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Biology subjects

Franke, B.

Publications and source records attributed to Franke, B..

At least 19 recordsLinked to original sources

A normative reference for large-scale human brain dynamics across the lifespan

Human brain function emerges from dynamic reconfigurations of large-scale neural networks. While population-level reference charts have transformed the study of static brain structure and connectivity, an equivalent normative framework for intrinsic brain dynamics has been lacking. This gap has limited our ability to characterize individual variability, development, ageing, and mental health conditions at scale. Here, we establish a population-level normative reference for large-scale human brain dynamics using resting-state fMRI data from more than 10,000 individuals spanning the lifespan and 91 scanning sites. We derive a compact set of recurring brain-state configurations that are reproducible across scanners and acquisition paradigms and that generalize to previously unseen cohorts. Anchoring these dynamic states to normative lifespan models enables the quantification of individual deviations relative to population reference distributions. We show that intrinsic brain dynamics undergo systematic reorganization across development and ageing, with pronounced changes before early adulthood and more gradual modulation thereafter. Applying this framework across multiple mental health conditions reveals disorder-specific and highly heterogeneous deviations in brain dynamics that are not captured by static neuroimaging measures. Robust transfer to independent cohorts and longitudinal analyses demonstrate that normative brain dynamics can be reliably assessed out of distribution. These results delineate a population-scale dynamic architecture of the human brain and extend normative brain mapping from static phenotypes to the temporal domain, providing a reference framework for studying brain function across the lifespan in health and disease.

neuroscience↗

Brain Age in Conduct Disorder: A Mega-Analysis of the ENIGMA Antisocial Behavior Working Group

Conduct disorder (CD) is the leading global cause of mental health burden in children and adolescents and has recently been hypothesized to be a neurodevelopmental disorder. Although prior research has identified neuroanatomical differences associated with CD, it remains unclear whether these differences reflect atypical brain development. Here, we investigated the difference between an individuals brain age and chronological age as a proxy for variations in brain maturation. Using a pretrained model, we estimated brain age from structural neuroimaging data obtained from 1,119 youth with CD and 1,183 typically developing controls across 14 international cohorts participating in the ENIGMA-Antisocial Behavior Working Group. Youth with CD exhibited a statistically robust but small acceleration in brain age compared to typically developing youth (around 0.50 years), which was restricted to the adolescence-onset subtype of the disorder. Our large-scale, coordinated analysis provides the first evidence of accelerated neurodevelopment as a potential mechanism underlying CD.

neuroscience↗

KCNQ1 regulates human neuronal development through mitochondrial and insulin signalling pathways

KCNQ1 encodes a voltage-gated potassium channel implicated in various peripheral and neurological disorders, yet its role during human neuronal development remains unclear. To investigate this, we generated KCNQ1 knockouts (KO) in human induced pluripotent stem cell lines and differentiated them into neural stem cells (NSCs) and cortical neurons. KCNQ1-deficient NSCs showed impaired neurite outgrowth, linked to reduced cell adhesion and disrupted neural cell adhesion molecule (NCAM) signalling. This phenotype was reproduced in wild-type and heterozygous lines by pharmacological KCNQ1 inhibition. Whole transcriptome, proteome, and follow-up analyses revealed mitochondrial dysfunction in KO NSCs, including reduced mitochondrial copy number and ATP synthase expression. Additionally, evidence was obtained for an impairment of insulin signalling in NSCs and neurons, with diminished insulin receptor gene expression and perturbation of key downstream signalling pathways (RAS-MAPK, PI3K-AKT). In neurons, KCNQ1 loss resulted in decreased synaptic activity and a more immature gene expression profile. Overall, our work reveals a novel role for KCNQ1 in human neurodevelopment by regulating cell adhesion, mitochondrial function, and insulin signalling. This work increases our understanding of KCNQ1 function in neurons and its contribution to neurological phenotypes observed in patients with KCNQ1-related diseases.

molecular biology↗

Thalamic Nuclei Volumes Across Psychiatric and Neurological Disorders: A Multi-Site Magnetic Resonance Imaging Study

