Biology subjects

Andreassen, O. A.

Publications and source records attributed to Andreassen, O. A..

At least 55 records · Page 3Linked to original sources

bioRxiv2019

Cannabis use is associated with increased levels of soluble gp130 in schizophrenia but not in bipolar disorder

The complex effects of plant cannabinoids on human physiology is not yet fully understood, but include a wide spectrum of effects on immune modulation. The immune system and its inflammatory effector pathways are recently emerging as possible causative factors in psychotic disorders. The present study aimed to investigate whether self-administered cannabis use was associated with changes in circulating immune and neuroendocrine markers in schizophrenia (SCZ, n=401) and bipolar disorder patients (BD, n=242). A screening of 13 plasma markers reflecting different inflammatory pathways was performed in SCZ and BD patients after subdividing each group into cannabis user and non-user subgroups. We found that i) soluble gp130 (sgp130) concentrations were significantly elevated among cannabis users in the SCZ group (p=0.002) after multiple testing correction, but not in BD. ii) Nominally significant differences were observed in the levels of IL-1RA (p=0.0059), YKL40 (p=0.0069), CatS (p=0.013), sTNFR1 (p=0.031), and BDNF (p=0.020), where these factors exhibited higher plasma levels in cannabis user SCZ patients than in non-users. iii) These differences in systemic levels were not reflected by altered mRNA expression of genes encoding sgp130, IL-1RA, YKL40, CatS, sTNFR1, and BDNF in whole blood. In sum, our results show that cannabis self-administration is associated with markedly higher sgp130 levels in SCZ, but not in BD, and that this phenomenon is independent of the modulation of peripheral immune cells. These findings warrant further investigation into the potential neuroimmune, anti-inflammatory, and biobehavioral-cognitive effects of cannabis use in SCZ.

pharmacology and toxicology↗

bioRxiv2019

Differences in directed functional brain connectivity related to age and sex

ObjectiveFunctional interconnections between brain regions define the connectome which is of central interest for understanding human brain function. Resting-state functional magnetic resonance (rsfMRI) work has revealed changes in static connectivity related to age, sex, cognitive abilities and psychiatric symptoms, yet little is known how these factors may alter the information flow. The commonly used approach infers functional brain connectivity using stationary coefficients yielding static estimates of the undirected connection strength between brain regions. Dynamic graphical models (DGMs) are a multivariate model with dynamic coefficients reflecting directed temporal associations between nodes, and can yield novel insight into directed functional connectivity. Here, we leveraged this approach to test for associations between edge-wise estimates of direction flow across the brain connectome and age, sex, intellectual abilities and mental health. MethodsWe applied DGM to investigate patterns of information flow in data from 984 individuals from the Human Connectome Project (HCP) and 10,249 individuals from the UK Biobank. ResultsOur analysis yielded patterns of directed connectivity in independent HCP and UK Biobank data similar to those previously reported, including that the cerebellum consistently receives information from other networks. We show robust associations between information flow and age and sex for several connections, with strongest effects of age observed in the sensorimotor network. Visual, auditory and sensorimotor nodes were also linked to mental health. DiscussionOur findings support the use of DGM as a measure of directed connectivity in rsfMRI data and provide new insight into the shaping of the connectome during aging.

neuroscience↗

bioRxiv2019

Annotation-Informed Causal Mixture Modeling (AI-MiXeR) reveals phenotype-specific differences in polygenicity and effect size distribution across functional annotation categories

Determining the contribution of functional genetic categories is fundamental to understanding the genetic etiology of complex human traits and diseases. Here we present Annotation Informed MiXeR: a likelihood-based method to estimate the number of variants influencing a phenotype and their effect sizes across different functional annotation categories of the genome using summary statistics from genome-wide association studies. Applying the model to 11 complex phenotypes suggests diverse patterns of functional category-specific genetic architectures across human diseases and traits.

