Background: Genetic vs. environmental contributions to the etiology of autism remain of ongoing research and debate, with twin studies being particularly informative. Concordance of twin pairs has varied significantly in published literature. An earlier comprehensive analysis of Autism Spectrum Disorder (ASD) twin data from the Interactive Autism Network (IAN) was conducted by Rosenberg et al.

1 There were 277 twin pairs, with pairwise

concordance in 59 out of the 67 monozygotic pairs (88.1%) and 64 out of the 210 dizygotic twins (30.5%).

Objective: The objective of this study is to construct a genetically driven, multi-mutation model for ASD development and test the model using monozygotic and dizygotic twin pair data where one member of each pair has developed the disorder. This model takes into account the varied ages of twin sets in autism research when evaluating autism concordance. This paper applies the successful modeling of schizophrenia by Kramer and Hong

2 to

ASD.

Methods: IAN is an online interactive research ASD registry (www.IANresearch.org) of over 20,000 individuals with ASD. Probands must have received an ASD diagnosis by a professional. Self-report cross-sectional data was provided online by parents with at least one twin with ASD aged <18 years. To be included in the current analysis:

Participants must be from twin sets with at least one twin affected by ASD. Higher levels of multiple births were excluded.

Affected twins must have provided their zygosity and date/age of diagnosis.

Results: There were a total of 518 twin cases in the IAN database during this study time period (2014):

123 monozygotic twins (concordance 77.2%); 395 dizygotic twins (concordance 27.8%).

A disproportionate number of concordant twin data for both monozygotic and dizygotic twins was missing the age of diagnosis of the co-twin and did not meet a necessary requirement to be included in this analysis. Concordant twins were less likely to be included in the data set because of the extra work required of the parents completing forms. For an unaffected twin to be included in the data set, no additional forms are required beyond basic registration.

Final data: 320 twin sets

60 monozygotic twins (41; 68.3% concordant); 260 dizygotic twins (49; 18.8% concordant).

References: 1Rosenberg RE, Law JK, Yenokyan G, McGready J, Kaufmann WE, Law PA. Characteristics and Concordance of

Autism Spectrum Disorders Among 277 Twin Pairs. Arch Pediatr Adolesc Med; 2009. 163 (10): 907-914. 2 Kramer I, Hong LE. A Novel Model of Schizophrenia Age-of-Onset Data Challenges the Conventional Interpretations

of the Discordance in Monozygote Twin Studies. ISRN Psychiatry. 2013; Article ID 604587, 10 pages. 3Ozonoff S, Young GS, Carter A, Messinger D, Yirmiya N, Zwaigenbaum L, Bryson S, Carver LJ, Constantino JN,

Dobkins K, Hutman T, Iverson JM, Landa R, Rogers SJ, Sigman M, Stone WL. Recurrence risk for autism spectrum disorders: a Baby Siblings Research Consortium study. Pediatrics. 2011 Sep;128(3):e488-95 4Kramer I, Lipkin PH, Marvin AR, Law PA. A Genetic Multimutation Model of Autism Spectrum Disorder Fits Disparate

Twin Concordance Data from the USA and Canada. ISRN Psychiatry. 2015; Article ID 519828, 10 pages. Acknowledgements: Individuals with ASD, families, researchers, and health care professionals make IAN possible through the generous

contribution of their time and effort.

The Interactive Autism Network (JHM IRB# NA_00002750; PI: Dr. Paul H. Lipkin) is a Partnership of Kennedy Krieger Institute and the Simons Foundation. Visit the Interactive Autism Network at www.IANcommunity.org.

Ivan Kramer, PhD.1; Alison R. Marvin, Ph.D.

2; Paul H. Lipkin, M.D.

2,3; Paul A. Law, M.D., M.P.H.

4,5

1Physics Department, University of Maryland Baltimore County, Baltimore, MD, USA;

2Department of Medical Informatics, Kennedy Krieger Institute, Baltimore, MD, USA;

3Department of Pediatrics, Johns Hopkins School of Medicine, Baltimore, MD, USA;

4Université Protestante au Congo, Kinshasa, Democratic Republic of Congo;

5Johns Hopkins University School of Public Health, Baltimore, MD, USA

A Genetic Multi-Mutation Model of Autism Spectrum Disorder

Results (Continued): Multi-Mutation Modeling Figures (1a) and (1b): Cumulative number, N(t), of males and females, respectively, who were officially diagnosed with ASD as a function of age t in the IAN database.

The data were fitted with the model function below, where Nb is a fourth parameter to be determined by the fit. Nb represents the total number of males or females that are susceptible to developing ASD in a lifetime.

Figure (2a): Plot of susceptible prevalence curve Ps(t) obtained by the model fit to the data predicts:

50% born with susceptibility to develop ASD will develop it by the age of 33 months (2.75 years);

98% will develop it by the age of 124 months (10.3 years).

Figure (2b): Plot of the slope of the Ps(t) curve, IRs(t) = dPs(t) / dt, shows:

Susceptible incidence rate curve IRs(t) peaks at the age of 25.6 months (2.13 years).

Note that the total area under the complete IRs(t) curve must equal 1.

Figure (3): Plots for concordance , discordance , and non-ASD probability functions for identical twins predicted by the model fit to IAN data.

The total of all three states must equal 100% at any given point for a given a cohort of identical twin pairs born with a susceptibility to develop ASD. Example: at the 40-month point, 41.2% are concordant, 46.0% are discordant, and in 12.8% of the pairs neither twin has yet developed the disorder. The concordance curve saturates at ~100% and is also shown in Figure(4).

Figure (4): Actual monozygotic concordance curve Cm(t) predicted by the model fit to IAN data.

50% of the monozygotic twin pairs become concordant at the age of 42 months;

96% concordance is reached by the age of 123 months (10.25 years).

The dizygotic concordance curve Sinher characteristically saturates at the value of 18.8%. This is almost identical to the estimates of recurrence risk (18.7%) by Ozonoff et al (2011).

3

Conclusions: 1. The multi-mutation model of ASD development agrees with other age-of-onset data. It assumes that

susceptibility to develop ASD is present at birth, and that the full manifestations occur when all of the specific brain mutations characteristic of ASD are acquired.

2. The corresponding male and female model fit parameters (Figures (1a) & (1b)) are practically identical, from which we may conclude that ASD develops the same way in males and females, and ASD may be an autosomal disorder.

3. Greater than 98% of those susceptible to developing ASD acquire it by age 124 months (10.33 years), based on the age-of-onset prevalence curve (Figure (2a)). We would therefore expect over 96% concordance in identical twin pairs by this age (Figure (4)).

4. Continuing to survey the concordance rate in susceptible twin pairs until they all exceed ~10 years of age will further test the genetic hypothesis of ASD development.

For more information about this study, please see Kramer et al.4