Evidence map›Paper›PMID 41852584›Full record

ReviewBiochemistry and biophysics reports2026

Preimplantation genetic testing for polygenic diseases: A novel paradigm in embryo selection.

Tianrui Jing, Yanzhi Du

Abstract readReview
In one paragraph

Review in Biochemistry and biophysics reports, 2026. 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

2 authors.

Tianrui JingDepartment of Reproductive Medicine, Ren Ji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, 200135, China.
Yanzhi DuDepartment of Reproductive Medicine, Ren Ji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, 200135, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Polygenic diseases are caused by a number of independently acting or interacting polymorphic genomic variants in conjunction with environmental factors, and show an increasing global prevalence, posing a significant threat to individual and public health. Given the limited curative prospects for polygenic diseases owing to their complex etiology, preventive strategies have gained paramount importance. Preimplantation genetic testing for polygenic disorders (PGT-P) is an advanced in vitro fertilization technique that screens embryos for genetic susceptibility to polygenic diseases, aiming to reduce the risk of offspring developing conditions such as diabetes, schizophrenia, polycystic ovary syndrome, and certain cancers. This technology calculates polygenic disease risks across sibling embryos relying on the use of a polygenic risk score (PRS) that counts the effects of risk alleles derived from genome-wide association studies. Using PGT-P, each embryo's PRS is computed for any disease or trait of interest, and the euploid embryo with the lowest PRS is prioritized for implantation. As a result, PGT-P may decrease the disease risk for future offspring and provide a novel strategy for embryo ranking, selection, or even discarding. In this review, we systematically summarize the current knowledge regarding PGT-P, detailing its workflow, potential benefits, predictive power, expected risk reduction and significant limitations. We further evaluate its utility and practical implementation in clinical practice and outline critical directions for future research.

Indexed as

Embryo selectionGenome-wide association studiesPolygenic diseasesPolygenic risk scorePreimplantation genetic testing for polygenic disease

Identifiers

PMID41852584
PMCPMC12994082

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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.