11.3 Running lm_lods example and sample output

Under the subdirectory `Lodscores/', run the lm_lods example on the phenotypic trait by typing:

lm_lods ped73_lods_ph.par

The main results of interest from lm_lods are the LOD scores which are given at the end of the output for each component (connected pedigree) at each position requested.

Since there are 73 individuals in the pedigree, it will take a while to finish. But the LOD scores from this example look like this (some outputs omitted to save space):

ESTIMATED LOD SCORES Component 1 The largest eigenvalue : 1.86626 The second largest eigenvalue : 1.57587 Cumulative from left : 2.21620 Cumulative from right : 0.45122 LodScore estimates: Trait pos # position (Haldane cM) or marker male female eigen left right 1 -115.129 -115.129 0.04481 0.00015 -0.34546 2 -80.472 -80.472 0.13091 -0.00410 -0.34971 3 -45.815 -45.815 0.28046 -0.05817 -0.40378 4 -17.834 -17.834 0.43549 -0.18552 -0.53113 5 -5.268 -5.268 0.83469 -0.13949 -0.48510 marker-1 0.000 0.000 NA NA NA 6 3.000 3.000 1.33851 0.00175 -0.34386 7 7.000 7.000 1.68532 0.05630 -0.28931 marker-2 10.000 10.000 NA NA NA 8 13.000 13.000 2.30193 0.17720 -0.16841 9 17.000 17.000 2.58626 0.23244 -0.11317 marker-3 20.000 20.000 NA NA NA 10 23.000 23.000 3.46676 0.62422 0.27861 11 27.000 27.000 3.95936 0.78769 0.44208 marker-4 30.000 30.000 NA NA NA 12 33.000 33.000 5.05419 0.97612 0.63051 13 37.000 37.000 5.42606 1.08006 0.73445 marker-5 40.000 40.000 NA NA NA 14 43.000 43.000 6.14399 1.17323 0.82762 15 47.000 47.000 6.39609 1.23478 0.88917 marker-6 50.000 50.000 NA NA NA 16 53.000 53.000 5.68067 1.06031 0.71470 17 57.000 57.000 5.37402 0.98868 0.64307 marker-7 60.000 60.000 NA NA NA 18 63.000 63.000 4.32190 1.05923 0.71362 19 67.000 67.000 3.91308 1.05841 0.71280 marker-8 70.000 70.000 NA NA NA 20 73.000 73.000 3.35744 1.01417 0.66856 21 77.000 77.000 3.07940 1.04257 0.69696 marker-9 80.000 80.000 NA NA NA 22 83.000 83.000 2.96763 1.33124 0.98563 23 87.000 87.000 2.79748 1.45101 1.10540 marker-10 90.000 90.000 NA NA NA 24 95.268 95.268 1.78970 1.13057 0.78496 25 107.834 107.834 1.13899 0.95320 0.60759 26 135.815 135.815 0.41807 0.59170 0.24609 27 170.472 170.472 0.10067 0.43978 0.09417 28 205.129 205.129 0.01432 0.39120 0.04559

As we mentioned earlier, there are three methods to combine the likelihood ratios (for each test position over the position to the left, and over the position to the right): the eigenvalue method, simple averaging starting from the left, and simple averaging starting from the right.

The largest real eigenvalue should, in theory, be equal to 2.0 and the eigenvector corresponding to the largest real eigenvalue is given as the LOD scores. However, when the second largest eigenvalue is very close to the largest one, the eigenvector can be very unstable and sometimes gives very bad LOD scores. When that happens, the "left" and "right" method, though, simpler, actually perform better.

The "Cumulative from left" and "Cumulative from right" values should, ideally, be one (their product is always one). Usually they are not and one can see that the LOD scores differ a lot for these three methods. This was a very short MCMC run. For longer runs, the LOD scores can be more consistent for the three methods. Nevertheless, lm_lods is now giving way to our newer method, lm_bayes.