A primer on the use of machine learning to distil knowledge from data in biological psychiatry.

Applications of machine learning in the biomedical sciences are growing rapidly. This growth has been spurred by diverse cross-institutional and interdisciplinary collaborations, public availability of large datasets, an increase in the accessibility of analytic routines, and the availability of powerful computing resources. With this increased access and exposure to machine learning comes a responsibility for education and a deeper understanding of its bases and bounds, borne equally by data scientists seeking to ply their analytic wares in medical research and by biomedical scientists seeking to harness such methods to glean knowledge from data. This article provides an accessible and critical review of machine learning for a biomedically informed audience, as well as its applications in psychiatry. The review covers definitions and expositions of commonly used machine learning methods, and historical trends of their use in psychiatry. We also provide a set of standards, namely Guidelines for REporting Machine Learning Investigations in Neuropsychiatry (GREMLIN), for designing and reporting studies that use machine learning as a primary data-analysis approach. Lastly, we propose the establishment of the Machine Learning in Psychiatry (MLPsych) Consortium, enumerate its objectives, and identify areas of opportunity for future applications of machine learning in biological psychiatry. This review serves as a cautiously optimistic primer on machine learning for those on the precipice as they prepare to dive into the field, either as methodological practitioners or well-informed consumers.

Assessing the quality and value of metabolic chart data for capturing core outcomes for pediatric medium-chain acyl-CoA dehydrogenase (MCAD) deficiency.

BACKGROUND: Generating rigorous evidence to inform care for rare diseases requires reliable, sustainable, and longitudinal measurement of priority outcomes. Having developed a core outcome set for pediatric medium-chain acyl-CoA dehydrogenase (MCAD) deficiency, we aimed to assess the feasibility of prospective measurement of these core outcomes during routine metabolic clinic visits. METHODS: We used existing cohort data abstracted from charts of 124 children diagnosed with MCAD deficiency who participated in a Canadian study which collected data from birth to a maximum of 11 years of age to investigate the frequency of clinic visits and quality of metabolic chart data for selected outcomes. We recorded all opportunities to collect outcomes from the medical chart as a function of visit rate to the metabolic clinic, by treatment centre and by child age. We applied a data quality framework to evaluate data based on completeness, conformance, and plausibility for four core MCAD outcomes: emergency department use, fasting time, metabolic decompensation, and death. RESULTS: The frequency of metabolic clinic visits decreased with increasing age, from a rate of 2.8 visits per child per year (95% confidence interval, 2.3-3.3) among infants 2 to 6 months, to 1.0 visit per child per year (95% confidence interval, 0.9-1.2) among those ≥ 5 years of age. Rates of emergency department visits followed anticipated trends by child age. Supplemental findings suggested that some emergency visits occur outside of the metabolic care treatment centre but are not captured. Recommended fasting times were updated relatively infrequently in patients' metabolic charts. Episodes of metabolic decompensation were identifiable but required an operational definition based on acute manifestations most commonly recorded in the metabolic chart. Deaths occurred rarely in these patients and quality of mortality data was not evaluated. CONCLUSIONS: Opportunities to record core outcomes at the metabolic clinic occur at least annually for children with MCAD deficiency. Methods to comprehensively capture emergency care received at outside institutions are needed. To reduce substantial heterogeneous recording of core outcome across treatment centres, improved documentation standards are required for recording of recommended fasting times and a consensus definition for metabolic decompensations needs to be developed and implemented.

Paper II: thematic framework analysis of registry-based randomized controlled trials provided insights for designing trial ready registries.

OBJECTIVES: Registry-based randomized controlled trials (RRCTs) are increasingly used, promising to address challenges associated with traditional randomized controlled trials. We identified strengths and limitations reported in planned and completed RRCTs to inform future RRCTs. STUDY DESIGN AND SETTING: We conducted an environmental scan of literature discussing conceptual or methodological strengths and limitations of using registries for trial design and conduct (n = 12), followed by an analysis of RRCT protocols (n = 13) and reports (n = 77) identified from a scoping review. Using framework analysis, we developed and refined a conceptual framework of RRCT-specific strengths and limitations. We mapped and interpreted strengths and limitations discussed by authors of RRCT articles using framework codes and quantified the frequencies at which these were mentioned. RESULTS: Our conceptual framework identified six main RRCT strengths and four main RRCT limitations. Considering implications for RRCT conduct and design, we formulated ten recommendations for registry designers, administrators, and trialists planning future RRCTs. CONCLUSION: Consideration and application of empirically underpinned recommendations for future registry design and trial conduct may help trialists utilize registries and RRCTs to their full potential.

