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Systematic reanalysis of genomic data improves quality of variant interpretation.

ABSTRACT: As genomic sequencing expands, so does our knowledge of the link between genetic variation and disease. Deeper catalogs of variant frequencies improve identification of benign variants, while sequencing affected individuals reveals disease-associated variation. Accumulation of human genetic data thus makes reanalysis a means to maximize the benefits of clinical sequencing. We implemented pipelines to systematically reassess sequencing data from 494 individuals with developmental disability. Reanalysis yielded pathogenic or likely pathogenic (P/LP) variants that were not initially reported in 23 individuals, 6 described here, comprising a 16% increase in P/LP yield. We also downgraded 3 LP and 6 variants of uncertain significance (VUS) due to updated population frequency data. The likelihood of identifying a new P/LP variant increased over time, as ~22% of individuals who did not receive a P/LP variant at their original analysis subsequently did after 3 years. We show here that reanalysis and data sharing increase the diagnostic yield and accuracy of clinical sequencing.

SUBMITTER: Hiatt SM

PROVIDER: S-EPMC5995667 | biostudies-literature | 2018 Jul

REPOSITORIES: biostudies-literature

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Systematic reanalysis of genomic data improves quality of variant interpretation.

Hiatt S M SM Amaral M D MD Bowling K M KM Finnila C R CR Thompson M L ML Gray D E DE Lawlor J M J JMJ Cochran J N JN Bebin E M EM Brothers K B KB East K M KM Kelley W V WV Lamb N E NE Levy S E SE Lose E J EJ Neu M B MB Rich C A CA Simmons S S Myers R M RM Barsh G S GS Cooper G M GM

Clinical genetics 20180510 1

As genomic sequencing expands, so does our knowledge of the link between genetic variation and disease. Deeper catalogs of variant frequencies improve identification of benign variants, while sequencing affected individuals reveals disease-associated variation. Accumulation of human genetic data thus makes reanalysis a means to maximize the benefits of clinical sequencing. We implemented pipelines to systematically reassess sequencing data from 494 individuals with developmental disability. Rean ...[more]

PMID: 29652076

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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

Systematic reanalysis of genomic data improves quality of variant interpretation.

Publications

Systematic reanalysis of genomic data improves quality of variant interpretation.

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