Principles and methods of in-silico prioritization of non-coding regulatory variants

Lee, Phil H.; Lee, Christian; Li, Xihao; Wee, Brian; Dwivedi, Tushar; Daly, Mark J.

doi:10.1007/s00439-017-1861-0

Cited by 44 publications

(32 citation statements)

References 128 publications

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“…When it comes to the analysis of non-coding variants, the large number of such variants present in each person's genome and the infancy of any robust clinical annotation means the application of WGS for rare disease patients diagnosis still presents a considerable challenge [40,67,68] . Therefore, whilst most clinical diagnostic labs now utilise whole exome sequencing, few have yet transitioned to whole genome sequencing [69,70] .…”

Section: Discussionmentioning

confidence: 99%

“…Several dedicated resources have subsequently been developed, integrating and extending these datasets to generate a more detailed picture of regulatory elements [21][22][23][24][25][26] . Meanwhile, the application of novel computational [26][27][28][29] and high-throughput screening methods [30][31][32][33] has substantially improved our understanding of how regulatory elements control their respective target genes while several in-silico methods have been developed to better predict the impact of non-coding regulatory variants [34][35][36][37][38][39][40] .…”

Section: Introductionmentioning

confidence: 99%

“…Whilst there is a large variety of annotation methods and databases available for coding variants [41,42] , resources for programmatic annotation of regulatory variants and their respective target gene(s) are still lacking. Ideally, such resources would include a catalog of regulatory regions and functional elements together with a set of impact prediction scores [40,43] . However, the resources and databases currently available in this field are often presented in a format not suitable to this task, and information about controlled gene(s) and tissue(s) of activity is difficult to access programmatically.…”

Section: Introductionmentioning

confidence: 99%

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GREEN-DB: A framework for the annotation and prioritization of non-coding regulatory variants from whole-genome sequencing data

Giacopuzzi

Popitsch

Taylor

2020

Preprint

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BackgroundNon-coding variants have emerged as important contributors to the pathogenesis of human diseases, not only as common susceptibility alleles but also as rare high-impact variants. Despite recent advances in the study of regulatory elements and the availability of specialized data collections, the systematic annotation of non-coding variants from genome sequencing remains challenging.ResultsWe integrated 24 data sources to develop a standardized collection of 2.4 million regulatory elements in the human genome, transcription factor binding sites, DNase peaks, ultra-conserved non-coding elements, and super-enhancers. Information on controlled gene(s), tissue(s) and associated phenotype(s) are provided for regulatory elements when possible. We also calculated a variation constraint metric for regulatory regions and showed that genes controlled by constrained regions are more likely to be disease-associated genes and essential genes from mouse knock-out screenings. Finally, we evaluated 16 non-coding impact prediction scores providing suggestions for variant prioritization. The companion tool allows for annotation of VCF files with information about the regulatory regions as well as non-coding prediction scores to inform variant prioritization. The proposed annotation framework was able to capture previously published disease-associated non-coding variants and its integration in a routine prioritization pipeline increased the number of candidate genes, including genes potentially correlated with patient phenotype, and established clinically relevant genes.ConclusionWe have developed a resource for the annotation and prioritization of regulatory variants in WGS analysis to support the discovery of candidate disease-associated variants in the non-coding genome.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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GREEN-DB: A framework for the annotation and prioritization of non-coding regulatory variants from whole-genome sequencing data

Giacopuzzi

Popitsch

Taylor

2020

Preprint

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“…Finally, PolyPhen2 and sorting intolerant from tolerant (SIFT) are structure-based approaches that infer functional impact according to sequence conservation and severity of the amino acid change. 21 Functional impact scores were interpreted here following developers’ recommendations. In addition, data from The Genotype-Tissue Expression (GTEx) project 22 were also analysed to search for eQTLs using each SNP as query in the GTEx portal.…”

Section: Methodsmentioning

confidence: 99%

Association between MBL2 haplotypes and dengue severity in children from Rio de Janeiro, Brazil

