Dataset Information

Disease-specific variant pathogenicity prediction significantly improves variant interpretation in inherited cardiac conditions.

ABSTRACT:

Purpose

Accurate discrimination of benign and pathogenic rare variation remains a priority for clinical genome interpretation. State-of-the-art machine learning variant prioritization tools are imprecise and ignore important parameters defining gene-disease relationships, e.g., distinct consequences of gain-of-function versus loss-of-function variants. We hypothesized that incorporating disease-specific information would improve tool performance.

Methods

We developed a disease-specific variant classifier, CardioBoost, that estimates the probability of pathogenicity for rare missense variants in inherited cardiomyopathies and arrhythmias. We assessed CardioBoost's ability to discriminate known pathogenic from benign variants, prioritize disease-associated variants, and stratify patient outcomes.

Results

CardioBoost has high global discrimination accuracy (precision recall area under the curve [AUC] 0.91 for cardiomyopathies; 0.96 for arrhythmias), outperforming existing tools (4-24% improvement). CardioBoost obtains excellent accuracy (cardiomyopathies 90.2%; arrhythmias 91.9%) for variants classified with >90% confidence, and increases the proportion of variants classified with high confidence more than twofold compared with existing tools. Variants classified as disease-causing are associated with both disease status and clinical severity, including a 21% increased risk (95% confidence interval [CI] 11-29%) of severe adverse outcomes by age 60 in patients with hypertrophic cardiomyopathy.

Conclusions

A disease-specific variant classifier outperforms state-of-the-art genome-wide tools for rare missense variants in inherited cardiac conditions ( https://www.cardiodb.org/cardioboost/ ), highlighting broad opportunities for improved pathogenicity prediction through disease specificity.

SUBMITTER: Zhang X

PROVIDER: S-EPMC7790749 | biostudies-literature | 2021 Jan

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Disease-specific variant pathogenicity prediction significantly improves variant interpretation in inherited cardiac conditions.

Zhang Xiaolei X Walsh Roddy R Whiffin Nicola N Buchan Rachel R Midwinter William W Wilk Alicja A Govind Risha R Li Nicholas N Ahmad Mian M Mazzarotto Francesco F Roberts Angharad A Theotokis Pantazis I PI Mazaika Erica E Allouba Mona M de Marvao Antonio A Pua Chee Jian CJ Day Sharlene M SM Ashley Euan E Colan Steven D SD Michels Michelle M Pereira Alexandre C AC Jacoby Daniel D Ho Carolyn Y CY Olivotto Iacopo I Gunnarsson Gunnar T GT Jefferies John L JL Semsarian Chris C Ingles Jodie J O'Regan Declan P DP Aguib Yasmine Y Yacoub Magdi H MH Cook Stuart A SA Barton Paul J R PJR Bottolo Leonardo L Ware James S JS

Genetics in medicine : official journal of the American College of Medical Genetics 20201013 1

<h4>Purpose</h4>Accurate discrimination of benign and pathogenic rare variation remains a priority for clinical genome interpretation. State-of-the-art machine learning variant prioritization tools are imprecise and ignore important parameters defining gene-disease relationships, e.g., distinct consequences of gain-of-function versus loss-of-function variants. We hypothesized that incorporating disease-specific information would improve tool performance.<h4>Methods</h4>We developed a disease-spe ...[more]

PMID: 33046849

Similar Datasets

Project description:The acceleration of DNA sequencing in samples from patients and population studies has resulted in extensive catalogues of human genetic variation, but the interpretation of rare genetic variants remains problematic. A notable example of this challenge is the existence of disruptive variants in dosage-sensitive disease genes, even in apparently healthy individuals. Here, by manual curation of putative loss-of-function (pLoF) variants in haploinsufficient disease genes in the Genome Aggregation Database (gnomAD)1, we show that one explanation for this paradox involves alternative splicing of mRNA, which allows exons of a gene to be expressed at varying levels across different cell types. Currently, no existing annotation tool systematically incorporates information about exon expression into the interpretation of variants. We develop a transcript-level annotation metric known as the 'proportion expressed across transcripts', which quantifies isoform expression for variants. We calculate this metric using 11,706 tissue samples from the Genotype Tissue Expression (GTEx) project2 and show that it can differentiate between weakly and highly evolutionarily conserved exons, a proxy for functional importance. We demonstrate that expression-based annotation selectively filters 22.8% of falsely annotated pLoF variants found in haploinsufficient disease genes in gnomAD, while removing less than 4% of high-confidence pathogenic variants in the same genes. Finally, we apply our expression filter to the analysis of de novo variants in patients with autism spectrum disorder and intellectual disability or developmental disorders to show that pLoF variants in weakly expressed regions have similar effect sizes to those of synonymous variants, whereas pLoF variants in highly expressed exons are most strongly enriched among cases. Our annotation is fast, flexible and generalizable, making it possible for any variant file to be annotated with any isoform expression dataset, and will be valuable for the genetic diagnosis of rare diseases, the analysis of rare variant burden in complex disorders, and the curation and prioritization of variants in recall-by-genotype studies.

Dataset Information

Disease-specific variant pathogenicity prediction significantly improves variant interpretation in inherited cardiac conditions.

Purpose

Methods

Results

Conclusions

Publications

Disease-specific variant pathogenicity prediction significantly improves variant interpretation in inherited cardiac conditions.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets