Genome-wide association studies in ADHD

Attention-deficit/hyperactivity disorder, ADHD, is a common and highly heritable neuropsychiatric disorder that is seen in children and adults. Although heritability is estimated at around 76%, it has been hard to find genes underlying the disorder. ADHD is a multifactorial disorder, in which many genes, all with a small effect, are thought to cause the disorder in the presence of unfavorable environmental conditions. Whole genome linkage analyses have not yet lead to the identification of genes for ADHD, and results of candidate gene-based association studies have been able to explain only a tiny part of the genetic contribution to disease, either. A novel way of performing hypothesis-free analysis of the genome suitable for the identification of disease risk genes of considerably smaller effect is the genome-wide association study (GWAS). So far, five GWAS have been performed on the diagnosis of ADHD and related phenotypes. Four of these are based on a sample set of 958 parent–child trio’s collected as part of the International Multicentre ADHD Genetics (IMAGE) study and genotyped with funds from the Genetic Association Information Network (GAIN). The other is a pooled GWAS including adult patients with ADHD and controls. None of the papers reports any associations that are formally genome-wide significant after correction for multiple testing. There is also very limited overlap between studies, apart from an association with CDH13, which is reported in three of the studies. Little evidence supports an important role for the ‘classic’ ADHD genes, with possible exceptions for SLC9A9, NOS1 and CNR1. There is extensive overlap with findings from other psychiatric disorders. Though not genome-wide significant, findings from the individual studies converge to paint an interesting picture: whereas little evidence—as yet—points to a direct involvement of neurotransmitters (at least the classic dopaminergic, noradrenergic and serotonergic pathways) or regulators of neurotransmission, some suggestions are found for involvement of ‘new’ neurotransmission and cell–cell communication systems. A potential involvement of potassium channel subunits and regulators warrants further investigation. More basic processes also seem involved in ADHD, like cell division, adhesion (especially via cadherin and integrin systems), neuronal migration, and neuronal plasticity, as well as related transcription, cell polarity and extracellular matrix regulation, and cytoskeletal remodeling processes. In conclusion, the GWAS performed so far in ADHD, though far from conclusive, provide a first glimpse at genes for the disorder. Many more (much larger studies) will be needed. For this, collaboration between researchers as well as standardized protocols for phenotyping and DNA-collection will become increasingly important

The IMAGE project: methodological issues for the molecular genetic analysis of ADHD

The genetic mechanisms involved in attention deficit hyperactivity disorder (ADHD) are being studied with considerable success by several centres worldwide. These studies confirm prior hypotheses about the role of genetic variation within genes involved in the regulation of dopamine, norepinephrine and serotonin neurotransmission in susceptibility to ADHD. Despite the importance of these findings, uncertainties remain due to the very small effects sizes that are observed. We discuss possible reasons for why the true strength of the associations may have been underestimated in research to date, considering the effects of linkage disequilibrium, allelic heterogeneity, population differences and gene by environment interactions. With the identification of genes associated with ADHD, the goal of ADHD genetics is now shifting from gene discovery towards gene functionality – the study of intermediate phenotypes ('endophenotypes'). We discuss methodological issues relating to quantitative genetic data from twin and family studies on candidate endophenotypes and how such data can inform attempts to link molecular genetic data to cognitive, affective and motivational processes in ADHD. The International Multi-centre ADHD Gene (IMAGE) project exemplifies current collaborative research efforts on the genetics of ADHD. This European multi-site project is well placed to take advantage of the resources that are emerging following the sequencing of the human genome and the development of international resources for whole genome association analysis. As a result of IMAGE and other molecular genetic investigations of ADHD, we envisage a rapid increase in the number of identified genetic variants and the promise of identifying novel gene systems that we are not currently investigating, opening further doors in the study of gene functionality

Reply to : On powerful GWAS in admixed populations

Non peer reviewe

Association of Clinician Diagnostic Performance With Machine Learning-Based Decision Support Systems: A Systematic Review.

