Le Messager, Lewiston, Maine 1880-1968

A typed outline of the history of the publication Le Messager from 1800-1968 by Paul-M. Paré.https://digitalcommons.usm.maine.edu/le-messager/1006/thumbnail.jp

Novel Association of HK1 with Glycated Hemoglobin in a Non-Diabetic Population: A Genome-Wide Evaluation of 14,618 Participants in the Women's Genome Health Study

Type 2 diabetes is a leading cause of morbidity and mortality. While genetic variants have been found to influence the risk of type 2 diabetes mellitus, relatively few studies have focused on genes associated with glycated hemoglobin, an index of the mean blood glucose concentration of the preceding 8–12 weeks. Epidemiologic studies and randomized clinical trials have documented the relationship between glycated hemoglobin levels and the development of long-term complications in diabetes; moreover, higher glycated hemoglobin levels in the subdiabetic range have been shown to predict type 2 diabetes risk and cardiovascular disease. To examine the common genetic determinants of glycated hemoglobin levels, we performed a genome-wide association study that evaluated 337,343 SNPs in 14,618 apparently healthy Caucasian women. The results show that glycated hemoglobin levels are associated with genetic variation at the GCK (rs730497; P = 2.8×10−12), SLC30A8 (rs13266634; P = 9.8×10−8), G6PC2 (rs1402837; P = 6.8×10−10), and HK1 (rs7072268; P = 6.4×10−9) loci. While associations at the GCK, SLC30A8, and G6PC2 loci are confirmatory, the findings at HK1 are novel. We were able to replicate this novel association in an independent validation sample of 455 additional non-diabetic men and women. HK1 encodes the enzyme hexokinase, the first step in glycolysis and a likely candidate for the control of glucose metabolism. This observed genetic association between glycated hemoglobin levels and HK1 polymorphisms paves the way for further studies of the role of HK1 in hemoglobin glycation, glucose metabolism, and diabetes

Ulcerative colitis-risk loci on chromosomes 1p36 and 12q15 found by genome-wide association study.

Ulcerative colitis is a chronic inflammatory disease of the colon that presents as diarrhea and gastrointestinal bleeding. We performed a genome-wide association study using DNA samples from 1,052 individuals with ulcerative colitis and preexisting data from 2,571 controls, all of European ancestry. In an analysis that controlled for gender and population structure, ulcerative colitis loci attaining genome-wide significance and subsequent replication in two independent populations were identified on chromosomes 1p36 (rs6426833, combined P = 5.1 x 10(-13), combined odds ratio OR = 0.73) and 12q15 (rs1558744, combined P = 2.5 x 10(-12), combined OR = 1.35). In addition, combined genome-wide significant evidence for association was found in a region spanning BTNL2 to HLA-DQB1 on chromosome 6p21 (rs2395185, combined P = 1.0 x 10(-16), combined OR = 0.66) and at the IL23R locus on chromosome 1p31 (rs11209026, combined P = 1.3 x 10(-8), combined OR = 0.56; rs10889677, combined P = 1.3 x 10(-8), combined OR = 1.29)

Ranking and characterization of established BMI and lipid associated loci as candidates for gene-environment interactions

Phenotypic variance heterogeneity across genotypes at a single nucleotide polymorphism (SNP) may reflect underlying gene-environment (G×E) or gene-gene interactions. We modeled variance heterogeneity for blood lipids and BMI in up to 44,211 participants and investigated relationships between variance effects (Pv), G×E interaction effects (with smoking and physical activity), and marginal genetic effects (Pm). Correlations between Pv and Pm were stronger for SNPs with established marginal effects (Spearman’s ρ = 0.401 for triglycerides, and ρ = 0.236 for BMI) compared to all SNPs. When Pv and Pm were compared for all pruned SNPs, only BMI was statistically significant (Spearman’s ρ = 0.010). Overall, SNPs with established marginal effects were overrepresented in the nominally significant part of the Pv distribution (Pbinomial <0.05). SNPs from the top 1% of the Pm distribution for BMI had more significant Pv values (PMann–Whitney= 1.46×10−5), and the odds ratio of SNPs with nominally significant (<0.05) Pm and Pv was 1.33 (95% CI: 1.12, 1.57) for BMI. Moreover, BMI SNPs with nominally significant G×E interaction P-values (Pint<0.05) were enriched with nominally significant Pv values (Pbinomial = 8.63×10−9 and 8.52×10−7 for SNP × smoking and SNP × physical activity, respectively). We conclude that some loci with strong marginal effects may be good candidates for G×E, and variance-based prioritization can be used to identify them

Bioactive Hydroperoxyl Cembranoids from the Red Sea Soft Coral Sarcophyton glaucum

