scholarly journals Evidence for GRN as part of a neuroinflammatory mechanism connecting common neurodegenerative diseases

2020 ◽  
Author(s):  
Mike A. Nalls ◽  
Cornelis Blauwendraat ◽  
Lana Sargent ◽  
Dan Vitale ◽  
Hampton Leonard ◽  
...  
Keyword(s):  
Amyotrophic Lateral Sclerosis ◽  
Neurodegenerative Diseases ◽  
Genetic Risk ◽  
Association Studies ◽  
Molecular Data ◽  
Genome Wide Association Studies ◽  
Data Types ◽  
Genome Wide ◽  
Trait Locus ◽  
Lateral Sclerosis

SUMMARYBackgroundPrevious research using genome wide association studies (GWAS) has identified variants that may contribute to lifetime risk of multiple neurodegenerative diseases. However, whether there are common mechanisms that link neurodegenerative diseases is uncertain. Here, we focus on one gene, GRN, encoding progranulin, and the potential mechanistic interplay between genetic risk, gene expression in the brain and inflammation across multiple common neurodegenerative diseases.MethodsWe utilized GWAS, expression quantitative trait locus (eQTL) mapping and Bayesian colocalization analyses to evaluate potential causal and mechanistic inferences. We integrate various molecular data types from public resources to infer disease connectivity and shared mechanisms using a data driven process.FindingseQTL analyses combined with GWAS identified significant functional associations between increasing genetic risk in the GRN region and decreased expression of the gene in Parkinson’s, Alzheimer’s and amyotrophic lateral sclerosis. Additionally, colocalization analyses show a connection between blood based inflammatory biomarkers relating to platelets and GRN expression in the frontal cortex.InterpretationGRN expression mediates neuroinflammation function related to general neurodegeneration. This analysis suggests shared mechanisms for Parkinson’s, Alzheimer’s and amyotrophic lateral sclerosis.FundingNational Institute on Aging, National Institute of Neurological Disorders and Stroke, and the Michael J. Fox Foundation.

scholarly journals Evidence for GRN connecting multiple neurodegenerative diseases

2021 ◽  
Author(s):  
Mike A Nalls ◽  
Cornelis Blauwendraat ◽  
Lana Sargent ◽  
Dan Vitale ◽  
Hampton Leonard ◽  
...  
Keyword(s):  
Quantitative Trait Locus ◽  
Amyotrophic Lateral Sclerosis ◽  
Neurodegenerative Diseases ◽  
Genetic Risk ◽  
Association Studies ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Genome Wide ◽  
Trait Locus ◽  
Lateral Sclerosis

Abstract Previous research using genome wide association studies has identified variants that may contribute to lifetime risk of multiple neurodegenerative diseases. However, whether there are common mechanisms that link neurodegenerative diseases is uncertain. Here, we focus on one gene, GRN, encoding progranulin, and the potential mechanistic interplay between genetic risk, gene expression in the brain and inflammation across multiple common neurodegenerative diseases. We utilized genome wide association studies, expression quantitative trait locus mapping and Bayesian colocalization analyses to evaluate potential causal and mechanistic inferences. We integrate various molecular data types from public resources to infer disease connectivity and shared mechanisms using a data driven process. Expression quantitative trait locus analyses combined with genome wide association studies identified significant functional associations between increasing genetic risk in the GRN region and decreased expression of the gene in Parkinson’s, Alzheimer’s and amyotrophic lateral sclerosis. Additionally, colocalization analyses show a connection between blood based inflammatory biomarkers relating to platelets and GRN expression in the frontal cortex. GRN expression mediates neuroinflammation function related to multiple neurodegenerative diseases. This analysis suggests shared mechanisms for Parkinson’s, Alzheimer’s and amyotrophic lateral sclerosis.

scholarly journals Multiple-Tissue Integrative Transcriptome-Wide Association Studies Discovered New Genes Associated With Amyotrophic Lateral Sclerosis

2020 ◽  
Vol 11 ◽  
Author(s):  
Lishun Xiao ◽  
Zhongshang Yuan ◽  
Siyi Jin ◽  
Ting Wang ◽  
Shuiping Huang ◽  
...  
Keyword(s):  
Amyotrophic Lateral Sclerosis ◽  
Type I Error ◽  
Association Studies ◽  
Type I ◽  
Genome Wide Association Studies ◽  
Combination Strategy ◽  
New Genes ◽  
Genome Wide ◽  
Causal Genes ◽  
Lateral Sclerosis