IMPORTANCE: An increasing number of machine learning (ML)-based clinical decision support systems (CDSSs) are described in the medical literature, but this research focuses almost entirely on comparing CDSS directly with clinicians (human vs computer). Little is known about the outcomes of these systems when used as adjuncts to human decision-making (human vs human with computer). OBJECTIVES: To conduct a systematic review to investigate the association between the interactive use of ML-based diagnostic CDSSs and clinician performance and to examine the extent of the CDSSs' human factors evaluation. EVIDENCE REVIEW: A search of MEDLINE, Embase, PsycINFO, and grey literature was conducted for the period between January 1, 2010, and May 31, 2019. Peer-reviewed studies published in English comparing human clinician performance with and without interactive use of an ML-based diagnostic CDSSs were included. All metrics used to assess human performance were considered as outcomes. The risk of bias was assessed using Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) and Risk of Bias in Non-Randomised Studies-Intervention (ROBINS-I). Narrative summaries were produced for the main outcomes. Given the heterogeneity of medical conditions, outcomes of interest, and evaluation metrics, no meta-analysis was performed. FINDINGS: A total of 8112 studies were initially retrieved and 5154 abstracts were screened; of these, 37 studies met the inclusion criteria. The median number of participating clinicians was 4 (interquartile range, 3-8). Of the 107 results that reported statistical significance, 54 (50%) were increased by the use of CDSSs, 4 (4%) were decreased, and 49 (46%) showed no change or an unclear change. In the subgroup of studies carried out in representative clinical settings, no association between the use of ML-based diagnostic CDSSs and improved clinician performance could be observed. Interobserver agreement was the commonly reported outcome whose change was the most strongly associated with CDSS use. Four studies (11%) reported on user feedback, and, in all but 1 case, clinicians decided to override at least some of the algorithms' recommendations. Twenty-eight studies (76%) were rated as having a high risk of bias in at least 1 of the 4 QUADAS-2 core domains, and 6 studies (16%) were considered to be at serious or critical risk of bias using ROBINS-I. CONCLUSIONS AND RELEVANCE: This systematic review found only sparse evidence that the use of ML-based CDSSs is associated with improved clinician diagnostic performance. Most studies had a low number of participants, were at high or unclear risk of bias, and showed little or no consideration for human factors. Caution should be exercised when estimating the current potential of ML to improve human diagnostic performance, and more comprehensive evaluation should be conducted before deploying ML-based CDSSs in clinical settings. The results highlight the importance of considering supported human decisions as end points rather than merely the stand-alone CDSSs outputs

Targeting alphas can make coyote control more effective and socially acceptable

Research at the UC Hopland Research and Extension Center (HREC) has improved our understanding of how to reduce sheep depredation while minimizing the impact on coyotes. Analysis of a 14-year data set of HREC coyote-control efforts found that sheep depredation losses were not correlated with the number of coyotes removed in any of three time scales analyzed (yearly, seasonally and monthly) during corresponding intervals for the next 2 years. Field research using radiotelemetry to track coyotes supported and explained this finding. For example, in 1995, dominant “alphas” from four territories were associated with 89% of 74 coyote-killed lambs; “betas” and transients were not associated with any of these kills. Relatively few coyotes were killing sheep, and these animals were difficult to capture by conventional methods at the time of year when depredation was highest. However, selective removal of only the problem alpha coyotes effectively reduced losses at HREC

Targeting alphas can make coyote control more effective and socially acceptable

Research at the UC Hopland Research and Extension Center (HREC) has improved our understanding of how to reduce sheep depredation while minimizing the impact on coyotes. Analysis of a 14-year data set of HREC coyote-control efforts found that sheep depredation losses were not correlated with the number of coyotes removed in any of three time scales analyzed (yearly, seasonally and monthly) during corresponding intervals for the next 2 years. Field research using radiotelemetry to track coyotes supported and explained this finding. For example, in 1995, dominant “alphas” from four territories were associated with 89% of 74 coyote-killed lambs; “betas” and transients were not associated with any of these kills. Relatively few coyotes were killing sheep, and these animals were difficult to capture by conventional methods at the time of year when depredation was highest. However, selective removal of only the problem alpha coyotes effectively reduced losses at HREC