An in-frame deletion affecting the critical acid loop of PPP2R5D is associated with a neonatal lethal form of PPP2R5D-related neurodevelopmental disorder.

Heterozygous pathogenic variants in PPP2R5D gene are associated with PPP2R5D-related neurodevelopmental disorder, a rare autosomal dominant condition, characterized by neurodevelopmental impairment in childhood, macrocephaly/megalencephaly, hypotonia, epilepsy, and dysmorphic features. Up-to-date, only approximately 100 cases have been published in the literature and the full phenotypic and genotypic spectrum have not yet been fully described. PPP2R5D gene encodes the B56δ subunit of the PP2A enzyme complex. We describe a neonatal form of PPP2R5D-related disorder with early infantile death, caused by a novel in-frame deletion causing loss of 8 amino acids and insertion of serine at position 201 (p.Phe194_Pro201delinsSer) of the B56δ subunit. This deletion is predicted to disrupt a critical acidic loop of amino acids important for binding other subunits of the PP2A enzyme complex, and harbors many of the residues previously reported to cause a mild-moderate form of this condition. This report describes a neonatal lethal presentation of the PPP2R5D-related neurodevelopmental disorder and provides additional evidence that disruption of the acidic loop is an important pathomechanism underlying PPP2R5D-related disorder.

PSMC3 proteasome subunit variants are associated with neurodevelopmental delay and type I interferon production.

A critical step in preserving protein homeostasis is the recognition, binding, unfolding, and translocation of protein substrates by six AAA-ATPase proteasome subunits (ATPase-associated with various cellular activities) termed PSMC1-6, which are required for degradation of proteins by 26S proteasomes. Here, we identified 15 de novo missense variants in the PSMC3 gene encoding the AAA-ATPase proteasome subunit PSMC3/Rpt5 in 23 unrelated heterozygous patients with an autosomal dominant form of neurodevelopmental delay and intellectual disability. Expression of PSMC3 variants in mouse neuronal cultures led to altered dendrite development, and deletion of the PSMC3 fly ortholog Rpt5 impaired reversal learning capabilities in fruit flies. Structural modeling as well as proteomic and transcriptomic analyses of T cells derived from patients with PSMC3 variants implicated the PSMC3 variants in proteasome dysfunction through disruption of substrate translocation, induction of proteotoxic stress, and alterations in proteins controlling developmental and innate immune programs. The proteostatic perturbations in T cells from patients with PSMC3 variants correlated with a dysregulation in type I interferon (IFN) signaling in these T cells, which could be blocked by inhibition of the intracellular stress sensor protein kinase R (PKR). These results suggest that proteotoxic stress activated PKR in patient-derived T cells, resulting in a type I IFN response. The potential relationship among proteosome dysfunction, type I IFN production, and neurodevelopment suggests new directions in our understanding of pathogenesis in some neurodevelopmental disorders.

Association of novel mutation in TRPV4 with familial nonsyndromic craniosynostosis with complete penetrance and variable expressivity.

OBJECTIVE: The aim of this study was to characterize a novel pathogenic variant in the transient receptor potential vanilloid 4 (TRPV4) gene, causing familial nonsyndromic craniosynostosis (CS) with complete penetrance and variable expressivity. METHODS: Whole-exome sequencing was performed on germline DNA of a family with nonsyndromic CS to a mean depth coverage of 300× per sample, with greater than 98% of the targeted region covered at least 25×. In this study, the authors detected a novel variant, c.496C>A in TRPV4, exclusively in the four affected family members. The variant was modeled using the structure of the TRPV4 protein from Xenopus tropicalis. In vitro assays in HEK293 cells overexpressing wild-type TRPV4 or TRPV4 p.Leu166Met were used to assess the effect of the mutation on channel activity and downstream MAPK signaling. RESULTS: The authors identified a novel, highly penetrant heterozygous variant in TRPV4 (NM_021625.4:c.496C>A) causing nonsyndromic CS in a mother and all three of her children. This variant results in an amino acid change (p.Leu166Met) in the intracellular ankyrin repeat domain distant from the Ca2+-dependent membrane channel domain. In contrast to other TRPV4 mutations in channelopathies, this variant does not interfere with channel activity as identified by in silico modeling and in vitro overexpression assays in HEK293 cells. CONCLUSIONS: Based on these findings, the authors hypothesized that this novel variant causes CS by modulating the binding of allosteric regulatory factors to TRPV4 rather than directly modifying its channel activity. Overall, this study expands the genetic and functional spectrum of TRPV4 channelopathies and is particularly relevant for the genetic counseling of CS patients.

