Biology subjects

Luykx, J. J.

Publications and source records attributed to Luykx, J. J..

5 recordsLinked to original sources

bioRxiv2021

A computational solution for bolstering reliability of epigenetic clocks: Implications for clinical trials and longitudinal tracking

Epigenetic clocks are widely used aging biomarkers calculated from DNA methylation data. Unfortunately, measurements for individual CpGs can be surprisingly unreliable due to technical noise, and this may limit the utility of epigenetic clocks. We report that noise produces deviations up to 3 to 9 years between technical replicates for six major epigenetic clocks. The elimination of low-reliability CpGs does not ameliorate this issue. Here, we present a novel computational multi-step solution to address this noise, involving performing principal component analysis on the CpG-level data followed by biological age prediction using principal components as input. This method extracts shared systematic variation in DNAm while minimizing random noise from individual CpGs. Our novel principal-component versions of six clocks show agreement between most technical replicates within 0 to 1.5 years, equivalent or improved prediction of outcomes, and more stable trajectories in longitudinal studies and cell culture. This method entails only one additional step compared to traditional clocks, does not require prior knowledge of CpG reliabilities, and can improve the reliability of any existing or future epigenetic biomarker. The high reliability of principal component-based epigenetic clocks will make them particularly useful for applications in personalized medicine and clinical trials evaluating novel aging interventions.

bioinformatics↗

bioRxiv2019

Interaction between polygenic liability for schizophrenia and childhood adversity influences daily-life emotional dysregulation and psychosis proneness

BackgroundThe earliest stages of the pluripotent psychopathology on the pathway to psychotic disorders is represented by emotional dysregulation and subtle psychosis expression, which can be measured using the Ecological Momentary Assessment (EMA). However, it is not clear to what degree common genetic and environmental risk factors for psychosis contribute to variation in these early expressions of psychopathology.\n\nMethodsIn this largest ever EMA study of a general population twin cohort including 593 adolescents and young adults between the ages of 15 and 35 years, we tested whether polygenic risk score for schizophrenia (PRS-S) interacts with childhood adversity (the Childhood Trauma Questionnaire score) and daily-life stressors to influence momentary mental state domains (negative affect, positive affect, and subtle psychosis expression) and stress-sensitivity measures.\n\nResultsBoth childhood adversity and daily-life stressors were associated with increased negative affect, decreased positive affect, and increased subtle psychosis expression, while PRS-S was only associated with increased positive affect. No gene-environment correlation was detected. We have provided novel evidence for interaction effects between PRS-S and childhood adversity to influence momentary mental states [negative affect (b = 0.07, 95% CI 0.01 to 0.13, P = 0.013), positive affect (b = -0.05, 95% CI -0.10 to -0.00, P = 0.043), and subtle psychosis expression (b = 0.11, 95% CI 0.03 to 0.19, P = 0.007)] and stress-sensitivity measures.\n\nConclusionExposure to childhood adversities, particularly in individuals with high PRS-S, is pleiotropically associated with emotional dysregulation and psychosis proneness.

genomics↗

bioRxiv2019

Phenome-wide and Genome-wide Analyses of Quality of Life in Schizophrenia

BackgroundSchizophrenia negatively impacts quality of life (QoL). A handful of variables from small studies have been reported to influence QoL of schizophrenia patients, but a study comprehensively dissecting the genetic and non-genetic contributing factors to QoL in these patients is currently lacking. We adopted a hypothesis-generating approach to assess the phenotypic and genotypic determinants of QoL in schizophrenia.\n\nMethodThe study population consisted of 1,119 patients with a psychotic disorder, 1,979 relatives and 586 healthy controls. Using linear regression, we tested >100 independent demographic, cognitive and clinical phenotypes for their association with QoL in patients. We then performed genome-wide association analyses of QoL and examined the association between polygenic risk scores (PRSs) for schizophrenia, major depressive disorder (MDD), and subjective wellbeing (SW) with QoL.\n\nResultsWe found nine phenotypes to be significantly and independently associated with QoL in patients, the most significant ones being negative (Beta=-1.17; SE=0.05, P=1x10-83; r2=53%), depressive (Beta=-1.07; SE=0.05; P=2x10-79; r2=51%) and emotional distress (Beta=-0.09; SE=0.01; P=4x10-59, r2=38%) symptoms. Schizophrenia and subjective wellbeing PRSs using various P-value thresholds were significantly and consistently associated with QoL (lowest association p-value = 6.8x10-6). Several sensitivity analyses confirmed the results.\n\nConclusionsVarious clinical phenotypes of schizophrenia as well as schizophrenia and subjective wellbeing polygenic risk scores are associated with QoL in schizophrenia patients and their relatives. These may be targeted by clinicians to more easily identify vulnerable schizophrenia patients for further social and clinical interventions to improve their QoL.

