Effects of Six Functional SNPs on the Urinary 8-Isoprostane Level in a General Japanese Population; Shimane COHRE Study

Suyama, Yoji; Matsuda, Chikashi; Isomura, Minoru; Hamano, Tsuyoshi; Karino, Kenji; Yamasaki, Masayuki; Yamaguchi, Shûhei; Shiwaku, Kuninori; Masuda, Junichi; Nabika, Toru

doi:10.1155/2011/469854

Cited by 2 publications

(2 citation statements)

References 33 publications

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“…Furthermore, GMDR can reduce the complexity substantially and permit adjustment for discrete and quantitative covariates[14]. GMDR has been performed to successfully identify the combinations of SNPs that significantly influenced complex diseases[28]. The model of A1, A2, B1 and B2 (rs4240711, rs4842194, rs2920502 and rs3856806) were the best model (Cross-validation consistency 10/10, P = 0.0447).…”

Section: Discussionmentioning

confidence: 99%

Application of the back-error propagation artificial neural network (BPANN) on genetic variants in the PPAR-γ and RXR-α gene and risk of metabolic syndrome in a Chinese Han population

Zhao¹,

Xu²,

Shi³

et al. 2014

J Biomed Res

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This study was aimed to explore the associations between the combined effects of several polymorphisms in the PPAR-γ and RXR-α gene and environmental factors with the risk of metabolic syndrome by back-error propagation artificial neural network (BPANN). We established the model based on data gathered from metabolic syndrome patients (n = 1012) and normal controls (n = 1069) by BPANN. Mean impact value (MIV) for each input variable was calculated and the sequence of factors was sorted according to their absolute MIVs. Generalized multifactor dimensionality reduction (GMDR) confirmed a joint effect of PPAR-γ and RXR-α based on the results from BPANN. By BPANN analysis, the sequences according to the importance of metabolic syndrome risk factors were in the order of body mass index (BMI), serum adiponectin, rs4240711, gender, rs4842194, family history of type 2 diabetes, rs2920502, physical activity, alcohol drinking, rs3856806, family history of hypertension, rs1045570, rs6537944, age, rs17817276, family history of hyperlipidemia, smoking, rs1801282 and rs3132291. However, no polymorphism was statistically significant in multiple logistic regression analysis. After controlling for environmental factors, A1, A2, B1 and B2 (rs4240711, rs4842194, rs2920502 and rs3856806) models were the best models (cross-validation consistency 10/10, P = 0.0107) with the GMDR method. In conclusion, the interaction of the PPAR-γ and RXR-α gene could play a role in susceptibility to metabolic syndrome. A more realistic model is obtained by using BPANN to screen out determinants of diseases of multiple etiologies like metabolic syndrome.

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Section: Discussionmentioning

confidence: 99%

Application of the back-error propagation artificial neural network (BPANN) on genetic variants in the PPAR-γ and RXR-α gene and risk of metabolic syndrome in a Chinese Han population

Zhao¹,

Xu²,

Shi³

et al. 2014

J Biomed Res

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“…The genes used for GMDR analysis were usually from one pathway which showed similar functions1115. It has been used for risk assessment on cancer and other diseases in humans142223. This method was recently introduced for genetic breeding of livestock and poultry11.…”

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confidence: 99%

Gene set based association analyses for the WSSV resistance of Pacific white shrimp Litopenaeus vannamei

et al. 2017

Sci Rep

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White Spot Syndrome Virus (WSSV) is regarded as a virus with the strongest pathogenicity to shrimp. For the threshold trait such as disease resistance, marker assisted selection (MAS) was considered to be a more effective approach. In the present study, association analyses of single nucleotide polymorphisms (SNPs) located in a set of immune related genes were conducted to identify markers associated with WSSV resistance. SNPs were detected by bioinformatics analysis on RNA sequencing data generated by Illimina sequencing platform and Roche 454 sequencing technology. A total of 681 SNPs located in the exons of immune related genes were selected as candidate SNPs. Among these SNPs, 77 loci were genotyped in WSSV susceptible group and resistant group. Association analysis was performed based on logistic regression method under an additive and dominance model in GenABEL package. As a result, five SNPs showed associations with WSSV resistance at a significant level of 0.05. Besides, SNP-SNP interaction analysis was conducted. The combination of SNP loci in TRAF6, Cu/Zn SOD and nLvALF2 exhibited a significant effect on the WSSV resistance of shrimp. Gene expression analysis revealed that these SNPs might influence the expression of these immune-related genes. This study provides a useful method for performing MAS in shrimp.

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Effects of Six Functional SNPs on the Urinary 8-Isoprostane Level in a General Japanese Population; Shimane COHRE Study

Cited by 2 publications

References 33 publications

Application of the back-error propagation artificial neural network (BPANN) on genetic variants in the PPAR-γ and RXR-α gene and risk of metabolic syndrome in a Chinese Han population

Application of the back-error propagation artificial neural network (BPANN) on genetic variants in the PPAR-γ and RXR-α gene and risk of metabolic syndrome in a Chinese Han population

Gene set based association analyses for the WSSV resistance of Pacific white shrimp Litopenaeus vannamei

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