scholarly journals Shared Genetic Control of Brain Activity During Sleep and Insulin Secretion: A Laboratory-Based Family Study

Diabetes ◽  
2017 ◽  
Vol 67 (1) ◽  
pp. 155-164
Author(s):  
Lisa L. Morselli ◽  
Eric R. Gamazon ◽  
Esra Tasali ◽  
Nancy J. Cox ◽  
Eve Van Cauter ◽  
...  
Keyword(s):  
Insulin Secretion ◽  
Genetic Control ◽  
Family Study ◽  
Brain Activity ◽  
Shared Genetic

scholarly journals Associated dental anomalies: case report

2005 ◽  
Vol 13 (4) ◽  
pp. 431-436 ◽  
Author(s):  
Daniela Gamba Garib ◽  
Nildiceli Leite Melo Zanella ◽  
Sheldon Peck
Keyword(s):  
Case Report ◽  
Early Diagnosis ◽  
Genetic Control ◽  
Treatment Planning ◽  
Interesting Case ◽  
Clinical Implications ◽  
Dental Anomalies ◽  
Dental Abnormalities ◽  
Developmental Disturbances ◽  
Shared Genetic

Certain human dental anomalies frequently occur together, supporting the accumulated evidence of the shared genetic control of dental developmental disturbances. The present study reports a rare and interesting case of a 12-year-old girl with an association of multiple dental abnormalities, including agenesis, tooth malposition and delayed development. The etiology and treatment planning are discussed with reference to the literature. The clinical implications of genetically controlled patterns of dental anomalies are important in the establishment of early diagnosis and appropriate orthodontic intervention.

Genetic and environmental influences on psychological distress in the population: General Health Questionnaire analyses in UK twins

2003 ◽  
Vol 33 (5) ◽  
pp. 793-801 ◽  
Author(s):  
F. V. RIJSDIJK ◽  
H. SNIEDER ◽  
J. ORMEL ◽  
P. SHAM ◽  
D. P. GOLDBERG ◽  
...  
Keyword(s):  
Genetic Control ◽  
General Health ◽  
Genetic Factors ◽  
General Health Questionnaire ◽  
Model Fitting ◽  
Regression Method ◽  
Health Questionnaire ◽  
Twin Data ◽  
Score Distribution ◽  
Shared Genetic

Background. The General Health Questionnaire (GHQ) is the most popular screening instrument for detecting psychiatric disorders in community samples. Using longitudinal data of a large sample of UK twin pairs, we explored (i) heritabilities of the four scales and the total score; (ii) the genetic stability over time; and (iii) the existence of differential heritable influences at the high (ill) and low (healthy) tail of the distribution.Method. At baseline we assessed the GHQ in 627 MZ and 1323 DZ female pairs and at a second occasion (3·5 years later) for a small subsample (90 MZ and 270 DZ pairs). Liability threshold models and raw ordinal maximum likelihood were used to estimate twin correlations and to fit longitudinal genetic models. We estimated extreme group heritabilities of the GHQ distribution by using a model-fitting implementation of the DeFries–Fulker regression method for selected twin data.Results. Heritabilities for Somatic Symptoms, Anxiety, Social Dysfunction, Depression and total score were 0·37, 0·40, 0·20, 0·42 and 0·44, respectively. The contribution of shared genetic factors to the correlations between time points is substantial for the total score (73%). Group heritabilities of 0·48 and 0·43 were estimated for the top and bottom 10% of the total GHQ score distribution, respectively.Conclusion. The overall heritability of the GHQ as a measure of psychosocial distress was substantial (44%), with all scales having significant additive genetic influences that persisted across time periods. Extreme group analyses suggest that the genetic control of resilience is as important as the genetic control of vulnerability.

A Statistical Model and Analysis for Genetic and Environmental Effects in Responses From Twin-Family Studies

1981 ◽  
Vol 30 (1) ◽  
pp. 9-38 ◽  
Author(s):  
James S. Williams ◽  
Hariharan Iyer
Keyword(s):  
Statistical Model ◽  
Genetic Control ◽  
Environmental Effects ◽  
Family Study ◽  
Smoking Habit ◽  
Cluster Sampling ◽  
Model Parameters ◽  
Social Environments ◽  
Correlation Data ◽  
Height Data

A statistical model and analysis for genetic and environmental effects in twin-family data are presented. The model is used to derive expressions for phenotypic correlations of 22 essential pair relationships in twin-family units. The analysis proceeds in two steps. First, differential effects of sex, generation, and sex-zygosity of twin-family units and correlations due to cluster sampling are eliminated from correlation data. Then, estimates and tests of model parameters are calculated from the adjusted data. The theory and methods were developed for a Swedish twin-family study of many behaviors possibly related to the smoking habit. There, it is important to screen for behaviors that clearly are under genetic control and to assess relative influences of various biological and social environments on the development of all behaviors. Height data from the Swedish study are used to illustrate concepts and methods presented in this paper.

scholarly journals A novel interaction between dietary composition and insulin secretion: effects on weight gain in the Quebec Family Study