genetics↗

bioRxiv2019

Making the MOSTest of imaging genetics

Regional brain morphology has a complex genetic architecture, consisting of many common polymorphisms with small individual effects, which has proven challenging for genome-wide association studies to date, despite its high heritability1,2. Given the distributed nature of the genetic signal across brain regions, joint analysis of regional morphology measures in a multivariate statistical framework provides a way to enhance discovery of genetic variants with current sample sizes. While several multivariate approaches to GWAS have been put forward over the past years3-5, none are optimally suited for complex, large-scale data. Here, we applied the Multivariate Omnibus Statistical Test (MOSTest), with an efficient computational design enabling rapid and reliable permutation-based inference, to 171 subcortical and cortical brain morphology measures from 26,502 participants of the UK Biobank (mean age 55.5 years, 52.0% female). At the conventional genome-wide significance threshold of =5x10-8, MOSTest identifies 347 genetic loci associated with regional brain morphology, more than any previous study, improving upon the discovery of established GWAS approaches more than threefold. Our findings implicate more than 5% of all protein-coding genes and provide evidence for gene sets involved in neuron development and differentiation. As such, MOSTest, which we have made publicly available, enhances our understanding of the genetic determinants of regional brain morphology.

neuroscience↗

bioRxiv2019

A genome-wide genetic pleiotropy approach identified shared loci between multiple system atrophy and inflammatory bowel disease

We aimed to identify shared genetic background between multiple system atrophy (MSA) and autoimmune diseases by using the conjFDR approach. Our study showed significant genetic overlap between MSA and inflammatory bowel disease and identified DENND1B, C7, and RSP04 loci, which are linked to significant changes in methylation or expression levels of adjacent genes. We obtained evidence of enriched heritability involving immune/digestive categories. Finally, an MSA mouse model showed dysregulation of the C7 gene in the degenerating midbrain compared to wildtype mice. The results identify novel molecular mechanisms and implicate immune and gut dysfunction in MSA pathophysiology.

genetics↗

bioRxiv2019

Shared Genetic Risk between Eating Disorder- and Substance-Use-Related Phenotypes: Evidence from Genome-Wide Association Studies

Eating disorders and substance use disorders frequently co-occur. Twin studies reveal shared genetic variance between liabilities to eating disorders and substance use, with the strongest associations between symptoms of bulimia nervosa (BN) and problem alcohol use (genetic correlation [rg], twin-based=0.23-0.53). We estimated the genetic correlation between eating disorder and substance use and disorder phenotypes using data from genome-wide association studies (GWAS). Four eating disorder phenotypes (anorexia nervosa [AN], AN with binge-eating, AN without binge-eating, and a BN factor score), and eight substance-use-related phenotypes (drinks per week, alcohol use disorder [AUD], smoking initiation, current smoking, cigarettes per day, nicotine dependence, cannabis initiation, and cannabis use disorder) from eight studies were included. Significant genetic correlations were adjusted for variants associated with major depressive disorder (MDD). Total sample sizes per phenotype ranged from ~2,400 to ~537,000 individuals. We used linkage disequilibrium score regression to calculate single nucleotide polymorphism-based genetic correlations between eating disorder and substance-use-related phenotypes. Significant positive genetic associations emerged between AUD and AN (rg=0.18; false discovery rate q=0.0006), cannabis initiation and AN (rg=0.23; q<0.0001), and cannabis initiation and AN with binge-eating (rg=0.27; q=0.0016). Conversely, significant negative genetic correlations were observed between three non-diagnostic smoking phenotypes (smoking initiation, current smoking, and cigarettes per day) and AN without binge-eating (rgs=-0.19 to -0.23; qs<0.04). The genetic correlation between AUD and AN was no longer significant after co-varying for MDD loci. The patterns of association between eating disorder- and substance-use-related phenotypes highlights the potentially complex and substance-specific relationships between these behaviors.

genetics↗

bioRxiv2019

Impact of second-generation antipsychotics on white matter microstructure in adolescent-onset psychosis.

White matter abnormalities are well-established in adult patients with psychosis. Yet less is known about changes in early onset psychosis (EOP) during adolescence, especially whether antipsychotic medication might impact white matter microstructure in this sensitive phase. Here, we utilized Magnetic Resonance Imaging (MRI) in unmedicated and medicated adolescent EOP patients in comparison to healthy controls to examine the impact of antipsychotic medication status on indices of white matter microstructure. Twenty-two EOP patients (11 unmedicated) and 33 healthy controls, aged between 12-18 years, underwent 3T diffusion-weighted MRI. Using Tract-based Spatial Statistics, we calculate case-control differences in scalar diffusion measures, i.e. fractional anisotropy (FA), axial diffusion (AD) and radial diffusion (RD), and investigated their association with antipsychotic medication. We found significantly lower mean FA and AD in largely overlapping areas, particularly in left anterior corona radiata (ACR), in EOP patients relative to healthy controls. Mean FA in the left ACR was significantly associated with antipsychotic medication status (t = 2.991, p = 0.008, R2 = 0.298), showing higher FA values in medicated compared to unmedicated EOP patients. The present study is the first to link antipsychotic medication status to altered regional FA in the left ACR, a region being discussed to contribute to the etiology of psychosis. Yet, further work with larger samples is needed to draw firm conclusions about putatively enhancing effects of antipsychotic medication on white matter microstructure early in the disease process.

