Dataset Information

Classification of Genes: Standardized Clinical Validity Assessment of Gene-Disease Associations Aids Diagnostic Exome Analysis and Reclassifications.

ABSTRACT: Ascertaining a diagnosis through exome sequencing can provide potential benefits to patients, insurance companies, and the healthcare system. Yet, as diagnostic sequencing is increasingly employed, vast amounts of human genetic data are produced that need careful curation. We discuss methods for accurately assessing the clinical validity of gene-disease relationships to interpret new research findings in a clinical context and increase the diagnostic rate. The specifics of a gene-disease scoring system adapted for use in a clinical laboratory are described. In turn, clinical validity scoring of gene-disease relationships can inform exome reporting for the identification of new or the upgrade of previous, clinically relevant gene findings. Our retrospective analysis of all reclassification reports from the first 4 years of diagnostic exome sequencing showed that 78% were due to new gene-disease discoveries published in the literature. Among all exome positive/likely positive findings in characterized genes, 32% were in genetic etiologies that were discovered after 2010. Our data underscore the importance and benefits of active and up-to-date curation of a gene-disease database combined with critical clinical validity scoring and proactive reanalysis in the clinical genomics era.

SUBMITTER: Smith ED

PROVIDER: S-EPMC5655771 | biostudies-literature | 2017 May

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Classification of Genes: Standardized Clinical Validity Assessment of Gene-Disease Associations Aids Diagnostic Exome Analysis and Reclassifications.

Smith Erica D ED Radtke Kelly K Rossi Mari M Shinde Deepali N DN Darabi Sourat S El-Khechen Dima D Powis Zöe Z Helbig Katherine K Waller Kendra K Grange Dorothy K DK Tang Sha S Farwell Hagman Kelly D KD

Human mutation 20170213 5

Ascertaining a diagnosis through exome sequencing can provide potential benefits to patients, insurance companies, and the healthcare system. Yet, as diagnostic sequencing is increasingly employed, vast amounts of human genetic data are produced that need careful curation. We discuss methods for accurately assessing the clinical validity of gene-disease relationships to interpret new research findings in a clinical context and increase the diagnostic rate. The specifics of a gene-disease scoring ...[more]

PMID: 28106320

Similar Datasets

Project description:Study questionWhich genes are confidently linked to human monogenic male infertility?Summary answerOur systematic literature search and clinical validity assessment reveals that a total of 78 genes are currently confidently linked to 92 human male infertility phenotypes.What is known alreadyThe discovery of novel male infertility genes is rapidly accelerating with the availability of next-generating sequencing methods, but the quality of evidence for gene-disease relationships varies greatly. In order to improve genetic research, diagnostics and counseling, there is a need for an evidence-based overview of the currently known genes.Study design, size, durationWe performed a systematic literature search and evidence assessment for all publications in Pubmed until December 2018 covering genetic causes of male infertility and/or defective male genitourinary development.Participants/materials, setting, methodsTwo independent reviewers conducted the literature search and included papers on the monogenic causes of human male infertility and excluded papers on genetic association or risk factors, karyotype anomalies and/or copy number variations affecting multiple genes. Next, the quality and the extent of all evidence supporting selected genes was weighed by a standardized scoring method and used to determine the clinical validity of each gene-disease relationship as expressed by the following six categories: no evidence, limited, moderate, strong, definitive or unable to classify.Main results and the role of chanceFrom a total of 23 526 records, we included 1337 publications about monogenic causes of male infertility leading to a list of 521 gene-disease relationships. The clinical validity of these gene-disease relationships varied widely and ranged from definitive (n = 38) to strong (n = 22), moderate (n = 32), limited (n = 93) or no evidence (n = 160). A total of 176 gene-disease relationships could not be classified because our scoring method was not suitable.Large scale dataNot applicable.Limitations, reasons for cautionOur literature search was limited to Pubmed.Wider implications of the findingsThe comprehensive overview will aid researchers and clinicians in the field to establish gene lists for diagnostic screening using validated gene-disease criteria and help to identify gaps in our knowledge of male infertility. For future studies, the authors discuss the relevant and important international guidelines regarding research related to gene discovery and provide specific recommendations for the field of male infertility.Study funding/competing interest(s)This work was supported by a VICI grant from The Netherlands Organization for Scientific Research (918-15-667 to J.A.V.), the Royal Society, and Wolfson Foundation (WM160091 to J.A.V.) as well as an investigator award in science from the Wellcome Trust (209451 to J.A.V.).Prospero registration numberNone.

Project description:ImportanceThe ability of present-day etiologic stroke classification systems to generate subtypes with discrete stroke characteristics is not known.ObjectiveTo test the hypothesis that etiologic stroke subtyping identifies different disease processes that can be recognized through their different clinical courses.Design, setting, and participantsWe performed a head-to-head evaluation of the ability of the Causative Classification of Stroke (CCS), Trial of Org 10172 in Acute Stroke Treatment (TOAST), and ASCO (A for atherosclerosis, S for small-vessel disease, C for cardiac source, and O for other cause) classification systems to generate etiologic subtypes with different clinical, imaging, and prognostic characteristics in 1816 patients with ischemic stroke. This study included 2 cohorts recruited at separate periods; the first cohort was recruited between April 2003 and June 2006 and the second between June 2009 and December 2011. Data analysis was performed between June 2014 and May 2016.Main outcomes and measuresSeparate teams of stroke-trained neurologists performed CCS, TOAST, and ASCO classifications based on information available at the time of hospital discharge. We assessed the association between etiologic subtypes and stroke characteristics by computing receiver operating characteristic curves for binary variables (90-day stroke recurrence and 90-day mortality) and by calculating the ratio of between-category to within-category variability from the analysis of variance for continuous variables (admission National Institutes of Health Stroke Scale score and acute infarct volume).ResultsAmong the 1816 patients included, the median age was 70 years (interquartile range, 58-80 years) (830 women [46%]). The classification systems differed in their ability to assign stroke etiologies into known subtypes; the size of the undetermined category was 33% by CCS, 53% by TOAST, and 42% by ASCO (P < .001 for all binary comparisons). All systems provided significant discrimination for the validation variables tested. For the primary validation variable (90-day recurrence), the area under the receiver operating characteristic curve was 0.71 (95% CI, 0.66-0.75) for CCS, 0.61 (95% CI, 0.56-0.67) for TOAST, and 0.66 (95% CI, 0.60-0.71) for ASCO (P = .01 for CCS vs ASCO; P < .001 for CCS vs TOAST; P = .13 for ASCO vs TOAST). The classification systems exhibited similar discrimination for 90-day mortality. For admission National Institutes of Health Stroke Scale score and acute infarct volume, CCS generated more distinct subtypes with higher between-category to within-category variability than TOAST and ASCO.Conclusions and relevanceOur findings suggest that the major etiologic stroke subtypes are distinct categories with different stroke characteristics irrespective of the classification system used to identify them. We further show that CCS generates discrete etiologic categories with more diverse clinical, imaging, and prognostic characteristics than either TOAST or ASCO.

Dataset Information

Classification of Genes: Standardized Clinical Validity Assessment of Gene-Disease Associations Aids Diagnostic Exome Analysis and Reclassifications.

Publications

Classification of Genes: Standardized Clinical Validity Assessment of Gene-Disease Associations Aids Diagnostic Exome Analysis and Reclassifications.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets