1. Department of Psychiatry, Washington University School of Medicine
Stability and Changes in Familial Influences on Attention and Activity during Adolescence: A longitudinal twin study using the SWAN
Chun-Zi Peng, ph.D., Julia D. Grant, ph.D., Andrew C. Heath, D.phil., Angela M. Reiersen, M.D., M.P.E., Richard C. Mulligan, Ph.D., Andrey P. Anokh in, Ph.D.
Department of Psychiatry, Washington University School of Medicine
St Louis, MO 63108

2. In the general population, 70-85% of children diagnosed with ADHD may continue to have the disorder during adolescence
The twin studies indicated:
ADHD is highly heritable (60%-90%)
The two symptoms (Inattention and Hyperactivity) were affected by different type of genetic influences
The heritability estimates were strongly influenced by rater effects and assessment instruments used in the studies
Symptomatic end only, non-affected phenotypes largely collapsed into one class
qualitative
quantitative

3. The Strengths and Weaknesses of ADHD Symptoms and Normal Behavior Scale (SWAN)
“Listen when spoken to directly”
symptomatic ends
far below=3 below=2
slightly below=1
Adaptive end
slightly above=-1 above=-2
far above=-3
average=0

4. Twin studies on the SWAN
Significant “C” affect found (Hay, Bennett, Levy, Sergeant, & Swanson, 2007)
The SWAN captures variance hidden in the “0” ratings of the CBCL (Polderman et al., 2007)
No longitudinal genetic study on the SWAN

5. Table1 Sample characteristics and means of Attention and activityN
% male
% MZ
ATTa
M(SD)
ACTb
Age 12
434
52.3%
55.3%
-0.36 (0.95)
-0.50(0.95)
Age 14
592
51.3%
46.5%
-0.56 (1.08)
-0.68(1.09)
Age 16
369
52.6%
47.3%
-0.81(1.15)
-0.96(1.14)
note: a all ATT means significantly different from each other, p<0.05; b all ACT means significantly different from each other, p<0.05.

6. * significant at the 0.05 level.
Table2 Cross-age within individual correlations (95% Confidence Interval) for attention(above diagonals) and activity (below diagonals)
Age 12
Age 14
Age 16 --
0.71 ( )*
0.66 ( )*
0.65 ( )*
0.74 ( )*
0.63 ( )*
0.67 ( )*
* significant at the 0.05 level.

7. Table3 Cross-twin correlations (95% Confidence Interval) for Attention and Activity
ATT
ACT
Age 12 MZ
0.74 ( )*
0.87( )* DZ
0.01( )
0.48 ( )*
Age 14
0.77 ( )*
0.88( )*
0.36( )*
0.70( )*
Age 16
0.84( )*
0.94( )*
0.48( )*
0.74( )*
Note: MZ: Monozygotic twins; DZ: Dizygotic twins; * correlation is significant at the 0.05 level

8. FIGURE1 Best fitting linear structural equation model for genetic and environmental determinants of Attention score. Note: Rectangular represent the observed variance for each age. The circles represent the latent factors including additive genetic influences (A12, A14 and A16) and shared environmental influences (C12, C14 and C16). A Simplex model is shown. A Cholesky (triangular decomposition) model is shown for the non-shared environmental effects (E12, E14 and E16)

9. FIGURE2 Best fitting linear structural equation model for genetic and environmental determinants of Activity score. Note: The rectangular represent the observed variance for each age. The circles represent the latent factors. For Additive genetic influences (A12, A14 and A16), shared environmental influences (C12, C14 and C16) and non-shared environmental effects (E12, E14 and E16), a simplex model is shown.

10. FIGURE1 Best fitting linear structural equation model for genetic and environmental determinants of Attention score. Note: Rectangular represent the observed variance for each age. The circles represent the latent factors including additive genetic influences (A12, A14 and A16) and shared environmental influences (C12, C14 and C16). A Simplex model is shown. A Cholesky (triangular decomposition) model is shown for the non-shared environmental effects (E12, E14 and E16).

11. Conclusions
ATT and ACT traits measured by SWAN are highly stable phenotypes affected by substantial familial influences
Their developmental mechanism consisted of transmission of previously existing genetic effects interacting with new genetic influences
The shared environmental influences also contribute to their stability and changes, which highlights the importance of using fully quantitative measure in behavior genetics study