Clinical Conditions and Their Impact on Utility of Genetic Scores for Prediction of Acute Coronary Syndrome

Background: Acute coronary syndrome (ACS) is a clinically significant presentation of coronary heart disease. Genetic information has been proposed to improve prediction beyond well-established clinical risk factors. While polygenic scores (PS) can capture an individual's genetic risk for ACS, its prediction performance may vary in the context of diverse correlated clinical conditions. Here, we aimed to test whether clinical conditions impact the association between PS and ACS. Methods: We explored the association between 405 clinical conditions diagnosed before baseline and 9080 incident cases of ACS in 387 832 individuals from the UK Biobank. Results were replicated in 6430 incident cases of ACS in 177 876 individuals from FinnGen. Results: We identified 80 conventional (eg, stable angina pectoris and type 2 diabetes) and unconventional (eg, diaphragmatic hernia and inguinal hernia) associations with ACS. The association between PS and ACS was consistent in individuals with and without most clinical conditions. However, a diagnosis of stable angina pectoris yielded a differential association between PS and ACS. PS was associated with a significantly reduced (interaction P=2.87x10(-8)) risk for ACS in individuals with stable angina pectoris (hazard ratio, 1.163 [95% CI, 1.082-1.251]) compared with individuals without stable angina pectoris (hazard ratio, 1.531 [95% CI, 1.497-1.565]). These findings were replicated in FinnGen (interaction P=1.38x10(-6)). Conclusions: In summary, while most clinical conditions did not impact utility of PS for prediction of ACS, we found that PS was substantially less predictive of ACS in individuals with prevalent stable coronary heart disease. PS may be more appropriate for prediction of ACS in asymptomatic individuals than symptomatic individuals with clinical suspicion for coronary heart disease.Peer reviewe

Lack of Association of Rare Functional Variants in TSC1/TSC2 Genes with Autism Spectrum Disorder

Background: Autism spectrum disorder (ASD) is reported in 30 to 60% of patients with tuberous sclerosis complex (TSC) but shared genetic mechanisms that exist between TSC-associated ASD and idiopathic ASD have yet to be determined. Through the small G-protein Rheb, the TSC proteins, hamartin and tuberin, negatively regulate mammalian target of rapamycin complex 1 (mTORC1) signaling. It is well established that mTORC1 plays a pivotal role in neuronal translation and connectivity, so dysregulation of mTORC1 signaling could be a common feature in many ASDs. Pam, an E3 ubiquitin ligase, binds to TSC proteins and regulates mTORC1 signaling in the CNS, and the FBXO45-Pam ubiquitin ligase complex plays an essential role in neurodevelopment by regulating synapse formation and growth. Since mounting evidence has established autism as a disorder of the synapses, we tested whether rare genetic variants in TSC1, TSC2, MYCBP2, RHEB and FBXO45, genes that regulate mTORC1 signaling and/or play a role in synapse development and function, contribute to the pathogenesis of idiopathic ASD. Methods: Exons and splice junctions of TSC1, TSC2, MYCBP2, RHEB and FBXO45 were resequenced for 300 ASD trios from the Simons Simplex Collection (SSC) using a pooled PCR amplification and next-generation sequencing strategy, targeted to the discovery of deleterious coding variation. These detected, potentially functional, variants were confirmed by Sanger sequencing of the individual samples comprising the pools in which they were identified. Results: We identified a total of 23 missense variants in MYCBP2, TSC1 and TSC2. These variants exhibited a near equal distribution between the proband and parental pools, with no statistical excess in ASD cases (P > 0.05). All proband variants were inherited. No putative deleterious variants were confirmed in RHEB and FBXO45. Three intronic variants, identified as potential splice defects in MYCBP2 did not show aberrant splicing upon RNA assay. Overall, we did not find an over-representation of ASD causal variants in the genes studied to support them as contributors to autism susceptibility. Conclusions: We did not observe an enrichment of rare functional variants in TSC1 and TSC2 genes in our sample set of 300 trios