsubscribe-auth/AM74NIPWX6GAKYUN36FMDTTWTEMJZANCNFSM6AAAAAAUAFULNU. You are receiving this because you are subscribed to this thread.Message ID: @.***>
Thank you! I've been struggling with more recent versions of Minimac4, not sure if it's just me or not. Versions 4.1.1 and 4.1.2 segfault for me when using their Linux sh install script. When I compiled 4.1.1 myself, it runs but the imputation quality was bad... not sure if I have a bad compile or that is just how Minimac4 is now... Version 1.0.2 seems to work fine for me.
If there is any doubt, check the vcf header as Minimac adds a line with the version into the header, e.g.:
Which exact version of Minimac 4 was used for training and testing in the associated paper?
That is: https://doi.org/10.1016/j.ajhg.2022.09.009 Sun, Q., Yang, Y., Rosen, J. D., Jiang, M. Z., Chen, J., Liu, W., Wen, J., Raffield, L. M., Pace, R. G., Zhou, Y. H., Wright, F. A., Blackman, S. M., Bamshad, M. J., Gibson, R. L., Cutting, G. R., Knowles, M. R., Schrider, D. R., Fuchsberger, C., & Li, Y. (2022). MagicalRsq: Machine-learning-based genotype imputation quality calibration. American journal of human genetics, 109(11), 1986–1997. https://doi.org/10.1016/j.ajhg.2022.09.009