Correction to: Large‑scale pathway specific polygenic risk and transcriptomic community network analysis identifies novel functional pathways in Parkinson disease

ABSTRACTPolygenic inheritance plays a central role in Parkinson disease (PD). A priority in elucidating PD etiology lies in defining the biological basis of genetic risk. Unraveling how risk leads to disruption will yield disease-modifying therapeutic targets that may be effective. Here, we utilized a high-throughput and hypothesis-free approach to determine biological pathways underlying PD using the largest currently available cohorts of genetic data and gene expression data from International Parkinson’s Disease Genetics Consortium (IPDGC) and the Accelerating Medicines Partnership - Parkinson’s disease initiative (AMP-PD), among other sources. We placed these insights into a cellular context. We applied large-scale pathway-specific polygenic risk score (PRS) analyses to assess the role of common variation on PD risk in a cohort of 457,110 individuals by focusing on a compilation of 2,199 publicly annotated gene sets representative of curated pathways, of which we nominate 46 pathways associated with PD risk. We assessed the impact of rare variation on PD risk in an independent cohort of whole-genome sequencing data, including 4,331 individuals. We explored enrichment linked to expression cell specificity patterns using single-cell gene expression data and demonstrated a significant risk pattern for adult dopaminergic neurons, serotonergic neurons, and radial glia. Subsequently, we created a novel way of building de novo pathways by constructing a network expression community map using transcriptomic data derived from the blood of 1,612 PD patients, which revealed 54 connecting networks associated with PD. Our analyses highlight several promising pathways and genes for functional prioritization and provide a cellular context in which such work should be done.

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Large-Scale Network Analysis for Online Social Brand Advertising

MIS Quarterly ◽

10.25300/misq/2016/40.4.03 ◽

2016 ◽

Vol 40 (4) ◽

pp. 849-868 ◽

Cited By ~ 27

Author(s):

Kunpeng Zhang ◽

◽

Siddhartha Bhattacharyya ◽

Sudha Ram ◽

◽

...

Keyword(s):

Network Analysis ◽

Large Scale ◽

Large Scale Network ◽

Brand Advertising ◽

Scale Network

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Polygenic risk for immuno-metabolic markers and specific depressive symptoms: A multi-sample network analysis study

Brain Behavior and Immunity ◽

10.1016/j.bbi.2021.03.024 ◽

2021 ◽

Author(s):

Nils Kappelmann ◽

Darina Czamara ◽

Nicolas Rost ◽

Sylvain Moser ◽

Vanessa Schmoll ◽

...

Keyword(s):

Depressive Symptoms ◽

Network Analysis ◽

Metabolic Markers ◽

Polygenic Risk ◽

Analysis Study

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Social Network Analysis and Mining to Monitor and Identify Problems with Large-Scale Information and Communication Technology Interventions

PLoS ONE ◽

10.1371/journal.pone.0146220 ◽

2016 ◽

Vol 11 (1) ◽

pp. e0146220 ◽

Cited By ~ 5

Author(s):

Aleksandra do Socorro da Silva ◽

Silvana Rossy de Brito ◽

Nandamudi Lankalapalli Vijaykumar ◽

Cláudio Alex Jorge da Rocha ◽

Maurílio de Abreu Monteiro ◽

...

Keyword(s):

Social Network ◽

Social Network Analysis ◽

Network Analysis ◽

Information And Communication Technology ◽

Communication Technology ◽

Large Scale ◽

Information And Communication

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Degree Adjusted Large-Scale Network Analysis Reveals Novel Putative Metabolic Disease Genes

Biology ◽

10.3390/biology10020107 ◽

2021 ◽

Vol 10 (2) ◽

pp. 107

Author(s):

Apurva Badkas ◽

Thanh-Phuong Nguyen ◽

Laura Caberlotto ◽

Jochen G. Schneider ◽

Sébastien De Landtsheer ◽

...

Keyword(s):

Network Analysis ◽

Network Topology ◽

Large Scale ◽

Metabolic Diseases ◽

Critical Factor ◽

Disease Genes ◽

Alcoholic Fatty Liver ◽

Large Scale Network ◽

Depth Study ◽

Public Datasets

A large percentage of the global population is currently afflicted by metabolic diseases (MD), and the incidence is likely to double in the next decades. MD associated co-morbidities such as non-alcoholic fatty liver disease (NAFLD) and cardiomyopathy contribute significantly to impaired health. MD are complex, polygenic, with many genes involved in its aetiology. A popular approach to investigate genetic contributions to disease aetiology is biological network analysis. However, data dependence introduces a bias (noise, false positives, over-publication) in the outcome. While several approaches have been proposed to overcome these biases, many of them have constraints, including data integration issues, dependence on arbitrary parameters, database dependent outcomes, and computational complexity. Network topology is also a critical factor affecting the outcomes. Here, we propose a simple, parameter-free method, that takes into account database dependence and network topology, to identify central genes in the MD network. Among them, we infer novel candidates that have not yet been annotated as MD genes and show their relevance by highlighting their differential expression in public datasets and carefully examining the literature. The method contributes to uncovering connections in the MD mechanisms and highlights several candidates for in-depth study of their contribution to MD and its co-morbidities.

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Analysis of blood-based gene expression in idiopathic Parkinson disease

Neurology ◽

10.1212/wnl.0000000000004516 ◽

2017 ◽

Vol 89 (16) ◽

pp. 1676-1683 ◽

Cited By ~ 36

Author(s):

Ron Shamir ◽

Christine Klein ◽

David Amar ◽

Eva-Juliane Vollstedt ◽

Michael Bonin ◽

...

Keyword(s):

Gene Expression ◽

Parkinson Disease ◽

Gene Networks ◽

Large Scale ◽

Expression Profiles ◽

Area Under The Curve ◽

Gene Expression Profiles ◽

Gene Signature ◽

Gene Profiles ◽

Independent Test

Objective:To examine whether gene expression analysis of a large-scale Parkinson disease (PD) patient cohort produces a robust blood-based PD gene signature compared to previous studies that have used relatively small cohorts (≤220 samples).Methods:Whole-blood gene expression profiles were collected from a total of 523 individuals. After preprocessing, the data contained 486 gene profiles (n = 205 PD, n = 233 controls, n = 48 other neurodegenerative diseases) that were partitioned into training, validation, and independent test cohorts to identify and validate a gene signature. Batch-effect reduction and cross-validation were performed to ensure signature reliability. Finally, functional and pathway enrichment analyses were applied to the signature to identify PD-associated gene networks.Results:A gene signature of 100 probes that mapped to 87 genes, corresponding to 64 upregulated and 23 downregulated genes differentiating between patients with idiopathic PD and controls, was identified with the training cohort and successfully replicated in both an independent validation cohort (area under the curve [AUC] = 0.79, p = 7.13E–6) and a subsequent independent test cohort (AUC = 0.74, p = 4.2E–4). Network analysis of the signature revealed gene enrichment in pathways, including metabolism, oxidation, and ubiquitination/proteasomal activity, and misregulation of mitochondria-localized genes, including downregulation of COX4I1, ATP5A1, and VDAC3.Conclusions:We present a large-scale study of PD gene expression profiling. This work identifies a reliable blood-based PD signature and highlights the importance of large-scale patient cohorts in developing potential PD biomarkers.

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Large-scale gene network analysis reveals the significance of extracellular matrix pathway and homeobox genes in acute myeloid leukemia: an introduction to the Pigengene package and its applications

BMC Medical Genomics ◽

10.1186/s12920-017-0253-6 ◽

2017 ◽

Vol 10 (1) ◽

Cited By ~ 11

Author(s):

Amir Foroushani ◽

Rupesh Agrahari ◽

Roderick Docking ◽

Linda Chang ◽

Gerben Duns ◽

...

Keyword(s):

Extracellular Matrix ◽

Acute Myeloid Leukemia ◽

Network Analysis ◽

Myeloid Leukemia ◽

Gene Network ◽

Large Scale ◽

Homeobox Genes ◽

Gene Network Analysis ◽

Acute Myeloid

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A Data-Driven Text Mining and Semantic Network Analysis for Design Information Retrieval

Journal of Mechanical Design ◽

10.1115/1.4037649 ◽

2017 ◽

Vol 139 (11) ◽

Cited By ~ 24

Author(s):

Feng Shi ◽

Liuqing Chen ◽

Ji Han ◽

Peter Childs

Keyword(s):

Information Retrieval ◽

Network Analysis ◽

Text Mining ◽

Engineering Design ◽

Large Scale ◽

Semantic Network ◽

Document Retrieval ◽

Design Information ◽

Improve Design ◽

Correlation Degree

With the advent of the big-data era, massive information stored in electronic and digital forms on the internet become valuable resources for knowledge discovery in engineering design. Traditional document retrieval method based on document indexing focuses on retrieving individual documents related to the query, but is incapable of discovering the various associations between individual knowledge concepts. Ontology-based technologies, which can extract the inherent relationships between concepts by using advanced text mining tools, can be applied to improve design information retrieval in the large-scale unstructured textual data environment. However, few of the public available ontology database stands on a design and engineering perspective to establish the relations between knowledge concepts. This paper develops a “WordNet” focusing on design and engineering associations by integrating the text mining approaches to construct an unsupervised learning ontology network. Subsequent probability and velocity network analysis are applied with different statistical behaviors to evaluate the correlation degree between concepts for design information retrieval. The validation results show that the probability and velocity analysis on our constructed ontology network can help recognize the high related complex design and engineering associations between elements. Finally, an engineering design case study demonstrates the use of our constructed semantic network in real-world project for design relations retrieval.

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An atlas of polygenic risk score associations to highlight putative causal relationships across the human phenome

10.1101/467910 ◽

2018 ◽

Cited By ~ 1

Author(s):

Tom G. Richardson ◽

Sean Harrison ◽

Gibran Hemani ◽

George Davey Smith

Keyword(s):

Web Application ◽

Large Scale ◽

Complex Disease ◽

Association Studies ◽

Risk Scores ◽

Polygenic Risk Score ◽

Genome Wide Association Studies ◽

Genetic Liability ◽

Polygenic Risk ◽

The Uk

AbstractThe age of large-scale genome-wide association studies (GWAS) has provided us with an unprecedented opportunity to evaluate the genetic liability of complex disease using polygenic risk scores (PRS). In this study, we have analysed 162 PRS (P<5×l0 05) derived from GWAS and 551 heritable traits from the UK Biobank study (N=334,398). Findings can be investigated using a web application (http://mrcieu.mrsoftware.org/PRS_atlas/), which we envisage will help uncover both known and novel mechanisms which contribute towards disease susceptibility.To demonstrate this, we have investigated the results from a phenome-wide evaluation of schizophrenia genetic liability. Amongst findings were inverse associations with measures of cognitive function which extensive follow-up analyses using Mendelian randomization (MR) provided evidence of a causal relationship. We have also investigated the effect of multiple risk factors on disease using mediation and multivariable MR frameworks. Our atlas provides a resource for future endeavours seeking to unravel the causal determinants of complex disease.

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