FIRE: functional inference of genetic variants that regulate gene expression

Ioannidis, Nilah M.; Davis, Joe R.; DeGorter, Marianne K.; Larson, Nicholas B.; McDonnell, Shannon K.; French, Amy J.; Battle, Alexis; Hastie, Trevor; Thibodeau, Stephen N.; Montgomery, Stephen B.; Bustamante, Carlos D.; Sieh, Weiva; Whittemore, Alice S.

doi:10.1093/bioinformatics/btx534

Cited by 34 publications

(33 citation statements)

References 40 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To further assess the performance of our epigenetic functional scoring, we compared the functional support on multiple immune-cell associated regulatory evidence between SNPs prioritized by our method and other five functional scoring methods [11-15]. Table S15 summarized the main characteristics between our method and other scoring methods (see discussion for comparison in detail).…”

Section: Resultsmentioning

confidence: 99%

“…In this study, we developed a new improved epigenetic functional scoring method to prioritize functional autoimmune SNPs through incorporating hundreds of immune cell-specific active epigenetic information. Some other comparable scoring methods are also developed, such as 3DSNP [13], FIRE [11], GWAS4D [14], IW-Scoring [15] or RegulomeDB [12]. Compared with these approaches, one distinct feature of our method was the integrating of immune cell-specific epigenetic information (Table S15), which might provide better evaluation for disease-specific functional autoimmune SNPs.…”

Section: Discussionmentioning

confidence: 99%

“…We compared our epigenetic functional scoring with five other functional scoring methods, including 3DSNP [13], FIRE [11], GWAS4D [14], IW-Scoring [15] and RegulomeDB [12]. The IW-Scoring [15] integrated eleven commonly used scoring methods to assign SNP a combined significance level ( P -value) and outperformed any single method.…”

Section: Methodsmentioning

confidence: 99%

“…In the past few years, many studies have integrated functional epigenetic data to predict function of noncoding SNPs. Many of these methods such as CADD [6], DeepSEA [7], GWAVA [8], FATHMM-MKL [9], ReMM [10] and FIRE [11], adopted machine learning algorithms to develop classifiers through integrating various annotations and labelled training data to distinguish potential functional/non-functional SNPs. However, the prior labelled training data may be inaccurate and impractical due to the current knowledge limitation in functional roles underlying noncoding SNPs.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Dissecting molecular regulatory mechanisms underlying noncoding susceptibility SNPs associated with 19 autoimmune diseases using multi-omics integrative analysis

Chen

Guo

Duan

et al. 2019

Preprint

View full text Add to dashboard Cite

14The genome-wide association studies (GWAS) have identified hundreds of 15 susceptibility loci associated with autoimmune diseases. However, over 90% of risk 16 variants are located in the noncoding regions, leading to great challenges in 17 deciphering the underlying causal functional variants/genes and biological 18 mechanisms. Previous studies focused on developing new scoring method to 19 prioritize functional/disease-relevant variants. However, they principally 20 incorporated annotation data across all cells/tissues while omitted the cell-specific or 21 context-specific regulation. Moreover, limited analyses were performed to dissect the 22 detailed molecular regulatory circuits linking functional GWAS variants to disease 23 etiology. Here we devised a new analysis frame that incorporate hundreds of immune 24 cell-specific multi-omics data to prioritize functional noncoding susceptibility SNPs 25 with gene targets and further dissect their downstream molecular mechanisms and 26 clinical applications for 19 autoimmune diseases. Most prioritized SNPs have genetic 27 associations with transcription factors (TFs) binding, histone modification or 28 chromatin accessibility, indicating their allelic regulatory roles on target genes. Their 29 target genes were significantly enriched in immunologically related pathways and 30other immunologically related functions. We also detected long-range regulation on 31 90.7% of target genes including 132 ones exclusively regulated by distal SNPs (eg, 32 CD28, IL2RA), which involves several potential key TFs (eg, CTCF), suggesting the 33 important roles of long-range chromatin interaction in autoimmune diseases. 34Moreover, we identified hundreds of known or predicted druggable genes, and 35 predicted some new potential drug targets for several autoimmune diseases, including 36 two genes (NFKB1, SH2B3) with known drug indications on other diseases, 37 highlighting their potential drug repurposing opportunities. In summary, our analyses 38 may provide unique resource for future functional follow-up and drug application on 39 autoimmune diseases, which are freely available at http://fngwas.online/. 40 41 42 3 Author Summary 43Autoimmune diseases are groups of complex immune system disorders with high 44 prevalence rates and high heritabilities. Previous studies have unraveled thousands 45 of SNPs associated with different autoimmune diseases. However, it remains largely 46 unknown on the molecular mechanisms underlying these genetic associations. 47Striking, over 90% of risk SNPs are located in the noncoding region. By leveraging 48 multiple immune cell-specific multi-omics data across genomic, epigenetic, 49 transcriptomic and 3D chromatin interaction information, we systematically analyzed 50 the functional variants/genes and biological mechanisms underlying genetic 51 association on 19 autoimmune diseases. We found that most functional SNPs may 52 affect target gene expression through altering transcription factors (TFs) binding, 53 histone modification or chromatin acce...

show abstract

Section: Resultsmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Dissecting molecular regulatory mechanisms underlying noncoding susceptibility SNPs associated with 19 autoimmune diseases using multi-omics integrative analysis

Chen

Guo

Duan

et al. 2019

Preprint

View full text Add to dashboard Cite

show abstract

“…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%

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

Giacopuzzi

Popitsch

Taylor

2020

Preprint

View full text Add to dashboard Cite

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.

show abstract

Whole-genome sequencing identifies functional noncoding variation in SEMA3C that cosegregates with dyslexia in a multigenerational family

et al. 2021

View full text Add to dashboard Cite

Dyslexia is a common heritable developmental disorder involving impaired reading abilities. Its genetic underpinnings are thought to be complex and heterogeneous, involving common and rare genetic variation. Multigenerational families segregating apparent monogenic forms of language-related disorders can provide useful entrypoints into biological pathways. In the present study, we performed a genome-wide linkage scan in a three-generational family in which dyslexia affects 14 of its 30 members and seems to be transmitted with an autosomal dominant pattern of inheritance. We identified a locus on chromosome 7q21.11 which cosegregated with dyslexia status, with the exception of two cases of phenocopy (LOD = 2.83). Whole-genome sequencing of key individuals enabled the assessment of coding and noncoding variation in the family. Two rare single-nucleotide variants (rs144517871 and rs143835534) within the first intron of the SEMA3C gene cosegregated with the 7q21.11 risk haplotype. In silico characterization of these two variants predicted effects on gene regulation, which we functionally validated for rs144517871 in human cell lines using luciferase reporter assays. SEMA3C encodes a secreted protein that acts as a guidance cue in several processes, including cortical neuronal migration and cellular polarization. We hypothesize that these intronic variants could have a cis-regulatory effect on SEMA3C expression, making a contribution to dyslexia susceptibility in this family.

show abstract

FIRE: functional inference of genetic variants that regulate gene expression

Abstract: Supplementary data are available at Bioinformatics online.

Cited by 34 publications

References 40 publications

Dissecting molecular regulatory mechanisms underlying noncoding susceptibility SNPs associated with 19 autoimmune diseases using multi-omics integrative analysis

Dissecting molecular regulatory mechanisms underlying noncoding susceptibility SNPs associated with 19 autoimmune diseases using multi-omics integrative analysis

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

Whole-genome sequencing identifies functional noncoding variation in SEMA3C that cosegregates with dyslexia in a multigenerational family

Contact Info

Product

Resources

About