Ethical and practical considerations related to data sharing when collecting patient-reported outcomes in care-based child health research.

PURPOSE: The collection and use of patient reported outcomes (PROs) in care-based child health research raises challenging ethical and logistical questions. This paper offers an analysis of two questions related to PROs in child health research: (1) Is it ethically obligatory, desirable or preferable to share PRO data collected for research with children, families, and health care providers? And if so, (2) What are the characteristics of a model best suited to guide the collection, monitoring, and sharing of these data? METHODS: A multidisciplinary team of researchers, providers, patient and family partners, and ethicists examined the literature and identified a need for focus on PRO sharing in pediatric care-based research. We constructed and analyzed three models for managing pediatric PRO data in care-based research, drawing on ethical principles, logistics, and opportunities to engage with children and families. RESULTS: We argue that it is preferable to share pediatric PRO data with providers, but to manage expectations and balance the risks and benefits of research, this requires a justifiable data sharing model. We argue that a successful PRO data sharing model will allow children and families to have access to and control over their own PRO data and be engaged in decision-making around how PROs collected for research may be integrated into care, but require support from providers. CONCLUSION: We propose a PRO data sharing model that can be used across diverse research settings and contributes to improved transparency, communication, and patient-centered care and research.

Could distal variants in ALG13 lead to atypical clinical presentation?

Congenital disorders of glycosylation (CDG) represent a wide range of some 150 inherited metabolic diseases, continually expanding in terms of newly identified genes and the heterogeneity of clinical and molecular presentations within each subtype. Heterozygous pathogenic variants in ALG13 are associated with early-onset epileptic encephalopathy, typically in females. The majority of subjects described so far harbour one of the two recurrent pathogenic variants, namely p.(Asn107Ser) and p.(Ala81Thr) in the C-terminal glycosyltransferase domain. We report a novel ALG13 variant (c.1709G > A, p.(Gly570Glu)) in an adult female with unremarkable past developmental and medical history, except for mild kinetic tremor. Our proband presented with acute onset of neurological and psychiatric features, along with liver dysfunction, during pregnancy, all of which gradually resolved after delivery. The proband's newborn baby died at 22 days of life from neonatal liver disease, due to gestational alloimmune liver disease (GALD). Functional assessment on fibroblasts derived from our case showed alterations in 2 of 3 cellular glycosylation markers (LAMP2, Factor IX), suggesting a functional effect of this novel ALG13 variant on glycosylation. This paper raises the possibility that variants outside the glycosyltransferase domain may have a hypomorphic effect leading to atypical clinical manifestations.

Families' healthcare experiences for children with inherited metabolic diseases: protocol for a mixed methods cohort study.

INTRODUCTION: Children with inherited metabolic diseases (IMDs) often have complex and intensive healthcare needs and their families face challenges in receiving high-quality, family centred health services. Improvement in care requires complex interventions involving multiple components and stakeholders, customised to specific care contexts. This study aims to comprehensively understand the healthcare experiences of children with IMDs and their families across Canada. METHODS AND ANALYSIS: A two-stage explanatory sequential mixed methods design will be used. Stage 1: quantitative data on healthcare networks and encounter experiences will be collected from 100 parent/guardians through a care map, 2 baseline questionnaires and 17 weekly diaries over 5-7 months. Care networks will be analysed using social network analysis. Relationships between demographic or clinical variables and ratings of healthcare experiences across a range of family centred care dimensions will be analysed using generalised linear regression. Other quantitative data related to family experiences and healthcare experiences will be summarised descriptively. Ongoing analysis of quantitative data and purposive, maximum variation sampling will inform sample selection for stage 2: a subset of stage 1 participants will participate in one-on-one videoconference interviews to elaborate on the quantitative data regarding care networks and healthcare experiences. Interview data will be analysed thematically. Qualitative and quantitative data will be merged during analysis to arrive at an enhanced understanding of care experiences. Quantitative and qualitative data will be combined and presented narratively using a weaving approach (jointly on a theme-by-theme basis) and visually in a side-by-side joint display. ETHICS AND DISSEMINATION: The study protocol and procedures were approved by the Children's Hospital of Eastern Ontario's Research Ethics Board, the University of Ottawa Research Ethics Board and the research ethics boards of each participating study centre. Findings will be published in peer-reviewed journals and presented at scientific conferences.

