Article

New genetic loci implicated in fasting glucose homeostasis and their impact on type 2 diabetes risk.

Department of Biostatistics, Boston University School of Public Health, Massachusetts, USA.
Nature Genetics (Impact Factor: 29.65). 02/2010; 42(2):105-16. DOI: 10.1038/ng.520
Source: PubMed

ABSTRACT Levels of circulating glucose are tightly regulated. To identify new loci influencing glycemic traits, we performed meta-analyses of 21 genome-wide association studies informative for fasting glucose, fasting insulin and indices of beta-cell function (HOMA-B) and insulin resistance (HOMA-IR) in up to 46,186 nondiabetic participants. Follow-up of 25 loci in up to 76,558 additional subjects identified 16 loci associated with fasting glucose and HOMA-B and two loci associated with fasting insulin and HOMA-IR. These include nine loci newly associated with fasting glucose (in or near ADCY5, MADD, ADRA2A, CRY2, FADS1, GLIS3, SLC2A2, PROX1 and C2CD4B) and one influencing fasting insulin and HOMA-IR (near IGF1). We also demonstrated association of ADCY5, PROX1, GCK, GCKR and DGKB-TMEM195 with type 2 diabetes. Within these loci, likely biological candidate genes influence signal transduction, cell proliferation, development, glucose-sensing and circadian regulation. Our results demonstrate that genetic studies of glycemic traits can identify type 2 diabetes risk loci, as well as loci containing gene variants that are associated with a modest elevation in glucose levels but are not associated with overt diabetes.

3 Followers
 · 
557 Views
  • [Show abstract] [Hide abstract]
    ABSTRACT: Although type 2 diabetes mellitus is a known risk factor for pancreatic cancer, the existence of shared genetic susceptibility is largely unknown. We evaluated whether any reported genetic risk variants of either disease found by genome-wide association studies reciprocally confer susceptibility. Data that were generated in previous genome-wide association studies (GENEVA Type 2 Diabetes; PanScan) were obtained through the National Institutes of Health database of Genotypes and Phenotypes (dbGaP). Using the PanScan datasets, we tested for association of 38 variants within 37 genomic regions known to be susceptibility factors for type 2 diabetes. We further examined whether type 2 diabetes variants predispose to pancreatic cancer risk stratified by diabetes status. Correspondingly, we examined the association of fourteen pancreatic cancer susceptibility variants within eight genomic regions in the GENEVA Type 2 Diabetes dataset. Four plausible associations of diabetes variants and pancreatic cancer risk were detected at a significance threshold of p = 0.05, and one pancreatic cancer susceptibility variant was associated with diabetes risk at threshold of p = 0.05, but none remained significant after correction for multiple comparisons. Currently identified GWAS susceptibility variants are unlikely to explain the potential shared genetic etiology between Type 2 diabetes and pancreatic cancer.
    PLoS ONE 02/2015; 10(2):e0117230. DOI:10.1371/journal.pone.0117230 · 3.53 Impact Factor
  • [Show abstract] [Hide abstract]
    ABSTRACT: Elevated serum uric acid concentration is an independent risk factor and predictor of type 2 diabetes (T2D). Whether the uric acid-associated genes have an impact on T2D remains unclear. We aimed to investigate the effects of the uric acid-associated genes on the risk of T2D as well as glucose metabolism and insulin secretion. We recruited 2,199 normal glucose tolerance subjects from the Shanghai Diabetes Study I and II and 2,999 T2D patients from the inpatient database of Shanghai Diabetes Institute. Fifteen single nucleotide polymorphisms (SNPs) mapped in or near 11 loci (PDZK1, GCKR, LRP2, SLC2A9, ABCG2, LRRC16A, SLC17A1, SLC17A3, SLC22A11, SLC22A12 and SF1) were genotyped and serum biochemical parameters related to uric acid and T2D were determined. SF1 rs606458 showed strong association to T2D in both males and females (p = 0.034 and 0.0008). In the males, LRRC16A was associated with 2-h insulin and insulin secretion (p = 0.009 and 0.009). SLC22A11 was correlated with HOMA-B and insulin secretion (p = 0.048 and 0.029). SLC2A9 rs3775948 was associated with 2-h glucose (p = 0.043). In the females, LRP2 rs2544390 and rs1333049 showed correlations with fasting insulin, HOMA-IR and insulin secretion (p = 0.028, 0.033 and 0.052 and p = 0.034, 0.047 and 0.038, respectively). SLC2A9 rs11722228 was correlated with 2-h glucose, 2-h insulin and insulin secretion (p = 0.024, 0.049 and 0.049, respectively). Our results indicated that the uric acid-associated genes have an impact on the risk of T2D, glucose metabolism and insulin secretion in a Chinese population.
    PLoS ONE 01/2015; 10(1):e0116714. DOI:10.1371/journal.pone.0116714 · 3.53 Impact Factor
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: Pleiotropic genetic variants have independent effects on different phenotypes. C-reactive protein (CRP) is associated with several cardiometabolic phenotypes. Shared genetic backgrounds may partially underlie these associations. We conducted a genome-wide analysis to identify the shared genetic background of inflammation and cardiometabolic phenotypes using published genome-wide association studies (GWAS). We also evaluated whether the pleiotropic effects of such loci were biological or mediated in nature. First, we examined whether 283 common variants identified for 10 cardiometabolic phenotypes in GWAS are associated with CRP level. Second, we tested whether 18 variants identified for serum CRP are associated with 10 cardiometabolic phenotypes. We used a Bonferroni corrected p-value of 1.1×10-04 (0.05/463) as a threshold of significance. We evaluated the independent pleiotropic effect on both phenotypes using individual level data from the Women Genome Health Study. Evaluating the genetic overlap between inflammation and cardiometabolic phenotypes, we found 13 pleiotropic regions. Additional analyses showed that 6 regions (APOC1, HNF1A, IL6R, PPP1R3B, HNF4A and IL1F10) appeared to have a pleiotropic effect on CRP independent of the effects on the cardiometabolic phenotypes. These included loci where individuals carrying the risk allele for CRP encounter higher lipid levels and risk of type 2 diabetes. In addition, 5 regions (GCKR, PABPC4, BCL7B, FTO and TMEM18) had an effect on CRP largely mediated through the cardiometabolic phenotypes. In conclusion, our results show genetic pleiotropy among inflammation and cardiometabolic phenotypes. In addition to reverse causation, our data suggests that pleiotropic genetic variants partially underlie the association between CRP and cardiometabolic phenotypes.
    PLoS ONE 10(3):e0118859. DOI:10.1371/journal.pone.0118859 · 3.53 Impact Factor

Full-text

Download
110 Downloads
Available from
Jun 3, 2014