scholarly journals Systematic tissue-specific functional annotation of the human genome highlights immune-related DNA elements for late-onset Alzheimer’s disease

PLoS Genetics ◽  
2017 ◽  
Vol 13 (7) ◽  
pp. e1006933 ◽  
Author(s):  
Qiongshi Lu ◽  
Ryan L. Powles ◽  
Sarah Abdallah ◽  
Derek Ou ◽  
Qian Wang ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Human Genome ◽  
Functional Annotation ◽  
Late Onset ◽  
Tissue Specific ◽  
Dna Elements

scholarly journals Systematic tissue-specific functional annotation of the human genome highlights immune-related DNA elements for late-onset Alzheimer’s disease

2016 ◽  
Author(s):  
Qiongshi Lu ◽  
Ryan L. Powles ◽  
Sarah Abdallah ◽  
Derek Ou ◽  
Qian Wang ◽  
...  
Keyword(s):  
Human Genome ◽  
Functional Annotation ◽  
Late Onset ◽  
Complex Diseases ◽  
Tissue Specific ◽  
Functional Regions ◽  
Genome Wide ◽  
Dna Elements ◽  
Human Complex

AbstractContinuing efforts from large international consortia have made genome-wide epigenomic and transcriptomic annotation data publicly available for a variety of cell and tissue types. However, synthesis of these datasets into effective summary metrics to characterize the functional non-coding genome remains a challenge. Here, we present GenoSkyline-Plus, an extension of our previous work through integration of an expanded set of epigenomic and transcriptomic annotations to produce high-resolution, single tissue annotations. After validating our annotations with a catalog of tissue-specific non-coding elements previously identified in the literature, we apply our method using data from 127 different cell and tissue types to present an atlas of heritability enrichment across 45 different GWAS traits. We show that broader organ system categories (e.g. immune system) increase statistical power in identifying biologically relevant tissue types for complex diseases while annotations of individual cell types (e.g. monocytes or B-cells) provide deeper insights into disease etiology. Additionally, we use our GenoSkyline-Plus annotations in an in-depth case study of late-onset Alzheimer’s disease (LOAD). Our analyses suggest a strong connection between LOAD heritability and genetic variants contained in regions of the genome functional in monocytes. Furthermore, we show that LOAD shares a similar localization of SNPs to monocyte-functional regions with Parkinson’s disease. Overall, we demonstrate that integrated genome annotations at the single tissue level provide a valuable tool for understanding the etiology of complex human diseases. Our GenoSkyline-Plus annotations are freely available at http://genocanyon.med.yale.edu/GenoSkyline.Author SummaryAfter years of community efforts, many experimental and computational approaches have been developed and applied for functional annotation of the human genome, yet proper annotation still remains challenging, especially in non-coding regions. As complex disease research rapidly advances, increasing evidence suggests that non-coding regulatory DNA elements may be the primary regions harboring risk variants in human complex diseases. In this paper, we introduce GenoSkyline-Plus, a principled annotation framework to identify tissue and cell type-specific functional regions in the human genome through integration of diverse high-throughput epigenomic and transcriptomic data. Through validation of known non-coding tissue-specific regulatory regions, enrichment analyses on 45 complex traits, and an in-depth case study of neurodegenerative diseases, we demonstrate the ability of GenoSkyline-Plus to accurately identify tissue-specific functionality in the human genome and provide unbiased, genome-wide insights into the genetic basis of human complex diseases.

scholarly journals Network propagation of rare variants in Alzheimer’s disease reveals tissue-specific hub genes and communities

2021 ◽  
Vol 17 (1) ◽  
pp. e1008517
Author(s):  
Marzia Antonella Scelsi ◽  
Valerio Napolioni ◽  
Michael D. Greicius ◽  
Andre Altmann ◽  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Rare Variants ◽  
Late Onset ◽  
Integrative Approach ◽  
Specific Gene ◽  
Tissue Specific ◽  
Association Testing ◽  
Wide Range ◽  
Network Propagation

State-of-the-art rare variant association testing methods aggregate the contribution of rare variants in biologically relevant genomic regions to boost statistical power. However, testing single genes separately does not consider the complex interaction landscape of genes, nor the downstream effects of non-synonymous variants on protein structure and function. Here we present the NETwork Propagation-based Assessment of Genetic Events (NETPAGE), an integrative approach aimed at investigating the biological pathways through which rare variation results in complex disease phenotypes. We applied NETPAGE to sporadic, late-onset Alzheimer’s disease (AD), using whole-genome sequencing from the AD Neuroimaging Initiative (ADNI) cohort, as well as whole-exome sequencing from the AD Sequencing Project (ADSP). NETPAGE is based on network propagation, a framework that models information flow on a graph and simulates the percolation of genetic variation through tissue-specific gene interaction networks. The result of network propagation is a set of smoothed gene scores that can be tested for association with disease status through sparse regression. The application of NETPAGE to AD enabled the identification of a set of connected genes whose smoothed variation profile was robustly associated to case-control status, based on gene interactions in the hippocampus. Additionally, smoothed scores significantly correlated with risk of conversion to AD in Mild Cognitive Impairment (MCI) subjects. Lastly, we investigated tissue-specific transcriptional dysregulation of the core genes in two independent RNA-seq datasets, as well as significant enrichments in terms of gene sets with known connections to AD. We present a framework that enables enhanced genetic association testing for a wide range of traits, diseases, and sample sizes.

