Marta Di Forti

Bio: Marta Di Forti is an academic researcher from King's College London. The author has contributed to research in topics: Cannabis & Psychosis. The author has an hindex of 29, co-authored 118 publications receiving 5849 citations. Previous affiliations of Marta Di Forti include University of Palermo & Mental Health Foundation.

Large recurrent microdeletions associated with schizophrenia

Abstract: Reduced fecundity, associated with severe mental disorders, places negative selection pressure on risk alleles and may explain, in part, why common variants have not been found that confer risk of disorders such as autism, schizophrenia and mental retardation. Thus, rare variants may account for a larger fraction of the overall genetic risk than previously assumed. In contrast to rare single nucleotide mutations, rare copy number variations (CNVs) can be detected using genome-wide single nucleotide polymorphism arrays. This has led to the identification of CNVs associated with mental retardation and autism. In a genome-wide search for CNVs associating with schizophrenia, we used a population-based sample to identify de novo CNVs by analysing 9,878 transmissions from parents to offspring. The 66 de novo CNVs identified were tested for association in a sample of 1,433 schizophrenia cases and 33,250 controls. Three deletions at 1q21.1, 15q11.2 and 15q13.3 showing nominal association with schizophrenia in the first sample (phase I) were followed up in a second sample of 3,285 cases and 7,951 controls (phase II). All three deletions significantly associate with schizophrenia and related psychoses in the combined sample. The identification of these rare, recurrent risk variants, having occurred independently in multiple founders and being subject to negative selection, is important in itself. CNV analysis may also point the way to the identification of additional and more prevalent risk variants in genes and pathways involved in schizophrenia.

Mapping genomic loci implicates genes and synaptic biology in schizophrenia

Abstract: Schizophrenia has a heritability of 60–80%1, much of which is attributable to common risk alleles. Here, in a two-stage genome-wide association study of up to 76,755 individuals with schizophrenia and 243,649 control individuals, we report common variant associations at 287 distinct genomic loci. Associations were concentrated in genes that are expressed in excitatory and inhibitory neurons of the central nervous system, but not in other tissues or cell types. Using fine-mapping and functional genomic data, we identify 120 genes (106 protein-coding) that are likely to underpin associations at some of these loci, including 16 genes with credible causal non-synonymous or untranslated region variation. We also implicate fundamental processes related to neuronal function, including synaptic organization, differentiation and transmission. Fine-mapped candidates were enriched for genes associated with rare disruptive coding variants in people with schizophrenia, including the glutamate receptor subunit GRIN2A and transcription factor SP4, and were also enriched for genes implicated by such variants in neurodevelopmental disorders. We identify biological processes relevant to schizophrenia pathophysiology; show convergence of common and rare variant associations in schizophrenia and neurodevelopmental disorders; and provide a resource of prioritized genes and variants to advance mechanistic studies. A genome-wide association study including over 76,000 individuals with schizophrenia and over 243,000 control individuals identifies common variant associations at 287 genomic loci, and further fine-mapping analyses highlight the importance of genes involved in synaptic processes.

High-potency cannabis and the risk of psychosis

Abstract: Background People who use cannabis have an increased risk of psychosis, an effect attributed to the active ingredient Δ9-tetrahydrocannabinol (Δ9-THC). There has recently been concern over an increase in the concentration of Δ9-THC in the cannabis available in many countries. Aims To investigate whether people with a first episode of psychosis were particularly likely to use high-potency cannabis. Method We collected information on cannabis use from 280 cases presenting with a first episode of psychosis to the South London & Maudsley National Health Service (NHS) Foundation Trust, and from 174 healthy controls recruited from the local population. Results There was no significant difference between cases and controls in whether they had ever taken cannabis, or age at first use. However, those in the cases group were more likely to be current daily users (OR = 6.4) and to have smoked cannabis for more than 5 years (OR = 2.1). Among those who used cannabis, 78% of the cases group used high-potency cannabis (sinsemilla, ‘ skunk’) compared with 37% of the control group (OR 6.8). Conclusions The finding that people with a first episode of psychosis had smoked higher-potency cannabis, for longer and with greater frequency, than a healthy control group is consistent with the hypothesis that Δ9-THC is the active ingredient increasing risk of psychosis. This has important public health implications, given the increased availability and use of high-potency cannabis.