Transcriptomics and machine learning to advance schizophrenia genetics: A case-control study using post-mortem brain data.

BACKGROUND AND OBJECTIVE: Alterations of the expression of a variety of genes have been reported in patients with schizophrenia (SCZ). Moreover, machine learning (ML) analysis of gene expression microarray data has shown promising preliminary results in the study of SCZ. Our objective was to evaluate the performance of ML in classifying SCZ cases and controls based on gene expression microarray data from the dorsolateral prefrontal cortex. METHODS: We apply a state-of-the-art ML algorithm (XGBoost) to train and evaluate a classification model using 201 SCZ cases and 278 controls. We utilized 10-fold cross-validation for model selection, and a held-out testing set to evaluate the model. The performance metric utilizes to evaluate classification performance was the area under the receiver-operator characteristics curve (AUC). RESULTS: We report an average AUC on 10-fold cross-validation of 0.76 and an AUC of 0.76 on testing data, not used during training. Analysis of the rolling balanced classification accuracy from high to low prediction confidence levels showed that the most certain subset of predictions ranged between 80-90%. The ML model utilized 182 gene expression probes. Further improvement to classification performance was observed when applying an automated ML strategy on the 182 features, which achieved an AUC of 0.79 on the same testing data. We found literature evidence linking all of the top ten ML ranked genes to SCZ. Furthermore, we leveraged information from the full set of microarray gene expressions available via univariate differential gene expression analysis. We then prioritized differentially expressed gene sets using the piano gene set analysis package. We augmented the ranking of the prioritized gene sets with genes from the complex multivariate ML model using hypergeometric tests to identify more robust gene sets. We identified two significant Gene Ontology molecular function gene sets: "oxidoreductase activity, acting on the CH-NH2 group of donors" and "integrin binding." Lastly, we present candidate treatments for SCZ based on findings from our study CONCLUSIONS: Overall, we observed above-chance performance from ML classification of SCZ cases and controls based on brain gene expression microarray data, and found that ML analysis of gene expressions could further our understanding of the pathophysiology of SCZ and help identify novel treatments.

Family Experiences with Care for Children with Inherited Metabolic Diseases in Canada: A Cross-Sectional Survey.

BACKGROUND AND OBJECTIVE: Children with inherited metabolic diseases often require complex and highly specialized care. Patient and family-centered care can improve health outcomes that are important to families. This study aimed to examine experiences of family caregivers (parents/guardians) of children diagnosed with inherited metabolic diseases with healthcare to inform strategies to improve those experiences. METHODS: A cross-sectional mailed survey was conducted of family caregivers recruited from an ongoing cohort study. Participants rated their healthcare experiences during their child's visits to five types of healthcare settings common for inherited metabolic diseases: the metabolic clinic, the emergency department, hospital inpatient units, the blood laboratory, and the pharmacy. Participants provided narrative descriptions of any memorable negative or positive experiences. RESULTS: There were 248 respondents (response rate 49%). Caregivers were generally very or somewhat satisfied with the care provided at each care setting. Appropriate treatment, provider knowledge, provider communication, and care coordination were deemed essential aspects of satisfaction with care by the majority of participants across many settings. Memorable negative experiences were reported by 8-22% of participants, varying by setting. Among participants who reported memorable negative experiences, contributing factors included providers' demeanor, lack of communication, lack of involvement of the family, and disregard of an emergency protocol letter provided by the family. CONCLUSIONS: While caregivers' satisfaction with care for children with inherited metabolic diseases was high, we identified gaps in family-centered care and factors contributing to negative experiences that are important to consider in the future development of strategies to improve pediatric care for inherited metabolic diseases.

Core Outcome Sets for Medium-Chain Acyl-CoA Dehydrogenase Deficiency and Phenylketonuria.