neuroscience↗

bioRxiv2019

Linkage Disequilibrium and Heterozygosity Modulate the Genetic Architecture of Human Complex Phenotypes: Evidence of Natural Selection from GWAS Summary Statistics

We propose an extended Gaussian mixture model for the distribution of causal effects of common single nucleotide polymorphisms (SNPs) for human complex phenotypes that depends on linkage disequilibrium (LD) and heterozygosity (H), while also allowing for independent components for small and large effects. Using a precise methodology showing how genome-wide association studies (GWAS) summary statistics (z-scores) arise through LD with underlying causal SNPs, we applied the model to GWAS of multiple human phenotypes. Our findings indicated that causal effects are distributed with dependence on total LD and H, whereby SNPs with lower total LD and H are more likely to be causal with larger effects; this dependence is consistent with models of the influence of negative pressure from natural selection. Compared with the basic Gaussian mixture model it is built on, the extended model - primarily through quantification of selection pressure - reproduces with greater accuracy the empirical distributions of z-scores, thus providing better estimates of genetic quantities, such as polygenicity and heritability, that arise from the distribution of causal effects.

genomics↗

bioRxiv2019

Maturation of cortical microstructure and cognitive development in childhood and adolescence: a T1w/T2w ratio MRI study

The restructuring and optimization of the cerebral cortex from early childhood and through adolescence is an essential feature of human brain development, underlying immense cognitive improvements. Beyond established morphometric cortical assessments, the T1w/T2w ratio quantifies partly separate biological processes, and might inform models of typical neurocognitive development and developmental psychopathology. In the present study, we computed vertex-wise T1w/T2w ratio across the cortical surface in 621 youths (3-21 years) sampled from the Pediatric Imaging, Neurocognition, and Genetics (PING) study and tested for associations with individual differences in age, sex, and both general and specific cognitive abilities. The results showed a near global linear age-related increase in T1w/T2w ratio across the brain surface, with a general posterior to anterior increasing gradient in association strength. Moreover, results indicated that boys in late adolescence had regionally higher T1w/T2w ratio as compared to girls. Across individuals, T1w/T2w ratio was negatively associated with general and several specific cognitive abilities mainly within anterior cortical regions. Our study indicates age-related differences in T1w/T2w ratio throughout childhood, adolescence and young adulthood, in line with the known protracted myelination of the cortex. Moreover, the study supports T1w/T2w ratio as a promising surrogate measure of individual differences in intracortical brain structure in neurodevelopment.

neuroscience↗

bioRxiv2019

Brain age estimation is a sensitive marker of processing speed in the early course of multiple sclerosis

Background and objectivesCognitive deficits in MS are common, also early in the disease course. We aimed to identify if estimated brain age from MRI could serve as an imaging marker for early cognitive symptoms in a longitudinal MS study.\n\nMethodsA group of 76 MS patients (mean age 34 years, 71% females, 96% relapsing-remitting) was examined 1, 2 and 5 years after diagnosis. A machine-learning model using Freesurfer-processed T1-weighted brain MRI data from 3208 healthy controls, was applied to develop a prediction model for brain age. The difference between estimated and chronological brain age was calculated (brain age gap). Tests of memory, attention and executive functions were performed. Associations between brain age gap and cognitive performance were assessed using linear mixed effects (LME) models and corrected for multiple testing.\n\nResultsLME models revealed a significant association between the Color Naming condition of Color-Word Interference Test and brain age gap (t=2.84, p=0.005).\n\nConclusionsIn this study, decreased information processing speed correlated with increased brain age gap. Our findings suggest that brain age estimation using MRI provides a useful semi-automated approach applying machine learning for individual level brain phenotyping and correlates with information processing speed in the early course of MS.

neuroscience↗

bioRxiv2019

Age dependence of modern clinical risk groups for localized prostate cancer -- a population-based study

