When we have to use covariate or not for QTL analysis?

[amira ndi]

Dear Dr. Karl,

I have one trait (phenotype) and around ~1000 genotype data. I would like to make QTL analysis. However, I fell a little bit confused when I use covariate or not? I read about this on https://rqtl.org/tutorials/rqtltour.pdf However, I am still confused. How to get the data "fake.bc" or listeria in excel or .csv format?

Could you please help me?

I appreciate your help!

Best,

Amirandi

[Karl Broman]

The use of covariates is discussed in Chapter 7 of the R/qtl book (https://rqtl.org/book). "If a covariate has a large effect on the phenotype, its inclusion in the analysis will result in reduced residual variation and so will enhance our ability to detect QTL. It is also of interest to assess possible QTL × covariate interactions. For example, does a QTL have different effects in the two sexes?"

The fake.bc and listeria datasets are distributed with R/qtl. You can load them with data() and write them to a file with write.cross().

The listeria data is also provided in several formats at https://rqtl.org/sampledata

karl

[amira ndi]

Dear Dr. Karl,

I have tried to understand the covariate is but I still do not understand yet.

In my case, I have the data about the length of wing spot and wing length in butterfly's wing. Do I have to apply covariate or not for this case?

Please give me your ideas. 

Best,

Amirandi

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