Neurological disorder-associated genetic variants in individuals with psychogenic nonepileptic seizures

Psychogenic nonepileptic seizures (PNES) are diagnosed in approximately 30% of patients referred to tertiary care epilepsy centers. Little is known about the molecular pathology of PNES, much less about possible underlying genetic factors. We generated whole-exome sequencing and whole-genome genotyping data to identify rare, pathogenic (P) or likely pathogenic (LP) variants in 102 individuals with PNES and 448 individuals with focal (FE) or generalized (GE) epilepsy. Variants were classified for all individuals based on the ACMG-AMP 2015 guidelines. For research purposes only, we considered genes associated with neurological or psychiatric disorders as candidate genes for PNES. We observe in this first genetic investigation of PNES that six (5.88%) individuals with PNES without coexistent epilepsy carry P/LP variants (deletions at 10q11.22-q11.23, 10q23.1-q23.2, distal 16p11.2, and 17p13.3, and nonsynonymous variants in NSD1 and GABRA5). Notably, the burden of P/LP variants among the individuals with PNES was similar and not significantly different to the burden observed in the individuals with FE (3.05%) or GE (1.82%) (PNES vs. FE vs. GE (3x2 chi (2)), P=0.30; PNES vs. epilepsy (2x2 chi (2)), P=0.14). The presence of variants in genes associated with monogenic forms of neurological and psychiatric disorders in individuals with PNES shows that genetic factors are likely to play a role in PNES or its comorbidities in a subset of individuals. Future large-scale genetic research studies are needed to further corroborate these interesting findings in PNES.Peer reviewe

Preeclampsia and Blood Pressure Trajectory during Pregnancy in Relation to Vitamin D Status

Every tenth pregnancy is affected by hypertension, one of the most common complications and leading causes of maternal death worldwide. Hypertensive disorders in pregnancy include pregnancy-induced hypertension and preeclampsia. The pathophysiology of the development of hypertension in pregnancy is unknown, but studies suggest an association with vitamin D status, measured as 25-hydroxyvitamin D (25(OH)D). The aim of this study was to investigate the association between gestational 25(OH)D concentration and preeclampsia, pregnancy-induced hypertension and blood pressure trajectory. This cohort study included 2000 women. Blood was collected at the first (T1) and third (T3) trimester (mean gestational weeks 10.8 and 33.4). Blood pressure at gestational weeks 10, 25, 32 and 37 as well as symptoms of preeclampsia and pregnancy-induced hypertension were retrieved from medical records. Serum 25(OH)D concentrations (LC-MS/MS) in T1 was not significantly associated with preeclampsia. However, both 25(OH)D in T3 and change in 25(OH)D from T1 to T3 were significantly and negatively associated with preeclampsia. Women with a change in 25(OH)D concentration of ≥30 nmol/L had an odds ratio of 0.22 (p = 0.002) for preeclampsia. T1 25(OH)D was positively related to T1 systolic (β = 0.03, p = 0.022) and T1 diastolic blood pressure (β = 0.02, p = 0.016), and to systolic (β = 0.02, p = 0.02) blood pressure trajectory during pregnancy, in adjusted analyses. There was no association between 25(OH)D and pregnancy-induced hypertension in adjusted analysis. In conclusion, an increase in 25(OH)D concentration during pregnancy of at least 30 nmol/L, regardless of vitamin D status in T1, was associated with a lower odds ratio for preeclampsia. Vitamin D status was significantly and positively associated with T1 blood pressure and gestational systolic blood pressure trajectory but not with pregnancy-induced hypertension

EpiDiP/NanoDiP: a versatile unsupervised machine learning edge computing platform for epigenomic tumour diagnostics.