Cannabis, the mind and society: the hash realities

Abstract: Cannabis has been known for at least 4,000 years to have profound effects on the mind--effects that have provoked dramatically divergent attitudes towards it. Some societies have regarded cannabis as a sacred boon for mankind that offers respite from the tribulations of everyday life, whereas others have demonized it as inevitably leading to 'reefer madness'. The debate between the protagonists and prohibitionists has recently been re-ignited, but unfortunately this debate continues mainly in ignorance of our new understanding of the effects of cannabis on the brain and of studies that have quantified the extent of the risks of long-term use.

Machine learning

Abstract: Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Finding the missing heritability of complex diseases

Abstract: Genome-wide association studies have identified hundreds of genetic variants associated with complex human diseases and traits, and have provided valuable insights into their genetic architecture. Most variants identified so far confer relatively small increments in risk, and explain only a small proportion of familial clustering, leading many to question how the remaining, 'missing' heritability can be explained. Here we examine potential sources of missing heritability and propose research strategies, including and extending beyond current genome-wide association approaches, to illuminate the genetics of complex diseases and enhance its potential to enable effective disease prevention or treatment.

Pharmacotherapy for neuropathic pain in adults: a systematic review and meta-analysis.

Abstract: Summary Background New drug treatments, clinical trials, and standards of quality for assessment of evidence justify an update of evidence-based recommendations for the pharmacological treatment of neuropathic pain. Using the Grading of Recommendations Assessment, Development, and E valuation (GRADE), we revised the Special Interest Group on Neuropathic Pain (NeuPSIG) recommendations for the pharmacotherapy of neuropathic pain based on the results of a systematic review and meta-analysis. Methods Between April, 2013, and January, 2014, NeuPSIG of the International Association for the Study of Pain did a systematic review and meta-analysis of randomised, double-blind studies of oral and topical pharmacotherapy for neuropathic pain, including studies published in peer-reviewed journals since January , 1966, and unpublished trials retrieved from ClinicalTrials.gov and websites of pharmaceutical companies. We used number needed to treat (NNT) for 50% pain relief as a primary measure and assessed publication bias; NNT was calculated with the fi xed-eff ects Mantel-Haenszel method. Findings 229 studies were included in the meta-analysis. Analysis of publication bias suggested a 10% overstatement of treatment eff ects. Studies published in peer-reviewed journals reported greater eff ects than did unpublished studies (r² 9·3%, p=0·009). T rial outcomes were generally modest: in particular, combined NNTs were 6·4 (95% CI 5·2–8·4) for serotonin-noradrenaline reuptake inhibitors, mainly including duloxetine (nine of 14 studies); 7·7 (6·5–9·4) for pregabalin; 7·2 (5·9–9·21) for gabapentin, including gabapentin extended release and enacarbil; and 10·6 (7·4–19·0) for capsaicin high-concentration patches. NNTs were lower for tricyclic antidepressants, strong opioids, tramadol, and botulinum toxin A, and undetermined for lidocaine patches. Based on GRADE, fi nal quality of evidence was moderate or high for all treatments apart from lidocaine patches; tolerability and safety, and values and preferences were higher for topical drugs; and cost was lower for tricyclic antidepressants and tramadol. These fi ndings permitted a strong recommendation for use and proposal as fi rst-line treatment in neuropathic pain for tricyclic antidepressants, serotonin-noradrenaline reuptake inhibitors, pregabalin, and gabapentin; a weak recommendation for use and proposal as second line for lidocaine patches, capsaicin high-concentration patches, and tramadol; and a weak recommendation for use and proposal as third line for strong opioids and botulinum toxin A. Topical agents and botulinum toxin A are recommended for peripheral neuropathic pain only. Interpretation Our results support a revision of the NeuPSIG recommendations for the pharmacotherapy of neuropathic pain. Inadequate response to drug treatments constitutes a substantial unmet need in patients with neuropathic pain. Modest effi cacy, large placebo responses, heterogeneous diagnostic criteria, and poor phenotypic profi ling probably account for moderate trial outcomes and should be taken into account in future studies. Funding NeuPSIG of the International Association for the Study of Pain.

