A Novel Approach to Finding Genetic Association in Attention-Deficit/Hyperactivity Disorder

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By: Dustin Sokolowski

Affiliations: Western University, 3rd year student in Genetics and Applied Statistics

Supervisor: Dr. Paul Arnold, Hospital for Sick Children, Peter Gilgan Centre for Research and Learning

Attention-deficit/hyperactivity disorder (ADHD) is a childhood-onset, psychiatric disorder that affects approximately 3.4% of people world-wide.1 Patients with ADHD are impulsive, hyperactive, and inattentive and often have trouble with various types of cognitive processes such as executive functions. For example, ADHD patients often have deficits in response inhibition, which refers to the ability to do the appropriate thing at the appropriate time (e.g., speak at appropriate times). Individuals with ADHD are also at risk for problems in school, work, driving, and in relationships.2 To mitigate the negative outcomes associated with ADHD, we need to develop better treatments, diagnostic screens, and early detection methods. Understanding the biological mechanisms underlying ADHD will improve our ability to develop more efficacious medications that target the specific proteins and pathways involved in ADHD and improve our ability to diagnose ADHD accurately and perhaps before symptom onset.

Genetics play an important role in ADHD. We know ADHD is heritable based on twin studies (60-90%),3 meaning that ADHD has a moderate to strong genetic component. Although we know that ADHD is genetic, the specific genes that confer risk of developing ADHD are still unknown. One method to finding novel genetic variants in genes associated with ADHD is genome-wide association studies (GWAS) which examine millions of genetic variants across the genome. Although several GWAS comparing patients with ADHD to healthy controls have been published, no genetic variants significantly associated with ADHD have been identified. One hurdle in finding genetic variants that are involved in ADHD is the amount of complexity in both the clinical presentation of ADHD and the genetics that likely underlie the disorder. ADHD is likely, in part, the result of hundreds of genes interacting in multiple pathways. Clinically, ADHD is also quite variable. For example, some children may have troubles with impulsivity, while others are less impulsive but hyperactive. Accordingly, researchers are exploring new ways of approaching genetic studies that can cut through some of the complexity and provide more power to detect novel variants in the GWAS framework.

Quantitative traits and endophenotypes are two novel approaches that may help identify genetic variants associated with ADHD using GWAS. Quantitative trait GWAS design, which uses continuous measures of a trait of interest, is more powerful than the typical GWAS case/control design.4 An example of a quantitative trait for ADHD is the number of inattentive, hyperactive, and impulsive symptoms. Endophenotypes (i.e., intermediate phenotypes) are traits that are associated with the disorder of interest but are believed to be more directly linked to the underlying genetics than the disorder, thus reducing some genetic, behavioural, and diagnostic complexity.5 An endophenotype for ADHD is stopping a behaviour that is either planned or in progress, which is a specific type of response inhibition that is common in patients with ADHD. A real life application of stopping is applying the breaks in a car when the traffic lights turn red.

Our group at the Hospital for Sick Children, under the supervision of Dr. Paul Arnold, Dr. Russell Schachar, and Dr. Jennifer Crosbie, is using both quantitative traits and endophenotypes in GWAS to understand the genetics of ADHD. In a pilot study, we examined if number of parent-rated ADHD symptoms and performance on the Stop-Signal task was associated with millions of genetic variants across the genome using GWAS. Despite a smaller sample size, we identified signals that approached genome-wide significance, showing potential for associations with stopping and ADHD symptoms. One genetic variant associated with stopping was in the SLC24A4 gene, which is expressed in the brain, among other bodily regions, and is responsible for transporting calcium in the cell. Calcium has recently been linked to ADHD and other major mental disorders.6 Next we will meta-analyze our data with a larger sample. Once we identify genetic variants associated with stopping and ADHD symptoms, we will test if these variants are associated with ADHD in another clinical sample.

Our study is just the first step in identifying the many genes likely to be involved in ADHD. ADHD is not going to be the result of a single gene, but likely hundreds of genes, similar to other complex psychiatric disorders like schizophrenia.7 GWAS studies will be necessary to help discover these genes. Pinpointing the genes involved in ADHD will be the first step in creating novel medications that specifically target the underlying mechanisms of ADHD. Furthermore, having proper genetic screens for ADHD will improve diagnosis and it could help tailor treatments for ADHD and improve the lives of children with ADHD and their families.

  1. Polanczyk, GV, Salum, GA, Sugaya, LS, Caye A, Rohde, LA. Annual Research Review: A meta‐analysis of the worldwide prevalence of mental disorders in children and adolescents. J Am Acad Child Adolesc Psychiatry. 2015; 56(3), 345–365.
  2. Barkley, RA. Major life activity and health outcomes associated with attention-deficit/hyperactivity disorder. J Clin Psychiatry. 2002; 63, 10–15.
  3. Todd RD. Genetics of attention deficit/hyperactivity disorder: are we ready for molecular genetic studies? Am J Med Genet Neuropsychiatry Genet. 2000; 96:241–243
  4. Van der Sluis S, Posthuma D, Nivard MG, Verhage M, Dolan CV. Power in GWAS: lifting the curse of the clinical cut-off. Mol Psychiatry. 2013; 18, 2–3.
  5. Crosbie J, Pérusse D, Barr CL, Schachar, RJ. Validating psychiatric endophenotypes: inhibitory control and attention deficit hyperactivity disorder. Neurosci Biobehav R. 2008; 32(1), 40–55.
  6. Cross-Disorder Group of the Psychiatric Genomics Consortium. Identification of risk loci with shared effects on five major psychiatric disorders: a genome-wide analysis. The Lancet. 2013; 381(9875), 137–1379.
  7. Schizophrenia Working Group of the Psychiatric Genomics Consortium. Biological insights from 108 schizophrenia-associated genetic loci. Nature. 2014; 511(7510), 421–427.