Researchers have found the first risk genes for ADHD

Our genes are very important for the development of mental disorders – including ADHD, where genetic factors capture up to 75% of the risk. Until now, the search for these genes had yet to deliver clear results.   In the 1990s, many of us were searching for genes that increased the risk for ADHD because we know from twin studies that ADHD had a robust genetic component.   Because I realized that solving this problem required many DNA samples from people with and without ADHD, I created the ADHD Molecular Genetics Network, funded by the US NIMH.  We later joined forces with the Psychiatric Genomics Consortium (PTC) and the Danish iPSYCH group, which had access to many samples.

The result is a study of over 20,000 people with ADHD and 35,000 who do not suffer from it – finding twelve locations (loci) where people with a particular genetic variant have an increased risk of ADHD compared to those who do not have the variant.  The results of the study have just been published in the scientific journal Nature Genetics,

These genetic discoveries provide new insights into the biology behind developing ADHD. For example, some of the genes have significance for how brain cells communicate with each other, while others are important for cognitive functions such as language and learning.

We study used genomewide association study (GWAS) methodology because it allowed us to discover genetic loci anywhere on the genome.  The method assays DNA variants throughout the genome and determines which variants are more common among ADHD vs. control participants.  It also allowed for the discovery of loci having very small effects.  That feature was essential because prior work suggested that, except for very rare cases, ADHD risk loci would individually have small effects.

The main findings are:

  1. A) we found 12 loci on the genome that we can be certain harbor DNA risk variants for ADHD. None of these loci were traditional ‘candidate genes’ for ADHD, i.e., genes involved in regulating neurotransmission systems that are affected by ADHD medications. Instead, these genes seem to be involved in the development of brain circuits.
  2. B) we found a significant polygenic etiology in our data, which means that there must be many loci (perhaps thousands) having variants that increase risk for ADHD. We will need to collect a much larger sample to find out which specific loci are involved;

We also compared the new results with those from a genetic study of continuous measures of ADHD symptoms in the general population. We found that the same genetic variants that give rise to an ADHD diagnosis also affect inattention and impulsivity in the general population.  This supports prior clinical research suggesting that, like hypertension and hypercholesteremia, ADHD is a continuous trait in the population.  These genetic data now show that the genetic susceptibility to ADHD is also a quantitative trait comprised of many, perhaps thousands, of DNA variants

The study also examined the genetic overlap with other disorders and traits in analyses that ask the questions:  Do genetic risk variants for ADHD increase or decrease the likelihood a person will express other traits and disorders.   These analyses found a strong negative genetic correlation between ADHD and education. This tell us that many of the genetic variants which increase the risk for ADHD also make it more likely that persons will perform poorly in educational settings. The study also found a positive correlation between ADHD and obesity, increased BMI and type-2 diabetes, which is to say that variants that increase the risk of ADHD also increase the risk of overweight and type-2 diabetes in the population.

This work has laid the foundation for future work that will clarify how genetic risks combine with environmental risks to cause ADHD.  When the pieces of that puzzle come together, researchers will be able to improve the diagnosis and treatment of ADHD.

Stephen Faraone is distinguished Professor of Psychiatry and of Neuroscience and Physiology at SUNY Upstate Medical University and is working on the H2020-funded project CoCA. 

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