Application of back propagation artificial neural network on genetic variants in adiponectin ADIPOQ, peroxisome proliferator-activated receptor-γ, and retinoid X receptor-α genes and type 2 diabetes risk in a Chinese Han population.
OBJECTIVE
Our study was designed to explore the applied characteristics of the back propagation artificial neural network (BPANN) on studying the genetic variants in adipnectin ADIPOQ, peroxisome proliferator-activated receptor (PPAR)-γ, and retinoid X receptor-α (RXR-α) genes and type 2 diabetes mellitus (T2DM) risks in a Chinese Han population.


METHODS
We used BPANN as the fitting model based on data gathered from T2DM patients (n=913) and normal controls (n=1,001). The mean impact value (MIV) for each input variables were calculated, and the sequence of the factors according to their absolute MIVs was sorted.


RESULTS
The results from BPANN were compared with multiple logistic regression analysis, and the generalized multifactor dimensionality reduction (GMDR) method was used to calculate the joint effects of ADIPOQ, PPAR-γ, and RXR-α genes. By BPANN analysis, the sequence according to the importance of the T2DM risk factors was in the order of serum adiponectin level, rs3856806, rs7649121, hypertension, rs3821799, rs17827276, rs12495941, rs4240711, age, rs16861194, waist circumference, rs2241767, rs2920502, rs1063539, alcohol drinking, smoking, hyperlipoproteinemia, gender, rs3132291, T2DM family history, rs4842194, rs822394, rs1801282, rs1045570, rs16861205, rs6537944, body mass index, rs266729, and rs1801282. However, compared with multiple logistic regression analysis, only 11 factors were statistically significant. After overweight and obesity were taken as environment adjustment factors into the analysis, model A2 B4 C5 C6 C8 (rs3856806, rs4240711, rs7649121, rs3821799, rs12495941) was the best model (coefficient of variation consistency=10/10, P=0.0107) in the GMDR method.


CONCLUSIONS
These results suggested the interactions of ADIPOQ, PPAR-γ, and RXR-α genes might play a role in susceptibility to T2DM. BPANN could be used to analyze the risk factors of diseases and provide more complicated relationships between inputs and outputs.
Department of Epidemiology and Biostatistics, School of Public Health, Nanjing Medical University, Nanjing, Jiangsu, China.
Hui
H
Shi
Ying
Y
Lu
Juan
J
Du
Wencong
W
Du
Xinhua
X
Ye
Xiaofang
X
Yu
Jianhua
J
Ma
Jinluo
J
Cheng
Yanqin
Y
Gao
Yuanyuan
Y
Cao
Ling
L
Zhou
Qian
Q
Li
D052242:Adiponectin / Q000235:genetics*; D044466:Asian People / Q000235:genetics*; D002681:China / Q000453:epidemiology; D003924:Diabetes Mellitus, Type 2 / Q000453:epidemiology / Q000235:genetics*; D005260:Female; D020022:Genetic Predisposition to Disease; D014644:Genetic Variation; D005838:Genotype; D006801:Humans; D008297:Male; D008875:Middle Aged; D016571:Neural Networks, Computer; D047495:PPAR gamma / Q000235:genetics*; D047490:Retinoid X Receptor alpha / Q000235:genetics*; D018570:Risk Assessment; D012307:Risk Factors
C499918:ADIPOQ protein, human; D052242:Adiponectin; D047495:PPAR gamma; D047490:Retinoid X Receptor alpha