genetics↗

bioRxiv2019

Genetic correlation between background EEG oscillations and genetic generalized epilepsy

ObjectiveParoxysmal epileptiform abnormalities on electroencephalography (EEG) are the hallmark of epilepsies, but it is uncertain to what extent epilepsy and background EEG oscillations share neurobiological underpinnings. Here, we aimed to assess the genetic correlation between epilepsy and background EEG oscillations. MethodsConfounding factors, including the heterogeneous etiology of epilepsies and medication effects hamper studies on background brain activity in people with epilepsy. To overcome this limitation, we compared genetic data from a GWAS on epilepsy (n=12,803 people with epilepsy and 24,218 controls) with that from a GWAS on background EEG (n=8,425 subjects without epilepsy), in which background EEG oscillation power was quantified in four different frequency bands: alpha, beta, delta and theta. We replicated our findings in an independent epilepsy replication dataset (n=4851 people with epilepsy and 20,428 controls). To assess the genetic overlap between these phenotypes, we performed genetic correlation analyses using linkage disequilibrium score regression, polygenic risk scores and Mendelian randomization analyses. ResultsOur analyses show strong genetic correlations between genetic generalized epilepsy (GGE) with background EEG oscillations, primarily with the beta frequency band. Furthermore, we show that subjects with higher beta and theta polygenic risk scores have a significantly higher risk of having generalized epilepsy. Mendelian randomization analyses suggest a causal effect of GGE genetic liability on beta oscillations. SignificanceOur results point to shared biological mechanisms underlying background EEG oscillations and the susceptibility for GGE, opening avenues to investigate the clinical utility of background EEG oscillations in the diagnostic work-up of epilepsy.

genetics↗

bioRxiv2017

Comprehensive Pathway Analyses Of Schizophrenia Risk Loci Point To Dysfunctional Postsynaptic Signaling

Large-scale genome-wide association studies (GWAS) have implicated many low-penetrance loci in schizophrenia. However, its pathological mechanisms are poorly understood, which in turn hampers the development of novel pharmacological treatments. Pathway and gene set analyses carry the potential to generate hypotheses about disease mechanisms and have provided biological context to genome-wide data of schizophrenia. We aimed to examine which biological processes are likely candidates to underlie schizophrenia by integrating novel and powerful pathway analysis tools using data from the largest Psychiatric Genomics Consortium schizophrenia GWAS (N = 79 845) and the most recent 2018 schizophrenia GWAS (N = 105 318). By applying a primary unbiased analysis (Multi-marker Analysis of GenoMic Annotation; MAGMA) to weigh the role of biological processes from the MSigDB database, we identified enrichment of common variants in synaptic plasticity and neuron differentiation gene sets. We supported these findings using MAGMA, Meta-Analysis Gene-set Enrichment of variaNT Associations (MAGENTA) and Interval Enrichment Analysis (INRICH) on detailed synaptic signaling pathways from the Kyoto Encyclopedia of Genes and Genomes (KEGG) and found enrichment in mainly the dopaminergic and cholinergic synapses. Moreover, shared genes involved in these neurotransmitter systems had a large contribution to the observed enrichment, protein products of top genes in these pathways showed more direct and indirect interactions than expected by chance, and expression profiles of these genes were largely similar among brain tissues. In conclusion, we provide strong and consistent genetics and protein-interaction informed evidence for the role of postsynaptic signaling processes in schizophrenia, opening avenues for future translational and psychopharmacological studies.

genetics↗