Annotating pathogenic non-coding variants in genic regions

Abstract

Identifying the underlying causes of disease requires accurate interpretation of genetic variants. Current methods ineffectively capture pathogenic non-coding variants in genic regions, resulting in overlooking synonymous and intronic variants when searching for disease risk. Here we present the Transcript-inferred Pathogenicity (TraP) score, which uses sequence context alterations to reliably identify non-coding variation that causes disease. High TraP scores single out extremely rare variants with lower minor allele frequencies than missense variants. TraP accurately distinguishes known pathogenic and benign variants in synonymous (AUC = 0.88) and intronic (AUC = 0.83) public datasets, dismissing benign variants with exceptionally high specificity. TraP analysis of 843 exomes from epilepsy family trios identifies synonymous variants in known epilepsy genes, thus pinpointing risk factors of disease from non-coding sequence data. TraP outperforms leading methods in identifying non-coding variants that are pathogenic and is therefore a valuable tool for use in gene discovery and the interpretation of personal genomes.

NBAS pathogenic variants: Defining the associated clinical and facial phenotype and genotype–phenotype correlations

“…This finding further highlights the current need to optimize the bioinformatics workflow required to attain an accurate functional annotation of the large number of variants that are routinely identified by genome-wide sequencing, as well as the importance of not underestimating synonymous variations when screening for disease-causing variants (Gelfman et al, 2017;Livingstone et al, 2017;Pantaleoni et al, 2017).…”

“…It is now well‐established that synonymous variants may alter gene expression through modulation of splicing, mRNA stability, and translation, and disturbing these processes may contribute to human disease (Mueller, Larsen, Garibaldi, Hatfield, & Hertel, ; Supek, Minana, Valcarcel, Gabaldon, & Lehner, ). This finding further highlights the current need to optimize the bioinformatics workflow required to attain an accurate functional annotation of the large number of variants that are routinely identified by genome‐wide sequencing, as well as the importance of not underestimating synonymous variations when screening for disease‐causing variants (Gelfman et al, ; Livingstone et al, ; Pantaleoni et al, ).…”

Genetic investigation of 93 families with microphthalmia or posterior microphthalmos

“…Variants found within genes previously reported to cause the condition and predicted to be pathogenic by at least 2 in silico prediction tools (PolyPhen, SIFT and CADD, http://cadd.gs.washington.edu/) were prioritized for segregation analysis. Transcript inferred Pathogenicity Score (TraP, http://trap-score.org/Search?version=v2) was used to predict the pathogencity of splice site variants . For simplex cases heterozygous variants were considered for all simplex cases irrespective of parental consanguinity.…”

“…version=v2) was used to predict the pathogencity of splice site variants. 25 For simplex cases heterozygous variants were considered for all simplex cases irrespective of parental consanguinity. Only those that were rare (<0.001 in gnomAD and <0.002 in our local database in the context of compound heterozygosity, or completely absent in our local database and <0.00002 in gnomAD in the context of dominant heterozygosity), predicted to be pathogenic and previously reported in the context of AD microphthalmia were considered as variants of interest and prioritized for segregation analysis.…”

MORFEE: a new tool for detecting and annotating single nucleotide variants creating premature ATG codons from VCF files

“…The latter generates an overlapping open reading frame that inhibits the production of the natural Protein S and causes familial inherited Protein S deficiency (Labrouche-Colomer et al, submitted). As this variant is located in the PROS1 promoter region, it was not predicted to be deleterious by popular bioinformatics prediction tools, including PolyPhen (http://genetics.bwh.harvard.edu/pph2/), SIFT (http://sift.bii.a-star.edu.sg/), CADD (Rentzsch et al, 2019), REVEL (Ioannidis et al, 2016), TraP (Gelfman et al, 2017) or RegulomeDB (Boyle et al, 2012), most of them mainly dedicated to coding SNVs. Even the PreTIS software (Reuter et al, 2016) dedicated to 5'UTR variants only predicted a "moderate" impact of the mutation.…”