ReviewFrontiers in immunology2018
Variation in the Untranslated Genome and Susceptibility to Infections.
Review in Frontiers in immunology, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
9 citing papers in PubMed, 23 citations in OpenAlex.
- Natural killer (NK) cells in bipolar disorders.Neuroscience applied · 2024Review
- Processing genome-wide association studies within a repository of heterogeneous genomic datasets.BMC genomic data · 2023Article
- Editorial: Noncoding RNA-based spatiotemporal modulation and therapeutics in neuroinflammation.Frontiers in immunology · 2023Article
- Competitive endogenous RNA network and pathway-based analysis of LncRNA single-nucleotide polymorphism in myasthenia gravis.Scientific reports · 2021Article
- Impact of rare and common genetic variation in the interleukin-1 pathway on human cytokine responses.Genome medicine · 2021Article
- Regulatory genome variants in human susceptibility to infection.Human genetics · 2020Review
- Disease-Causing Mutations and Rearrangements in Long Non-coding RNA Gene Loci.Frontiers in genetics · 2020Review
- Loci Associated With Antibody Response in Feral Swine (Frontiers in veterinary science · 2020Article
- The challenge of determining the impact of FUT3 tumor-associated polymorphism rs2306969 (-6951 C> T) in invasive breast cancer cells.Molecular biology reports · 2019Article
Corrections and comments
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Authors and funding
4 authors at 2 institutions in 2 countries.
Funding
Abstract
The clinical outcomes of infections are highly variable among individuals and are determined by complex host-pathogen interactions. Genome-wide association studies (GWAS) are powerful tools to unravel common genetic variations that are associated with disease risk and clinical outcomes. However, GWAS has only rarely revealed information on the exact genetic elements and their effects underlying an association because the majority of the hits are within non-coding regions. Some of the variants or the linked polymorphisms are now being discovered to have functional significance, such as regulatory elements in the promoter and enhancer regions or the microRNA binding sites in the 3'untranslated region of the protein-coding genes, which influence transcription, RNA stability, and translation of the protein-coding genes. However, only 3% of the entire transcriptome is protein-coding, signifying that non-coding RNAs represent most of the transcripts. Thus, a large portion of previously identified intergenic GWAS single nucleotide polymorphisms (SNPs) is in the non-coding RNAs. The non-coding RNAs form a large-scale regulatory network across the transcriptome, greatly expanding the complexity of gene regulation. Accumulating evidence also suggests that the "non-coding" genome regions actively regulate the highly dynamic three dimensional (3D) chromatin structures, which are critical for genome function. Epigenetic modulation like DNA methylation and histone modifications further affect chromatin accessibility and gene expression adding another layer of complexity to the functional interpretation of genetic variation associated with disease outcomes. We provide an overview of the current information on the influence of variation in these "untranslated" regions of the human genome on infectious diseases. The focus of this review is infectious disease-associated polymorphisms and gene regulatory mechanisms of pathophysiological relevance.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.