Biology subjects

Kahn, R. S.

Publications and source records attributed to Kahn, R. S..

14 recordsLinked to original sources

bioRxiv2025

Quantitative trait loci mapping of gene expression and chromatin accessibility in primary fibroblast reveals shared allelic effects between Latin American and European ancestries

Quantitative Trait Locus (QTL) analysis of molecular data has identified genetic variants associated with traits such as gene expression, and colocalization of these functional QTL with GWAS risk loci has offered insights into the genetic basis of human disease. We employed gene expression (RNA-seq) and chromatin accessibility (ATAC-seq) obtained from human primary fibroblasts to investigate quantitative trait loci (QTLs) in cohorts ascertained for bipolar disorder of European (n=150) and Latin American (n=96) ancestries. Leveraging data from three countries of origin (The Netherlands, Colombia, Costa Rica) within our cohort, we characterized differences among individuals at the SNP, gene, and accessible-chromatin levels to compute ancestry-specific expression (e)QTLs and chromatin-accessibility (ca)QTLs. Across ancestries, we observed R{superscript 2} [≥] 0.93 for eQTL effect sizes and R{superscript 2} [≥] 0.95 for caQTLs, indicating a high degree of concordance. Integrating chromatin data with expression and genotype information enabled precise fine-mapping of eQTLs, yielding 203 high-confidence (posterior probability > 90 %) regulatory pathways. In downstream analyses, transcriptome-wide (TWAS) and chromatin-wide (CWAS) association studies with brain- and skin-related GWAS identified 36 TWAS-significant genes and 77 CWAS-significant open chromatin regions. These findings underscore the shared genetic regulatory mechanisms across European and Latin American ancestries, while demonstrating that ancestry-specific reference panels enhance the accuracy of TWAS and CWAS in diverse populations.

genomics↗

bioRxiv2025

Accurate DNA Methylation Predictor for C9orf72 Repeat Expansion Alleles in the Pathogenic Range

The hexanucleotide (G4C2) repeat expansion in the promoter region of C9orf72 is the most frequent genetic cause of frontotemporal dementia (FTD) and amyotrophic lateral sclerosis (ALS). In this study, we conducted a genome-wide DNA methylation (DNAm) analysis using EPIC version 2 (EPICv2) arrays on an FTD cohort comprising 27 carriers and 250 non-carriers of the pathogenic C9orf72 repeat expansion from the Amsterdam Dementia Cohort. We identified differentially methylated CpGs probes associated with the pathogenic C9orf72 expansion and used these findings to create a DNAm Least Absolute Shrinkage and Selection Operator (LASSO) predictor to identify repeat expansion carriers. Eight CpG sites at the C9orf72 locus were significantly differentially hypermethylated in repeat expansion carriers compared to non-carriers. The LASSO model predicted repeat expansion status with an average accuracy of 98.6%. The LASSO predictor was further validated in an independent cohort of 2,548 subjects with available EPICv2 data, identifying four C9orf72 repeat expansion carriers, subsequently confirmed by repeat-primed PCR. This result not only illustrates the accuracy of the DNAm predictor of C9orf72 repeat expansion carriers but also suggests that repeat expansion carriers may be more prevalent than expected. The identification of a highly accurate DNAm biomarker for a repeat expansion locus associated with neurodegenerative disorders may provide great value for studying this locus. The approach holds significant promise for investigating this and other repeat expansion loci, particularly given the growing interest in epigenetic epidemiological studies involving large cohorts with available DNAm data. Graphical abstract (optional) O_FIG O_LINKSMALLFIG WIDTH=155 HEIGHT=200 SRC="FIGDIR/small/643775v1_ufig1.gif" ALT="Figure 1"> View larger version (44K): org.highwire.dtl.DTLVardef@9d7879org.highwire.dtl.DTLVardef@505560org.highwire.dtl.DTLVardef@154720org.highwire.dtl.DTLVardef@f0c063_HPS_FORMAT_FIGEXP M_FIG C_FIG

genetics↗

bioRxiv2023

Cell type deconvolution of bulk blood RNA-Seq to reveal biological insights of neuropsychiatric disorders

