Researchers have found the first risk genes for ADHD

Posted on by Prof. Stephen Faraone

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, https://www.nature.com/articles/s41588-018-0269-7.

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. 

The first risk genes for ADHD has been identified

Posted on by dittedemontis

A major international collaboration headed by researchers from the Danish iPSYCH project, the Broad Institute of Harvard and MIT, Massachusetts General Hospital, SUNY Upstate Medical University, and the Psychiatric Genomics Consortium has for the first time identified genetic variants which increase the risk of ADHD. The new findings provide a completely new insight into the biology behind ADHD.

 

Risk variants for  ADHD
Our genes are very important for the development of ADHD, where genetic factors capture up to 75% of the risk. Until now, the search for locations in the genome with genetic variation that is involved in ADHD has not delivered clear results. A large genetic study performed by researchers from the Psychiatric Genomics Consortium have compared genetic variation across the entire genome for over 20,000 people with ADHD and 35,000 who do not suffer from it – finding twelve locations where people with a particular genetic variant have an increased risk of ADHD compared to those who do not have the variant.

The special about the new study is the large amount of data. The search for genetic risk variants for ADHD has spanned decades but without obtaining robust results. This time the study really expanded the number of study subjects substantially, increasing the power to obtain conclusive results.

The results of the study have just been published in the scientific journal Nature Genetics.

The new 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. Overall, the results show that the risk variants typically regulate how much a gene is expressed, and that the genes affected are primarily expressed in the brain.

The same genes affect impulsivity in healthy people
 In the study, the researchers have also compared the new results with those from a genetic study of continuous measures of ADHD behaviours in the general population. The researchers discovered that the same genetic variants that give rise to an ADHD diagnosis also affect inattention and impulsivity in the general population. This result tells us, that the risk variants are  widespread in the population. The more risk variants a person has, the greater the tendency to have ADHD-like characteristics will be as well as the risk of developing ADHD.
The study also evaluated the genetic overlap with other diseases and traits, and a strong negative genetic correlation between ADHD and education was identified. This means that on average genetic variants which increase the risk of ADHD also influence performance in the education system negatively for people in the general population who carry these variants without having ADHD.

Conversely, the study 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.

What´s next?
The new findings mean that the scientists now – after many years of research – finally have robust genetic findings that can inform about the underlying biology and what role genetics plays in the diseases and traits that are often cooccurring with ADHD. In addition, the study is an important foundation for further research into ADHD. Studies can now be targeted, to focus on the genes and biological mechanisms identified in the new study in order to achieve a deeper understanding of how the genetic risk variants affect the development of ADHD with the aim of ultimately providing better help for people with ADHD.


References:

Demontis and Walters et al. Discovery of the first genome-wide significant risk loci for attention deficit/hyperactivity disorder. Nature Genetics, 2018. https://doi.org/10.1038/s41588-018-0269-7

https://www.nature.com/articles/s41588-018-0269-7

What tube maps tell us about adhd and the brain

Posted on by Oliver Grimm

Most people consider the tube maps of big cities e.g. London’s tube map complex and irritating.

On the other hand, people immediately spot patterns in most tube maps. Most often, you recognize a limited number of hubs e.g. the central station. If the central station is left out, traveling gets much harder. A tube map is a compromise between a completely random net between nodes (or call it stations) and a simple, extremely hierarchical one.tube-map

Mathematicians call this a small network and developed a whole theory for better characterization of the lines and nodes (vertices and edges, in the mathematicians’ slang).

So, what has this to do with ADHD? If brain connectivity plays a role for attention and better organization of your behavior, maybe the “tube map” of an ADHD patient is not efficiently organized. This could be analyzed by measuring the brain’s activity in several regions over time and then correlating these timecourses to look which regions are tightly linked (form connections) and which regions are not (no “subway connection available between these regions”). Than one could use this theory for characterizing the brain network. That’s exactly what Robert Cary and colleagues report in the Journal Cerebral Cortex (2016;1–10; doi: 10.1093/cercor/bhw209).

They investigated 22 patients with ADHD with and without their medication and compared it to the network pattern of 31 controls.

For this, they calculated for each point in the resting-state data of the brain (so-called “voxel”) a measure they termed “node dissociation index” (NDI). The basic idea is that for a part of the brain nodes, we can define a “module”. A module for a given node is its number of links to different nodes in relation to the sum of all links (a bit more complicated, but that’s the basic idea). The Modularity in a region is the sum of the modularity of all nodes in this region. The node dissociation index is the sum of all modules in relation to the number of connections (in the specific node i and across all nodes).

When we look at the nodes number 1,5 and six, we see two highly interconnected networks in green and blue. These form two different “modules”. While node 1 has three connections to 2,3, and 4, it does not connect to the blue module, therefore its NDI is zero. Node number six is highly connected within the blue module but not to the green module, its NDI is zero. Node number 5 has four connections and one connection to the green module, therefore its NDI is 0.25.

ndi-example

In the analysis by Cary et al. the NDI (summed for specific networks like “visual”, “default mode” or “salience” ) takes values between 0.1 (visual) or almost 0.7 (salience). These measures give us a clue how tightly these networks are linked to nodes within their own communities (low dissociation indices) or whether they “dissolve” their connections and have connections to nodes which are not grouped into their own node community.

What was the effect when patients were scanned after having had a short medication wash-out? And how does this compare to the healthy controls?

The controls had lower values of the dissociation index than the patients. In patients, the visual system was not affected (very plausible!), but a variety of networks showed a decrease of the dissociation index when patients were on medication. The largest differences in networks were found in the visual attention network, the salience network and the fronto-parietal network. These networks are involved in higher order cognitive functioning and mediate psychological functions which are implicated in ADHD. The interesting take home message is, that by giving stimulant medications to patients a confuse and badly organized “tube map” (or brain network) gets a more concise structure. Graph theory offers an interesting perspective on brain networks. Future work might look in detail at how clinical phenomena are connected to brain networks or how specific comorbidities (e.g. additional addiction disorders) influence brain networks.