Table 3.

Main effect of sequence variants on lung cancer risk estimated by hierarchical models

Case, nControl, nConventional 1,* OR (95% CI)Conventional 2, OR (95% CI)Pathway HM (EB), OR (95% CI)SIFT HM (EB),§ OR (95% CI)SIFT HM (SB), OR (95% CI)
APEX 148E, 51H1,1441,5180.85 (0.75-0.98)0.90 (0.76-1.06)0.95 (0.81-1.10)0.95 (0.81-1.11)0.93 (0.79-1.08)
LIG3 250T1,2321,5231.10 (0.96-1.27)1.13 (0.95-1.34)1.13 (0.97-1.32)1.13 (0.97-1.32)1.13 (0.96-1.33)
OGG1 326C/326C84631.57 (1.14-2.17)2.03 (1.39-2.95)1.40 (1.03-1.90)1.45 (1.05-2.00)1.59 (1.17-2.18)
PARP 762A5006680.98 (0.86-1.12)1.03 (0.88-1.21)1.05 (0.91-1.21)1.04 (0.90-1.21)1.04 (0.89-1.21)
XRCC1 194R, 280H, 399Q1,5912,0420.60 (0.28-1.25)0.49 (0.20-1.19)0.97 (0.64-1.45)0.98 (0.63-1.51)0.97 (0.62-1.50)
LIG1 7T- int9G1551851.19 (1.02-1.38)1.19 (0.99-1.42)1.17 (0.99-1.37)1.16 (0.99-1.37)1.17 (0.99-1.39)
ERCC1 8092A, 354C1091.90 (1.02-3.56)1.34 (0.63-2.86)1.06 (0.75-1.51)1.09 (0.75-1.57)1.11 (0.74-1.68)
XPA 23A9591,2151.00 (0.89-1.14)1.03 (0.88-1.19)1.02 (0.89-1.17)1.02 (0.89-1.17)1.03 (0.89-1.18)
XPC 939Q1,0391,3131.04 (0.91-1.18)1.02 (0.88-1.19)1.02 (0.89-1.17)1.02 (0.88-1.17)1.02 (0.88-1.18)
XPD 312N1,0221,3151.01 (0.89-1.15)0.99 (0.81-1.22)0.99 (0.84-1.16)0.99 (0.84-1.17)0.99 (0.83-1.19)
XPD 751Q1,0261,3320.98 (0.86-1.11)0.96 (0.78-1.18)0.97 (0.83-1.14)0.97 (0.82-1.14)0.97 (0.81-1.16)
XPF 379S, 415Q2112850.97 (0.81-1.16)0.95 (0.77-1.18)0.97 (0.81-1.16)0.98 (0.81-1.19)0.97 (0.80-1.19)
XPG 1104H1,5211,9501.05 (0.79-1.40)0.95 (0.68-1.33)0.98 (0.78-1.24)0.98 (0.77-1.24)0.97 (0.74-1.28)
MGMT 171T, 84F4616140.98 (0.86-1.12)0.95 (0.81-1.12)0.97 (0.84-1.13)0.97 (0.83-1.12)0.96 (0.82-1.12)
MGMT 143V, 178R3734121.09 (0.94-1.26)1.23 (1.03-1.47)1.18 (1.00-1.39)1.18 (1.01-1.40)1.20 (1.02-1.43)
MLH1 219V8271,0780.99 (0.88-1.12)1.02 (0.88-1.18)1.02 (0.89-1.17)1.02 (0.89-1.18)1.02 (0.89-1.18)
XRCC2 188H1792151.02 (0.84-1.23)1.08 (0.85-1.36)1.12 (0.91-1.36)1.11 (0.91-1.36)1.10 (0.89-1.37)
XRCC3 241T1,4291,7971.11 (0.91-1.35)1.18 (0.94-1.49)1.18 (0.97-1.44)1.19 (0.97-1.45)1.19 (0.96-1.47)
XRCC4 298N3474511.04 (0.90-1.21)1.02 (0.85-1.22)1.06 (0.90-1.25)1.06 (0.90-1.25)1.05 (0.88-1.24)
CHEK2 157I1,5621,9452.26 (1.57-3.25)2.15 (1.44-3.23)1.55 (1.13-2.13)1.58 (1.14-2.17)1.72 (1.24-2.39)
CCND1 870A1,1611,4580.98 (0.86-1.13)1.02 (0.87-1.20)1.04 (0.89-1.20)1.03 (0.89-1.20)1.03 (0.88-1.20)
MDM2 162G1872411.07 (0.88-1.29)0.96 (0.76-1.21)1.01 (0.84-1.22)1.01 (0.83-1.22)0.99 (0.80-1.22)
MDM2 309G9551,2050.96 (0.85-1.09)1.01 (0.87-1.18)1.03 (0.90-1.18)1.02 (0.89-1.17)1.02 (0.88-1.18)
MMP1 insG1,1601,4581.02 (0.89-1.17)1.09 (0.92-1.28)1.09 (0.94-1.26)1.09 (0.94-1.26)1.09 (0.93-1.27)
CDKN2A 148T951141.03 (0.79-1.35)0.98 (0.71-1.35)1.04 (0.83-1.31)1.06 (0.83-1.35)1.04 (0.80-1.37)
CDKN1A 31R2432971.16 (0.97-1.39)1.10 (0.89-1.35)1.10 (0.92-1.31)1.10 (0.92-1.32)1.11 (0.91-1.34)
TP53 Int3A2 -72P39241.83 (1.14-2.92)1.96 (1.10-3.51)1.26 (0.90-1.77)1.29 (0.91-1.83)1.38 (0.95-2.00)
P73 14T4285161.07 (0.93-1.23)1.08 (0.92-1.28)1.09 (0.94-1.27)1.09 (0.94-1.27)1.09 (0.93-1.28)
Estimated τ20.01880.0208Set to 0.05
Pathway estimation
    BER1.10 (0.95-1.28)1.09 (0.93-1.29)1.09 (0.86-1.37)
    NER1.01 (0.89-1.14)1.00 (0.87-1.14)0.99 (0.82-1.20)
    MMR/DR1.05 (0.88-1.26)1.03 (0.83-1.28)1.02 (0.76-1.38)
    DSB1.18 (0.98-1.42)1.12 (0.83-1.52)1.12 (0.74-1.68)
    Cell cycle1.10 (0.97-1.24)1.09 (0.96-1.24)1.11 (0.93-1.32)
    SIFT1.06 (0.80-1.40)1.09 (0.75-1.60)
  • * Logistic regression with single marker in each regression.

  • Logistic regression with all markers in one single model.

  • Hierarchical modeling with empirical Bayes approach and pathway indicators only.

  • § Hierarchical modeling with the empirical Bayes approach, pathway indicators and a SIFT covariate.

  • Hierarchal modeling with semi-Bayes approach, pathway indicators and a SIFT covariate.