Genome-wide association studies (GWAS) have uncovered susceptibility loci associated with psychiatric disorders like bipolar disorder (BP) and schizophrenia (SCZ). However, most of these loci are in non-coding regions of the genome with unknown causal mechanisms of the link between genetic variation and disease risk. Expression quantitative trait loci (eQTL) analysis of bulk tissue is a common approach to decipher underlying mechanisms, though this can obscure cell-type specific signals thus masking trait-relevant mechanisms. While single-cell sequencing can be prohibitively expensive in large cohorts, computationally inferred cell type proportions and cell type gene expression estimates have the potential to overcome these problems and advance mechanistic studies. Using bulk RNA-Seq from 1,730 samples derived from whole blood in a cohort ascertained for individuals with BP and SCZ this study estimated cell type proportions and their relation with disease status and medication. We found between 2,875 and 4,629 eGenes for each cell type, including 1,211 eGenes that are not found using bulk expression alone. We performed a colocalization test between cell type eQTLs and various traits and identified hundreds of associations between cell type eQTLs and GWAS loci that are not detected in bulk eQTLs. Finally, we investigated the effects of lithium use on cell type expression regulation and found examples of genes that are differentially regulated dependent on lithium use. Our study suggests that computational methods can be applied to large bulk RNA-Seq datasets of non-brain tissue to identify disease-relevant, cell type specific biology of psychiatric disorders and psychiatric medication.

genomics↗

bioRxiv2022

Monozygotic twins discordant for schizophrenia differ in maturation and synaptic transmission

Schizophrenia affects approximately 1% of the world population. Genetics, epigenetics, and environmental factors are known to play a role in this psychiatric disorder. While there is a high concordance in monozygotic twins, about half of twin pairs are discordant for schizophrenia. We characterized human-induced pluripotent stem cell (iPSC)-derived hippocampal neurons from two pairs of monozygotic twins that are discordant for a schizophrenia diagnosis. We compared the affected and the non-affected siblings and compared all of them to twin sets where none of the siblings suffered from schizophrenia. We found that the neurons derived from the schizophrenia patients were less arborized, were hypoexcitable with immature spike features, and exhibited a significant reduction in synaptic activity with dysregulation in synapse-related genes. Interestingly, the neurons derived from the co-twin siblings who did not have schizophrenia formed another distinct group that was different from the neurons in the group of the affected twin siblings but also different from the neurons in the group of the control twins. The neurons in the unaffected co-twin group were also less arborized than the neurons from controls but more arborized than those from affected siblings. Some of their spike features were immature (but less immature than neurons derived from the affected siblings). Importantly, their synaptic activity was not affected. Since schizophrenia is a genetically complex disorder, our twin study allows the measurement of neuronal phenotypes with a similar genetic background. The differences between the siblings may arise due to changes that occurred after the split of the egg into twins. Therefore, our study confirms that dysregulation of synaptic pathways, as well as changes in the rate of synaptic events, distinguishes between individuals affected with schizophrenia and unaffected individuals, even in those having a very similar genetic background.

neuroscience↗

bioRxiv2021

Systematic Evaluation of Machine Learning Algorithms for Neuroanatomically-Based Age Prediction in Youth

