ReviewImaging genetics in ADHD
Introduction
This special issue of Neuroimage focuses on imaging genetics in the brain, both in the healthy brain and in disorders. This is relevant as although many psychiatric disorders have well-established heritable bases, psychiatric genetic research has not been able to uncover the genetic causes underlying them. Attention Deficit Hyperactivity Disorder (ADHD) is a prevalent neuropsychiatric disorder, with 5% of school age children receiving the diagnosis, compared to prevalence rates of .5% for schizophrenia and .5–1.0% for epilepsy. However, its prevalence is not necessarily reflected in the amount of studies published on it: a Pubmed search in July 2009 gave nearly 16 000 hits for ADHD, but over 84 000 for schizophrenia, and 116 000 for epilepsy. ADHD is a heritable disorder, making it a prime candidate for imaging genetics studies. Up to 80% of the phenotypic variance can be explained by additive genetic factors (Faraone et al., 2005), similar to estimates for schizophrenia and some (familial) forms of epilepsy (Cardno and Gottesman, 2000, Kjeldsen et al., 2003). Of the approximately 16 000 publications to date that mention ADHD, only 14.5% mention imaging modalities (MRI, EEG, or PET/SPECT). That is similar to the total number of publications on ADHD that mention neuropsychology (13.5%).
Section snippets
The intermediate or endophenotype approach
The intermediate or endophenotype approach is the focus of this special issue and is explained in some detail by Bigos & Weinberger (2010). As such, it is not outlined in detail here. Briefly, the intermediate or endophenotype approach allows for mapping of the effect of individual risk genes on neurobiological parameters, such as brain structure, brain activity or neurochemistry. Criteria for an endophenotype in psychiatry include being continuously quantifiable, stable, closer to the
Modalities used to date in endophenotype approaches to ADHD
As Fig. 1 illustrates, most studies to date that have applied an endophenotype approach to ADHD have used neuropsychology as an outcome measure. The advantages are obvious: the methods are cheap and readily available. Furthermore, neuropsychological measures have been shown to meet a number of criteria for endophenotypes, including being (1) affected in ADHD; (2) under familial influences in ADHD and (3) relatively stable over time (for review, see Bellgrove et al., 2008, Nigg, 2005, Rommelse
Imaging genetics in ADHD
A systematic review of the published literature on imaging genetics in ADHD shows that to date, only fourteen imaging genetics studies have been published in ADHD (see Table 1). These papers were retrieved through a pubmed search using the search terms “ADHD” and “MRI” “fMRI” “SPECT” “PET” “EEG” or “ERP” and “gene” “DAT1” “DAT” or “DRD4”. The reference lists of retrieved papers (and reviews) were further checked for other publications in this area. Of the fourteen publications to date, eight
Concluding remarks
Imaging genetics in ADHD is in its infancy. To date, only fourteen studies have used neuroimaging methods to assess the effect of ADHD risk genes on the brain. However, this is of pivotal importance if we want to address how genetic risk can impact a biological system and ultimately result in ADHD. Genetic variations that affect gene expression in the brain can affect brain function. Imaging approaches permit us to visualize these changes in vivo. This is essentially the simple approach that we
Acknowledgments
The author gratefully acknowledges two anonymous reviewers for their helpful suggestions. The work by the author's group described here was supported by an NWO VIDI grant to SD.
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2015, NeuroImageCitation Excerpt :Understanding such genetic and environmental factors is an important step for the development of urgently needed approaches to the prevention, diagnosis, and treatment of these complex diseases. Such studies and research projects include the Philadelphia Neurodevelopmental Cohort (PNC), the Alzheimer's Disease Neuroimaging Initiative (ADNI), and the Longitudinal Study of Early Brain Development (LSEBD), among others (NIH; Durston, 2010; Shen et al., 2010; Satterthwaite et al., 2014; Gilmore et al., 2010; Knickmeyer et al., 2014). These initiatives have generated many high-dimensional and complex data sets, referred to as big data, whose size is beyond the ability of commonly used software tools to capture, manage, and process data within a tolerable elapsed time.