INFERNO - INFERring the molecular mechanisms of NOncoding genetic variants

Amlie‐Wolf, Alexandre; Tang, Mitchell; Mlynarski, Elisabeth E.; Kuksa, Pavel P.; Valladares, Otto; Katanić, Živadin; Tsuang, Debby W.; Brown, Christopher D.; Schellenberg, Gerard D.; Wang, Li-San

doi:10.1101/211599

Cited by 19 publications

(33 citation statements)

References 64 publications

(68 reference statements)

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“…Moreover, development of PTSD biomarkers have focused on the stress response, peripheral inflammation (inflammatory cytokines, C‐reactive protein), neural‐immune communication, 55 and response of the blood immune cells to glucocorticoid activation 53 . Collectively, the results of the current study support and extend the previously suggested role of the immune system and regulation of cell activation in both LOAD 10,56‐60 and PTSD 61,62 …”

Section: Discussionsupporting

confidence: 82%

Shared genetic etiology underlying late‐onset Alzheimer's disease and posttraumatic stress syndrome

Lutz

Luo

Williamson

et al. 2020

Alzheimer's & Dementia

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Introduction: Late-onset Alzheimer's disease (LOAD) manifests comorbid neuropsychiatric symptoms and posttraumatic stress disorder (PTSD) is associated with an increased risk for dementia in late life, suggesting the two disorders may share genetic etiologies. Methods: We performed genetic pleiotropy analysis using LOAD and PTSD genomewide association study (GWAS) datasets from white and African-American populations, followed by functional-genomic analyses. Results: We found an enrichment for LOAD across increasingly stringent levels of significance with the PTSD GWAS association (LOAD|PTSD) in the discovery and replication cohorts and a modest enrichment for the reverse conditional association (PTSD|LOAD). LOAD|PTSD association analysis identified and replicated the MS4A genes region. These genes showed similar expression pattern in brain regions affected in LOAD, and across-brain-tissue analysis identified a significant association for MS4A6A. The African-American samples showed moderate enrichment; however, no false discovery rate-significant associations. Discussion: We demonstrated common genetic signatures for LOAD and PTSD and suggested immune response as a common pathway for these diseases.

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Section: Discussionsupporting

confidence: 82%

Shared genetic etiology underlying late‐onset Alzheimer's disease and posttraumatic stress syndrome

Lutz

Luo

Williamson

et al. 2020

Alzheimer's & Dementia

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“…Genome-wide association studies (GWASs) have successfully identified over 70,000 genetic variants associated with more than 3,000 human diseases and phenotypes (Buniello et al, 2019). Interpretation of these associations remain difficult (Watanabe et al, 2017;Amlie-Wolf et al, 2018) as most GWAS hits are in the noncoding genome. Resolution of GWAS is limited as neighboring variants have similar associations due to linkage disequilibrium (LD) (Amlie-Wolf et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

“…Interpretation of these associations remain difficult (Watanabe et al, 2017;Amlie-Wolf et al, 2018) as most GWAS hits are in the noncoding genome. Resolution of GWAS is limited as neighboring variants have similar associations due to linkage disequilibrium (LD) (Amlie-Wolf et al, 2018). Our recently developed INFERNO method (Amlie-Wolf et al, 2018) focuses on identifying potentially causal variants underlying observed GWAS associations by integrating with hundreds of functional genomics datasets.…”

Section: Introductionmentioning

confidence: 99%

“…Resolution of GWAS is limited as neighboring variants have similar associations due to linkage disequilibrium (LD) (Amlie-Wolf et al, 2018). Our recently developed INFERNO method (Amlie-Wolf et al, 2018) focuses on identifying potentially causal variants underlying observed GWAS associations by integrating with hundreds of functional genomics datasets. The current INFERNO implementation is not optimized for big data, and a scalable framework for annotating genetic variants and genomic regions generated by various human genetic studies in a high throughput manner is in need for systematic large scale genomic and genetic analyses.…”

Section: Introductionmentioning

confidence: 99%

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SparkINFERNO: A scalable high-throughput pipeline for inferring molecular mechanisms of non-coding genetic variants

Kuksa

Lee

Amlie‐Wolf

et al. 2020

Preprint

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AbstractSummaryWe report SparkINFERNO (Spark-based INFERence of the molecular mechanisms of NOn-coding genetic variants), a scalable bioinformatics pipeline characterizing noncoding GWAS association findings. SparkINFERNO prioritizes causal variants underlying GWAS association signals and reports relevant regulatory elements, tissue contexts, and plausible target genes they affect. To achieve this, the SparkINFERNO algorithm integrates GWAS summary statistics with large-scale collection of functional genomics datasets spanning enhancer activity, transcription factor binding, expression quantitative trait loci, and other functional datasets across more than 400 tissues and cell types. Scalability is achieved by an underlying API implemented using Apache Spark and Giggle-based genomic indexing. We evaluated SparkINFERNO on large GWAS studies and show that SparkINFERNO is more than 60-times efficient and scales with data size and amount of computational resources.AvailabilitySparkINFERNO runs on clusters or a single server with Apache Spark environment, and is available at https://bitbucket.org/wanglab-upenn/SparkINFERNO or https://hub.docker.com/r/wanglab/spark-inferno.Contactlswang@pennmedicine.upenn.edu

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“…The database is currently undergoing a major overhaul and will eventually be superseded by POSTGAP . A valid and recent alternative is INFERNO 23 , though it does only rely on eQTL data for target gene assignment. These resources implement some or all of the approaches that will be reviewed in the workflow and constitute good entry points for identifying the most likely target gene(s) of regulatory SNPs.…”

Section: Introductionmentioning

confidence: 99%

Using regulatory genomics data to interpret the function of disease variants and prioritise genes from expression studies

Ferrero

2018

F1000Res

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The identification of therapeutic targets is a critical step in the research and developement of new drugs, with several drug discovery programmes failing because of a weak linkage between target and disease. Genome-wide association studies and large-scale gene expression experiments are providing insights into the biology of several common diseases, but the complexity of transcriptional regulation mechanisms often limits our understanding of how genetic variation can influence changes in gene expression. Several initiatives in the field of regulatory genomics are aiming to close this gap by systematically identifying and cataloguing regulatory elements such as promoters and enhacers across different tissues and cell types. In this Bioconductor workflow, we will explore how different types of regulatory genomic data can be used for the functional interpretation of disease-associated variants and for the prioritisation of gene lists from gene expression experiments.

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INFERNO - INFERring the molecular mechanisms of NOncoding genetic variants

Cited by 19 publications

References 64 publications

Shared genetic etiology underlying late‐onset Alzheimer's disease and posttraumatic stress syndrome

Shared genetic etiology underlying late‐onset Alzheimer's disease and posttraumatic stress syndrome

SparkINFERNO: A scalable high-throughput pipeline for inferring molecular mechanisms of non-coding genetic variants

Using regulatory genomics data to interpret the function of disease variants and prioritise genes from expression studies

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