Multiple QTL mapping in r/qtl2

[Sylvia Durkin]

Hi Karl, 

Question and initial response pasted below (exchange started over email), with my follow-up to the response.

Original question: I am interested in doing multiple QTL mapping using r/qtl2, or something similar to the r/qtl function scantwo. To my knowledge, this functionality is not built in r/qtl2, and I am concerned about building my cross object in r/qtl as opposed to r/qtl2 because it is an F3 design.

My question is: Is multiple QTL mapping in an F3 cross design something that would be ill-advised, or would designing the cross object in r/qtl2, converting it to be functional in r/qtl, and carrying out MQM in r/qtl be ok? 

All the best, Sylvia

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Response: Currently, R/qtl2 has only the single-qtl method scan1 for mapping, though you could include additional QTL as covariates.

How is it that you want to handle the F3 population that couldn’t be done in R/qtl1? Is it that you want to fit a linear mixed model to account for population structure?

Karl

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Including the genotype of significant QTL as covariates would make sense! Would that look like specifying the genotype of each significant QTL as individual additive covariates when calling scan1?

For the second point, My main reason for not using R/qtl1 was R/qtl1 not being recommended for F3 cross designs specifically, whereas R/qtl2 has options to specify the cross generation. I was looking at using the mqm functions of R/qtl1, but was not sure my data were suitable to be processed with R/qtl1. Hopefully that makes sense!

Thanks so much!

Sylvia

[Karl Broman]

Unless you have particularly sparse marker data, I think treating the F3 cross as if it were an F2 will work fine.

And so I'd lean towards using R/qtl1, given your interest in fitting multiple-QTL models.

The main advantage of R/qtl2 for these data would be to account for population structure, for example if your F3 data included sets of sibships.

To include multiple QTL as covariates in scan1() in R/qtl2, I would use pull_genoprobpos() to pull out the genotype probabilities for a given QTL and then cbind() those together (dropping one column per QTL since they sum to 1), and then passing that to scan1() with the addcovar argument.

karl

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[Sylvia Durkin]

This is super helpful - thank you!!

There are sets of siblings included in the F3 data, so that may be an issue for the R/qtl1 route...but marker density should be perfectly fine.

I'll try both options!