2008 ◽  
Vol 87 (2) ◽  
pp. 303-309 ◽  
Author(s):  
Jean-Philippe Chaput ◽  
Angelo Tremblay ◽  
Eric B Rimm ◽  
Claude Bouchard ◽  
David S Ludwig
Keyword(s):  
Insulin Secretion ◽  
Weight Gain ◽  
Family Study ◽  
Dietary Composition ◽  
Quebec Family Study

scholarly journals Prediabetes Blunts CD26/DPP4 Genetic Control of Postprandial Glycemia and Insulin Secretion

2021 ◽  
Author(s):  
Rita S Patarrao ◽  
Nadia Duarte ◽  
Rogerio Ribeiro ◽  
Rita Andrade ◽  
Joao Costa ◽  
...  
Keyword(s):  
Insulin Secretion ◽  
Genetic Control ◽  
Molecular Mechanisms ◽  
Glucose Sensing ◽  
Chow Diet ◽  
Single Nucleotide Variants ◽  
Metabolic Disturbances ◽  
C Peptide ◽  
Postprandial Glycemia ◽  
Peptide Release

Aims/hypothesis Imbalances in glucose metabolism are hallmarks of clinically silent prediabetes representing dysmetabolism trajectories leading to type 2 diabetes (T2D). CD26/DPP4 is a clinically proven molecular target of diabetes-controlling drugs but the CD26 gene control of disglycemia is unsettled. Methods We dissected the genetic control of postprandial glycemic and insulin release responses by the CD26/DPP4 gene in an European/Portuguese population-based cohort that comprised individuals with normoglycemia and prediabetes, and in mouse experimental models of CD26 deficiency and hypercaloric diet. Results In individuals with normoglycemia, CD26/DPP4 single-nucleotide variants governed glycemic excursions (rs4664446, P=1.63X10-7) and C-peptide release responses (rs2300757, P=6.86X10-5) upon OGTT. Association with glycemia was stronger at 30min OGTT but the higher association of the genetic control of insulin secretion was detected in later phases of the postprandial response suggesting that CD26/DDP4 gene directly senses glucose challenges. Accordingly, in mice fed normal chow diet but not high fat diet, we found that under OGTT expression of CD26/DPP4 gene is strongly down-regulated at 30min in the mouse liver. Strikingly, no genetic association was found in prediabetic individuals indicating that postprandial glycemic control by CD26/DPP4 is abrogated in prediabetes. Furthermore, CD26 null mice provided concordant evidence that CD26/DPP4 modulates postprandial C-peptide release in normoglycemic but not in dysmetabolic states. Conclusions/interpretation These results unveiled CD26/DPP4 gene as a strong determinant of postprandial glycemia via glucose sensing mechanisms that are abrogated in prediabetes. We propose that impairments in CD26/DDP4 control of postprandial glucose-insulin responses are part of molecular mechanisms underlying early metabolic disturbances associated with T2D.

scholarly journals Multivariate analysis of the cotton seed ionome reveals a shared genetic architecture

2017 ◽  
Author(s):  
Duke Pauli ◽  
Greg Ziegler ◽  
Min Ren ◽  
Matthew A. Jenks ◽  
Douglas J. Hunsaker ◽  
...  
Keyword(s):  
Qtl Mapping ◽  
Genetic Control ◽  
Environmental Conditions ◽  
Cotton Seed ◽  
Genetic Architecture ◽  
Physiological Responses ◽  
Arid Environment ◽  
Machine Learning Algorithms ◽  
Physiological Status ◽  
Shared Genetic

ABSTRACTTo mitigate the effects of heat and drought stress, a better understanding of the genetic control of physiological responses to these environmental conditions is needed. To this end, we evaluated an upland cotton (Gossypium hirsutum L.) mapping population under water-limited and well-watered conditions in a hot, arid environment. The elemental concentrations (ionome) of seed samples from the population were profiled in addition to those of soil samples taken from throughout the field site to better model environmental variation. The elements profiled in seeds exhibited moderate to high heritabilities, as well as strong phenotypic and genotypic correlations between elements that were not altered by the imposed irrigation regimes. Quantitative trait loci (QTL) mapping results from a Bayesian classification method identified multiple genomic regions where QTL for individual elements colocalized, suggesting that genetic control of the ionome is highly interrelated. To more fully explore this genetic architecture, multivariate QTL mapping was implemented among groups of biochemically related elements. This analysis revealed both additional and pleiotropic QTL responsible for coordinated control of phenotypic variation for elemental accumulation. Machine learning algorithms that utilized only ionomic data predicted the irrigation regime under which genotypes were evaluated with very high accuracy. Taken together, these results demonstrate the extent to which the seed ionome is genetically interrelated and predictive of plant physiological responses to adverse environmental conditions.One sentence summaryThe cotton seed ionome has a shared genetic basis that provides insight into the physiological status of the plant.

scholarly journals Insulin Sensitivity, Insulin Secretion, and Abdominal Fat: The Insulin Resistance Atherosclerosis Study (IRAS) Family Study

Diabetes ◽  
2003 ◽  
Vol 52 (10) ◽  
pp. 2490-2496 ◽  
Author(s):  
L. E. Wagenknecht ◽  
C. D. Langefeld ◽  
A. L. Scherzinger ◽  
J. M. Norris ◽  
S. M. Haffner ◽  
...  
Keyword(s):  
Insulin Resistance ◽  
Insulin Secretion ◽  
Insulin Sensitivity ◽  
Family Study ◽  
Abdominal Fat ◽  
Insulin Resistance Atherosclerosis Study
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