scholarly journals Network propagation of rare mutations in Alzheimer’s disease reveals tissue-specific hub genes and communities

2019 ◽  
Author(s):  
Marzia A. Scelsi ◽  
Valerio Napolioni ◽  
Michael D. Greicius ◽  
Andre Altmann ◽  
◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Gene Networks ◽  
Complex Disease ◽  
Late Onset ◽  
Integrative Approach ◽  
Tissue Specific ◽  
Association Testing ◽  
Wide Range ◽  
Network Propagation

ABSTRACTBackgroundState-of-the-art rare variant association testing methods aggregate the contribution of rare variants in biologically relevant genomic regions to boost statistical power. However, testing single genes separately does not consider the complex interaction landscape of genes, nor the downstream effects of non-synonymous variants on protein structure and function. Here we present the NETwork Propagation-based Assessment of Genetic Events (NETPAGE), an integrative approach aimed at investigating the biological pathways through which rare variation results in complex disease phenotypes.ResultsWe applied NETPAGE to sporadic, late-onset Alzheimer’s disease (AD), using whole-genome sequencing from the AD Neuroimaging Initiative (ADNI) cohort, as well as whole-exome sequencing from the AD Sequencing Project (ADSP). NETPAGE is based on network propagation, a framework that models information flow on a graph and simulates the percolation of genetic variation through gene networks. The result of network propagation is a set of smoothed gene scores used to predict disease status through sparse regression. The application of NETPAGE to AD enabled the identification of a set of connected genes whose smoothed mutation profile acted as a robust predictor of case-control status, based on gene interactions in the hippocampus. Additionally, smoothed scores significantly correlated with risk of conversion to AD in Mild Cognitive Impairment (MCI) subjects. Lastly, we showed tissue-specific transcriptional dysregulation of the core genes in two independent RNA-seq datasets, as well as significant enrichments in terms and gene sets with known connections to AD.ConclusionsThe presented framework enables enhanced genetic association testing for a wide range of traits, diseases, and sample sizes.

scholarly journals Inferring the molecular mechanisms of noncoding Alzheimer’s disease-associated genetic variants

2018 ◽  
Author(s):  
Alexandre Amlie-Wolf ◽  
Mitchell Tang ◽  
Jessica Way ◽  
Beth Dombroski ◽  
Ming Jiang ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Molecular Mechanisms ◽  
Target Genes ◽  
Genome Wide Association Study ◽  
Late Onset ◽  
Regulatory Mechanisms ◽  
Luciferase Expression ◽  
Tissue Specific ◽  
Causal Variants

Structured AbstractINTRODUCTIONWe set out to characterize the causal variants, regulatory mechanisms, tissue contexts, and target genes underlying noncoding late-onset Alzheimer’s Disease (LOAD)-associated genetic signals.METHODSWe applied our INFERNO method to the IGAP genome-wide association study (GWAS) data, annotating all potentially causal variants with tissue-specific regulatory activity. Bayesian co-localization analysis of GWAS summary statistics and eQTL data was performed to identify tissue-specific target genes.RESULTSINFERNO identified enhancer dysregulation in all 19 tag regions analyzed, significant enrichments of enhancer overlaps in the immune-related blood category, and co-localized eQTL signals overlapping enhancers from the matching tissue class in ten regions (ABCA7, BIN1, CASS4, CD2AP, CD33, CELF1, CLU, EPHA1, FERMT2, ZCWPW1). We validated the allele-specific effects of several variants on enhancer function using luciferase expression assays.DISCUSSIONIntegrating functional genomics with GWAS signals yielded insights into the regulatory mechanisms, tissue contexts, and genes affected by noncoding genetic variation associated with LOAD risk.

scholarly journals Functional annotation of genomic variants in studies of late-onset Alzheimer’s disease

2018 ◽  
Vol 34 (16) ◽  
pp. 2724-2731 ◽  
Author(s):  
Mariusz Butkiewicz ◽  
Elizabeth E Blue ◽  
Yuk Yee Leung ◽  
Xueqiu Jian ◽  
Edoardo Marcora ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Functional Annotation ◽  
Late Onset ◽  
Genomic Variants

Familial Patterns of Risk in Very Late-Onset Alzheimer's Disease

2003 ◽  
Author(s):  
J. M. Silverman ◽  
C. J. Smith ◽  
D. B. Marin ◽  
R. C. Mohs ◽  
C. B. Propper
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Late Onset ◽  
Familial Patterns
Sign in / Sign up

Export Citation Format

Share Document