The human thalamus is an integrative hub for multiple cortical and subcortical circuits involved in sensory processing and higher cognitive functions. Thalamic volume differences have been reported across multiple psychiatric and neurological disorders, but previous studies have typically relied on small samples, focused on one or a limited number of disorders, or investigated the thalamus as a whole without considering its functional subdivisions. In this multi-site study, we compared thalamic nuclei volumes across mild cognitive impairment (MCI), dementia (DEM), major depressive disorder, schizophrenia spectrum disorder (SCZ), clinical high risk for schizophrenia, bipolar spectrum disorder, autism spectrum disorder, attention-deficit/hyperactivity disorder, Parkinsons disease, multiple sclerosis (MS), and healthy controls (N > 8 000). Using structural MRI, we segmented 25 bilateral thalamic nuclei, corresponding to six anatomical groups. Linear models revealed that anterior, medial and lateral regions of the thalamus were significantly smaller in several conditions, with largest effects observed for MCI, DEM, SCZ and MS. In contrast, the ventral and intralaminar groups were relatively normal. This pattern of effects largely corresponds to the canonical functional subdivision of the thalamus into higher-order and sensory regions. At the level of individual nuclei, the clinical conditions were associated with distinct patterns of alterations, while left and right lateral geniculate nuclei were implicated in six of the disorders, suggesting a possible relation with circadian and sleep disturbances. Together, the results highlight a role for the higher-order thalamus in common brain disorders and a differential involvement at the nuclei level, refining our understanding of thalamic pathology across common brain disorders.

neuroscience↗

Multiscale heterogeneity of white matter morphometry in psychiatric disorders

BackgroundInter-individual variability in neurobiological and clinical characteristics in mental illness is often overlooked by classical group-mean case-control studies. Studies using normative modelling to infer person-specific deviations of grey matter volume have indicated that group means are not representative of most individuals. The extent to which this variability is present in white matter morphometry, which is integral to brain function, remains unclear. MethodsWe applied Warped Bayesian Linear Regression normative models to T1- weighted magnetic resonance imaging data and mapped inter-individual variability in person-specific white matter volume deviations in 1,294 cases (58% male) diagnosed with one of six disorders (attention-deficit/hyperactivity, autism, bipolar, major depressive, obsessive-compulsive and schizophrenia) and 1,465 matched controls (54% male) recruited across 25 scan sites. We developed a framework to characterize deviation heterogeneity at multiple spatial scales, from individual voxels, through inter- regional connections, specific brain regions, and spatially extended brain networks. ResultsThe specific locations of white matter volume deviations were highly heterogeneous across participants, affecting the same voxel in fewer than 8% of individuals with the same diagnosis. For autism and schizophrenia, negative deviations (i.e., areas where volume is lower than normative expectations) aggregated into common tracts, regions and large-scale networks in up to 35% of individuals. ConclusionsThe prevalence of white matter volume deviations was lower than previously observed in grey matter, and the specific location of these deviations was highly heterogeneous when considering voxel-wise spatial resolution. Evidence of aggregation within common pathways and networks was apparent in schizophrenia and autism but not other disorders.

neuroscience↗

Liver Receptor Homolog-1 (LRH-1/NR5A2) orchestrates hepatic inflammation and TNF-induced cell death

Liver Receptor Homolog-1 (LRH-1/NR5A2) is a nuclear receptor that has been shown to promote apoptosis resistance in various tissues and disease contexts, however, its role in liver cell death remains unexplored. Deletion of LRH-1 in hepatocytes developed into a mild steatosis and inflammation already under steady-state conditions. Unexpectedly, hepatocyte-specific deletion of LRH-1 also resulted in a profound protection of mice from TNF-induced hepatocyte apoptosis and associated hepatitis. LRH-1-deficient hepatocytes showed elevated NF-B activity, while LRH-1 overexpression inhibited NF-B activity. This inhibition was based on direct physical interaction of the ligand-binding domain of LRH-1 and the Rel homology domain of NF-B subunit RelA. Mechanistically, we found that increased transcription of anti-apoptotic NF-B target genes, together with proteasomal degradation of pro-apoptotic BIM via regeneration-driven EGF receptor signaling, prevented mitochondrial apoptosis, ultimately protecting mice from TNF-induced liver damage. Collectively, our study demonstrates that LRH-1 is a critical modulator of cell death and inflammation in the healthy and diseased liver. HighlightsO_LIHepatic LRH-1 deletion causes mild liver steatosis, fibrosis, and inflammation. C_LIO_LIFemale LRH-1-deficient mice are protected from TNF-induced liver damage. C_LIO_LILRH-1 interacts with NF-B and inhibits its activity. C_LIO_LILRH-1 deletion-provoked inflammation causes degradation of pro-apoptotic protein BIM. C_LI Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=125 SRC="FIGDIR/small/542039v1_ufig1.gif" ALT="Figure 1"> View larger version (35K): org.highwire.dtl.DTLVardef@13cd609org.highwire.dtl.DTLVardef@1080012org.highwire.dtl.DTLVardef@3cd9c4org.highwire.dtl.DTLVardef@fd0438_HPS_FORMAT_FIGEXP M_FIG C_FIG