DNA methylation analysis based on supervised machine learning algorithms with static reference data, allowing diagnostic tumour typing with unprecedented precision, has quickly become a new standard of care. Whereas genome-wide diagnostic methylation profiling is mostly performed on microarrays, an increasing number of institutions additionally employ nanopore sequencing as a faster alternative. In addition, methylation-specific parallel sequencing can generate methylation and genomic copy number data. Given these diverse approaches to methylation profiling, to date, there is no single tool that allows (1) classification and interpretation of microarray, nanopore and parallel sequencing data, (2) direct control of nanopore sequencers, and (3) the integration of microarray-based methylation reference data. Furthermore, no software capable of entirely running in routine diagnostic laboratory environments lacking high-performance computing and network infrastructure exists. To overcome these shortcomings, we present EpiDiP/NanoDiP as an open-source DNA methylation and copy number profiling suite, which has been benchmarked against an established supervised machine learning approach using in-house routine diagnostics data obtained between 2019 and 2021. Running locally on portable, cost- and energy-saving system-on-chip as well as gpGPU-augmented edge computing devices, NanoDiP works in offline mode, ensuring data privacy. It does not require the rigid training data annotation of supervised approaches. Furthermore, NanoDiP is the core of our public, free-of-charge EpiDiP web service which enables comparative methylation data analysis against an extensive reference data collection. We envision this versatile platform as a useful resource not only for neuropathologists and surgical pathologists but also for the tumour epigenetics research community. In daily diagnostic routine, analysis of native, unfixed biopsies by NanoDiP delivers molecular tumour classification in an intraoperative time frame

Risk and predictors of dementia and parkinsonism in idiopathic REM sleep behaviour disorder: a multicentre study

Idiopathic REM sleep behaviour disorder (iRBD) is a powerful early sign of Parkinson's disease, dementia with Lewy bodies, and multiple system atrophy. This provides an unprecedented opportunity to directly observe prodromal neurodegenerative states, and potentially intervene with neuroprotective therapy. For future neuroprotective trials, it is essential to accurately estimate phenoconversion rate and identify potential predictors of phenoconversion. This study assessed the neurodegenerative disease risk and predictors of neurodegeneration in a large multicentre cohort of iRBD. We combined prospective follow-up data from 24 centres of the International RBD Study Group. At baseline, patients with polysomnographically-confirmed iRBD without parkinsonism or dementia underwent sleep, motor, cognitive, autonomic and special sensory testing. Patients were then prospectively followed, during which risk of dementia and parkinsonsim were assessed. The risk of dementia and parkinsonism was estimated with Kaplan-Meier analysis. Predictors of phenoconversion were assessed with Cox proportional hazards analysis, adjusting for age, sex, and centre. Sample size estimates for disease-modifying trials were calculated using a time-to-event analysis. Overall, 1280 patients were recruited. The average age was 66.3 \ub1 8.4 and 82.5% were male. Average follow-up was 4.6 years (range = 1-19 years). The overall conversion rate from iRBD to an overt neurodegenerative syndrome was 6.3% per year, with 73.5% converting after 12-year follow-up. The rate of phenoconversion was significantly increased with abnormal quantitative motor testing [hazard ratio (HR) = 3.16], objective motor examination (HR = 3.03), olfactory deficit (HR = 2.62), mild cognitive impairment (HR = 1.91-2.37), erectile dysfunction (HR = 2.13), motor symptoms (HR = 2.11), an abnormal DAT scan (HR = 1.98), colour vision abnormalities (HR = 1.69), constipation (HR = 1.67), REM atonia loss (HR = 1.54), and age (HR = 1.54). There was no significant predictive value of sex, daytime somnolence, insomnia, restless legs syndrome, sleep apnoea, urinary dysfunction, orthostatic symptoms, depression, anxiety, or hyperechogenicity on substantia nigra ultrasound. Among predictive markers, only cognitive variables were different at baseline between those converting to primary dementia versus parkinsonism. Sample size estimates for definitive neuroprotective trials ranged from 142 to 366 patients per arm. This large multicentre study documents the high phenoconversion rate from iRBD to an overt neurodegenerative syndrome. Our findings provide estimates of the relative predictive value of prodromal markers, which can be used to stratify patients for neuroprotective trials

The Genetic Structure of the Swedish Population

Peer reviewe

Analysis of shared heritability in common disorders of the brain

ience, this issue p. eaap8757 Structured Abstract INTRODUCTION Brain disorders may exhibit shared symptoms and substantial epidemiological comorbidity, inciting debate about their etiologic overlap. However, detailed study of phenotypes with different ages of onset, severity, and presentation poses a considerable challenge. Recently developed heritability methods allow us to accurately measure correlation of genome-wide common variant risk between two phenotypes from pools of different individuals and assess how connected they, or at least their genetic risks, are on the genomic level. We used genome-wide association data for 265,218 patients and 784,643 control participants, as well as 17 phenotypes from a total of 1,191,588 individuals, to quantify the degree of overlap for genetic risk factors of 25 common brain disorders. RATIONALE Over the past century, the classification of brain disorders has evolved to reflect the medical and scientific communities' assessments of the presumed root causes of clinical phenomena such as behavioral change, loss of motor function, or alterations of consciousness. Directly observable phenomena (such as the presence of emboli, protein tangles, or unusual electrical activity patterns) generally define and separate neurological disorders from psychiatric disorders. Understanding the genetic underpinnings and categorical distinctions for brain disorders and related phenotypes may inform the search for their biological mechanisms. RESULTS Common variant risk for psychiatric disorders was shown to correlate significantly, especially among attention deficit hyperactivity disorder (ADHD), bipolar disorder, major depressive disorder (MDD), and schizophrenia. By contrast, neurological disorders appear more distinct from one another and from the psychiatric disorders, except for migraine, which was significantly correlated to ADHD, MDD, and Tourette syndrome. We demonstrate that, in the general population, the personality trait neuroticism is significantly correlated with almost every psychiatric disorder and migraine. We also identify significant genetic sharing between disorders and early life cognitive measures (e.g., years of education and college attainment) in the general population, demonstrating positive correlation with several psychiatric disorders (e.g., anorexia nervosa and bipolar disorder) and negative correlation with several neurological phenotypes (e.g., Alzheimer's disease and ischemic stroke), even though the latter are considered to result from specific processes that occur later in life. Extensive simulations were also performed to inform how statistical power, diagnostic misclassification, and phenotypic heterogeneity influence genetic correlations. CONCLUSION The high degree of genetic correlation among many of the psychiatric disorders adds further evidence that their current clinical boundaries do not reflect distinct underlying pathogenic processes, at least on the genetic level. This suggests a deeply interconnected nature for psychiatric disorders, in contrast to neurological disorders, and underscores the need to refine psychiatric diagnostics. Genetically informed analyses may provide important "scaffolding" to support such restructuring of psychiatric nosology, which likely requires incorporating many levels of information. By contrast, we find limited evidence for widespread common genetic risk sharing among neurological disorders or across neurological and psychiatric disorders. We show that both psychiatric and neurological disorders have robust correlations with cognitive and personality measures. Further study is needed to evaluate whether overlapping genetic contributions to psychiatric pathology may influence treatment choices. Ultimately, such developments may pave the way toward reduced heterogeneity and improved diagnosis and treatment of psychiatric disorders

Genome-wide identification and phenotypic characterization of seizure-associated copy number variations in 741,075 individuals

Copy number variants (CNV) are established risk factors for neurodevelopmental disorders with seizures or epilepsy. With the hypothesis that seizure disorders share genetic risk factors, we pooled CNV data from 10,590 individuals with seizure disorders, 16,109 individuals with clinically validated epilepsy, and 492,324 population controls and identified 25 genome-wide significant loci, 22 of which are novel for seizure disorders, such as deletions at 1p36.33, 1q44, 2p21-p16.3, 3q29, 8p23.3-p23.2, 9p24.3, 10q26.3, 15q11.2, 15q12-q13.1, 16p12.2, 17q21.31, duplications at 2q13, 9q34.3, 16p13.3, 17q12, 19p13.3, 20q13.33, and reciprocal CNVs at 16p11.2, and 22q11.21. Using genetic data from additional 248,751 individuals with 23 neuropsychiatric phenotypes, we explored the pleiotropy of these 25 loci. Finally, in a subset of individuals with epilepsy and detailed clinical data available, we performed phenome-wide association analyses between individual CNVs and clinical annotations categorized through the Human Phenotype Ontology (HPO). For six CNVs, we identified 19 significant associations with specific HPO terms and generated, for all CNVs, phenotype signatures across 17 clinical categories relevant for epileptologists. This is the most comprehensive investigation of CNVs in epilepsy and related seizure disorders, with potential implications for clinical practice

