More Mixed Model Methods!

Thanks to everyone for the great webcast yesterday. We had over 850 people register for the event and actually broke the record! Take that Bryce and Gabe!

If you would like to see the recording, view it at: Mixed Models: How to Effectively Account for Inbreeding and Population Structure in GWAS.

While preparing for this webcast, we chose to focus specifically on a few methods. But soon people in the community reached out and provided information on additional methods. (Thanks for sharing!) So here is a list of more mixed models if you want to dig in!

Methods We Covered:
EMMAX: Efficient Mixed-Model Association eXpedited (based on EMMA, which we didn’t cover)
Kang HM, Sul JH, Service SK, Zaitlen NA, Kong SY, Freimer NB, Sabatti C, Eskin E and

GBLUP: Genomic Best Linear Unbiased Prediction
VanRaden PM

MLMM: Multi-locus mixed model
Segura V, Vilhjálmsson B, Platt A, Korte A, Seren U, Long Q, Nordborg, M

Other Methods We Didn’t Cover:
Gilmour A

Bayes-A,B,C and Cπ
Habier D, Fernando R, Kizilkaya K, and Garrick D

Compressed MLM and P3D: Population Parameters Previously Determined
Zhang Z, Ersoz E, Lai CQ, Todhunter RJ, Tiwari HK, Gore MA, Bradbury PJ, Yu J, Arnett DK, Ordovas JM, and Buckler ES

FaST-LMM: FActored Spectrally Transformed Linear Mixed Models
Listgarten J, Lippert C, Kadie CM, Davidson RI, Eskin E, and Heckerman D

GENMIX: Genealogy based Mixed Model
Sahana G, Mailund T, Lund MS, and Guldbrandtsen B

GRAMMAR-Gamma (GenABEL R package)
Svishcheva G, Axenovich T, Belonogova N, van Duijn C, and Aulchenko Y

MASTOR: Mixed-model Association Score Test On Related individuals
Jakobsdottir J, McPeek MS

MixABEL package within GenABEL
Aulchenko YS, Ripke S, Isaacs A, and van Duijn CM

MTMM: Multi-Trait Mixed Model
Korte A, Vilhjálmsson B, Segura V, Platt A, Long Q, and Nordborg M

Cheng R, Abney M, Palmer AA and Skol AD

Unified Mixed Model in TASSEL
Yu J, Pressoir G, Briggs WH, Bi IV, Yamasaki M, Doebley JF, McMullen MD, Gaut BS, Nielsen MD, Holland JB, Kresovich S, and Buckler ES

What did I miss? I’m sure there are more out there – let me know in the comments!

Greta Linse Peterson

About Greta Linse Peterson

Greta Peterson is Golden Helix’s Director of Product Management and Quality and as such, Greta is quite busy. Her main duty is managing SNP & Variation Suite (SVS) from strategic planning to tactical activities, including defining market requirements and working with developers to ensure timely releases. On the "Quality" side, she is responsible for software testing, quality control, and maintaining technical documentation. In addition to those duties, Greta also manages Golden Helix's Technical Support team, writes Python scripts for extending SVS functionality, and conducts software training for customers and prospects. Greta joined Golden Helix in 2008 when she completed her Masters degree in both Mathematics and Statistics at Montana State University in Bozeman. When Greta is not working, she enjoys spending time with her family and hiking the surrounding areas of Bozeman.
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4 Responses to More Mixed Model Methods!

  1. Michael says:

    Dear Greta Linse Peterson!
    Could you answer me on a question. Is SVS7 package working with ILLUMINA files (for example, BovineSNP50)?
    Sincerely, Michael

    • Greta Peterson says:

      Dear Michael,
      Thank you for your question! Yes, if you have your data in a Final Report Text File format you can import that into SVS ( However, the preferred method is to install the Golden Helix GenomeStudio DSF Plug-in and export DSF files from GenomeStudio. ( Note, if you only have iDat files, or do not have access to the GenomeStudio project you will need to contact the person that provided those and request the final reports or ask them to install the DSF plug-in and send you the DSF files. If you have any questions about this process please feel free to contact and we would be more than happy to help you import your Final Report Text Files or DSF files into an SVS Project.

  2. Jiaren Zhang says:

    Dear Greta Linse Peterson,

    I have a question about the best step selection of MLMM model using SVS7 package. If there are several optimal steps according to different criteria, which step should i select?

    • Greta Peterson says:

      Dear Jiaren Zhang,

      Thank you for your question! The Segura 2012 paper recommends Extended BIC (Ext. BIC in SVS) or multiple-Bonferroni criterion (Bonferroni in SVS). They claimed that Ext. BIC was more stringent than Bonferroni. However, I found that it really depended on the model and the data itself, as sometimes Bonferroni was more stringent than Ext. BIC. I would look at Bonferroni, Mod. BIC and Ext. BIC and pick the first step that is optimal for any one of those criteria. I.e. the one that includes the fewest co-factors. If you had to pick just one criteria to use, I think the Ext. BIC is a good one to use, but I encourage you to read the Segura 2012 paper for their justification on these two criteria:

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