cell biology↗

A human in vitro neuronal model for studying homeostatic plasticity at the network level

Mechanisms that underlie homeostatic plasticity have been extensively investigated at single-cell levels in animal models, but are less well understood at the network level. Here, we used microelectrode arrays to characterize neuronal networks following induction of homeostatic plasticity in human induced pluripotent stem cell (hiPSC)-derived glutamatergic neurons co-cultured with rat astrocytes. Chronic suppression of neuronal activity through tetrodotoxin (TTX) elicited a time-dependent network re-arrangement. Increased expression of AMPA receptors and the elongation of axon initial segments were associated with increased network excitability following TTX treatment. Transcriptomic profiling of TTX-treated neurons revealed up-regulated genes related to extracellular matrix organization, while down-regulated genes related to cell communication; also astrocytic gene expression was found altered. Overall, our study shows that hiPSC-derived neuronal networks provide a reliable in vitro platform to measure and characterize homeostatic plasticity at network and single-cell level; this platform can be extended to investigate altered homeostatic plasticity in brain disorders.

neuroscience↗

A comparison of bioinformatics pipelines for compositional analysis of the human gut microbiome

Investigating the impact of gut microbiome on human health is a rapidly growing area of research. A significant limiting factor in the progress in this field is the lack of consistency between study results, which hampers the correct biological interpretation of findings. One of the reasons is variation of the applied bioinformatics analysis pipelines. This study aimed to compare five frequently used bioinformatics pipelines (NG-Tax 1.0, NG-Tax 2.0, QIIME, QIIME2 and mothur) for the analysis of 16S rRNA marker gene sequencing data and determine whether and how the analytical methods affect the downstream statistical analysis results. Based on publicly available case-control analysis of ADHD and two mock communities, we show that the choice of bioinformatic pipeline does not only impact the analysis of 16S rRNA gene sequencing data but consequently also the downstream association results. The differences were observed in obtained number of ASVs/OTUs (range: 1,958 - 20,140), number of unclassified ASVs/OTUs (range: 210 - 8,092) or number of genera (range: 176 - 343). Also, the case versus control comparison resulted in different results across the pipelines. Based on our results we recommend: i) QIIME1 and mothur when interested in rare and/or low-abundant taxa, ii) NG-Tax1 or NG-Tax2 when favouring stringent artefact filtering, iii) QIIME2 for a balance between two abovementioned points, and iv) to use at least two pipelines to assess robustness of the results. This work illustrates the strengths and limitations of frequently used microbial bioinformatics pipelines in the context of biological conclusions of case-control comparisons. With this, we hope to contribute to "best practice" approaches for microbiome analysis, promoting methodological consistency and replication of microbial findings. Author SummaryStudies increasingly demonstrate the relevance of gut microbiota in understanding human health and disease. However, the lack of consistency between study results is a significant limiting factor of progress in this field. The reasons for this include variation in study design, sample size, bacterial DNA extraction and sequencing method, bioinformatics analysis pipeline and statistical analysis methodology. This paper focuses on the variation generated by bioinformatics pipelines. A choice of a bioinformatic pipeline can influence the assessment of microbial diversity. However, it is unclear to what extent and how the results and conclusion of a case-control study can be influenced. Therefore, we compared the results of a case-control study across different pipelines (applying default settings) while using the same dataset. Our results indicate a lack of consistency across the pipelines. We show that the choice of bioinformatic pipeline not only affects the analysis results of 16S rRNA gene sequencing data from the gut microbiome, but also the associated conclusions for the case-control study. This means different conclusions would be drawn from the same data analysed with different bioinformatic pipeline.

bioinformatics↗

Normative Modeling of Brain Morphometry Across the Lifespan using CentileBrain: Algorithm Benchmarking and Model Optimization

