Assessment of Susceptibility Risk Factors for ADHD in Imaging Genetic Studies
N Vilor-Tejedor1,2,3, S Alemany1,2,3, J Forns1,2,3,4
11 SGlobal - Centre for Research in Environmental Epidemiology, Barcelona, Spain.
Journal of Attention Disorders
|August 19, 2016
Summary
A new statistical model, the zero-inflated negative binomial (ZINB) distribution, better analyzes Attention Deficit Hyperactivity Disorder (ADHD) symptoms. This approach identified a genetic variant linked to ADHD and brain structure, unlike traditional methods.
Area of Science:
- Neuroscience
- Genetics
- Biostatistics
Background:
- Attention Deficit Hyperactivity Disorder (ADHD) symptom counts exhibit heterogeneity, including overdispersion and excess zeros.
- Traditional statistical inference for ADHD often relies on underpowered dichotomous outcomes.
Purpose of the Study:
- To identify a suitable probability distribution for analyzing ADHD symptoms within Imaging Genetic studies.
- To enhance statistical power for detecting ADHD risk factors.
Main Methods:
- Evaluated standard count data probability distributions using two independent population samples of children.
- Applied the zero-inflated negative binomial (ZINB) distribution to model ADHD symptoms.
Main Results:
- The ZINB distribution demonstrated superior power for modeling ADHD symptoms compared to other distributions.
- ZINB identified a significant association between the genetic variant rs273342 (MAPRE2) and ADHD (p = 2.73E-05).
- The identified genetic variant was also associated with perivascular volumes (Virchow-Robin spaces; p < 1E-03).
- No significant associations were detected using a dichotomous definition of ADHD.
Conclusions:
- Appropriate statistical modeling of ADHD symptoms significantly increases the power to identify risk factors.
- The ZINB distribution offers a more robust approach for ADHD symptom analysis in genetic studies.
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