Genome-wide association studies (GWAS) have identified multiple causal genes associated with amyotrophic lateral sclerosis (ALS); however, the genetic architecture of ALS remains completely unknown and a large number of causal genes have yet been discovered. To full such gap in part, we implemented an integrative analysis of transcriptome-wide association study (TWAS) for ALS to prioritize causal genes with summary statistics from 80,610 European individuals and employed 13 GTEx brain tissues as reference transcriptome panels. The summary-level TWAS analysis with single brain tissue was first undertaken and then a flexible p-value combination strategy, called summary data-based Cauchy Aggregation TWAS (SCAT), was proposed to pool association signals from single-tissue TWAS analysis while protecting against highly positive correlation among tests. Extensive simulations demonstrated SCAT can produce well-calibrated p-value for the control of type I error and was often much more powerful to identify association signals across various scenarios compared with single-tissue TWAS analysis. Using SCAT, we replicated three ALS-associated genes (i.e., ATXN3, SCFD1, and C9orf72) identified in previous GWASs and discovered additional five genes (i.e., SLC9A8, FAM66D, TRIP11, JUP, and RP11-529H20.6) which were not reported before. Furthermore, we discovered the five associations were largely driven by genes themselves and thus might be new genes which were likely related to the risk of ALS. However, further investigations are warranted to verify these results and untangle the pathophysiological function of the genes in developing ALS.

scholarly journals Investigating Casual Associations among Gut Microbiota, Metabolites and Neurodegenerative Diseases: A Mendelian Randomization Study

2021 ◽  
Author(s):  
Jin-Tai Yu ◽  
Jing Ning ◽  
Shu-Yi Huang ◽  
Shi-Dong Chen ◽  
Yu-Xiang Yang ◽  
...  
Keyword(s):  
Gut Microbiota ◽  
Neurodegenerative Diseases ◽  
Protective Factor ◽  
Mendelian Randomization ◽  
Association Studies ◽  
Genome Wide Association Studies ◽  
Causal Relationships ◽  
Genome Wide ◽  
Underlying Mechanisms ◽  
Lateral Sclerosis

Abstract Background Recent studies had explored that the gut microbiota was associated with neurodegenerative diseases (including Alzheimer’s disease (AD), Parkinson’s disease (PD) and amyotrophic lateral sclerosis (ALS)) through the gut-brain axis, among which metabolic pathways played an important role. However, the underlying causality remained unclear. Our study aimed to evaluate potential causal relationships between gut microbiota, metabolites and neurodegenerative diseases through Mendelian randomization (MR) approach. Methods We selected genetic variants associated with gut microbiota traits (N = 18340) and gut microbiota-derived metabolites (N = 7824) from genome-wide association studies (GWASs). Summary statistics of neurodegenerative diseases were obtained from IGAP (AD: 17008 cases; 37154 controls), IPDGC (PD: 37 688 cases; 141779 controls) and IALSC (ALS: 20806 cases; 59804 controls) respectively. Results A total of 19 gut microbiota traits were found to be causally associated with risk of neurodegenerative diseases, including 1 phylum, 2 classes, 2 orders, 2 families and 12 genera. We found genetically predicted greater abundance of Ruminococcus, at genus level (OR:1.245, 95%CI:1.103,1.405; P = 0.0004) was significantly related to higher risk of ALS. We also found suggestive association between 12 gut microbiome-dependent metabolites and neurodegenerative diseases. For serotonin pathway, our results revealed serotonin as protective factor of PD, and kynurenine as risk factor of ALS. Besides, reduction of glutamine was found causally associated with occurrence of AD. Conclusions Our study firstly applied a two-sample MR approach to detect causal relationships among gut microbiota, gut metabolites and the risk of AD, PD and ALS, and we revealed several causal relationships. These findings may provide new targets for treatment of these neurodegenerative diseases, and may offer valuable insights for further researches on the underlying mechanisms.

scholarly journals Searching Far and Genome-Wide: The Relevance of Association Studies in Amyotrophic Lateral Sclerosis

2021 ◽  
Vol 14 ◽  
Author(s):  
Kelly A. Rich ◽  
Jennifer Roggenbuck ◽  
Stephen J. Kolb
Keyword(s):  
Amyotrophic Lateral Sclerosis ◽  
Complex Disease ◽  
Association Studies ◽  
Genetic Association Studies ◽  
Disease Status ◽  
Therapeutic Interventions ◽  
Genome Wide Association Studies ◽  
Genome Wide ◽  
Disease Associated Genes ◽  
Lateral Sclerosis

Genome-wide association studies (GWAS) and rare variant association studies (RVAS) are applied across many areas of complex disease to analyze variation in whole genomes of thousands of unrelated patients. These approaches are able to identify variants and/or biological pathways which are associated with disease status and, in contrast to traditional linkage studies or candidate gene approaches, do so without requiring multigenerational affected families, prior hypotheses, or known genes of interest. However, the novel associations identified by these methods typically have lower effect sizes than those found in classical family studies. In the motor neuron disease amyotrophic lateral sclerosis (ALS), GWAS, and RVAS have been used to identify multiple disease-associated genes but have not yet resulted in novel therapeutic interventions. There is significant urgency within the ALS community to identify additional genetic markers of disease to uncover novel biological mechanisms, stratify genetic subgroups of disease, and drive drug development. Given the widespread and increasing application of genetic association studies of complex disease, it is important to recognize the strengths and limitations of these approaches. Here, we review ALS gene discovery via GWAS and RVAS.