BACKGROUND: Evidence to guide treatment of pediatric medium-chain acyl-coenzyme A dehydrogenase (MCAD) deficiency and phenylketonuria (PKU) is fragmented because of large variability in outcome selection and measurement. Our goal was to develop core outcome sets (COSs) for these diseases to facilitate meaningful future evidence generation and enhance the capacity to compare and synthesize findings across studies. METHODS: Parents and/or caregivers, health professionals, and health policy advisors completed a Delphi survey and participated in a consensus workshop to select core outcomes from candidate lists of outcomes for MCAD deficiency and PKU. Delphi participants rated the importance of outcomes on a nine-point scale (1-3: not important, 4-6: important but not critical, 7-9: critical). Candidate outcomes were progressively narrowed down over 3 survey rounds. At the workshop, participants evaluated the remaining candidate outcomes using an adapted nominal technique, open discussion, and voting. After the workshop, we finalized the COSs and recommended measurement instruments for each outcome. RESULTS: There were 85, 61, and 53 participants across 3 Delphi rounds, respectively. The candidate core outcome lists were narrowed down to 20 outcomes per disease to be discussed at the consensus workshop. Voting by 18 workshop participants led to COSs composed of 8 and 9 outcomes for MCAD deficiency and PKU, respectively, with measurement recommendations. CONCLUSIONS: These are the first known pediatric COSs for MCAD deficiency and PKU. Adoption in future studies will help to ensure best use of limited research resources to ultimately improve care for children with these rare diseases.

Machine learning and bioinformatic analysis of brain and blood mRNA profiles in major depressive disorder: A case-control study.

This study analyzed gene expression messenger RNA data, from cases with major depressive disorder (MDD) and controls, using supervised machine learning (ML). We built on the methodology of prior studies to obtain more generalizable/reproducible results. First, we obtained a classifier trained on gene expression data from the dorsolateral prefrontal cortex of post-mortem MDD cases (n = 126) and controls (n = 103). An average area-under-the-receiver-operating-characteristics-curve (AUC) from 10-fold cross-validation of 0.72 was noted, compared to an average AUC of 0.55 for a baseline classifier (p = .0048). The classifier achieved an AUC of 0.76 on a previously unused testing-set. We also performed external validation using DLPFC gene expression values from an independent cohort of matched MDD cases (n = 29) and controls (n = 29), obtained from Affymetrix microarray (vs. Illumina microarray for the original cohort) (AUC: 0.62). We highlighted gene sets differentially expressed in MDD that were enriched for genes identified by the ML algorithm. Next, we assessed the ML classification performance in blood-based microarray gene expression data from MDD cases (n = 1,581) and controls (n = 369). We observed a mean AUC of 0.64 on 10-fold cross-validation, which was significantly above baseline (p = .0020). Similar performance was observed on the testing-set (AUC: 0.61). Finally, we analyzed the classification performance in covariates subgroups. We identified an interesting interaction between smoking and recall performance in MDD case prediction (58% accurate predictions in cases who are smokers vs. 43% accurate predictions in cases who are non-smokers). Overall, our results suggest that ML in combination with gene expression data and covariates could further our understanding of the pathophysiology in MDD.

Psychiatric features and variable neurodevelopment outcome in four females with IQSEC2 spectrum disorder.

IQSEC2 is an X-linked gene highly expressed at the excitatory synapses where it plays a crucial role in α-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid receptor trafficking and synaptic plasticity. To date, several males and females with severe to profound intellectual disability have been reported harbouring frameshift and nonsense variants in this gene, whereas a milder phenotype has been recognized in females carrying missense pathogenic variants. Here, we report two novel IQSEC2 variants in four females with psychiatric features and otherwise variable cognitive impairment. A female (case 1) with severe verbal language learning disorder and a psychotic episode (precipitated by exposure to anti-contraceptive pill) harboured a de novo pathogenic frameshift variant (c.1170dupG,p.Gln391Alafs*5), whereas the female proband of family 2, displaying severe psychomotor regression and complex psychiatric features carried a missense variant of uncertain significance (c.770G[A,p.Ser257Asn) that was maternally inherited. Skewed X-inactivation was noted in the carrier mother. The maternal aunt, affected by schizophrenia, was found to bear the same IQSEC2 variant. We discuss the variable clinical presentation of IQSEC2 spectrum disorders and the challenging genotype-phenotype correlation, including the possible role of environmental factors as triggers for decompensation. Our report highlights how psychiatric features may be the main clinical presentation in subtle IQSEC2 phenotype, suggesting that the prevalence of IQSEC2 mutations in patients with psychiatric disorders may be underestimated.

