9 research outputs found
Ancestral diversity improves discovery and fine-mapping of genetic loci for anthropometric traits — The Hispanic/Latino Anthropometry Consortium
Hispanic/Latinos have been underrepresented in genome-wide association studies (GWAS) for anthropometric traits despite their notable anthropometric variability, ancestry proportions, and high burden of growth stunting and overweight/obesity. To address this knowledge gap, we analyzed densely imputed genetic data in a sample of Hispanic/Latino adults to identify and fine-map genetic variants associated with body mass index (BMI), height, and BMI-adjusted waist-to-hip ratio (WHRadjBMI). We conducted a GWAS of 18 studies/consortia as part of the Hispanic/Latino Anthropometry (HISLA) Consortium (stage 1, n = 59,771) and generalized our findings in 9 additional studies (stage 2, n = 10,538). We conducted a trans-ancestral GWAS with summary statistics from HISLA stage 1 and existing consortia of European and African ancestries. In our HISLA stage 1 + 2 analyses, we discovered one BMI locus, as well as two BMI signals and another height signal each within established anthropometric loci. In our trans-ancestral meta-analysis, we discovered three BMI loci, one height locus, and one WHRadjBMI locus. We also identified 3 secondary signals for BMI, 28 for height, and 2 for WHRadjBMI in established loci. We show that 336 known BMI, 1,177 known height, and 143 known WHRadjBMI (combined) SNPs demonstrated suggestive transferability (nominal significance and effect estimate directional consistency) in Hispanic/Latino adults. Of these, 36 BMI, 124 height, and 11 WHRadjBMI SNPs were significant after trait-specific Bonferroni correction. Trans-ancestral meta-analysis of the three ancestries showed a small-to-moderate impact of uncorrected population stratification on the resulting effect size estimates. Our findings demonstrate that future studies may also benefit from leveraging diverse ancestries and differences in linkage disequilibrium patterns to discover novel loci and additional signals with less residual population stratification
Gene expression in thiazide diuretic or statin users in relation to incident type 2 diabetes.
Thiazide diuretics and statins are used to improve cardiovascular outcomes, but may also cause type 2 diabetes (T2DM), although mechanisms are unknown. Gene expression studies may facilitate understanding of these associations. Participants from ongoing population-based studies were sampled for these longitudinal studies of peripheral blood microarray gene expression, and followed to incident diabetes. All sampled subjects were statin or thiazide users. Those who developed diabetes during follow-up comprised cases (44 thiazide users; 19 statin users), and were matched to drug-using controls who did not develop diabetes on several factors. Supervised normalization, surrogate variable analyses removed technical bias and confounding. Differentially-expressed genes were those with a false discovery rate Q-value<0.05. Among thiazide users, diabetes cases had significantly different expression of CCL14 (down-regulated 6%, Q-value=0.0257), compared with controls. Among statin users, diabetes cases had marginal but insignificantly different expression of ZNF532 (up-regulated 15%, Q-value=0.0584), CXORF21 (up-regulated 11%, Q-value=0.0584), and ZNHIT3 (up-regulated 19%, Q-value=0.0959), compared with controls. These genes comprise potential targets for future expression or mechanistic research on medication-related diabetes development
Genome-wide association study of the modified Stumvoll Insulin Sensitivity Index identifies BCL2 and FAM19A2 as novel insulin sensitivity loci
Genome-wide association studies (GWAS) have found few common variants that influence fasting measures of insulin sensitivity. We hypothesized that a GWAS of an integrated assessment of fasting and dynamic measures of insulin sensitivity would detect novel common variants. We performed GWAS of the modified Stumvoll Insulin Sensitivity Index (ISI) within the Meta-Analyses of Glucose and Insulin-related traits Consortium. Discovery was performed in 16,753 individuals, and replication was attempted for the 23 most significant novel loci in 13,354 independent individuals. Association with ISI was tested in models adjusted for age, sex, body mass index (BMI) and in a model ("Model 3") analyzing the combined influence of the genotype effect adjusted for BMI and the interaction effect between the genotype and BMI on ISI. In Model 3, three variants reached genome-wide significance: rs13422522 (NYAP2, P=8.87 ×10(-11)), rs12454712 (BCL2, P=2.7×10(-8)) and rs10506418 (FAM19A2, P=1.9×10(-8)). The association at NYAP2 was eliminated by conditioning on the known IRS1 insulin sensitivity locus; the BCL2 and FAM19A2 associations were independent of known cardio-metabolic loci. In conclusion, we identified two novel loci and replicated known variants associated with insulin sensitivity. Further studies are needed to clarify the causal variant and function at the BCL2 and FAM19A2 loci
Molecularly imprinted gelatin nanoparticles for DNA delivery and in-situ fluorescence imaging of telomerase activity
Sensing mechanism of a ratiometric near-infrared fluorescent chemosensor for cysteine hydropersulfide: Intramolecular charge transfer
Genome-wide association identifies nine common variants associated with fasting proinsulin levels and provides new insights into the pathophysiology of type 2 diabetes.
OBJECTIVE: Proinsulin is a precursor of mature insulin and C-peptide. Higher circulating proinsulin levels are associated with impaired \u3b2-cell function, raised glucose levels, insulin resistance, and type 2 diabetes (T2D). Studies of the insulin processing pathway could provide new insights about T2D pathophysiology.RESEARCH DESIGN AND METHODS: We have conducted a meta-analysis of genome-wide association tests of 3c2.5 million genotyped or imputed single nucleotide polymorphisms (SNPs) and fasting proinsulin levels in 10,701 nondiabetic adults of European ancestry, with follow-up of 23 loci in up to 16,378 individuals, using additive genetic models adjusted for age, sex, fasting insulin, and study-specific covariates.RESULTS: Nine SNPs at eight loci were associated with proinsulin levels (P < 5
7 10(-8)). Two loci (LARP6 and SGSM2) have not been previously related to metabolic traits, one (MADD) has been associated with fasting glucose, one (PCSK1) has been implicated in obesity, and four (TCF7L2, SLC30A8, VPS13C/C2CD4A/B, and ARAP1, formerly CENTD2) increase T2D risk. The proinsulin-raising allele of ARAP1 was associated with a lower fasting glucose (P = 1.7
7 10(-4)), improved \u3b2-cell function (P = 1.1
7 10(-5)), and lower risk of T2D (odds ratio 0.88; P = 7.8
7 10(-6)). Notably, PCSK1 encodes the protein prohormone convertase 1/3, the first enzyme in the insulin processing pathway. A genotype score composed of the nine proinsulin-raising alleles was not associated with coronary disease in two large case-control datasets.CONCLUSIONS: We have identified nine genetic variants associated with fasting proinsulin. Our findings illuminate the biology underlying glucose homeostasis and T2D development in humans and argue against a direct role of proinsulin in coronary artery disease pathogenesis
Targeting epigenetic regulators for cancer therapy: mechanisms and advances in clinical trials
Genetic studies of body mass index yield new insights for obesity biology
Note: A full list of authors and affiliations appears at the end of the article.
Obesity is heritable and predisposes to many diseases. To understand the genetic basis of obesity better, here we conduct a genome-wide association study and Metabochip meta-analysis of body mass index (BMI), a measure commonly used to define obesity and assess adiposity, in up to 339,224 individuals. This analysis identifies 97 BMI-associated loci (P 20% of BMI variation. Pathway analyses provide strong support for a role of the central nervous system in obesity susceptibility and implicate new genes and pathways, including those related to synaptic function, glutamate signalling, insulin secretion/action, energy metabolism, lipid biology and adipogenesis.</p