scholarly journals Association analysis of juvenile idiopathic arthritis genetic susceptibility factors in Estonian patients

2021 ◽  
Author(s):  
Tiit Nikopensius ◽  
Priit Niibo ◽  
Toomas Haller ◽  
Triin Jagomägi ◽  
Ülle Voog-Oras ◽  
...  
Keyword(s):  
Rheumatoid Arthritis ◽  
Juvenile Idiopathic Arthritis ◽  
Autoimmune Diseases ◽  
Genetic Risk ◽  
Association Studies ◽  
Control Sample ◽  
Case Control ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Genome Wide

Abstract Background Juvenile idiopathic arthritis (JIA) is the most common chronic rheumatic condition of childhood. Genetic association studies have revealed several JIA susceptibility loci with the strongest effect size observed in the human leukocyte antigen (HLA) region. Genome-wide association studies have augmented the number of JIA-associated loci, particularly for non-HLA genes. The aim of this study was to identify new associations at non-HLA loci predisposing to the risk of JIA development in Estonian patients. Methods We performed genome-wide association analyses in an entire JIA case–control sample (All-JIA) and in a case–control sample for oligoarticular JIA, the most prevalent JIA subtype. The entire cohort was genotyped using the Illumina HumanOmniExpress BeadChip arrays. After imputation, 16,583,468 variants were analyzed in 263 cases and 6956 controls. Results We demonstrated nominal evidence of association for 12 novel non-HLA loci not previously implicated in JIA predisposition. We replicated known JIA associations in CLEC16A and VCTN1 regions in the oligoarticular JIA sample. The strongest associations in the All-JIA analysis were identified at PRKG1 (P = 2,54 × 10−6), LTBP1 (P = 9,45 × 10−6), and ELMO1 (P = 1,05 × 10−5). In the oligoarticular JIA analysis, the strongest associations were identified at NFIA (P = 5,05 × 10−6), LTBP1 (P = 9,95 × 10−6), MX1 (P = 1,65 × 10−5), and CD200R1 (P = 2,59 × 10−5). Conclusion This study increases the number of known JIA risk loci and provides additional evidence for the existence of overlapping genetic risk loci between JIA and other autoimmune diseases, particularly rheumatoid arthritis. The reported loci are involved in molecular pathways of immunological relevance and likely represent genomic regions that confer susceptibility to JIA in Estonian patients. Key Points• Juvenile idiopathic arthritis (JIA) is the most common childhood rheumatic disease with heterogeneous presentation and genetic predisposition.• Present genome-wide association study for Estonian JIA patients is first of its kind in Northern and Northeastern Europe.• The results of the present study increase the knowledge about JIA risk loci replicating some previously described associations, so adding weight to their relevance and describing novel loci.• The study provides additional evidence for the existence of overlapping genetic risk loci between JIA and other autoimmune diseases, particularly rheumatoid arthritis.

scholarly journals Genome-wide genetic links between amyotrophic lateral sclerosis and autoimmune diseases

BMC Medicine ◽  
2021 ◽  
Vol 19 (1) ◽  
Author(s):  
Chun Yu Li ◽  
Tian Mi Yang ◽  
Ru Wei Ou ◽  
Qian Qian Wei ◽  
Hui Fang Shang
Keyword(s):  
Amyotrophic Lateral Sclerosis ◽  
Autoimmune Diseases ◽  
Genetic Correlation ◽  
Functional Enrichment ◽  
Genome Wide Association Studies ◽  
Risk Genes ◽  
Genome Wide ◽  
Shared Risk ◽  
Lateral Sclerosis ◽  
Shared Genetic

