Data from: Genetic variants in HLA-DQA1/DQB1 genes modulate the risk of gestational diabetes mellitus in a southern Chinese population
Data files
Aug 29, 2025 version files 293.76 KB
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Data_from__Genetic_variants_in_HLA-DQA1_DQB1_genes.xlsx
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README.md
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Abstract
Background: Gestational diabetes mellitus (GDM) is an endocrine disorder that occurs easily in women during pregnancy. HLA-DQA1/DQB1 genes play a crucial role in the regulation of the human immune and endocrine systems, potentially influencing the pathogenesis of GDM.
Objective: To explore the associations between single nucleotide polymorphisms (SNPs) in HLA-DQA1/DQB1 genes and the risk of GDM.
This dataset includes 523 gestational diabetes mellitus (GDM) patients and 638 healthy pregnancies’ baseline information obtained from a unique questionnaire and medical records, and the genetic loci were genotyped by the Sequenom MassARRAY platform. Evaluate the relationship between certain genetic variations and the risk of GDM by integrating clinical, biochemical, and genetic data.
Subjects’ baseline data involve age, pre-BMI, systolic blood pressure (SBP, mmHg), diastolic blood pressure (DBP, mmHg), triglyceride (TG, mmol/L), total cholesterol (TC, mmol/L), high-density lipoprotein cholesterol (HDL-c, mmol/L), low-density lipoprotein cholesterol (LDL-c, mmol/L), fasting plasma glucose (FPG, mmol/L), oral glucose tolerance test 1h plasma glucose (1hPG, mmol/L), oral glucose tolerance test 2h plasma glucose (2hPG, mmol/L), glycated hemoglobin (HbA1c, %). The data of genetic polymorphism include rs1391371 A>T, rs9272425 T>C, rs9272426 A>G, rs9272460 A>G, rs9273368 A>G, rs9273505 C>,T and rs9274666 A>G and their corresponding dominant and recessive genetic model.
The meaning of the different assignments in the dataset:
(1)Group: 1=Control group; 2=GDM
(2)Sample ID: Identification number of test sample
(3)Variants and genotypes assignment
- rs1391371 A>T: 1=AA genotype; 2=AT genotype;3=TT genotype
- rs9272425 T>C: 1=TT genotype; 2=TC genotype;3=CC genotype
- rs9272426 A>G: 1=AA genotype; 2=AG genotype;3=GG genotype
- rs9272460 A>G: 1=AA genotype; 2=AG genotype;3=GG genotype
- rs9273368 A>G: 1=AA genotype; 2=AG genotype;3=GG genotype
- rs9273505 C>T: 1=CC genotype; 2=CT genotype;3=TT genotype
- rs9274666 A>G: 1=AA genotype; 2=AG genotype;3=GG genotype
- rs1391371 Dominant model (AT/TT vs. AA): 1=AA genotype; 4=AT/TT genotypes
- rs9272425 Dominant model (TC/CC vs. TT): 1=TT genotype; 4=TC/CC genotypes
- rs9272426 Dominant model (AG/GG vs. AA): 1=AA genotype; 4=AG/GG genotypes
- rs9272460 Dominant model (AG/GG vs. AA): 1=GG genotype; 4=AG/GG genotypes
- rs9273368 Dominant model (AG/GG vs. AA): 1=AA genotype; 4=AG/GG genotypes
- rs9273505 Dominant model (CT/TT vs. CC): 1=CC genotype; 4=CT/TT genotypes
- rs9274666 Dominant model (AG/GG vs. AA): 1=AA genotype; 4=AG/GG genotypes
- rs1391371 Recessive model (TT vs. AA/AT): 3=TT genotype; 5=AA/AT genotypes
- rs9272425 Recessive model (CC vs. TT/TC): 3=CC genotype; 5=TT/TC genotypes
- rs9272426 Recessive model (GG vs. AA/AG): 3=GG genotype; 5=AA/AG genotypes
- rs9272460 Recessive model (GG vs. AA/AG): 3=GG genotype; 5=AA/AG genotypes
- rs9273368 Recessive model (GG vs. AA/AG): 3=GG genotype; 5=AA/AG genotypes
- rs9273505 Recessive model (TT vs. CC/CT): 3=TT genotype; 5=CC/CT genotypes
- rs9274666 Recessive model (GG vs. AA/AG): 3=GG genotype; 5=AA/AG genotypes
(4)Number of risk genotypes: Cumulative number of dangerous genotypes in the test samples
(4)Variables for stratification analysis - Age_M (Mean Age value:30.04 mmHg,1="≤30.04 years old";2= “>30.04 years old” );
- pre-BMI_M (Mean pre-BMI value:22.2 kg/m2),1="≤22.2 kg/m2";2= “>22.2 kg/m2" );
- SBP_M (Mean SBP value:110.03 mmHg,1="≤110.03 mmHg";2= “>110.03mmHg” );
- DBP_M (Mean DBP value:69.44 mmHg,1="≤69.44 mmHg";2= “>69.44 mmHg );
- TG_M (Mean TG value:2.53 mmol/L,1="≤2.53 mmol/L";2= “>2.53 mmol/L”);
- TC_M (Mean TG value:5.33 mmol/L,1="≤5.33 mmol/L";2= “>5.33 mmol/L”);
- HDL-c_M (Mean TG value:1.65 mmol/L,1="≤1.65 mmol/L";2= “>1.65 mmol/L”);
(5)NA: missing value
Detailed data can be found in the table file: Data_from__Genetic_variants_in_HLA-DQA1_DQB1_genes.xlsx
Human subjects data
All participants were provided written informed consent. The personal identity information data of the subjects have all been anonymized.
Seven functional SNPs of HLA-DQA1/DQB1 genes were selected and genotyped in 523 GDM patients and 638 normal pregnant women. The odds ratio (OR) and its corresponding 95% confidence interval (CI) were utilized to assess the association between candidate SNPs and the risk of GDM. And then, false positive report probability (FPRP), multifactor dimensionality reduction (MDR) and haplotype analysis were employed to validate the statistically significant associations between studied SNPs and GDM risk.