Application of machine learning algorithms to structural magnetic resonance imaging (sMRI) data has yielded behaviorally meaningful estimates of the biological age of the brain (brain-age). The choice of the machine learning approach in estimating brain-age in children and adolescents is important because age-related brain changes in these age-groups are dynamic. However, the comparative performance of the multiple machine learning algorithms available has not been systematically appraised. To address this gap, the present study evaluated the accuracy (Mean Absolute Error; MAE) and computational efficiency of 21 machine learning algorithms using sMRI data from 2,105 typically developing individuals aged 5 to 22 years from five cohorts. The trained models were then tested in two independent holdout datasets, comprising 4,078 pre-adolescents aged 9-10 years and another sample of 594 individuals aged 5-21 years. The algorithms encompassed parametric and nonparametric, Bayesian, linear and nonlinear, tree-based, and kernel-based models. Sensitivity analyses were performed for parcellation scheme, number of neuroimaging input features, number of cross-validation folds, number of extreme outliers, and sample size. The best performing algorithms were Extreme Gradient Boosting (MAE of 1.49), Random Forest Regression (MAE of 1.58) and Support Vector Regression with Radial Basis Function Kernel (MAE of 1.64) which had acceptable and comparable computational efficiency. Findings of the present study could be used as a guide for optimizing methodology when quantifying age-related changes in youth.

neuroscience↗

bioRxiv2021

Powerful eQTL mapping through low coverage RNA sequencing

Mapping genetic variants that regulate gene expression (eQTL mapping) in large-scale RNA sequencing (RNA-seq) studies is often employed to understand functional consequences of regulatory variants. However, the high cost of RNA-Seq limits sample size, sequencing depth, and therefore, discovery power. In this work, we demonstrate that, given a fixed budget, eQTL discovery power can be increased by lowering the sequencing depth per sample and increasing the number of individuals sequenced in the assay. We perform RNA-Seq of whole blood tissue across 1490 individuals at low-coverage (5.9 million reads/sample) and show that the effective power is higher than that of an RNA-Seq study of 570 individuals at high-coverage (13.9 million reads/sample). Next, we leverage synthetic datasets derived from real RNA-Seq data to explore the interplay of coverage and number individuals in eQTL studies, and show that a 10-fold reduction in coverage leads to only a 2.5-fold reduction in statistical power. Our study suggests that lowering coverage while increasing the number of individuals is an effective approach to increase discovery power in RNA-Seq studies.

genomics↗

bioRxiv2020

Atlas of genetic effects in human microglia transcriptome across brain regions, aging and disease pathologies

Microglial cells have emerged as potential key players in brain aging and pathology. To capture the heterogeneity of microglia across ages and regions, and to understand how genetic risk for neurological and psychiatric brain disorders is related to microglial function, large transcriptome studies are essential. Here, we describe the transcriptome analysis of 255 primary human microglia samples isolated at autopsy from multiple brain regions of 100 human subjects. We performed systematic analyses to investigate various aspects of microglial heterogeneities, including brain region, age and sex. We mapped expression and splicing quantitative trait loci and showed that many neurological disease susceptibility loci are mediated through gene expression or splicing in microglia. Fine-mapping of these loci nominated candidate causal variants that are within microglia-specific enhancers, including novel associations with microglia expression of USP6NL for Alzheimers disease, and P2RY12 for Parkinsons disease. In summary, we have built the most comprehensive catalog to date of genetic effects on the microglia transcriptome and propose molecular mechanisms of action of candidate functional variants in several neurological and psychiatric diseases.

genetics↗

bioRxiv2020

Sex differences in lifespan trajectories and variability of human sulcal and gyral morphology

Sex differences in development and aging of human sulcal morphology have been understudied. We charted sex differences in trajectories and inter-individual variability of global sulcal depth, width, and length, pial surface area, exposed (hull) gyral surface area, unexposed sulcal surface area, cortical thickness, and cortex volume across the lifespan in a longitudinal sample (700 scans, 194 participants two scans, 104 three scans, age range: 16-70 years) of neurotypical males and females. After adjusting for brain volume, females had thicker cortex and steeper thickness decline until age 40 years; trajectories converged thereafter. Across sexes, sulcal shortening was faster before age 40, while sulcal shallowing and widening were faster thereafter. While hull area remained stable, sulcal surface area declined and was more strongly associated with sulcal shortening than with sulcal shallowing and widening. Males showed greater variability for cortex volume and thickness and lower variability for sulcal width. Across sexes, variability decreased with age for all measures except for cortical volume and thickness. Our findings highlight the association between loss of sulcal area, notably through sulcal shortening, with cortex volume loss. Studying sex differences in lifespan trajectories may improve knowledge of individual differences in brain development and the pathophysiology of neuropsychiatric conditions.

