Dataset Information

CAGI4 SickKids clinical genomes challenge: A pipeline for identifying pathogenic variants.

ABSTRACT: Compared with earlier more restricted sequencing technologies, identification of rare disease variants using whole-genome sequence has the possibility of finding all causative variants, but issues of data quality and an overwhelming level of background variants complicate the analysis. The CAGI4 SickKids clinical genome challenge provided an opportunity to assess the landscape of variants found in a difficult set of 25 unsolved rare disease cases. To address the challenge, we developed a three-stage pipeline, first carefully analyzing data quality, then classifying high-quality gene-specific variants into seven categories, and finally examining each candidate variant for compatibility with the often complex phenotypes of these patients for final prioritization. Variants consistent with the phenotypes were found in 24 out of the 25 cases, and in a number of these, there are prioritized variants in multiple genes. Data quality analysis suggests that some of the selected variants are likely incorrect calls, complicating interpretation. The data providers followed up on three suggested variants with Sanger sequencing, and in one case, a prioritized variant was confirmed as likely causative by the referring physician, providing a diagnosis in a previously intractable case.

SUBMITTER: Pal LR

PROVIDER: S-EPMC5577808 | biostudies-literature | 2017 Sep

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

CAGI4 SickKids clinical genomes challenge: A pipeline for identifying pathogenic variants.

Pal Lipika R LR Kundu Kunal K Yin Yizhou Y Moult John J

Human mutation 20170627 9

Compared with earlier more restricted sequencing technologies, identification of rare disease variants using whole-genome sequence has the possibility of finding all causative variants, but issues of data quality and an overwhelming level of background variants complicate the analysis. The CAGI4 SickKids clinical genome challenge provided an opportunity to assess the landscape of variants found in a difficult set of 25 unsolved rare disease cases. To address the challenge, we developed a three-s ...[more]

PMID: 28512736

Similar Datasets

Project description:Rationale & objectivePathogenic variants in type IV collagen have been reported to account for a significant proportion of chronic kidney disease. Accordingly, genetic testing is increasingly used to diagnose kidney diseases, but testing also may reveal rare missense variants that are of uncertain clinical significance. To aid in interpretation, computational prediction (called in silico) programs may be used to predict whether a variant is clinically important. We evaluate the performance of in silico programs for COL4A3/A4/A5 variants.Study design setting & participantsRare missense variants in COL4A3/A4/A5 were identified in disease cohorts, including a local focal segmental glomerulosclerosis (FSGS) cohort and publicly available disease databases, in which they are categorized as pathogenic or benign based on clinical criteria.Tests compared & outcomesAll rare missense variants identified in the 4 disease cohorts were subjected to in silico predictions using 12 different programs. Comparisons between the predictions were compared with: (1) variant classification (pathogenic or benign) in the cohorts and (2) functional characterization in a randomly selected smaller number (17) of pathogenic or uncertain significance variants obtained from the local FSGS cohort.ResultsIn silico predictions correctly classified 75% to 97% of pathogenic and 57% to 100% of benign COL4A3/A4/A5 variants in public disease databases. The congruency of in silico predictions was similar for variants categorized as pathogenic and benign, with the exception of benign COL4A5 variants, in which disease effects were overestimated. By contrast, in silico predictions and functional characterization classified all 9 pathogenic COL4A3/A4/A5 variants correctly that were obtained from a local FSGS cohort. However, these programs also overestimated the effects of genomic variants of uncertain significance when compared with functional characterization. Each of the 12 in silico programs used yielded similar results.LimitationsOverestimation of in silico program sensitivity given that they may have been used in the categorization of variants labeled as pathogenic in disease repositories.ConclusionsOur results suggest that in silico predictions are sensitive but not specific to assign COL4A3/A4/A5 variant pathogenicity, with misclassification of benign variants and variants of uncertain significance. Thus, we do not recommend in silico programs but instead recommend pursuing more objective levels of evidence suggested by medical genetics guidelines.

Project description:BackgroundThe clinical significance of SPINK1 intronic variants in chronic pancreatitis has been previously assessed by various approaches including a cell culture-based full-length gene assay. A close correlation between the results of this assay and in silico splicing prediction was apparent. However, until now, a clinical diagnostic pipeline specifically designed to classify SPINK1 intronic variants accurately and efficiently has been lacking. Herein, we present just such a pipeline and explore its efficacy and potential utility in potentiating the classification of newly described SPINK1 intronic variants.ResultsWe confirm a close correlation between in silico splicing prediction and results from the cell culture-based full-length gene assay in the context of three recently reported pathogenic SPINK1 intronic variants. We then integrated in silico splicing prediction and the full-length gene assay into a stepwise approach and tested its utility in the classification of two novel datasets of SPINK1 intronic variants. The first dataset comprised 16 deep intronic variants identified in 52 genetically unexplained Chinese chronic pancreatitis patients by sequencing the entire intronic sequence of the SPINK1 gene. The second dataset comprised five novel rare proximal intronic variants identified through the routine analysis of the SPINK1 gene in French pancreatitis patients. Employing a minor allele frequency of > 5% as a population frequency filter, 6 of the 16 deep intronic variants were immediately classified as benign. In silico prediction of the remaining ten deep intronic variants and the five rare proximal intronic variants with respect to their likely impact on splice site selection suggested that only one proximal intronic variant, c.194 + 5G > A, was likely to be of functional significance. Employing the cell culture-based full-length gene assay, we functionally analyzed c.194 + 5G > A, together with seven predicted non-functional variants, thereby validating their predicted effects on splicing in all cases.ConclusionsWe demonstrated the accuracy and efficiency of in silico prediction in combination with the cell culture-based full-length gene assay for the classification of SPINK1 intronic variants. Based upon these findings, we propose an operational pipeline for classifying SPINK1 intronic variants in the clinical diagnostic setting.

Dataset Information

CAGI4 SickKids clinical genomes challenge: A pipeline for identifying pathogenic variants.

Publications

CAGI4 SickKids clinical genomes challenge: A pipeline for identifying pathogenic variants.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets