CIM. n.marcovar, and sample size

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Oct 20, 2021, 7:12:25 AMOct 20
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Hi Karl,

cim() seems to give a different LOD value for a QTL peak as n.marcovar goes from 3 to 11 when the sample size is low(~100).
Based on your answer here ( and looking at the code, I am guessing cim() uses forward selection, is there a value in using exhaustive search just for QTL peaks that pass LOD=3 but do not pass the permutation-based threshold, especially when the sample size is low and you want to rescue small effect QTLs?


Karl Broman

Oct 20, 2021, 3:19:39 PMOct 20
to R/qtl discussion
The value of n.marcovar can make a big difference even for much larger sample sizes.
And yes cim() uses forward selection, as mentioned in its help page:

"We first use fill.geno to impute any missing marker genotype data, either via a simple random imputation or using the Viterbi algorithm.

"We then perform forward selection to a fixed number of markers. These will be used (again, with any missing data filled in) as covariates in the subsequent genome scan."

Personally, I don't think the search algorithm is as important as the criterion for comparing models of different sizes. See, for example, Broman and Speed (2002)


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