neuroscience↗

bioRxiv2019

Schizophrenia is characterized by age- and sex-specific effects on epigenetic aging

BackgroundThe study of biological age acceleration may help identify at-risk individuals and contribute to reduce the rising global burden of age-related diseases. Using DNA methylation (DNAm) clocks, we investigated biological aging in schizophrenia (SCZ), a severe mental illness that is associated with an increased prevalence of age-related disabilities and morbidities. In a multi-cohort whole blood sample consisting of 1,090 SCZ cases and 1,206 controls, we investigated differential aging using three DNAm clocks (i.e. Hannum, Horvath, Levine). These clocks are highly predictive of chronological age and are known to capture different processes of biological aging. ResultsWe found that blood-based DNAm aging is significantly altered in SCZ with age- and sex-specific effects that differ between clocks and map to distinct chronological age windows. Most notably, differential phenotypic age (Levine clock) was most pronounced in female SCZ patients in later adulthood compared to matched controls. Female patients with high SCZ polygenic risk scores (PRS) present the highest age acceleration in this age group with +4.30 years (CI: 2.40-6.20, P=1.3E-05). Phenotypic age and SCZ PRS contribute additively to the illness and together explain up to 22.4% of the variance in disease status in this study. This suggests that combining genetic and epigenetic predictors may improve predictions of disease outcomes. ConclusionsSince increased phenotypic age is associated with increased risk of all-cause mortality, our findings indicate that specific and identifiable patient groups are at increased mortality risk as measured by the Levine clock. These results provide new biological insights into the aging landscape of SCZ with age- and sex-specific effects and warrant further investigations into the potential of DNAm clocks as clinical biomarkers that may help with disease management in schizophrenia.

genomics↗

bioRxiv2019

Changes in the intracranial volume from early adulthood to the sixth decade of life: A longitudinal study

Normal brain-aging occurs at all structural levels. Excessive pathophysiological changes in the brain, beyond the normal one, are implicated in the etiology of brain disorders such as severe forms of the schizophrenia spectrum and dementia. To account for brain-aging in health and disease, it is critical to study the age-dependent trajectories of brain biomarkers at various levels and among different age groups. The intracranial volume (ICV) is a key biological marker, and changes in the ICV during the lifespan can teach us about the biology of development, aging, and gene X environment interactions. However, whether ICV changes with age in adulthood is not resolved. Applying a semi-automatic in-house-built algorithm for ICV extraction on T1w MR brain scans in the Dutch longitudinal cohort (GROUP), we measured ICV changes. Individuals between the ages of 16 and 55 years were scanned up to three consecutive times with 3.32{+/-}0.32 years between consecutive scans (N=482, 359, 302). Using the extracted ICVs, we calculated ICV longitudinal aging-trajectories based on three analysis methods; direct calculation of ICV differences between the first and the last scan, fitting all ICV measurements of individuals to a straight line and applying a global linear mixed model fitting. We report statistically significant increases in the ICV in adulthood until the fourth decade of life (average change +0.03%/y, or about 0.5 ml/y, at age 20), and decreases in the ICV afterward (-0.09%/y, or about -1.2 ml/y, at age 55). To account for previous cross-sectional reports of ICV changes, we analyzed the same data using a cross-sectional approach. Our cross-sectional analysis detected ICV changes consistent with the previously reported cross-sectional effect. However, the reported amount of cross-sectional changes within this age range was significantly larger than the longitudinal changes. We attribute the cross-sectional results to a generational effect. In conclusion, the human intracranial volume does not stay constant during adulthood but instead shows a small increase during young adulthood and a decrease thereafter from the fourth decade of life. The age-related changes in the longitudinal setup are smaller than those reported using cross-sectional approaches and unlikely to affect structural brain imaging studies correcting for intracranial volume considerably. As to the possible mechanisms involved, this awaits further study, although thickening of the meninges and skull bones have been proposed, as well as a smaller amount of brain fluids addition above the overall loss of brain tissue.

