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lm_lods
parameter file
Here is an annotated parameter file `par15_lods' for lm_lods
.
input pedigree file 'ped15' input seed file 'seed15' select all markers traits 1 map markers recomb fracts .2 .2 .2 .2 set markers 1 2 3 4 5 freqs .2 .2 .4 .2 set trait 1 freqs .95 .05 set marker data 5 333 1 3 1 3 1 3 1 3 1 3 331 3 4 3 4 3 4 3 4 3 4 334 2 3 2 3 2 3 2 3 2 3 431 3 4 3 4 3 4 3 4 3 4 531 3 3 3 3 3 3 3 3 3 3 |
These should already be familiar. The input pedigree file is `ped15', the JV pedigree. There are five equally-spaced markers and one trait. The marker and trait allele frequencies are given in the `set ... freqs' statements. `set marker data' specifies the marker data of observed individuals. The trait data is specified as coded genotype, see Sample lm_auto parameter file. Note that there is no need to give the position of the trait locus because we are going to calculate LOD scores for varied positions of the trait.
map trait 1 all interval proportions .2 .5 .8 map trait 1 external recomb fracts .05 .15 .25 .35 .45 |
The above two statements request at which trait locus positions the LOD scores should be calculated. Between two marker loci, the positions are specified by proportions, at 20, 50, and 80 percent of the interval (it can deal with gender--specific maps easily this way). Outside the marker map, the positions are specified explicitly in terms of recombination fractions with the nearest marker locus. Note that an external recombination fraction of 0.5 is not necessary since the likelihood of unlinked trait locus is always used as reference when computing the LOD scores.
sample by step |
The sampling is done by step, that is, each iteration updates one locus (L-sampler) or one meiosis (M-sampler) only. An alternative is sampling by scan where each iteration updates all the meiosis indicators, S.
set burn-in iterations 100 |
Do 100 iterations of burn-in, with the trait being unlinked.
set L-sampler probability 0.2 |
Using the L-sampler 20 percent of the time for the MCMC iterations seems to to be a good choice.
set MC iterations 200 |
Now, for each test position for the trait, do 200 MCMC iterations. This is for demonstration only. For real data analysis, use longer runs!
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