Abstract
Background
Identifying genetic variants contributing to attention-deficit/hyperactivity disorder (ADHD) is complicated by the involvement of numerous common genetic variants with small effects, interacting with each other as well as with environmental factors, such as stress exposure. Random forest regression is well-suited to explore this complexity, as it allows for the analysis of many predictors simultaneously, taking into account any higher-order interactions among them.
Methods
Using random forest regression, we predicted ADHD severity, measured by Conners’ Parent Rating Scales, from 686 adolescents and young adults (including 281 diagnosed with ADHD). The analysis included 17,374 single nucleotide polymorphisms (SNPs) across 29 genes previously linked to hypothalamic-pituitary-adrenal (HPA) axis activity, together with information on exposure to 24 individual long-term difficulties or stressful life events.
Results
The model explained 12.5% of variance in ADHD severity. The most important SNP for prediction, which also showed the strongest interaction with stress exposure, was located in a region regulating the expression of telomerase reverse transcriptase (TERT). Other high-ranking SNPs were found in or near NPSR1, ESR1, GABRA6, PER3, NR3C2, and DRD4. Chronic stressors were more influential than single, severe, life events.
Conclusions
Random forest regression may be used to investigate how multiple genetic and environmental factors jointly contribute to ADHD. It is able to implicate novel SNPs of interest, interacting with stress exposure, and may explain inconsistent findings in ADHD genetics. This exploratory approach may be best combined with more hypothesis-driven research; top predictors, and their interactions with one another, should be replicated in independent samples.
Identifying genetic variants contributing to attention-deficit/hyperactivity disorder (ADHD) is complicated by the involvement of numerous common genetic variants with small effects, interacting with each other as well as with environmental factors, such as stress exposure. Random forest regression is well-suited to explore this complexity, as it allows for the analysis of many predictors simultaneously, taking into account any higher-order interactions among them.
Methods
Using random forest regression, we predicted ADHD severity, measured by Conners’ Parent Rating Scales, from 686 adolescents and young adults (including 281 diagnosed with ADHD). The analysis included 17,374 single nucleotide polymorphisms (SNPs) across 29 genes previously linked to hypothalamic-pituitary-adrenal (HPA) axis activity, together with information on exposure to 24 individual long-term difficulties or stressful life events.
Results
The model explained 12.5% of variance in ADHD severity. The most important SNP for prediction, which also showed the strongest interaction with stress exposure, was located in a region regulating the expression of telomerase reverse transcriptase (TERT). Other high-ranking SNPs were found in or near NPSR1, ESR1, GABRA6, PER3, NR3C2, and DRD4. Chronic stressors were more influential than single, severe, life events.
Conclusions
Random forest regression may be used to investigate how multiple genetic and environmental factors jointly contribute to ADHD. It is able to implicate novel SNPs of interest, interacting with stress exposure, and may explain inconsistent findings in ADHD genetics. This exploratory approach may be best combined with more hypothesis-driven research; top predictors, and their interactions with one another, should be replicated in independent samples.
| Original language | English |
|---|---|
| Pages (from-to) | 367-367 |
| Number of pages | 1 |
| Journal | Biological Psychiatry |
| Volume | 81 |
| Issue number | 10, Supplement |
| Early online date | 29 Apr 2017 |
| DOIs | |
| Publication status | Published - 15 May 2017 |