neuroscience↗

bioRxiv2018

Whole blood transcriptome analysis in bipolar disorder reveals strong lithium effect

Bipolar disorder (BD) is a highly heritable mood disorder with complex genetic architecture and poorly understood etiology. We performed a whole blood transcriptome analysis in a BD case-control sample (Nsubjects = 480) by RNA sequencing. While we observed widespread differential gene expression patterns between affected and unaffected individuals, these effects were largely linked to lithium treatment at the time of blood draw (FDR < 0.05, Ngenes = 976) rather than BD diagnosis itself (FDR < 0.05, Ngenes = 6). These lithium-associated genes were enriched for cell signaling and immune response functional annotations, among others, and were associated with neutrophil cell-type proportions, which were elevated in lithium users. Neither genes with altered expression in cases nor in lithium users were enriched for BD, schizophrenia, and depression genetic risk based on information from genome-wide association studies, nor was gene expression associated with polygenic risk scores for BD. Our findings suggest that BD is associated with minimal changes in whole blood gene expression independent of medication use but underline the importance of accounting for medication use and cell type heterogeneity in psychiatric transcriptomic studies. The results of our study add to mounting evidence of lithium's cell signaling and immune-related mechanisms.

genomics↗

bioRxiv2018

Overlapping but asymmetrical relationships between schizophrenia and autism revealed by brain connectivity

(Abstract included 248 words)Although the relationship between schizophrenia spectrum disorder (SSD) and autism spectrum disorder (ASD) has long been debated, it has not yet been fully elucidated. To address this issue, we took advantage of dual (ASD and SSD) classifiers that discriminate patients from their controls based on resting state brain functional connectivity. An SSD classifier using sophisticated machine-learning algorithms that automatically selected SSD- specific functional connections was applied to Japanese datasets including adult patients with SSD in a chronic stage. We demonstrated good performance of the SSD classification for independent validation cohorts. The generalizability was tested by USA and European cohorts in a chronic stage, and one USA cohort including first episode schizophrenia. The specificity was tested by two adult Japanese cohorts of ASD and major depressive disorder, and one European cohort of attention-deficit hyperactivity disorder. The weighted linear summation of the classifiers functional connections constituted the biological dimensions representing neural liability to the disorders. Our previously developed robust ASD classifier constituted the ASD dimension. Distributions of individuals with SSD, ASD and healthy controls were examined on the SSD and ASD biological dimensions. The SSD and ASD populations exhibited overlapping but asymmetrical patterns on the two biological dimensions. That is, the SSD population showed increased liability on the ASD dimension, but not vice versa. Furthermore, the two dimensions were correlated within the ASD population but not the SSD population. Using the two biological dimensions based on resting-state functional connectivity enabled us to quantify and visualize the relationships between SSD and ASD.

neuroscience↗

bioRxiv2018

Shared vulnerability for connectome alterations across psychiatric and neurological brain disorders

Macroscale white matter pathways form the infrastructure for large-scale communication in the human brain, a prerequisite for healthy brain function. Conversely, disruptions in the brains connectivity architecture are thought to play an important role in a wide range of psychiatric and neurological brain disorders. Here we show that especially connections important for global communication and network integration are involved in a wide range of brain disorders. We report on a meta-analytic connectome study comprising in total 895 patients and 1,016 controls across twelve neurological and psychiatric disorders. We extracted disorder connectome fingerprints for each of these twelve disorders, which were then combined into a cross-disorder disconnectivity involvement map, representing the involvement of each brain pathway across brain disorders. Our findings show connections central to the brains infrastructure are disproportionally involved across a wide range of disorders. Connections critical for global network communication and integration display high disturbance across disorders, suggesting a general cross-disorder involvement and importance of these pathways in normal function. Taken together, our cross-disorder study suggests a convergence of disconnectivity across disorders to a partially shared disconnectivity substrate of central connections.

neuroscience↗

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↗