Evidence map›Paper›PMID 31147632›Full record

ArticleGenetics in medicine : official journal of the American College of Medical Genetics2019

Considerations for clinical curation, classification, and reporting of low-penetrance and low effect size variants associated with disease risk.

Ozlem Senol-Cosar, Ryan J Schmidt, Emily Qian, Derick Hoskinson, Heather Mason-Suares, Birgit Funke, Matthew S Lebo

Abstract read
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In one paragraph

Article in Genetics in medicine : official journal of the American College of Medical Genetics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
24citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

24 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Patient Perspectives on Psychiatric Polygenic Risk Scores in Reproductive Decision-Making and Polygenic Embryo Screening.American journal of medical genetics. Part B, Neuropsychiatric genetics : the official publication of the International Society of Psychiatric Genetics · 2026
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  15. Implications of combinedFrontiers in immunology · 2023
    Article
  16. The CommonHuman mutation · 2023
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  20. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Ozlem Senol-CosarLaboratory for Molecular Medicine, Partners HealthCare Personalized Medicine, Cambridge, MA, USA.ORCID http://orcid.org/0000-0003-1575-0544
Ryan J SchmidtDepartment of Pathology and Laboratory Medicine, Children's Hospital Los Angeles, Keck School of Medicine of USC, Los Angeles, CA, USA.ORCID http://orcid.org/0000-0001-8789-9917
Emily QianVeritas Genetics, Cambridge, MA, USA.ORCID http://orcid.org/0000-0002-3304-4400
Derick HoskinsonLaboratory for Molecular Medicine, Partners HealthCare Personalized Medicine, Cambridge, MA, USA.ORCID http://orcid.org/0000-0002-5096-3249
Heather Mason-SuaresLaboratory for Molecular Medicine, Partners HealthCare Personalized Medicine, Cambridge, MA, USA.
Birgit FunkeLaboratory for Molecular Medicine, Partners HealthCare Personalized Medicine, Cambridge, MA, USA. bfunke@veritasgenetics.com.ORCID http://orcid.org/0000-0001-6643-3640
Matthew S LeboLaboratory for Molecular Medicine, Partners HealthCare Personalized Medicine, Cambridge, MA, USA. mlebo@bwh.harvard.edu.ORCID http://orcid.org/0000-0002-9733-5207

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposeClinically relevant variants exhibit a wide range of penetrance. Medical practice has traditionally focused on highly penetrant variants with large effect sizes and, consequently, classification and clinical reporting frameworks are tailored to that variant type. At the other end of the penetrance spectrum, where variants are often referred to as "risk alleles," traditional frameworks are no longer appropriate. This has led to inconsistency in how such variants are interpreted and classified. Here, we describe a conceptual framework to begin addressing this gap.

methodsWe used a set of risk alleles to define data elements that can characterize the validity of reported disease associations. We assigned weight to these data elements and established classification categories expressing confidence levels. This framework was then expanded to develop criteria for inclusion of risk alleles on clinical reports.

resultsFoundational data elements include cohort size, quality of phenotyping, statistical significance, and replication of results. Criteria for determining inclusion of risk alleles on clinical reports include presence of clinical management guidelines, effect size, severity of the associated phenotype, and effectiveness of intervention.

conclusionThis framework represents an approach for classifying risk alleles and can serve as a foundation to catalyze community efforts for refinement.

Indexed as

AllelesData CurationDisease SusceptibilityGenetic Predisposition to DiseaseGenetic VariationHumansPenetranceRisk Assessmentclassification frameworklow penetranceodds ratiorisk allelevariant interpretation

Identifiers

What OpenQuestion holds

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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.