Table 2.

Bivariate genetic analysis.

Model 1: Friends' alcohol use with quantity of alcohol use
Friends' alcohol use			Quantity of alcohol use			Correlations			Model fit statistics
a2	c2	e2	a2	c2	e2	rg	rc	re	−2LL	d.f.	AIC	Δχ2from saturated model	Δd.f. from saturated model
0.27*	0.37*	0.35*	0.61*	0.20*	0.19*	0.70*	0.91*	0.15	5770.204	3193	−615.796	15.871	20
(0.05–0.51)	(0.18–0.55)	(0.27–0.45)	(0.34–0.81)	(0.03–0.41)	(0.13–0.29)	(0.36–1.00)	(0.39–1.00)	(−0.08–0.36)

Model 2: Friends' alcohol use with problem alcohol use
Friends' alcohol use			Problem alcohol use			Correlations			Model fit statistics
a2	c2	e2	a2	c2	e2	rg	rc	re	−2LL	d.f.	AIC	Δχ2from saturated model	Δd.f. from saturated model
0.29*	0.36*	0.36*	0.46*	0.29*	0.25*	0.60*	0.94*	0.34*
(05–0.52)	(0.16–0.54)	(0.28–0.45)	(0.22–0.68)	(0.10–0.48)	(0.19–0.34)	(0.21–1.0)	(0.55–1.0)	(0.15–0.51)	5982.081	3223	−463.919	14.353	20

Full models with 95% confidence intervals are presented.

^*P < 0.05, a², c², e² are the genetic, common environment and unique environment estimates. rg, rc and re are the correlations between the genetic, common environment and unique environment factors influencing the phenotypes. −2LL = minus twice the log likelihood, d.f. = degrees of freedom. AIC: Aikake's information criterion.