GWAS meta-analysis of over 29,000 people with epilepsy identifies 26 risk loci and subtype-specific genetic architecture

Epilepsy is a highly heritable disorder affecting over 50 million people worldwide, of which about one-third are resistant to current treatments. Here we report a multi-ancestry genome-wide association study including 29,944 cases, stratified into three broad categories and seven subtypes of epilepsy, and 52,538 controls. We identify 26 genome-wide significant loci, 19 of which are specific to genetic generalized epilepsy (GGE). We implicate 29 likely causal genes underlying these 26 loci. SNP-based heritability analyses show that common variants explain between 39.6% and 90% of genetic risk for GGE and its subtypes. Subtype analysis revealed markedly different genetic architectures between focal and generalized epilepsies. Gene-set analyses of GGE signals implicate synaptic processes in both excitatory and inhibitory neurons in the brain. Prioritized candidate genes overlap with monogenic epilepsy genes and with targets of current antiseizure medications. Finally, we leverage our results to identify alternate drugs with predicted efficacy if repurposed for epilepsy treatment

Truncation and missing family links in population-based Registers

Studies of familial aggregation of disease routinely use linked population registers to construct retrospective cohorts. Although such resources have provided numerous estimates of familial risk, little is known regarding the sensitivity of the estimates to assumed disease models and incompleteness of the data, such as truncation and/or missing family links. Furthermore, there are no standard tools for testing the validity of estimates from standard epidemiologic designs using register data. We first introduce a software package, Poplab for simulating realistic populations of related individuals, with complete family information, using easily available vital statistics (fertility and mortality rates) and disease incidence rates. Incidence events are influenced by the familial model of disease, and other disease-related features such as value of familial association, case mortality ratio or biological relationship of aggregation. We illustrate by mimicking the Swedish population evolving dynamically over the calendar period 1955 - 2002, with female breast cancer aggregating in families. The simulated population agrees well on important demographic features with the real population, and the simulated parameters of familial aggregation are faithfully recovered. Next, virtual populations are used to investigate the impact of left-truncation of family history and missing family links due to death on familial risk estimates. The missing familial links had no effect, except when there was differential mortality for familial and non-familial cases. Bias due to left-truncation is most pronounced for high familial risks and for registers with a short life-span. The age distribution of disease and the magnitude of background incidence rates also affected the magnitude of bias. In the third study, we develop a method for correcting the bias in familial risk estimates due to left-truncation. The required sensitivity of exposure is estimated from virtual populations, and was found non-differential for cases and healthy individuals. In all the situations studied, the bias-corrected estimates are in excellent agreement with the true values. In our last study, we use the bias-correction methodology to evaluate the bias in the apparent familial risks due to left-truncation for the common cancer sites in Sweden. The study cohorts are based on the Swedish MultiGeneration Register linked to the Swedish Cancer Register for the period 1961-2002. We found that corrected age-group specific and overall estimates of the familial risks for colorectum, lung, breast and prostate cancer were close to the apparent relative risks, with overall values of 1.99 95%CI (1.85, 2.14), 2.05 (1.86, 2.26), 1.84 (1.76, 1.92) and 2.33 (2.19, 2.48), respectively. For melanoma, the apparent estimate, 2.68 (2.35, 3.07) was somewhat smaller than the corrected estimate, 3.18 (2.73, 3.64), and was dramatically different with the exposure defined as a parent affected at a younger age, thus changing from 4.07 (3.21, 5.16) to 5.67 (4.51, 6.83) after correction. In conclusion, we found that left-truncation induces bias in familial risk estimates especially for high values of risk. For common cancers, analyses of the Swedish MultiGeneration cohorts produced generally unbiased familial risk estimates, which was in agreement with our expectations for diseases with older ages of onset and familial risks of relatively low magnitude. However, where the exposure of interest is early age of onset in a parent, commonly considered to be an indication of genetically determined cancers, estimates may be biased, especially where familial risk is high. Our simulation method can correct for such biases and offers a feasible alternative to the use of validation samples