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Table 2 Summary of linear regression fit of p-values between imputed (Y) and actual (X)
SNPs for non-matched scenario
LD group
Reg. Statistics
Mean
Sta. Dev
Min
Max
R 2
0.8401
0.0233
0.7895
0.8803
1
Intercept
0.0338
0.0057
0.0223
0.0474
Slope
0.9204
0.0176
0.8831
0.9535
R 2
0.7228
0.0451
0.6159
0.8143
[0.9-1.0)
Intercept
0.0625
0.0126
0.0367
0.0912
Slope
0.8499
0.0374
0.7672
0.9281
R 2
0.4878
0.061
0.3286
0.6091
[0.8-0.9)
Intercept
0.1182
0.0203
0.0713
0.1782
Slope
0.6975
0.0568
0.5094
0.7935
R 2
0.2946
0.0756
0.1449
0.4358
[0.7-0.8)
Intercept
0.1782
0.0331
0.1164
0.2688
Slope
0.5517
0.083
0.3683
0.7181
R 2
0.2057
0.0836
0.0288
0.4487
[0.6-0.7)
Intercept
0.2332
0.0428
0.15
0.3481
Slope
0.4542
0.1081
0.1729
0.7369
R 2
0.0707
0.0487
-0.0042
0.2084
[0.5-0.6)
Intercept
0.2853
0.0417
0.1992
0.3761
Slope
0.2748
0.1132
0.0561
0.6046
R 2
0.0076
0.0121
-0.0029
0.0411
[0,0.5)
Intercept
0.3486
0.0278
0.299
0.4203
Slope
0.0837
0.0683
-0.0139
0.2604
R 2
0.5179
0.0421
0.444
0.5968
Over All
Intercept
0.1126
0.0101
0.094
0.1384
Slope
0.7236
0.0334
0.6454
0.7893
The gain in imputation accuracy under the second scenario, when there is a 2/3
match between the reference set and sample, also suggests that the conditional
probability P(X|A-B) in the Step 2 of the MiDCoP method should not be fixed but
should be adjusted to match with the sample data to be imputed.
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