Evidence map›Paper›PMID 41824440›Full record

ArticlePloS one2026

A new perspective on population genetics: Deciphering the relationship between genetic variants and disease prevalence in Psoriasis.

Yuanjing Zhang, Weiran Li, Wanrong Wang, Kejia Wu, Feiran Zhou, Xiaodong Zheng

Abstract read
In one paragraph

Article in PloS one, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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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

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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

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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

6 authors.

Yuanjing ZhangDepartment of Dermatology, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, Anhui, China.
Weiran LiDepartment of Dermatology, the First Affiliated Hospital of Anhui Medical University, Institute of Dermatology, Anhui Medical University, Hefei, China.
Wanrong WangFirst Clinical Medical College, Anhui Medical University, Hefei, Anhui Province, China.
Kejia WuFirst Clinical Medical College, Anhui Medical University, Hefei, Anhui Province, China.
Feiran ZhouFirst Clinical Medical College, Anhui Medical University, Hefei, Anhui Province, China.
Xiaodong ZhengDepartment of Dermatology, the First Affiliated Hospital of Anhui Medical University, Institute of Dermatology, Anhui Medical University, Hefei, China.ORCID https://orcid.org/0000-0002-6658-6492

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In the quest to identify the genetic underpinnings of complex diseases, we developed a novel approach called Causal Genotype Combination Patterns (CGCP) to uncover characteristic genetic signatures of common diseases. In this study, we applied the CGCP method to a whole-exome sequencing dataset of 781 psoriasis cases and 676 healthy controls from the Chinese Han population. Our analysis revealed 620 genotype combinations specific to psoriasis, covering 4.7% to 10% of all cases, with each genotype having a frequency of at least 1%. These genotypes converged into 134 genes, including 41 previously reported to be associated with psoriasis. By leveraging public data from the 1000 Genomes Project Phase III and literature reviews on psoriasis prevalence in various ethnic populations, we established a strong positive correlation and linear regression model (y = 61.72x + 0.48, 95% CI [21.60, 101.84]) between the average frequency of these psoriasis-specific genotype combinations and disease prevalence across populations. This finding may explain the varying prevalence of psoriasis in different populations. Our strategy offers a new perspective on understanding the characteristics of population genetics in common diseases.

Indexed as

Genetic Predisposition to DiseaseGenetics, PopulationGenetic VariationPsoriasisCase-Control StudiesChinaEast Asian PeopleExome SequencingGene FrequencyGenotypeHumansPolymorphism, Single NucleotidePrevalence

Identifiers

PMID41824440
PMCPMC12987435

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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.