Table 2 Capacity of a polygenic risk ratio (PRR) summarizing the non-IOP-dependent GRS vs. IOP-dependent component GRS to predict POAG traits

From: GWAS-by-subtraction reveals an IOP-independent component of primary open angle glaucoma

Trait prediction

GRS

OR 1

95% CI 1

P-value 1

OR 2

95% CI 2

P-value 2

POAG vs. Control (7205 vs. 191710)a

 

Non-IOP-dependent component

1.22

1.20–1.25

2.00E−16

1.22

1.19–1.25

2.00E−16

 

IOP-dependent component

1.27

1.24–1.30

2.00E−16

1.25

1.22–1.28

2.00E−16

HTG vs. Control (4821 vs. 191710)b

 

Non-IOP-dependent component

1.30

1.26–1.34

2.00E−16

1.29

1.25–1.33

2.00E−16

 

IOP-dependent component

1.38

1.34–1.42

2.00E−16

1.35

1.32–1.39

2.00E−16

NTG vs. Control (2384 vs. 191710)c

 

Non-IOP-dependent component

1.08

1.04–1.13

1.27E−04

1.09

1.04–1.13

9.73E−05

 

IOP-dependent component

1.07

1.03–1.12

5.24E−04

1.07

1.03–1.12

5.66E−04

NTG vs. HTG (2384 vs. 4821)d

 

PRR

1.05

1.01–1.09

0.012

1.05

1.01–1.09

0.009

  1. PRR = GRS_non-IOP-dependent/GRS_IOP-dependent.
  2. OR1: Main analysis. The SNP effect size for the GRS was taken from the discovery analysis.
  3. OR2: Sensitivity analysis. The SNP effect size for the GRS was taken from validation analysis 3 (independent of UK Biobank).
  4. Logistic regression was used to derive the results, P is the two-sided p-value.
  5. aOutcome: POAG vs. Control; Model: outcome ~ Age + Sex + GRS_IOP-dependent + GRS_non-IOP-dependent.
  6. bOutcome: HTG vs. Control; Model: outcome ~ Age + Sex + GRS_IOP-dependent + GRS_non-IOP-dependent.
  7. cOutcome: NTG vs. Control; Model: outcome ~ Age + Sex + GRS_IOP-dependent + GRS_non-IOP-dependent.
  8. dOutcome: NTG vs. HTG; Model: outcome ~ Age + Sex + PRR + GRS_POAG.
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