Machine Learning Analysis of Blood microRNA Data in Major Depression: A Case-Control Study for Biomarker Discovery.

BACKGROUND: There is a lack of reliable biomarkers for major depressive disorder (MDD) in clinical practice. However, several studies have shown an association between alterations in microRNA levels and MDD, albeit none of them has taken advantage of machine learning (ML). METHOD: Supervised and unsupervised ML were applied to blood microRNA expression profiles from a MDD case-control dataset (n = 168) to distinguish between (1) case vs control status, (2) MDD severity levels defined based on the Montgomery-Asberg Depression Rating Scale, and (3) antidepressant responders vs nonresponders. RESULTS: MDD cases were distinguishable from healthy controls with an area-under-the receiver-operating characteristic curve (AUC) of 0.97 on testing data. High- vs low-severity cases were distinguishable with an AUC of 0.63. Unsupervised clustering of patients, before supervised ML analysis of each cluster for MDD severity, improved the performance of the classifiers (AUC of 0.70 for cluster 1 and 0.76 for cluster 2). Antidepressant responders could not be successfully separated from nonresponders, even after patient stratification by unsupervised clustering. However, permutation testing of the top microRNA, identified by the ML model trained to distinguish responders vs nonresponders in each of the 2 clusters, showed an association with antidepressant response. Each of these microRNA markers was only significant when comparing responders vs nonresponders of the corresponding cluster, but not using the heterogeneous unclustered patient set. CONCLUSIONS: Supervised and unsupervised ML analysis of microRNA may lead to robust biomarkers for monitoring clinical evolution and for more timely assessment of treatment in MDD patients.

Evaluation of the quality of clinical data collection for a pan-Canadian cohort of children affected by inherited metabolic diseases: lessons learned from the Canadian Inherited Metabolic Diseases Research Network.

BACKGROUND: The Canadian Inherited Metabolic Diseases Research Network (CIMDRN) is a pan-Canadian practice-based research network of 14 Hereditary Metabolic Disease Treatment Centres and over 50 investigators. CIMDRN aims to develop evidence to improve health outcomes for children with inherited metabolic diseases (IMD). We describe the development of our clinical data collection platform, discuss our data quality management plan, and present the findings to date from our data quality assessment, highlighting key lessons that can serve as a resource for future clinical research initiatives relating to rare diseases. METHODS: At participating centres, children born from 2006 to 2015 who were diagnosed with one of 31 targeted IMD were eligible to participate in CIMDRN's clinical research stream. For all participants, we collected a minimum data set that includes information about demographics and diagnosis. For children with five prioritized IMD, we collected longitudinal data including interventions, clinical outcomes, and indicators of disease management. The data quality management plan included: design of user-friendly and intuitive clinical data collection forms; validation measures at point of data entry, designed to minimize data entry errors; regular communications with each CIMDRN site; and routine review of aggregate data. RESULTS: As of June 2019, CIMDRN has enrolled 798 participants of whom 764 (96%) have complete minimum data set information. Results from our data quality assessment revealed that potential data quality issues were related to interpretation of definitions of some variables, participants who transferred care across institutions, and the organization of information within the patient charts (e.g., neuropsychological test results). Little information was missing regarding disease ascertainment and diagnosis (e.g., ascertainment method - 0% missing). DISCUSSION: Using several data quality management strategies, we have established a comprehensive clinical database that provides information about care and outcomes for Canadian children affected by IMD. We describe quality issues and lessons for consideration in future clinical research initiatives for rare diseases, including accurately accommodating different clinic workflows and balancing comprehensiveness of data collection with available resources. Integrating data collection within clinical care, leveraging electronic medical records, and implementing core outcome sets will be essential for achieving sustainability.

Machine learning analysis of exome trios to contrast the genomic architecture of autism and schizophrenia.