BackgroundOptimal prostate cancer (PCa) screening strategies will focus on men most likely to have potentially-lethal, localized disease. Age-specific incidence rates (ASIRs) for clinical risk groups could guide risk-stratified screening.\n\nObjectiveDetermine ASIRs and proportions of PCa diagnoses in Norway for modern risk-group and Gleason score categories.\n\nDesign, Setting, and ParticipantsAll men diagnosed with PCa in Norway in 2014-2017 (n=20,356).\n\nOutcome Measurements and Statistical AnalysisPatients were assigned to clinical risk groups: low, favorable-intermediate, unfavorable-intermediate, high, regional, and metastatic, using Gleason score and clinical stage. Associations were assessed between age and (1) Gleason score (including Gleason 3+4 and 4+3) and (2) PCa risk group. Risk-group ASIRs were calculated by multiplying the overall Norwegian ASIR by the proportions observed for each category.\n\nResultsOlder age was significantly associated with higher Gleason score and more advanced disease. For example, among men aged 55-59, 65-69, 75-79, and 85-89 years, the percentage with Gleason 8-10 disease was 16.5%, 23.4%, 37.2%, and 59.9%, respectively (p<0.001); the percentage with at least high-risk disease was 29.3%, 39.1%, 60.4%, and 90.6%, respectively. Corresponding percentages for low-risk PCa were 24.0%, 17.9%, 10.2%, and 4.1% (p<0.001). The respective maximum ASIRs (per 100,000 men) for low-risk, favorable-intermediate-risk, unfavorable-intermediate-risk, high-risk, regional, and metastatic disease were: 157.1, 183.8, 194.8, 408.3, 172.3, and 330.0; incidence for low-risk and favorable-intermediate-risk PCa peaked before age 70, while more advanced categories peaked after 70. At age 75-79 years, the ASIR of high-risk disease was approximately 6 times greater than at 55-59 years.\n\nConclusionsRisk of clinically-significant, localized PCa increases with age. Healthy older men may be among those most likely to benefit from PCa screening.

epidemiology↗

bioRxiv2019

Pupillary dilation responses as a midlife indicator of risk for Alzheimer’s Disease: Association with Alzheimer’s disease polygenic risk

Pathological changes in Alzheimers disease (AD) begin decades before dementia onset. Because locus coeruleus tau pathology is the earliest occurring AD pathology, targeting indicators of locus coeruleus (dys)function may improve midlife screening for earlier identification of AD risk. Pupillary responses during cognitive tasks are driven by the locus coeruleus and index cognitive effort. Several findings suggest task-associated pupillary response as an early marker of AD risk. Requiring greater effort suggests being closer to ones compensatory capacity, and adults with mild cognitive impairment (MCI) have greater pupil dilation during digit span tasks than cognitively normal individuals, despite equivalent task performance. Higher AD polygenic risk scores (AD-PRSs) are associated with increased odds of MCI and tau positivity. We hypothesized that AD-PRSs would be associated with pupillary responses in cognitively normal middle-aged adults. We demonstrated that pupillary responses during digit span tasks were heritable (h2=.30-.36) in 1119 men ages 56-66. We then examined associations between AD-PRSs and pupillary responses in a cognitively normal subset who all had comparable span capacities (n=539). Higher AD-PRSs were associated with greater pupil dilation/effort in a high (9-digit recall) cognitive load condition; Cohens d=.36 for the upper versus lower quartile of the AD-PRS distribution. Results held up after controlling for APOE genotype. The results support pupillary response--and by inference, locus coeruleus dysfunction--as a genetically-mediated biomarker of early MCI/AD risk. In some studies, cognition predicted disease progression earlier than biomarkers. Pupillary responses might improve screening and early identification of genetically at-risk individuals even before cognitive performance declines.

genetics↗

bioRxiv2019

Sex-dependent polygenic effects on the clinical progressions of Alzheimer’s disease

