Table 3.

Comparisons between models predicting sensation seeking from 1) only covariates; and 2) covariates and all SNPs meeting p < 0.05 and FDR < 0.10 in the association tests.

Model	SNPs	R2	ΔR2	−2LL	df	p	AIC	BIC
1. Covariates only	0	27.9%		4113.0	8		4129.0	4164.7
2. All significant SNPs	12	31.7%	3.9%	4027.5	20	< 4 × 10−13	4067.5	4156.5

Note. SNPs = number of single nucleotide polymorphisms included in the model; R² = proportion of variance in the dependent variable explained by the covariates and any SNPs included in that regression model; ΔR² = additional variance explained by adding SNPs (model 2) to the covariate model (model 1); −2LL = −2 times the log-likelihood of the regression model; df = degrees of freedom in the regression model; p = the p-value when comparing model 2 to model 1, estimated by the change in −2LL on a chi-square distribution with df equal to the difference in dfs between the models; AIC = Akaike’s Information Criterion, where lower values indicate better model fit to the data; BIC = Bayesian Information Criterion, where lower values indicate better model fit to the data.