Data from: Association and function analysis of genetic variants and the risk of gestational diabetes mellitus in a southern Chinese population
Data files
Dec 07, 2024 version files 115.24 KB
Abstract
This dataset include 538 gestational diabetes mellitus (GDM) patients and 626 healthy pregnancies’ baseline information obtained from unique questionnaires and medical records, and the genetic loci were genotyped by the Sequenom MassARRAY platform. The clinical indicators and genetic variants that are statistically associated with GDM were used to construct a nomogram model. The nomogram model is formulated as a standard of scoring based on the regression coefficient (β) of indicators. Each level of the indicators will be given a specific score and the scores of each factor are added up to get the total point, which can be used to predict the probability of GDM occurrence. Subjects’ baseline data involve systolic blood pressure (SBP, mmHg), diastolic blood pressure (DBP, mmHg), 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, %), 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). The data of genetic polymorphism include rs4134819 C>T, rs720918 A>G, rs2034410 T>C, rs11109509 A>G, and rs12524768 G>A and their corresponding dominant and recessive genetic model.
README: Data from: Association and function analysis of genetic variants and the risk of Gestational Diabetes Mellitus in a southern Chinese population
Descriptions
The meaning of the different assignments in the dataset:
- Group: 1=GDM; 0=Control group
- Sample ID: Identification number of the test sample
- Variants and genotypes assignment
1)rs4134819 C>T: 1=CC genotype; 2=CT genotype;3=TT genotype
2)rs720918 A>G: 1=AA genotype; 2=AG genotype;3=GG genotype
3)rs2034410 T>C: 1=TT genotype; 2=TC genotype;3=CC genotype
4) rs11109509 A>G: 1=AA genotype; 2=AG genotype;3=GG genotype
5) rs12524768 G>A : 1=GG genotype; 2=GA genotype;3=AA genotype
6)rs4134819 Dominant model(CT/TT vs.CC): 1=CC genotype; 4=CT/TT genotypes
7) rs720918 Dominant model(AG/GG vs. AA): 1=AA genotype; 4=AG/GG genotypes
8) rs2034410 Dominant model(TC /CC vs.TT): 1=TT genotype; 4=TC /CC genotypes
9) rs11109509 Dominant model(AG/GG vs. AA): 1=AA genotype; 4=AG/GG genotypes
10) rs12524768 Dominant model(GA/AA vs. GG): 1=GG genotype; 4=GA/AA genotypes
11) rs4134819 Recessive model(TT vs. CC/CT): 3=TT genotype; 5=CC/CT genotypes
12) rs720918 Recessive model (GG vs. AA/AG): 3=GG genotype; 5=AA/AG genotypes
13) rs2034410 Recessive model(CC vs. TT/TC): 3=CC genotype; 5=TT/TC genotypes
14) rs11109509 Recessive model (GG vs. AA/AG): 3=GG genotype; 5=AA/AG genotypes
15) rs12524768 Recessive model(AA vs. GG/GA): 3=AA genotype; 5=GG/GA genotypes
- Variables for stratification analysis
1) SBP_M (Mean SBP value:110.03 mmHg,1="≤110.03 mmHg"；2= “＞110.03mmHg” );
2) DBP_M (Mean DBP value:69.44 mmHg,1="≤69.44 mmHg"；2= “＞69.44 mmHg );
3) FPG_M (Mean FPG value:4.78 mmol/L,1="≤4.78 mmol/L"；2= “＞4.78 mmol/L );
4) 1hPG_M (Mean 1hPG value:8.24 mmol/L,1="≤8.24 mmol/L"；2= “＞8.24 mmol/L”);
5)2hPG_M (Mean 2hPG value:7.10 mmol/L,1="≤7.10 mmol/L"；2= “＞7.10 mmol/L” );
6)HbA1c_M (Mean HbA1c(%) value:5.20%,1="≤5.20%"；2= “＞5.20%”);
7)TG_M (Mean TG value:2.53 mmol/L,1="≤2.53 mmol/L"；2= “＞2.53 mmol/L”);
- NA: missing value