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8.3 Sample lm_lods output

Run the lm_lods example by typing:

 
lm_lods par15_lods

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.

The LOD scores from this example look like this (some outputs omitted to save space):

 
ESTIMATED LOD SCORES
                                                                                
 Component   1
                                                                                
   The largest eigenvalue        :  2.12703
                                                                                
   The second largest eigenvalue :  1.96799
                                                                                
   Cumulative from left          :  0.12331
                                                                                
   Cumulative from right         :  8.10940
                                                                                
LodScore estimates:
                                                                                
Trait pos #     position (Haldane cM)
  or marker        male     female         eigen       left      right
                                                                                
          1    -115.129   -115.129       0.16014   -0.02971    0.87928
          2     -60.199    -60.199       0.38449   -0.13656    0.77243
          3     -34.657    -34.657       0.58356   -0.14461    0.76438
          4     -17.834    -17.834       0.90068   -0.04843    0.86056
          5      -5.268     -5.268       1.53650    0.15850    1.06749
   marker-1       0.000      0.000            NA         NA         NA
          6       5.108      5.108       2.00899    0.33920    1.24819
          7      12.771     12.771       2.07100    0.30338    1.21237
          8      20.433     20.433       2.05423    0.31048    1.21947
   marker-2      25.541     25.541            NA         NA         NA
          9      30.650     30.650       1.61970    0.24335    1.15234
         10      38.312     38.312       1.32440    0.26820    1.17719
         11      45.974     45.974       1.13920    0.18546    1.09445
   marker-3      51.083     51.083            NA         NA         NA
         12      56.191     56.191       0.78887    0.12538    1.03436
         13      63.853     63.853       0.74783    0.17440    1.08339
         14      71.516     71.516       0.64426    0.11037    1.01936
   marker-4      76.624     76.624            NA         NA         NA
         15      81.732     81.732       0.64836    0.24527    1.15426
         16      89.394     89.394       0.39596    0.06815    0.97714
         17      97.057     97.057       0.33385    0.02844    0.93743
   marker-5     102.165    102.165            NA         NA         NA
         18     107.433    107.433       0.08793   -0.21787    0.69112
         19     119.999    119.999      -0.13217   -0.47354    0.43545
         20     136.822    136.822      -0.30042   -0.75551    0.15348
         21     162.364    162.364      -0.25966   -0.89959    0.00940
         22     217.294    217.294      -0.11343   -0.89276    0.01623

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.


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