11.5 Running lm_bayes examples and sample output

If you have been following the tutorial in order, you have altered the parameter file `ped73_ph.par' in order to reduce the number of MCMC iterations for running the lm_lods example. Since lm_bayes DOES NOT perform MCMC at each position, it is not advisable to reduce the number of iterations like we did in the lm_lods example. Before running the example for lm_bayes, ensure that the parameter file `ped73_ph.par' has the larger MC iterations option selected. The relevant section of your parameter file should look like this, with the `set MC iterations 300' commented out and the `set MC iterations 3000' not commented out:

#For actual analyses, recommended number of iterations is #on the order of 10^5 set MC iterations 3000 #1 check progress MC iterations 1000 set global MCMC #TO RUN LM_LODS, comment out the line marked #1 (above), #and uncomment the line marked #2 (below). This effectively #reduces the number of MC iterations #Recall that lm_lods and lm_shnell require less iterations #(order of 10^4) than other programs #set MC iterations 300

Under the subdirectory `Lodscores/', run the lm_bayes example by typing:

./lm_bayes ped73_ph.par

The results from lm_bayes are the LOD scores toward the end of the output. Two methods of computing the LOD scores are available: (1) count realizations of locations sampled to estimate the posterior probability (crude) and (2) Rao-Blackwellized estimator (R-B). The latter is the preferred method.

LodScore estimates: Trait pos # position (Haldane cM) pseudo freq LodScore or marker male female prior visited crude R-B 0 unlinked unlinked 0.020178 116 NA NA 1 -115.129 -115.129 0.020560 100 -0.0726 -0.0080 2 -80.472 -80.472 0.021170 143 0.0700 -0.0203 3 -45.815 -45.815 0.023718 165 0.0828 -0.0681 4 -17.834 -17.834 0.035627 122 -0.2250 -0.2402 5 -5.268 -5.268 0.060088 122 -0.4520 -0.4592 marker-1 0.000 0.000 NA NA NA NA 6 3.000 3.000 0.089923 127 -0.6097 -0.6299 7 7.000 7.000 0.098887 150 -0.5786 -0.6763 marker-2 10.000 10.000 NA NA NA NA 8 13.000 13.000 0.106325 102 -0.7776 -0.7095 9 17.000 17.000 0.105926 111 -0.7393 -0.7091 marker-3 20.000 20.000 NA NA NA NA 10 23.000 23.000 0.067969 93 -0.6234 -0.5206 11 27.000 27.000 0.043461 105 -0.3765 -0.3216 marker-4 30.000 30.000 NA NA NA NA 12 33.000 33.000 0.022509 118 -0.0401 -0.0447 13 37.000 37.000 0.014511 87 0.0182 0.1340 marker-5 40.000 40.000 NA NA NA NA 14 43.000 43.000 0.009146 157 0.4751 0.3324 15 47.000 47.000 0.007793 82 0.2625 0.4142 marker-6 50.000 50.000 NA NA NA NA 16 53.000 53.000 0.010758 101 0.2130 0.3376 17 57.000 57.000 0.017022 121 0.0922 0.1807 marker-7 60.000 60.000 NA NA NA NA 18 63.000 63.000 0.025113 117 -0.0913 -0.0129 19 67.000 67.000 0.027450 45 -0.5449 -0.1115 marker-8 70.000 70.000 NA NA NA NA 20 73.000 73.000 0.027063 83 -0.2729 -0.1503 21 77.000 77.000 0.026552 67 -0.3576 -0.1337 marker-9 80.000 80.000 NA NA NA NA 22 83.000 83.000 0.023659 96 -0.1513 -0.0742 23 87.000 87.000 0.019262 55 -0.3039 0.0179 marker-10 90.000 90.000 NA NA NA NA 24 95.268 95.268 0.012658 81 0.0465 0.2023 25 107.834 107.834 0.011441 87 0.1215 0.2475 26 135.815 135.815 0.014517 76 -0.0406 0.1441 27 170.472 170.472 0.017645 90 -0.0520 0.0588 28 205.129 205.129 0.019069 81 -0.1314 0.0248