Functional annotation of noncoding sequence variants

Ritchie, Graham R. S.; Dunham, Ian; Zeggini, Eleftheria; Flicek, Paul

doi:10.1038/nmeth.2832

Cited by 510 publications

(577 citation statements)

References 25 publications

(32 reference statements)

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“…Among these features, H3K36me3 is associated with actively transcribed genes, and H3K4me3 is a hallmark of actively transcribed protein-coding promoters in eukaryotes (51). These findings support the fact that conserved and regulatory elements are critical to the formation and functionality of pathogenic variants in the non-coding genome (52). The area under the ROC curve was 0.89, which outperformed two well-known tools CADD and funSeq2 (14), however, more stringent comparison must be conducted to obtain a final conclusion.…”

Section: Discussionsupporting

confidence: 55%

Functional annotation of noncoding variants and prioritization of cancer-associated lncRNAs in lung cancer

2016

Oncology Letters

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Abstract. Multiple computational tools have been widely applied to the detection of coding driver mutations in cancer; however, the prioritization of pathogenic non-coding variants remains a difficult and demanding task. The present study was performed to distinguish non-coding disease-causing mutations from neutral ones, and to prioritize potential cancer-associated long non-coding RNAs (lncRNAs) with a logistic regression model in lung cancer. A logistic regression model was constructed, combining 19,153 disease-associated ClinVar and Human Gene Mutation Database pathogenic variants as the response variable and non-coding features as the predictor variable. Validation of the model was conducted with genome-wide association study (GWAS) disease-or trait-associated single nucleotide polymorphisms (SNPs) and recurrent somatic mutations. High scoring regions were characterized with respect to their distribution in various features and gene classes; potential cancer-associated lncRNA candidates were prioritized, combining the fraction of high-scoring regions and average score predicted by the logistic regression model. H3K79me2 was the most negative factor that contributed to the model, while conserved regions were most positively informative to the model. The area under the receiver operating characteristic curve of the model was 0.89. The model assigned a significantly higher score to GWAS SNPs and recurrent somatic mutations compared with neutral SNPs (mean, 5.9012 vs. 5.5238; P<0.001, Mann-Whitney U test) and non-recurrent mutations (mean, 5.4677 vs. 5.2277, P<0.001, Mann-Whitney U test), respectively. It was observed that regions, including splicing sites and untranslated regions, and gene classes, including cancer genes and cancer-associated lncRNAs, had an increased enrichment of high-scoring regions. In total, 2,679 cancer-associated lncRNAs were determined and characterized. A total of 104 of these lncRNAs were differentially expressed between lung cancer and normal specimens. The logistic regression model is a useful and efficient scoring system to prioritize non-coding pathogenic variants and lncRNAs, and may provide the basis for detecting non-coding driver lncRNAs in lung cancer.

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Section: Discussionsupporting

confidence: 55%

Functional annotation of noncoding variants and prioritization of cancer-associated lncRNAs in lung cancer

2016

Oncology Letters

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“…Abbreviations are as follows: BMI, body mass index; WHR, waist to hip ratio; WaistBMIadj, waist circumference adjusted for BMI; HipBMIadj, hip circumference adjusted for BMI; WHRBMIadj, waist to hip ratio adjusted for BMI; TFM, total fat mass; TLM, total lean mass; TRFM, trunk fat mass. from the two fine-mapping methods, two functional prediction scores (Genome Wide Annotation of Variants 54 [GWAVA] and GERP scores), and eQTL analysis (Figures 3 and S28). Of the 30 regions, 6 were fine-mapped to a coding variant (5 missense and 1 synonymous) and 9 were fine-mapped to a variant that was identified as an eQTL.…”

Section: Fine-mappingmentioning

confidence: 99%

Whole-Genome Sequencing Coupled to Imputation Discovers Genetic Signals for Anthropometric Traits

Tachmazidou

Süveges

Min

et al. 2017

The American Journal of Human Genetics

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141

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Deep sequence-based imputation can enhance the discovery power of genome-wide association studies by assessing previously unexplored variation across the common-and low-frequency spectra. We applied a hybrid whole-genome sequencing (WGS) and deep imputation approach to examine the broader allelic architecture of 12 anthropometric traits associated with height, body mass, and fat distribution in up to 267,616 individuals. We report 106 genome-wide significant signals that have not been previously identified, including 9 low-frequency variants pointing to functional candidates. Of the 106 signals, 6 are in genomic regions that have not been implicated with related traits before, 28 are independent signals at previously reported regions, and 72 represent previously reported signals for a different anthropometric trait. 71% of signals reside within genes and fine mapping resolves 23 signals to one or two likely causal variants. We confirm genetic overlap between human monogenic and polygenic anthropometric traits and find signal enrichment in cis expression QTLs in relevant tissues. Our results highlight the potential of WGS strategies to enhance biologically relevant discoveries across the frequency spectrum.

