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. 2020 Feb 27;5(1):105–116. doi: 10.1089/can.2018.0015

Table 1.

Parameter Estimates (±Unconditional Standard Error) for Models of the Genetic Architecture of Cannabinoid Inheritance: Δ-9-Tetrahydrocannabinol

Model
AICC weight (wi)
Estimate (SE)
(1) de Meijer et al.2 Aa Ad Ca Med AaAa AdAd Mea
Aa 0.285 2.43 (1.27)          
Aa, Ad 0.255 3.28 (0.17) −1.25 (0.11)          
Ca 0.146     1.12 (0.78)        
Med 0.095       0.68 (0.61)      
AaAa 0.076         −1.71 (1.77)    
AdAd 0.045           −0.47 (0.93)  
Ad 0.038   −0.36 (1.33)          
Mea 0.036             −0.11 (0.68)
Variable importance -> 0.551 0.293 0.146 0.095 0.076 0.065 0.052
Model
AICC weight (wi)
Estimate (SE)
       
(2) Staginnus et al.29 Aa Ca AaAa        
Aa
0.442
44.57 (0.53)
 
 
       
Ca, AaAa
0.176
 
44.50 (0.08)
53.37 (0.92)
       
Ca, Aa
0.176
53.37 (0.92)
−8.86 (0.92)
 
       
Aa, AaAa
0.176
44.50 (0.08)
8.86 (0.92)
 
       
Variable importance -> 0.822 0.352 0.351        

Cannabinoid inheritance is described for THC based on data from either (1) de Meijer et al.2 or (2) Staginnus et al.29 and an assessment of variable importance within the model and the Akaike weight associated with each genetic effect. AIC corrected for finite sample size (AICC) weights were used to select the models for presentation when the minimum number of models whose weights sum to >95%.

AIC, Akaike information criterion; CBC, cannabichromene; CBD, cannabidiol; SE, standard error; THC, Δ-9-tetrahydrocannabinol.