We present an empirically benchmarked framework for sex-specific normative modeling of brain morphometry that can inform about the biological and behavioral significance of deviations from typical age-related neuroanatomical changes and support future study designs. This framework was developed using regional morphometric data from 37,407 healthy individuals (53% female; aged 3-90 years) following a comparative evaluation of eight algorithms and multiple covariate combinations pertaining to image acquisition and quality, parcellation software versions, global neuroimaging measures, and longitudinal stability. The Multivariate Factorial Polynomial Regression (MFPR) emerged as the preferred algorithm optimized using nonlinear polynomials for age and linear effects of global measures as covariates. The MFPR models showed excellent accuracy across the lifespan and within distinct age-bins, and longitudinal stability over a 2-year period. The performance of all MFPR models plateaued at sample sizes exceeding 3,000 study participants. The model and scripts described here are freely available through CentileBrain (https://centilebrain.org/).

bioinformatics↗

Dissociable Cellular and Genetic Mechanisms of Cortical Thinning at Different Life Stages

Mechanisms underpinning age-related variations in cortical thickness in the human brain remain poorly understood. We investigated whether inter-regional age-related variations in cortical thinning (in a multicohort neuroimaging dataset from the ENIGMA Lifespan Working Group totalling 14,248 individuals, aged 4-89 years) depended on cell-specific marker gene expression levels. We found differences amidst early-life (<20 years), mid-life (20-60 years), and late-life (>60 years) in the patterns of association between inter-regional profiles of cortical thickness and expression profiles of marker genes for CA1 and S1 pyramidal cells, astrocytes, and microglia. Gene ontology and enrichment analyses indicated that each of the three life-stages was associated with different biological processes and cellular components: synaptic modeling in early life, neurotransmission in mid-life, and neurodegeneration in late-life. These findings provide mechanistic insights into age-related cortical thinning during typical development and aging.

neuroscience↗

Molecular and connectomic vulnerability shape cross-disorder cortical abnormalities

Numerous brain disorders demonstrate structural brain abnormalities, which are thought to arise from molecular perturbations or connectome miswiring. The unique and shared contributions of these molecular and connectomic vulnerabilities to brain disorders remain unknown, and has yet to be studied in a single multi-disorder framework. Using MRI morphometry from the ENIGMA consortium, we construct maps of cortical abnormalities for thirteen neurodevelopmental, neurological, and psychiatric disorders from N = 21 000 patients and N = 26 000 controls, collected using a harmonized processing protocol. We systematically compare cortical maps to multiple micro-architectural measures, including gene expression, neurotransmitter density, metabolism, and myelination (molecular vulnerability), as well as global connectomic measures including number of connections, centrality, and connection diversity (connectomic vulnerability). We find that regional molecular vulnerability and macroscale brain network architecture interact to drive the spatial patterning of cortical abnormalities in multiple disorders. Local attributes, particularly neurotransmitter receptor profiles, constitute the best predictors of both disorder-specific cortical morphology and cross-disorder similarity. Finally, we find that cross-disorder abnormalities are consistently subtended by a small subset of network epicentres in bilateral sensory-motor, medial temporal lobe, precuneus, and superior parietal cortex. Collectively, our results highlight how local biological attributes and global connectivity jointly shape cross-disorder cortical abnormalities.

neuroscience↗

Missing genetic links between general factors of brain resting-state functional magnetic resonance imaging, cognition and psychopathology

General factors capturing the shared genetics in psychiatric (genomic p-factor) and cognitive traits (genomic g-factor), and more recently in resting-state functional magnetic resonance imaging-derived brain networks, have contributed to our increased understanding of the etiologies in their respective domains. Yet it remains unclear whether general factors can capture the three-way genetic overlap of psychopathology, cognition and brain function. Here we tested for the presence of this genetic overlap via genetic correlation analyses using summary statistics of genome-wide association studies of the p-factor (N = 162,151 cases and 276,846 controls), the g-factor (N = 269,867), and the two genomic factors estimated from the amplitude in resting-state functional magnetic resonance imaging-derived brain networks (N = 31,688). Unlike hypothesized, only the genetic correlation between the p-factor and the g-factor was significant. We conclude that specific functional brain network constructs may have more potential than their derived general dimensions to capture relevant genetic variation for cognition and psychopathology.

