Best Way to Identify Multiple QTL in F1 (Outbred, 4-way Phase Known)

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Joshua Havill

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Mar 13, 2021, 12:46:46 PMMar 13
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Hi Karl et al.

Following the various tutorials, I came to realize that 4-way crosses were limited to standard interval mapping (CIM and MQM won't work with 4-way crosses).

I detect what appears to be several QTL on a single linkage group but I'm having trouble trying to decipher the next steps to control for the 1 major QTL to decipher whether the other QTL are still significant.

Would the best approach be to use the scantwo() to identify interacting loci?

Pasted below is a picture from the scanone() output (in red) and the addpair() output using the original QTL as the formula (in black). I'm unsure though whether this was the appropriate approach though.

Cheers,
Josh

QTL_LG4.jpeg

Karl Broman

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Mar 14, 2021, 4:28:39 PMMar 14
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I think makeqtl/fitqtl/addqtl/etc work for “4way” crosses, as well as stepwiseqtl().

karl

havi...@umn.edu

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Mar 14, 2021, 5:35:18 PMMar 14
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Hi Karl,

I guess my question then is, what is the appropriate method for using addqtl()?

I fit a singular qtl using fitqtl() for the largest peak, then restricted the addqtl() to only LG 4 where the QTL was detected using that QTL and the formula as “binary ~ Q1”.

If the goal was to detect additional QTL, the manual describes leaving out the QTL of interest on the restricted chromosome but an error is thrown because no QTL is included.

-------
Joshua Havill

On Mar 14, 2021, at 3:28 PM, Karl Broman <kbr...@gmail.com> wrote:

I think makeqtl/fitqtl/addqtl/etc work for “4way” crosses, as well as stepwiseqtl().
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Joshua Havill

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Mar 15, 2021, 12:30:02 AMMar 15
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Hi Karl,

As a follow-up, I re-ran the analysis with scantwo() using a small number of permutations restricted to the linkage group that originally exhibited multiple QTL.

The output suggests there are two QTL, but I want to make sure that I am interpreting the output correctly. There is strong evidence for two two significant QTL, with non-significant interactions between loci, but a demonstrable additive effect of genotype on the phenotype? There also appears to be evidence for epistatic effects between loci on the phenotype of interest?

summary(pm.scan, perms = perm.a, pvalue = T)
                                 pos1f pos2f lod.full pval lod.fv1 pval lod.int pval     pos1a pos2a lod.add pval lod.av1 pval
com_04:com_04   100     156     24.2     0      10.7     0       3.52    1         100      156      20.7      0      7.16      0

If this is the case, is the plot for the plotLodProfile the correct plot to use to demonstrate that the third "QTL" is spurious or do I just expand upon the analysis in the text to describe the subsequent analyses taken to tease apart these relationships?

Thanks,
Josh

Karl Broman

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Mar 15, 2021, 10:41:18 AMMar 15
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"If this is the case, is the plot for the plotLodProfile the correct plot to use to demonstrate that the third "QTL" is spurious or do I just expand upon the analysis in the text to describe the subsequent analyses taken to tease apart these relationships?"

I would use makeqtl() to create a qtl object with the two QTL on the chromosome, and then addqtl() to look for evidence to add a third QTL.

karl

Joshua Havill

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Mar 15, 2021, 2:23:13 PMMar 15
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Hi Karl,

Can you specify how you would write the model to search for additional QTL after fitting just a single one using fitqtl() with the largest QTl effect fitted first?

qtl <- makeqtl(cross, chr = c(LG), pos = c(POS), what = c("prob"))
fit <- fitqtl(cross, pheno.col = "binary", qtl, formula = binary ~ Q1, get.ests = TRUE, method = "hk", model = "binary")
add <- addqtl(cross, pheno.col = "binary", qtl = qtl, formula = binary ~ Q1, method = "hk", model = "binary")

Thanks,
Josh





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Karl Broman

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Mar 15, 2021, 4:30:35 PMMar 15
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Take a look at the tutorial on multiple-QTL models, https://rqtl.org/tutorials/new_multiqtl.pdf
In particular, the section on addqtl starting on page 6.
Alternatively, look at chapter 9 of the R/qtl book, and particularly section 9.3.4 starting on page 267.

In that example, the call to addqtl looks like this:

    out.aq <- addqtl(hyper, qtl=rqtl, formula=y~Q1+Q2+Q3*Q4)

The rqtl object has 4 QTL, and we're considering a model where the 3rd and 4th QTLs interact; that's why the formula is included.
The result of addqtl() will be similar to results of scanone(), but at each position we fit a five QTL model: the four QTL in the rqtl object
(with the 3rd and 4th interacting) plus an additional QTL at sliding position across the genome.

karl

Joshua Havill

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Mar 16, 2021, 2:10:25 PMMar 16
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Hi Karl,

I've figured out the multiple QTL part with regards to specifying the model, etc. but now I'm trying to interpret the resulting LOD profile after (using the plotLodProfile()). These results are supported by scanone() and scantwo() results.

Two_qtls.jpeg
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