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“…We used several bioinformatics approaches (LDlink,30 RegulomeDB,31 Genome‐Wide Annotation of Variants [GWAVA],32 and Data‐driven Expression Prioritized Integration for Complex Traits [DEPICT]33) to search and annotate SNPs in the regions containing genome‐wide significant SNPs. Publicly available reference haplotypes from Phase 3 (Version 5) of the 1000 Genomes Project (1000G)34 were used to calculate population‐specific measures of linkage disequilibrium (LD)30 in whites.…”

Section: Methodsmentioning

confidence: 99%

“…RegulomeDB31 integrates the RoadMap Epigenomics and ENCODE projects to identify variants which have potential or demonstrated regulatory function, and predicts potential mechanisms of functional involvement. We used a GWAVA32 score to identify loci with likely functional non‐coding variants. We used an unmatched GWAVA score threshold ≥0.35, GWAVA transcription start site (TSS) score threshold ≥0.45, and region GWAVA score threshold ≥0.55 to mark potentially functional loci.…”

Section: Methodsmentioning

confidence: 99%

Genome‐Wide Associations of Global Electrical Heterogeneity ECG Phenotype: The ARIC (Atherosclerosis Risk in Communities) Study and CHS (Cardiovascular Health Study)

Tereshchenko

Sotoodehnia

Sitlani

et al. 2018

JAHA

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Background ECG global electrical heterogeneity (GEH) is associated with sudden cardiac death. We hypothesized that a genome‐wide association study would identify genetic loci related to GEH.Methods and ResultsWe tested genotyped and imputed variants in black (N=3057) and white (N=10 769) participants in the ARIC (Atherosclerosis Risk in Communities) study and CHS (Cardiovascular Health Study). GEH (QRS‐T angle, sum absolute QRST integral, spatial ventricular gradient magnitude, elevation, azimuth) was measured on 12‐lead ECGs. Linear regression models were constructed with each GEH variable as an outcome, adjusted for age, sex, height, body mass index, study site, and principal components to account for ancestry. GWAS identified 10 loci that showed genome‐wide significant association with GEH in whites or joint ancestry. The strongest signal (rs7301677, near TBX3) was associated with QRS‐T angle (white standardized β+0.16 [95% CI 0.13–0.19]; P=1.5×10−26), spatial ventricular gradient elevation (+0.11 [0.08–0.14]; P=2.1×10−12), and spatial ventricular gradient magnitude (−0.12 [95% CI −0.15 to −0.09]; P=5.9×10−15). Altogether, GEH‐SNPs explained 1.1% to 1.6% of GEH variance. Loci on chromosomes 4 (near HMCN2), 5 (IGF1R), 11 (11p11.2 region cluster), and 7 (near ACTB) are novel ECG phenotype‐associated loci. Several loci significantly associated with gene expression in the left ventricle (HMCN2 locus—with HMCN2;IGF1R locus—with IGF1R), and atria (RP11‐481J2.2 locus—with expression of a long non‐coding RNA and NDRG4).ConclusionsWe identified 10 genetic loci associated with ECG GEH. Replication of GEH GWAS findings in independent cohorts is warranted. Further studies of GEH‐loci may uncover mechanisms of arrhythmogenic remodeling in response to cardiovascular risk factors.

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Functional annotation of noncoding sequence variants

Cited by 510 publications

References 25 publications

Functional annotation of noncoding variants and prioritization of cancer-associated lncRNAs in lung cancer

Functional annotation of noncoding variants and prioritization of cancer-associated lncRNAs in lung cancer

Whole-Genome Sequencing Coupled to Imputation Discovers Genetic Signals for Anthropometric Traits

Genome‐Wide Associations of Global Electrical Heterogeneity ECG Phenotype: The ARIC (Atherosclerosis Risk in Communities) Study and CHS (Cardiovascular Health Study)

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