neuroscience↗

Longitudinal changes of ADHD symptoms in association with white matter microstructure: a tract-specific fixel-based analysis

BackgroundVariation in the longitudinal course of childhood attention deficit/hyperactivity disorder (ADHD) coincides with neurodevelopmental maturation of brain structure and function. Prior work has attempted to determine how alterations in white matter (WM) relate to changes in symptom severity, but much of that work has been done in smaller cross-sectional samples using voxel-based analyses. Using standard diffusion-weighted imaging (DWI) methods, we previously showed WM alterations were associated with ADHD symptom remission over time in a longitudinal sample of probands, siblings, and unaffected individuals. Here, we extend this work by further assessing the nature of these changes in WM microstructure by including an additional follow-up measurement (aged 18 - 34 years), and using the more physiologically informative fixel-based analysis (FBA). MethodsData were obtained from 139 participants over 3 clinical and 2 follow-up DWI waves, and analyzed using FBA in regions-of-interest based on prior findings. We replicated previously reported significant models and extended them by adding another time-point, testing whether changes in combined ADHD and hyperactivity-impulsivity (HI) continuous symptom scores are associated with fixel metrics at follow-up. ResultsClinical improvement in HI symptoms over time was associated with more fiber density at follow-up in the left corticospinal tract (lCST) (tmax=1.092, standardized effect[SE]=0.044, pFWE=0.016). Improvement in combined ADHD symptoms over time was associated with more fiber cross-section at follow-up in the lCST (tmax=3.775, SE=0.051, pFWE=0.019). Conclusions: Aberrant white matter development involves both lCST micro- and macrostructural alterations, and its path may be moderated by preceding symptom trajectory.

neuroscience↗

Multilevel neural gradients reflect transdiagnostic effects of major psychiatric conditions on cortical morphology

AO_SCPLOWBSTRACTC_SCPLOWIt is increasingly recognized that multiple psychiatric conditions are underpinned by shared neural pathways, affecting similar brain systems. Here, we assessed i) shared dimensions of alterations in cortical morphology across six major psychiatric conditions (autism spectrum disorder, attention deficit/hyperactivity disorder, major depression, obsessive-compulsive disorder, bipolar disorder, schizophrenia) and ii) carried out a multiscale neural contextualization, by cross-referencing shared anomalies against cortical myeloarchitecture and cytoarchitecture, as well as connectome and neurotransmitter organization. Pooling disease-related effects on MRI-based cortical thickness measures across six ENIGMA working groups, including a total of 28,546 participants (12,876 patients and 15,670 controls), we computed a shared disease dimension on cortical morphology using principal component analysis that described a sensory-fugal pattern with paralimbic regions showing the most consistent abnormalities across conditions. The shared disease dimension was closely related to cortical gradients of microstructure and intrinsic connectivity, as well as neurotransmitter systems, specifically serotonin and dopamine. Our findings embed the shared effects of major psychiatric conditions on brain structure in multiple scales of brain organization and may provide novel insights into neural mechanisms into transdiagnostic vulnerability.

neuroscience↗

Subtly altered topological asymmetry of brain structural covariance networks in autism spectrum disorder across 43 datasets from the ENIGMA consortium

Small average differences in the left-right asymmetry of cerebral cortical thickness have been reported in individuals with autism spectrum disorder (ASD) compared to typically developing controls. Although these alterations affect multiple and widespread cortical regional asymmetries, the extent to which specific structural networks might be affected remains unknown. Inter-regional morphological covariance analysis can capture network connectivity relations between different cortical areas at the macroscale level. Here, we used cortical thickness data from 1,455 individuals with ASD and 1,560 controls, across 43 independent datasets of the ENIGMA consortiums ASD Working Group, to assess hemispheric asymmetries of intra-individual structural covariance networks, using graph theory-based topological metrics. Compared with typical features of small-world architecture in controls, the ASD sample showed significantly altered asymmetry of hemispheric networks involving the fusiform, rostral middle frontal, and medial orbitofrontal cortex, driven by shifts toward higher randomization of the corresponding right-hemispheric networks in ASD. A network involving the superior frontal cortex showed decreased right-hemisphere randomization. Based on comparisons with meta-analyzed functional neuroimaging data, the altered connectivity asymmetry particularly affected networks that subserve working memory, executive functions, language, reading, and sensorimotor processes. Taken together, these findings provide new insights into how altered brain left-right asymmetry in ASD affects specific structural and functional brain networks. Altered asymmetrical brain development in ASD may be partly propagated among spatially distant regions through structural connectivity.

