Evidence map›Paper›PMID 32602275›Full record

ArticleDiabetes & metabolism journal2021

Enhancer-Gene Interaction Analyses Identified the Epidermal Growth Factor Receptor as a Susceptibility Gene for Type 2 Diabetes Mellitus.

Yang Yang, Shi Yao, Jing-Miao Ding, Wei Chen, Yan Guo

Open access · goldAbstract read
In one paragraph

Article in Diabetes & metabolism journal, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.

0numbers the graph read from it
0cells of the map it votes in
10citing papers in PubMed
0.6field-weighted citation impact, top 34% of its field
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

10 citing papers in PubMed, 14 citations in OpenAlex.

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  10. Co-expression Network Revealed Roles of RNA mFrontiers in cell and developmental biology · 2021
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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

5 authors at 3 institutions in 1 country.

Yang Yang *Clinical Laboratory, The First Affiliated Hospital, Xi'an Jiaotong University, Xi'an, China.
Shi Yao *Key Laboratory of Biomedical Information Engineering of Ministry of Education, and Biomedical Informatics & Genomics Center, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, China.
Jing-Miao DingKey Laboratory of Biomedical Information Engineering of Ministry of Education, and Biomedical Informatics & Genomics Center, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, China.
Wei ChenClinical Laboratory, The First Affiliated Hospital, Xi'an Jiaotong University, Xi'an, China.
Yan GuoKey Laboratory of Biomedical Information Engineering of Ministry of Education, and Biomedical Informatics & Genomics Center, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, China.
Xi'an Jiaotong University · CNFirst Affiliated Hospital of Xi'an Jiaotong University · CNXian Center for Disease Control and Prevention · CN

Funding

CSCFundamental Research Fund for Central UniversitiesFundamental Research Funds for the Central UniversitiesInnovative Talent Promotion Plan of Shaanxi Province for Young Sci-Tech New Star 2018KJXX-010Major Science and Technology Projects in Xiaoshan District 2018224National Natural Science Foundation of China 31871264National Natural Science Foundation of China 81872490Natural Science Basic Research Program Shaanxi Province 2018JQ3058Natural Science Foundation of Zhejiang Province LGF18C060002Natural Science Foundation of Zhejiang Province LWY20H060001XJTU
6 · The paper itself

Abstract

Background: Genetic interactions are known to play an important role in the missing heritability problem for type 2 diabetes mellitus (T2DM). Interactions between enhancers and their target genes play important roles in gene regulation and disease pathogenesis. In the present study, we aimed to identify genetic interactions between enhancers and their target genes associated with T2DM. Methods: We performed genetic interaction analyses of enhancers and protein-coding genes for T2DM in 2,696 T2DM patients and 3,548 controls of European ancestry. A linear regression model was used to identify single nucleotide polymorphism (SNP) pairs that could affect the expression of the protein-coding genes. Differential expression analyses were used to identify differentially expressed susceptibility genes in diabetic and nondiabetic subjects. Results: We identified one SNP pair, rs4947941×rs7785013, significantly associated with T2DM (combined P=4.84×10-10). The SNP rs4947941 was annotated as an enhancer, and rs7785013 was located in the epidermal growth factor receptor (EGFR) gene. This SNP pair was significantly associated with EGFR expression in the pancreas (P=0.033), and the minor allele "A" of rs7785013 decreased EGFR gene expression and the risk of T2DM with an increase in the dosage of "T" of rs4947941. EGFR expression was significantly upregulated in T2DM patients, which was consistent with the effect of rs4947941×rs7785013 on T2DM and EGFR expression. A functional validation study using the Mouse Genome Informatics (MGI) database showed that EGFR was associated with diabetes-relevant phenotypes. Conclusion: Genetic interaction analyses of enhancers and protein-coding genes suggested that EGFR may be a novel susceptibility gene for T2DM.

Indexed as

Diabetes Mellitus, Type 2AllelesAnimalsEpistasis, GeneticErbB ReceptorsHumansMicePolymorphism, Single NucleotideErbB ReceptorsDiabetes mellitus, type 2Epistasis, geneticErbB receptorsGene regulatory networks

Identifiers

PMID32602275
PMCPMC8024152
OpenAlexW3159101924

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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.