I have 822K variants in 12 samples (which are separated into 2 groups) to detect selective adaption using bayescan. All parameters I choose were default except for pr_odds. I set pr_odds to 100.

The results I get are weird. The q values are pretty high (like 0.9) when p values are very small (less than 0.01). And the smallest q is only 0.27, which is higher than normally expected, right?

Here are the top 10 smallest p values results:

prop    log10(PO)   qval    alpha   fst
0.0036007   -2.442  0.98906 8.68E-04    0.11363
0.0038008   -2.4185 0.98906 -7.29E-04   0.11348
0.0038008   -2.4185 0.98906 -9.46E-04   0.11346
0.0040008   -2.3961 0.98906 -1.56E-03   0.11337
0.0040008   -2.3961 0.98906 -8.98E-04   0.11342
0.0040008   -2.3961 0.98906 -6.81E-04   0.11349
0.0042008   -2.3748 0.98906 -4.37E-04   0.11348
0.0042008   -2.3748 0.98906 1.44E-03    0.11368
0.0042008   -2.3748 0.98906 -6.05E-04   0.11349
0.0042008   -2.3748 0.98906 5.63E-04    0.1136

You can see the q values are very high.

Else, here are the top 10 smallest q value results,

prop    log10(PO)   qval    alpha   fst
0.72735 0.42613 0.27265 1.5232  0.3536
0.72414 0.41914 0.27425 1.5163  0.35244
0.72394 0.41871 0.27485 1.5188  0.35296
0.72374 0.41827 0.27521 1.4988  0.34917
0.72334 0.41741 0.2755  1.4995  0.34929
0.72234 0.41524 0.27586 1.4986  0.34924
0.71994 0.41005 0.27646 1.4917  0.34802
0.71874 0.40747 0.27706 1.5086  0.3515
0.71754 0.4049  0.27766 1.4945  0.34894
0.71734 0.40447 0.27856 1.4936  0.34871

No q value is less than 0.05, so as prop either.

Could anyone explain this situation? Many thanks.