A chemical investigation of an ethyl acetate extract of the Red Sea soft coral Sarcophyton glaucum has led to the isolation of two peroxide diterpenes, 11(S) hydroperoxylsarcoph-12(20)-ene (1), and 12(S)-hydroperoxylsarcoph-10-ene (2), as well as 8-epi-sarcophinone (3). In addition to these three new compounds, two known structures were identified including: ent-sarcophine (4) and sarcophine (5). Structures were elucidated by spectroscopic analysis, with the relative configuration of 1 and 2 confirmed by X-ray diffraction. Isolated compounds were found to be inhibitors of cytochrome P450 1A activity as well as inducers of glutathione S-transferases (GST), quinone reductase (QR), and epoxide hydrolase (mEH) establishing chemo-preventive and tumor anti-initiating activity for these characterized metabolites

On the Use of Variance per Genotype as a Tool to Identify Quantitative Trait Interaction Effects: A Report from the Women's Genome Health Study

Testing for genetic effects on mean values of a quantitative trait has been a very successful strategy. However, most studies to date have not explored genetic effects on the variance of quantitative traits as a relevant consequence of genetic variation. In this report, we demonstrate that, under plausible scenarios of genetic interaction, the variance of a quantitative trait is expected to differ among the three possible genotypes of a biallelic SNP. Leveraging this observation with Levene's test of equality of variance, we propose a novel method to prioritize SNPs for subsequent gene–gene and gene–environment testing. This method has the advantageous characteristic that the interacting covariate need not be known or measured for a SNP to be prioritized. Using simulations, we show that this method has increased power over exhaustive search under certain conditions. We further investigate the utility of variance per genotype by examining data from the Women's Genome Health Study. Using this dataset, we identify new interactions between the LEPR SNP rs12753193 and body mass index in the prediction of C-reactive protein levels, between the ICAM1 SNP rs1799969 and smoking in the prediction of soluble ICAM-1 levels, and between the PNPLA3 SNP rs738409 and body mass index in the prediction of soluble ICAM-1 levels. These results demonstrate the utility of our approach and provide novel genetic insight into the relationship among obesity, smoking, and inflammation

Gene × Physical Activity Interactions in Obesity: Combined Analysis of 111,421 Individuals of European Ancestry

Numerous obesity loci have been identified using genome-wide association studies. A UK study indicated that physical activity may attenuate the cumulative effect of 12 of these loci, but replication studies are lacking. Therefore, we tested whether the aggregate effect of these loci is diminished in adults of European ancestry reporting high levels of physical activity. Twelve obesity-susceptibility loci were genotyped or imputed in 111,421 participants. A genetic risk score (GRS) was calculated by summing the BMI-associated alleles of each genetic variant. Physical activity was assessed using self-administered questionnaires. Multiplicative interactions between the GRS and physical activity on BMI were tested in linear and logistic regression models in each cohort, with adjustment for age, age2, sex, study center (for multicenter studies), and the marginal terms for physical activity and the GRS. These results were combined using meta-analysis weighted by cohort sample size. The meta-analysis yielded a statistically significant GRS × physical activity interaction effect estimate (Pinteraction = 0.015). However, a statistically significant interaction effect was only apparent in North American cohorts (n = 39,810, Pinteraction = 0.014 vs. n = 71,611, Pinteraction = 0.275 for Europeans). In secondary analyses, both the FTO rs1121980 (Pinteraction = 0.003) and the SEC16B rs10913469 (Pinteraction = 0.025) variants showed evidence of SNP × physical activity interactions. This meta-analysis of 111,421 individuals provides further support for an interaction between physical activity and a GRS in obesity disposition, although these findings hinge on the inclusion of cohorts from North America, indicating that these results are either population-specific or non-causal

Hundreds of variants clustered in genomic loci and biological pathways affect human height

Most common human traits and diseases have a polygenic pattern of inheritance: DNA sequence variants at many genetic loci influence the phenotype. Genome-wide association (GWA) studies have identified more than 600 variants associated with human traits, but these typically explain small fractions of phenotypic variation, raising questions about the use of further studies. Here, using 183,727 individuals, we show that hundreds of genetic variants, in at least 180 loci, influence adult height, a highly heritable and classic polygenic trait. The large number of loci reveals patterns with important implications for genetic studies of common human diseases and traits. First, the 180 loci are not random, but instead are enriched for genes that are connected in biological pathways (P = 0.016) and that underlie skeletal growth defects (P < 0.001). Second, the likely causal gene is often located near the most strongly associated variant: in 13 of 21 loci containing a known skeletal growth gene, that gene was closest to the associated variant. Third, at least 19 loci have multiple independently associated variants, suggesting that allelic heterogeneity is a frequent feature of polygenic traits, that comprehensive explorations of already-discovered loci should discover additional variants and that an appreciable fraction of associated loci may have been identified. Fourth, associated variants are enriched for likely functional effects on genes, being over-represented among variants that alter amino-acid structure of proteins and expression levels of nearby genes. Our data explain approximately 10% of the phenotypic variation in height, and we estimate that unidentified common variants of similar effect sizes would increase this figure to approximately 16% of phenotypic variation (approximately 20% of heritable variation). Although additional approaches are needed to dissect the genetic architecture of polygenic human traits fully, our findings indicate that GWA studies can identify large numbers of loci that implicate biologically relevant genes and pathways.