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Table 4 Results for the simulation study for the case of having polymorphic CNVs

From: Bayesian model to detect phenotype-specific genes for copy number data

     

Bayesian Shared Model

    

Multinomial

Posterior

Normal

Posterior

 

# SNPs

χ 2

K-W

regression

Distribution

Approximation

Probability

moderate risk scenario (OR=2.0)

    

TPR

2000

48.50

0

52.25

75.25

74.25

75.50

TNR

2000

100.00

100

100.00

100.00

100.00

100.00

TPR

500

46.25

0

42.50

64.50

64.75

64.25

TNR

500

100.00

100

100.00

100.00

100.00

100.00

moderate risk scenario (OR=1.5)

    

TPR

2000

30.25

0

35.45

58.50

58.50

57.75

TNR

2000

100.00

100

100.00

99.98

99.99

99.97

TPR

500

20.50

0

23.25

44.25

44.25

44.50

TNR

500

99.99

100

99.99

99.96

99.96

99.94

low risk scenario (OR=1.2)

    

TPR

2000

0.70

0

0.70

20.25

20.25

20.75

TNR

2000

99.98

100

99.99

99.97

99.99

99.98

TPR

500

0.50

0

0.50

16.25

16.25

15.75

TNR

500

99.99

100

99.99

99.99

99.99

99.98

  1. Results for the simulation described in Simulation Studies Section for the case of having polymorphic CNVs with major allele frequency simulated from U(0.01, 0.1). The different scenarios are described in that section. We compare four different approaches: χ2 test, Kruskall-Wallis (K-W), Multinomial regression using likelihood ratio test, and our proposed Bayesian model. The comparison was based on computing the True Positive and Negative Rates, TPR and TNR respectively. Results are expressed in %.