Sex differences in the manifestations of Alzheimers disease (AD) are under intense investigations 1,2. Despite the emerging importance of polygenic predictions for AD 3-8, the sex-dependent polygenic effects have not been demonstrated. Here, using a sex crossover analysis, we show that sex-dependent autosomal genetic effects on AD can be revealed by characterizing disease progress via the hazard function. We first performed sex-stratified genome-wide associations, and then applied derived sex-dependent weights to two independent cohorts. Sex-matched polygenic hazard scores (PHS) have significantly stronger associations with age-at-disease-onset, clinical progressions, amyloid depositions, neurofibrillary tangles, and composite neuropathological scores, than sex-mismatched PHS, independent of apolipoprotein E. Models without using hazard weights, i.e. polygenic risk scores (PRS), have lower predictive power than PHS and show no evidence for sex differences. Our results indicate revealing sex-dependent genetic architecture requires the consideration of temporal processes of AD. This has strong implications not only for the genetic underpinning of AD but also for how we estimate sex-dependent polygenic effects for clinical use.

genetics↗

bioRxiv2019

Predicting cognitive and mental health traits and their polygenic architecture using large-scale brain connectomics

Cognitive abilities and mental disorders are complex traits sharing a largely unknown neuronal basis and aetiology. Their genetic architectures are highly polygenic and overlapping, which is supported by heterogeneous phenotypic expression and substantial clinical overlap. Brain network analysis provides a non-invasive means of dissecting biological heterogeneity yet its sensitivity, specificity and validity in clinical applications remains a major challenge. We used machine learning on static and dynamic temporal synchronization between all brain network nodes in 10,343 healthy individuals from the UK Biobank to predict (i) cognitive and mental health traits and (ii) their genetic underpinnings. We predicted age and sex to serve as our reference point. The traits of interest included individual level educational attainment and fluid intelligence (cognitive) and dimensional measures of depression, anxiety, and neuroticism (mental health). We predicted polygenic scores for educational attainment, fluid intelligence, depression, anxiety, and different neuroticism traits, in addition to schizophrenia. Beyond high accuracy for age and sex, permutation tests revealed above chance-level prediction accuracy for educational attainment and fluid intelligence. Educational attainment and fluid intelligence were mainly negatively associated with static brain connectivity in frontal and default mode networks, whereas age showed positive correlations with a more widespread pattern. In comparison, prediction accuracy for polygenic scores was at chance level across traits, which may serve as a benchmark for future studies aiming to link genetic factors and fMRI-based brain connectomics.\n\nSignificanceAlthough cognitive abilities and susceptibility to mental disorders reflect individual differences in brain function, neuroimaging is yet to provide a coherent account of the neuronal underpinnings. Here, we aimed to map the brain functional connectome of (i) cognitive and mental health traits and (ii) their polygenic architecture in a large population-based sample. We discovered high prediction accuracy for age and sex, and above-chance accuracy for educational attainment and intelligence (cognitive). In contrast, accuracies for dimensional measures of depression, anxiety and neuroticism (mental health), and polygenic scores across traits, were at chance level. These findings support the link between cognitive abilities and brain connectomics and provide a reference for studies mapping the brain connectomics of mental disorders and their genetic architectures.

neuroscience↗

bioRxiv2019

Brain age prediction reveals aberrant brain white matter in schizophrenia and bipolar disorder: A multi-sample diffusion tensor imaging study

BackgroundSchizophrenia (SZ) and bipolar disorders (BD) share substantial neurodevelopmental components affecting brain maturation and architecture. This necessitates a dynamic lifespan perspective in which brain aberrations are inferred from deviations from expected lifespan trajectories. We applied machine learning to diffusion tensor imaging (DTI) indices of white matter structure and organization to estimate and compare brain age between patients with SZ, BD, and healthy controls across 10 cohorts. MethodsWe trained six cross-validated models using different combinations of DTI data from 927 healthy controls (HC, 18-94 years), and applied the models to the test sets including 648 SZ (18-66 years) patients, 185 BD patients (18-64 years), and 990 HC (17-68 years), estimating brain age for each participant. Group differences were assessed using linear models, accounting for age, sex, and scanner. A meta-analytic framework was applied to assess the heterogeneity and generalizability of the results. Results10-fold cross-validation revealed high accuracy for all models. Compared to controls, the model including all feature sets significantly over-estimated the age of patients with SZ (d=-.29) and BD (d=.18), with similar effects for the other models. The meta-analysis converged on the same findings. Fractional anisotropy (FA) based models showed larger group differences than the models based on other DTI-derived metrics. ConclusionsBrain age prediction based on DTI provides informative and robust proxies for brain white matter integrity. Our results further suggest that white matter aberrations in SZ and BD primarily consist of anatomically distributed deviations from expected lifespan trajectories that generalize across cohorts and scanners.

