Evidence map›Paper›PMID 40282361›Full record

ReviewGenes2025

Genetically Transitional Disease and the Road to Personalized Medicine.

Qingping Yao, Peter D Gorevic, Greg Gibson

Abstract readReview
In one paragraph

Review in Genes, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

3 authors.

Qingping YaoDivision of Rheumatology, Allergy, and Immunology, Stony Brook University Renaissance School of Medicine, Stony Brook, NY 11794, USA.ORCID 0000-0002-6539-5803
Peter D GorevicDivision of Rheumatology, Allergy, and Immunology, Stony Brook University Renaissance School of Medicine, Stony Brook, NY 11794, USA.
Greg GibsonCenter for Integrative Genomics, School of Biology, Georgia Institute of Technology, Atlanta, GA 30332, USA.ORCID 0000-0002-5352-5877

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Genetically transitional disease (GTD) is emerging as a new concept in genomic medicine to straddle between the traditional binary classification of monogenic and polygenic disease. Genetic testing result reports in molecular laboratories have been predicated on the monogenic disease model, which focuses on pathogenic and likely pathogenic variants. While variants of uncertain significance (VUS) are reported by laboratories, there are challenges with regard to their clinical application so that these variants are often dismissed by ordering physicians. Unlike Mendelian disorders, where genetic variants are of high penetrance and highly probabilistic, the GTD concept is employed to highlight the impact of low-to-moderate effect gene variants whose influence on disease is modified by the genetic background. The GTD concept may explain health conditions associated with variants that are necessary but not sufficient for pathogenesis, lying in the mid gray zone between Mendelian and polygenic diseases. Although VUSs may not reach the level of pathogenicity based on American College of Medical Genetics and Genomics guidelines, they could be provisionally classified as GTD-associated variants to annotate and interpret the relationship between VUS and human genetic disease. The appropriate implementation of the GTD concept could impact patient care and research by focusing attention on the individual variability of responses in various diseases.

Indexed as

Genetic Diseases, InbornGenetic Predisposition to DiseasePrecision MedicineGenetic TestingGenetic VariationGenomic MedicineHumansMultifactorial Inheritanceautismautoimmunecardiomyopathygenetically transitional diseasegeneticsmonogenicprecision medicinevariantVUS

Identifiers

PMID40282361
PMCPMC12026687

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Registered trials

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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.