NDLI: Protein Interaction Networks Reveal Novel Autism Risk Genes within GWAS Statistical Noise

Content Provider	PubMed Central
Author	Correia, Catarina Oliveira, Guiomar Vicente, Astrid M.
Editor	Aleksic, Branko
Copyright Year	2014
Abstract	Genome-wide association studies (GWAS) for Autism Spectrum Disorder (ASD) thus far met limited success in the identification of common risk variants, consistent with the notion that variants with small individual effects cannot be detected individually in single SNP analysis. To further capture disease risk gene information from ASD association studies, we applied a network-based strategy to the Autism Genome Project (AGP) and the Autism Genetics Resource Exchange GWAS datasets, combining family-based association data with Human Protein-Protein interaction (PPI) data. Our analysis showed that autism-associated proteins at higher than conventional levels of significance (P<0.1) directly interact more than random expectation and are involved in a limited number of interconnected biological processes, indicating that they are functionally related. The functionally coherent networks generated by this approach contain ASD-relevant disease biology, as demonstrated by an improved positive predictive value and sensitivity in retrieving known ASD candidate genes relative to the top associated genes from either GWAS, as well as a higher gene overlap between the two ASD datasets. Analysis of the intersection between the networks obtained from the two ASD GWAS and six unrelated disease datasets identified fourteen genes exclusively present in the ASD networks. These are mostly novel genes involved in abnormal nervous system phenotypes in animal models, and in fundamental biological processes previously implicated in ASD, such as axon guidance, cell adhesion or cytoskeleton organization. Overall, our results highlighted novel susceptibility genes previously hidden within GWAS statistical “noise” that warrant further analysis for causal variants.
Related Links	http://dx.doi.org/10.1371/journal.pone.0112399
Starting Page	112399
File Format	PDF
ISSN	19326203
e-ISSN	19326203
Journal	PLoS ONE
Issue Number	11
Volume Number	9
Language	English
Publisher	Public Library of Science
Publisher Date	2014-11-01
Access Restriction	Open
Rights Holder	Public Library of Science
Subject Keyword	Biochemistry, Genetics and Molecular Biology(all) Agricultural and Biological Sciences(all) Medicine(all) Research in Higher Education
Content Type	Text
Resource Type	Article
Subject	Multidisciplinary

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

Protein Similarity Networks Reveal Relationships among Sequence, Structure, and Function within the Cupin Superfamily

t-LSE: A Novel Robust Geometric Approach for Modeling Protein-Protein Interaction Networks

Copy Number Variants in Extended Autism Spectrum Disorder Families Reveal Candidates Potentially Involved in Autism Risk

Novel Autism Subtype-Dependent Genetic Variants Are Revealed by Quantitative Trait and Subphenotype Association Analyses of Published GWAS Data

Quantitative Proteomics Reveal Distinct Protein Regulations Caused by Aggregatibacter actinomycetemcomitans within Subgingival Biofilms

Multi-Edge Gene Set Networks Reveal Novel Insights into Global Relationships between Biological Themes

Simulated Evolution of Protein-Protein Interaction Networks with Realistic Topology

Diffusion Model Based Spectral Clustering for Protein-Protein Interaction Networks

Assembly and Interrogation of Alzheimer’s Disease Genetic Networks Reveal Novel Regulators of Progression

Protein Interaction Networks Reveal Novel Autism Risk Genes within GWAS Statistical Noise

Similar Documents

Protein Similarity Networks Reveal Relationships among Sequence, Structure, and Function within the Cupin Superfamily

t-LSE: A Novel Robust Geometric Approach for Modeling Protein-Protein Interaction Networks

Copy Number Variants in Extended Autism Spectrum Disorder Families Reveal Candidates Potentially Involved in Autism Risk

Novel Autism Subtype-Dependent Genetic Variants Are Revealed by Quantitative Trait and Subphenotype Association Analyses of Published GWAS Data

Quantitative Proteomics Reveal Distinct Protein Regulations Caused by Aggregatibacter actinomycetemcomitans within Subgingival Biofilms

Multi-Edge Gene Set Networks Reveal Novel Insights into Global Relationships between Biological Themes

Simulated Evolution of Protein-Protein Interaction Networks with Realistic Topology

Diffusion Model Based Spectral Clustering for Protein-Protein Interaction Networks

Assembly and Interrogation of Alzheimer’s Disease Genetic Networks Reveal Novel Regulators of Progression

Protein Interaction Networks Reveal Novel Autism Risk Genes within GWAS Statistical Noise