neuroscience↗

bioRxiv2019

Genome-wide association study of Parkinson's disease progression biomarkers in 12 longitudinal patients' cohorts

BackgroundSeveral reports have identified different patterns of Parkinsons disease progression in individuals carrying missense variants in the GBA or LRRK2 genes. The overall contribution of genetic factors to the severity and progression of Parkinsons disease, however, has not been well studied. ObjectivesTo test the association between genetic variants and the clinical features and progression of Parkinsons disease on a genome-wide scale. MethodsWe accumulated individual data from 12 longitudinal cohorts in a total of 4,093 patients with 25,254 observations over a median of 3.81 years. Genome-wide associations were evaluated for 25 cross-sectional and longitudinal phenotypes. Specific variants of interest, including 90 recently-identified disease risk variants, were also investigated for the associations with these phenotypes. ResultsTwo variants were genome-wide significant. Rs382940(T>A), within the intron of SLC44A1, was associated with reaching Hoehn and Yahr stage 3 or higher faster (HR 2.04 [1.58, 2.62], P-value = 3.46E-8). Rs61863020(G>A), an intergenic variant and eQTL for ADRA2A, was associated with a lower prevalence of insomnia at baseline (OR 0.63 [0,52, 0.75], P-value = 4.74E-8). In the targeted analysis, we found nine associations between known Parkinsons risk variants and more severe motor/cognitive symptoms. Also, we replicated previous reports of GBA coding variants (rs2230288: p.E365K, rs75548401: p.T408M) being associated with greater motor and cognitive decline over time, and APOE E4 tagging variant (rs429358) being associated with greater cognitive deficits in patients. ConclusionsWe identified novel genetic factors associated with heterogeneity of progression in Parkinsons disease. The results provide new insights into the pathogenesis of Parkinsons disease as well as patient stratification for clinical trials.

genetics↗

bioRxiv2019

Association between schizophrenia polygenic score and psychotic symptoms in Alzheimer disease: meta-analysis of 10 cohort studies

BackgroundPsychosis (delusions and hallucinations) is common in Alzheimers disease (AD) and associated with worse clinical outcomes including accelerated cognitive decline and shorter time to nursing home admission. Atypical antipsychotics have limited efficacy which, along with emerging genomic research, suggests some overlapping mechanisms with other disorders characterized by psychosis, like schizophrenia. In this study, we tested whether polygenic risk score (PRS) for schizophrenia was associated with psychotic symptoms in AD. MethodsSchizophrenia PRS was calculated using Psychiatric Genomics Consortium data at 10 GWAS p-value thresholds (PT) in 3,173 AD cases from 11 cohort studies. Association between PRS and AD psychosis status was tested by logistic regression in each cohort individually and the results meta-analyzed. ResultsThe schizophrenia PRS was associated with psychosis in AD at an optimum PT of The strongest association was for delusions where a one standard deviation increase in PRS was associated with a 1.17-fold increased risk (95% CI: 1.07-1.3; p=0.001). ConclusionThese new findings point towards psychosis in AD - and particularly delusions - sharing some genetic liability with schizophrenia, and support a transdiagnostic view of psychotic symptoms across the lifespan.

genomics↗

bioRxiv2018

Partitioning the genetic architecture of amyotrophic lateral sclerosis

The genetic basis of sporadic amyotrophic lateral sclerosis (ALS) is not well understood. Using large genome-wide association studies and validated tools to quantify genetic overlap, we systematically identified single nucleotide polymorphisms (SNPs) associated with ALS conditional on genetic data from 65 different traits and diseases from >3 million people. We found strong genetic enrichment between ALS and a number of disparate traits including frontotemporal dementia, coronary artery disease, C-reactive protein, celiac disease and memory function. Beyond C9ORF72, we detected novel genetic signal within numerous loci including GIPC1, ELMO1 and COL16A and confirmed previously reported variants, such as ATXN2, KIF5A, UNC13A and MOBP. We found that ALS variants form a small-world co-expression network characterized by highly inter-connected hub genes. This network clustered into smaller sub-networks, each associated with a unique function. Altered gene expression of several sub-networks and hubs was over-represented in neuropathological samples from ALS patients and SOD1 G93A mice. Our collective findings indicate that the genetic architecture of ALS can be partitioned into distinct components where some genes are highly important for developing disease. These findings have implications for stratification and enrichment strategies for ALS clinical trials.

genetics↗