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Beta-actin (ACTB) loss-of-function mutations result in a pleiotropic developmental disorder of kidney. The present study aims to explore whether the common variants at the ACTB gene contribute to diabetic kidney disease (DKD) susceptibility in patients with type 2 diabetes mellitus (T2DM). From the baseline population of 20,340 diabetic patients, 1,510 DKD cases and 1,510 age-matched T2DM controls were selected. All subjects were Han Chinese. Three tagging single nucleotide polymorphisms (SNPs), rs852423, rs852426, and rs2966449, at the ACTB gene were genotyped. Logistic regression was performed to estimate the association with DKD. SNPs, rs852426 and rs2966449, were significantly associated with DKD [additive model; odds ratio (OR), 1.217 and 1.151; P = 0.001 and 0.018, respectively]. The association of rs852426 with DKD still remained statistically significant after Bonferroni correction and particularly significant in the population older than 70 years rather than the 70 years or younger (P = 0.047 for heterogeneity test). Furthermore, the association of rs852426 with DKD was observed in populations of male and females without smoking, drinking, and with duration for T2DM 10-20 years. The association of rs2966449 with DKD was also found in the populations older than 70 years, male, not smoking, not drinking, and with duration for T2DM over 20 years. The estimated glomerular filtration rate (eGFR) levels of the individuals with TT or CC genotypes of rs2966449 were significantly lower than that of TC genotype in DKD cases (P = 0.021). The present study provides evidence that the ACTB variants, i.e., rs852426 and rs2966449, may confer the genetic susceptibility to DKD in a Han Chinese population.
Pubmed ID: 31396261
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View all literature mentionsSNPinfo Web Server is a set of freely available web-based SNP selection tools where investigators can specify genes or linkage regions and select SNPs based on GWAS results, linkage disequilibrium (LD), and predicted functional characteristics of both coding and non-coding SNPs. The algorithm uses GWAS SNP P-value data and finds all SNPs in high LD with GWAS SNPs, so that selection is from a much larger set of SNPs than the GWAS itself. The program can also identify and choose tag SNPs for SNPs not in high LD with any GWAS SNP. We incorporate functional predictions of protein structure, gene regulation, splicing and miRNA binding, and consider whether the alternative alleles of a SNP are likely to have differential effects on function. Users can assign weights for different functional categories of SNPs to further tailor SNP selection. The program accounts for LD structure of different populations so that a GWAS study from one ethnic group can be used to choose SNPs for one or more other ethnic groups. SNP Selection and Functional Information *Candidate Gene SNP Selection (GenePipe):SNP selection for candidate genes based on Genome Wide Association Study (GWAS) results, functional SNP prediction and Linkage Disequilibrium (LD) information. *GWAS Functional SNP Selection (GenomePipe):Functional SNP selection from SNPs that are in high LD with GWAS SNPs *GWAS SNP Selection in Linkage Loci (LinkagePipe):GWAS SNP selection in candidate genomic regions (such as linkage loci) *LD TAG SNP Selection (TagSNP):LD tag SNP selection and visualization for single or multiple populations. Finalization of SNP list from various queries. *SNP Function Prediction (FuncPred): Querying SNP function predictions and ethnic-specific allele frequencies. *SNP Information in DNA Sequence (SNPseq):Visualization of SNP related information in the context of DNA sequence. Preparing DNA Sequence for PCR Primer Design considering SNP information. Detailed information of CpG region.
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