The Dopamine Hypothesis of Schizophrenia: Version III—The Final Common Pathway

Abstract: The dopamine hypothesis of schizophrenia has been one of the most enduring ideas in psychiatry. Initially, the emphasis was on a role of hyperdopaminergia in the etiology of schizophrenia (version I), but it was subsequently reconceptualized to specify subcortical hyperdopaminergia with prefrontal hypodopaminergia (version II). However, these hypotheses focused too narrowly on dopamine itself, conflated psychosis and schizophrenia, and predated advances in the genetics, molecular biology, and imaging research in schizophrenia. Since version II, there have been over 6700 articles about dopamine and schizophrenia. We selectively review these data to provide an overview of the 5 critical streams of new evidence: neurochemical imaging studies, genetic evidence, findings on environmental risk factors, research into the extended phenotype, and animal studies. We synthesize this evidence into a new dopamine hypothesis of schizophrenia-version III: the final common pathway. This hypothesis seeks to be comprehensive in providing a framework that links risk factors, including pregnancy and obstetric complications, stress and trauma, drug use, and genes, to increased presynaptic striatal dopaminergic function. It explains how a complex array of pathological, positron emission tomography, magnetic resonance imaging, and other findings, such as frontotemporal structural and functional abnormalities and cognitive impairments, may converge neurochemically to cause psychosis through aberrant salience and lead to a diagnosis of schizophrenia. The hypothesis has one major implication for treatment approaches. Current treatments are acting downstream of the critical neurotransmitter abnormality. Future drug development and research into etiopathogenesis should focus on identifying and manipulating the upstream factors that converge on the dopaminergic funnel point.

Consensus Statement : Chromosomal Microarray Is a First-Tier Clinical Diagnostic Test for Individuals with Developmental Disabilities or Congenital Anomalies

Abstract: Chromosomal microarray (CMA) is increasingly utilized for genetic testing of individuals with unexplained developmental delay/intellectual disability (DD/ID), autism spectrum disorders (ASD), or multiple congenital anomalies (MCA). Performing CMA and G-banded karyotyping on every patient substantially increases the total cost of genetic testing. The International Standard Cytogenomic Array (ISCA) Consortium held two international workshops and conducted a literature review of 33 studies, including 21,698 patients tested by CMA. We provide an evidence-based summary of clinical cytogenetic testing comparing CMA to G-banded karyotyping with respect to technical advantages and limitations, diagnostic yield for various types of chromosomal aberrations, and issues that affect test interpretation. CMA offers a much higher diagnostic yield (15%–20%) for genetic testing of individuals with unexplained DD/ID, ASD, or MCA than a G-banded karyotype (~3%, excluding Down syndrome and other recognizable chromosomal syndromes), primarily because of its higher sensitivity for submicroscopic deletions and duplications. Truly balanced rearrangements and low-level mosaicism are generally not detectable by arrays, but these are relatively infrequent causes of abnormal phenotypes in this population (<1%). Available evidence strongly supports the use of CMA in place of G-banded karyotyping as the first-tier cytogenetic diagnostic test for patients with DD/ID, ASD, or MCA. G-banded karyotype analysis should be reserved for patients with obvious chromosomal syndromes (e.g., Down syndrome), a family history of chromosomal rearrangement, or a history of multiple miscarriages.