BACKGROUND: Machine learning (ML) algorithms and methods offer great tools to analyze large complex genomic datasets. Our goal was to compare the genomic architecture of schizophrenia (SCZ) and autism spectrum disorder (ASD) using ML. METHODS: In this paper, we used regularized gradient boosted machines to analyze whole-exome sequencing (WES) data from individuals SCZ and ASD in order to identify important distinguishing genetic features. We further demonstrated a method of gene clustering to highlight which subsets of genes identified by the ML algorithm are mutated concurrently in affected individuals and are central to each disease (i.e., ASD vs. SCZ "hub" genes). RESULTS: In summary, after correcting for population structure, we found that SCZ and ASD cases could be successfully separated based on genetic information, with 86-88% accuracy on the testing dataset. Through bioinformatic analysis, we explored if combinations of genes concurrently mutated in patients with the same condition ("hub" genes) belong to specific pathways. Several themes were found to be associated with ASD, including calcium ion transmembrane transport, immune system/inflammation, synapse organization, and retinoid metabolic process. Moreover, ion transmembrane transport, neurotransmitter transport, and microtubule/cytoskeleton processes were highlighted for SCZ. CONCLUSIONS: Our manuscript introduces a novel comparative approach for studying the genetic architecture of genetically related diseases with complex inheritance and highlights genetic similarities and differences between ASD and SCZ.

Outcomes in pediatric studies of medium-chain acyl-coA dehydrogenase (MCAD) deficiency and phenylketonuria (PKU): a review.

BACKGROUND: Inherited metabolic diseases (IMDs) are a group of individually rare single-gene diseases. For many IMDs, there is a paucity of high-quality evidence that evaluates the effectiveness of clinical interventions. Clinical effectiveness trials of IMD interventions could be supported through the development of core outcome sets (COSs), a recommended minimum set of standardized, high-quality outcomes and associated outcome measurement instruments to be incorporated by all trials in an area of study. We began the process of establishing pediatric COSs for two IMDs, medium-chain acyl-CoA dehydrogenase (MCAD) deficiency and phenylketonuria (PKU), by reviewing published literature to describe outcomes reported by authors, identify heterogeneity in outcomes across studies, and assemble a candidate list of outcomes. METHODS: We used a comprehensive search strategy to identify primary studies and guidelines relevant to children with MCAD deficiency and PKU, extracting study characteristics and outcome information from eligible studies including outcome measurement instruments for select outcomes. Informed by an established framework and a previously published pediatric COS, outcomes were grouped into five, mutually-exclusive, a priori core areas: growth and development, life impact, pathophysiological manifestations, resource use, and death. RESULTS: For MCAD deficiency, we identified 83 outcomes from 52 articles. The most frequently represented core area was pathophysiological manifestations, with 33 outcomes reported in 29/52 articles (56%). Death was the most frequently reported outcome. One-third of outcomes were reported by a single study. The most diversely measured outcome was cognition and intelligence/IQ for which eight unique measurement instruments were reported among 14 articles. For PKU, we identified 97 outcomes from 343 articles. The most frequently represented core area was pathophysiological manifestations with 31 outcomes reported in 281/343 articles (82%). Phenylalanine concentration was the most frequently reported outcome. Sixteen percent of outcomes were reported by a single study. Similar to MCAD deficiency, the most diversely measured PKU outcome was cognition and intelligence/IQ with 39 different instruments reported among 82 articles. CONCLUSIONS: Heterogeneity of reported outcomes and outcome measurement instruments across published studies for both MCAD deficiency and PKU highlights the need for COSs for these diseases, to promote the use of meaningful outcomes and facilitate comparisons across studies.

Balance Problems, Paralysis, and Angina as Clinical Markers for Severity in Major Depression.

Major depressive disorder (MDD) is a heterogeneous disorder. Our hypothesis is that neurological symptoms correlate with the severity of MDD symptoms. One hundred eighty-four outpatients with MDD completed a self-report questionnaire on past and present medical history. Patients were divided into three roughly equal depression severity levels based on scores from the APA Severity Measure for Depression-Adult (n = 66, 58, 60, for low, medium, high severity, respectively). We saw a significant and gradual increase in the frequency of "muscular paralysis" (1.5-5.2-16.7%) and "balance problems" (21.2-36.2-46.6%) from low to medium to high severity groups. We repeated the analysis using only the two most extreme severity categories: low severity (66 samples) vs. high severity (60 samples). High severity patients were also found to experience more "angina" symptoms than low severity patients (27.3 vs. 50%). The three significant clinical variables identified were introduced into a binary logistic regression model as the independent variables with high or low severity as the dependent variable. Both "muscular paralysis" and "balance problems" were significantly associated with increased severity of depression (odds ratio of 13.5 and 2.9, respectively), while "angina" was associated with an increase in severity with an odds ratio of 2.0, albeit not significantly. We show that neurological exam or clinical history could be useful biomarkers for depression severity. Our findings, if replicated, could lead to a simple clinical scale administered regularly for monitoring patients with MDD.