neuroscience↗

Conformational changes in twitchin kinase in vivo revealed by FRET imaging of freely moving C. elegans

The force-induced unfolding and refolding of proteins is speculated to be a key mechanism in the sensing and transduction of mechanical signals in the living cell. Yet, little evidence has been gathered for its existence in vivo. Prominently, stretch-induced unfolding is postulated to be the activation mechanism of the twitchin/titin family of autoinhibited sarcomeric kinases linked to the mechanical stress response of muscle. To test the occurrence of mechanical kinase activation in living working muscle, we generated transgenic C. elegans expressing twitchin containing FRET moieties flanking the kinase domain and developed a quantitative technique for extracting FRET signals in freely moving C. elegans, using tracking and simultaneous imaging of animals in three channels (donor fluorescence, acceptor fluorescence, and transmitted light). Computer vision algorithms were used to extract fluorescence signals and muscle contraction states in each frame, in order to obtain fluorescence and body curvature measurements with spatial and temporal precision in vivo. The data revealed statistically significant periodic changes in FRET signals during muscle activity, consistent with a periodic change in the conformation of twitchin kinase. We conclude that stretch-unfolding of twitchin kinase occurs in the active muscle, whereby mechanical activity titrates the signalling pathway of this cytoskeletal kinase. We anticipate that the methods we have developed here could be applied to obtaining in vivo evidence for force-induced conformational changes or elastic behavior of other proteins not only in C. elegans but in other animals in which there is optical transparency (e.g zebrafish).

biophysics↗

Shared genetic influences on resting-state functional networks of the brain

The amplitude of activation in brain resting state networks (RSNs), measured with resting-state functional MRI, is heritable and genetically correlated across RSNs, indicating pleiotropy. Recent univariate genome-wide association studies (GWAS) explored the genetic underpinnings of individual variation in RSN activity. Yet univariate genomic analyses do not describe the pleiotropic nature of RSNs. In this study we used a novel multivariate method called genomic SEM to model latent factors that capture the shared genomic influence on RSNs and to identify SNPs and genes driving this pleiotropy. Using summary statistics from GWAS of 21 RSNs reported in UK Biobank (N = 31,688), the genomic latent factor analysis was first conducted in a discovery sample (N = 21,081), and then tested in an independent sample from the same cohort (N = 10,607). In the discovery sample, we show that the genetic organization of RSNs can be best explained by two distinct but correlated genetic factors that divide multimodal association networks and sensory networks. Eleven of the 17 factor loadings were replicated in the independent sample. With the multivariate GWAS, we found and replicated nine independent SNPs associated with the joint architecture of RSNs. Further, by combining the discovery and replication samples, we discovered additional SNP and gene associations with the two factors of RSN amplitude. We conclude that modelling the genetic effects on brain function in a multivariate way is a powerful approach to learn more about the biological mechanisms involved in brain function.

neuroscience↗

Quantitative Genetic Scoring, or how to put a number on an arbitrary genetic region

MotivationWith the increasing availability of genome-wide genetic data, methods to combine genetic variables with other sources of data in statistical models are required. This paper introduces quantitative genetic scoring (QGS), a dimensionality reduction method to create quantitative genetic variables representing arbitrary genetic regions. MethodsQGS is defined as the sum of absolute differences in the genetic sequence between a subject and a reference population. QGS properties such as distribution and sensitivity to region size were examined, and QGS was tested in six different existing genomic data sets of various sizes and various phenotypes. ResultsQGS can reduce genetic information by >98% yet explain phenotypic variance at low, medium, and high level of granularity. Associations based on QGS are independent of both size and linkage disequilibrium structure of the underlying region. In combination with stability selection, QGS finds significant results where a traditional genome-wide association approaches struggle. In conclusion, QGS preserves phenotypically significant genetic variance while reducing dimensionality, allowing researchers to include quantitative genetic information in any type of statistical analysis. Availabilityhttps://github.com/machine2learn/QGS Contactgido.schoenmacker@radboudumc.nl Supplemental informationSupplemental data are available online.

bioinformatics↗