Abstract Background Epidemiological and clinical studies have suggested comorbidity between amyotrophic lateral sclerosis (ALS) and autoimmune disorders. However, little is known about their shared genetic architecture. Methods To examine the relation between ALS and 10 autoimmune diseases, including asthma, celiac disease (CeD), Crohn’s disease (CD), inflammatory bowel disease (IBD), multiple sclerosis (MS), psoriasis, rheumatoid arthritis (RA), systemic lupus erythematosus (SLE), type 1 diabetes (T1D), and ulcerative colitis (UC), and identify shared risk loci, we first estimated the genetic correlation using summary statistics from genome-wide association studies, and then analyzed the genetic enrichment leveraging the conditional false discovery rate statistical method. Results We identified a significant positive genetic correlation between ALS and CeD, MS, RA, and SLE, as well as a significant negative genetic correlation between ALS and IBD, UC, and CD. Robust genetic enrichment was observed between ALS and CeD and MS, and moderate enrichment was found between ALS and UC and T1D. Thirteen shared genetic loci were identified, among which five were suggestively significant in another ALS GWAS, namely rs3828599 (GPX3), rs3849943 (C9orf72), rs7154847 (G2E3), rs6571361 (SCFD1), and rs9903355 (GGNBP2). By integrating cis-expression quantitative trait loci analyses in Braineac and GTEx, we further identified GGNBP2, ATXN3, and SLC9A8 as novel ALS risk genes. Functional enrichment analysis indicated that the shared risk genes were involved in four pathways including membrane trafficking, vesicle-mediated transport, ER to Golgi anterograde transport, and transport to the Golgi and subsequent modification. Conclusions Our findings demonstrate a specific genetic correlation between ALS and autoimmune diseases and identify shared risk loci, including three novel ALS risk genes. These results provide a better understanding for the pleiotropy of ALS and have implications for future therapeutic trials.

scholarly journals Leukocyte telomere length and amyotrophic lateral sclerosis: a Mendelian randomization study

2021 ◽  
Author(s):  
Kailin Xia ◽  
Linjing Zhang ◽  
Gan Zhang ◽  
Yajun Wang ◽  
Tao Huang ◽  
...  
Keyword(s):  
Amyotrophic Lateral Sclerosis ◽  
Telomere Length ◽  
Causal Relationship ◽  
Mendelian Randomization ◽  
Association Studies ◽  
Sensitivity Analyses ◽  
Genome Wide Association Studies ◽  
Leukocyte Telomere Length ◽  
Weighted Median ◽  
Lateral Sclerosis

Abstract Background Observational studies have suggested that telomere length is associated with amyotrophic lateral sclerosis (ALS). However, it remains unclear whether this association is causal. We employed a two-sample Mendelian randomization (MR) approach to explore the causal relationship between leukocyte telomere length (LTL) and ALS based on the most cited and most recent and largest LTL genome-wide association studies (GWASs) that measured LTL with the Southern blot method (n = 9190) and ALS GWAS summary data (n = 80,610). We adopted the inverse variance weighted (IVW) method to examine the effect of LTL on ALS and used the weighted median method, simple median method, MR Egger method and MR PRESSO method to perform sensitivity analyses. Results We found that genetically determined longer LTL was inversely associated with the risk of ALS (OR = 0.846, 95% CI: 0.744–0.962, P = 0.011), which was mainly driven by rs940209 in the OBFC1 gene, suggesting a potential effect of OBFC1 on ALS. In sensitivity analyses, that was confirmed in MR Egger method (OR = 0.647,95% CI = 0.447–0.936, P = 0.050), and a similar trend was shown with the weighted median method (OR = 0.893, P = 0.201) and simple median method (OR = 0.935 P = 0.535). The MR Egger analyses did not suggest directional pleiotropy, showing an intercept of 0.025 (P = 0.168). Neither the influence of instrumental outliers nor heterogeneity was found. Conclusions Our results suggest that genetically predicted longer LTL has a causal relationship with a lower risk of ALS and underscore the importance of protecting against telomere loss in ALS.

A coordinate descent approach for sparse Bayesian learning in high dimensional QTL mapping and genome-wide association studies

2019 ◽  
Vol 35 (21) ◽  
pp. 4327-4335
Author(s):  
Meiyue Wang ◽  
Shizhong Xu
Keyword(s):  
Quantitative Trait Locus ◽  
Quantitative Trait Locus Mapping ◽  
Bayesian Learning ◽  
Association Studies ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Sparse Bayesian Learning ◽  
Genome Wide ◽  
Trait Locus ◽  
Locus Mapping

AbstractMotivationGenomic scanning approaches that detect one locus at a time are subject to many problems in genome-wide association studies and quantitative trait locus mapping. The problems include large matrix inversion, over-conservativeness for tests after Bonferroni correction and difficulty in evaluation of the total genetic contribution to a trait’s variance. Targeting these problems, we take a further step and investigate a multiple locus model that detects all markers simultaneously in a single model.ResultsWe developed a sparse Bayesian learning (SBL) method for quantitative trait locus mapping and genome-wide association studies. This new method adopts a coordinate descent algorithm to estimate parameters (marker effects) by updating one parameter at a time conditional on current values of all other parameters. It uses an L2 type of penalty that allows the method to handle extremely large sample sizes (>100 000). Simulation studies show that SBL often has higher statistical powers and the simulated true loci are often detected with extremely small P-values, indicating that SBL is insensitive to stringent thresholds in significance testing.Availability and implementationAn R package (sbl) is available on the comprehensive R archive network (CRAN) and https://github.com/MeiyueComputBio/sbl/tree/master/R%20packge.Supplementary informationSupplementary data are available at Bioinformatics online.

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