Ornelas

Xavier-de-Carvalho

Alvarado-Arnez

et al. 2019

Mem. Inst. Oswaldo Cruz

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BACKGROUND Dengue is an arthropod-borne viral disease with a majority of asymptomatic individuals and clinical manifestations varying from mild fever to severe and potentially lethal forms. An increasing number of genetic studies have outlined the association between host genetic variations and dengue severity. Genes associated to viral recognition and entry, as well as those encoding mediators of the immune response against infection are strong candidates for association studies. OBJECTIVES The aim of this study was to investigate the association between MBL2, CLEC5A, ITGB3 and CCR5 genes and dengue severity in children. METHODS A matched case-control study was conducted and 19 single nucleotide polymorphisms (SNPs) were investigated. FINDINGS No associations were observed in single SNP analysis. However, when MBL2 SNPs were combined in haplotypes, the allele rs7095891G/rs1800450C/ rs1800451C/rs4935047A/rs930509G/rs2120131G/rs2099902C was significantly associated to risk of severe dengue under α = 0.05 (aOR = 4.02; p = 0.02). A second haplotype carrying rs4935047G and rs7095891G alleles was also associated to risk (aOR = 1.91; p = 0.04). MAIN CONCLUSIONS This is the first study to demonstrate the association between MBL2 haplotypes and dengue severity in Brazilians including adjustment for genetic ancestry. These results reinforce the role of mannose binding lectin in immune response to DENV.

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“…Traditionally, to understand how these variants may function they have been assigned to the nearest gene or genes within a defined linear distance. However this approach ignores the role of three-dimensional connectivity by which enhancers and repressors function to regulate transcription(135)(136)(137).Recent approaches use statistical co-localization tests to link potential causal SNPs and quantitative trait loci (QTLs) to identify the genes regulated by GWAS loci(138). These methods require many samples in the correct cell type or physiological context and to date work best for local/cis QTLs, generally less than 1Mb in linear distance(135).…”

mentioning

confidence: 99%

3DFAACTS-SNP: Using regulatory T cell-specific epigenomics data to uncover candidate mechanisms of Type-1 Diabetes (T1D) risk

Liu

Sadlon

Wong

et al. 2020

Preprint

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Background: Genome-wide association and fine-mapping studies have enabled the discovery of single nucleotide polymorphisms (SNPs) and other variants that are significantly associated with many autoimmune diseases including type 1 diabetes (T1D). However, many of the SNPs lie in non-coding regions, limiting the identification of mechanisms that contribute to autoimmune disease progression. Methods: Autoimmunity results from a failure of immune tolerance, suggesting that regulatory T cells (Treg) are likely a significant point of impact for this genetic risk, as Treg are critical for immune tolerance. Focusing on T1D as a model of defective function of Treg in autoimmunity, we designed a SNPs filtering workflow called 3 Dimensional Functional Annotation of Accessible Cell Type Specific SNPs (3DFAACTS-SNP) that utilises overlapping profiles of Treg-specific epigenomic data (ATAC-seq, Hi-C and FOXP3-ChIP) to identify regulatory elements potentially driving the effect of variants associated with T1D, and the gene(s) that they control. Results: Using 3DFAACTS-SNP we identified 36 SNPs with plausible Treg-specific mechanisms of action contributing to T1D from 1,228 T1D fine-mapped variants, identifying 119 novel interacting regions resulting in the identification of 51 candidate target genes. We further demonstrated the utility of the workflow by applying it to three other fine-mapped/meta-analysed SNP autoimmune datasets, identifying 17 Treg-centric candidate variants and 35 interacting genes. Finally, we demonstrate the broad utility of 3DFAACTS-SNP for functional annotation of any genetic variation using all common (>10% allele frequency) variants from the Genome Aggregation Database (gnomAD). We identified 7,900 candidate variants and 3,245 candidate target genes, generating a list of potential sites for future T1D or autoimmune research. Conclusions: We demonstrate that it is possible to further prioritise variants that contribute to T1D based on regulatory function and illustrate the power of using cell type specific multi-omics datasets to determine disease mechanisms. The 3DFAACTS-SNP workflow can be customised to any cell type for which the individual datasets for functional annotation have been generated, giving broad applicability and utility.

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Principles and methods of in-silico prioritization of non-coding regulatory variants

Cited by 44 publications

References 128 publications

GREEN-DB: A framework for the annotation and prioritization of non-coding regulatory variants from whole-genome sequencing data

GREEN-DB: A framework for the annotation and prioritization of non-coding regulatory variants from whole-genome sequencing data

Association between MBL2 haplotypes and dengue severity in children from Rio de Janeiro, Brazil

3DFAACTS-SNP: Using regulatory T cell-specific epigenomics data to uncover candidate mechanisms of Type-1 Diabetes (T1D) risk

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