Genome-Wide Interactions with Dairy Intake for Body Mass Index in Adults of European Descent

Scope: Body weight responds variably to the intake of dairy foods. Genetic variation may contribute to inter‐individual variability in associations between body weight and dairy consumption. Methods and results: A genome‐wide interaction study to discover genetic variants that account for variation in BMI in the context of low‐fat, high‐fat and total dairy intake in cross‐sectional analysis was conducted. Data from nine discovery studies (up to 25 513 European descent individuals) were meta‐analyzed. Twenty‐six genetic variants reached the selected significance threshold (p‐interaction \u3c10−7), and six independent variants (LINC01512‐rs7751666, PALM2/AKAP2‐rs914359, ACTA2‐rs1388, PPP1R12A‐rs7961195, LINC00333‐rs9635058, AC098847.1‐rs1791355) were evaluated meta‐analytically for replication of interaction in up to 17 675 individuals. Variant rs9635058 (128 kb 3’ of LINC00333) was replicated (p‐interaction = 0.004). In the discovery cohorts, rs9635058 interacted with dairy (p‐interaction = 7.36 × 10−8) such that each serving of low‐fat dairy was associated with 0.225 kg m−2 lower BMI per each additional copy of the effect allele (A). A second genetic variant (ACTA2‐rs1388) approached interaction replication significance for low‐fat dairy exposure. Conclusion: Body weight responses to dairy intake may be modified by genotype, in that greater dairy intake may protect a genetic subgroup from higher body weight

Gene-Environment Interactions of Circadian-Related Genes for Cardiometabolic Traits

OBJECTIVE Common circadian-related gene variants associate with increased risk for metabolic alterations including type 2 diabetes. However, little is known about whether diet and sleep could modify associations between circadian-related variants (CLOCK-rs1801260, CRY2-rs11605924, MTNR1B-rs1387153, MTNR1B-rs10830963, NR1D1-rs2314339) and cardiometabolic traits (fasting glucose [FG], HOMA-insulin resistance, BMI, waist circumference, and HDL-cholesterol) to facilitate personalized recommendations. RESEARCH DESIGN AND METHODS We conducted inverse-variance weighted, fixed-effect meta-analyses of results of adjusted associations and interactions between dietary intake/sleep duration and selected variants on cardiometabolic traits from 15 cohort studies including up to 28,190 participants of European descent from the Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) Consortium. RESULTS We observed significant associations between relative macronutrient intakes and glycemic traits and short sleep duration (<7 h) and higher FG and replicated known MTNR1B associations with glycemic traits. No interactions were evident after accounting for multiple comparisons. However, we observed nominally significant interactions (all P < 0.01) between carbohydrate intake and MTNR1B-rs1387153 for FG with a 0.003 mmol/L higher FG with each additional 1% carbohydrate intake in the presence of the T allele, between sleep duration and CRY2-rs11605924 for HDL-cholesterol with a 0.010 mmol/L higher HDL-cholesterol with each additional hour of sleep in the presence of the A allele, and between long sleep duration (≥9 h) and MTNR1B-rs1387153 for BMI with a 0.60 kg/m2 higher BMI with long sleep duration in the presence of the T allele relative to normal sleep duration (≥7 to <9 h). CONCLUSIONS Our results suggest that lower carbohydrate intake and normal sleep duration may ameliorate cardiometabolic abnormalities conferred by common circadian-related genetic variants. Until further mechanistic examination of the nominally significant interactions is conducted, recommendations applicable to the general population regarding diet—specifically higher carbohydrate and lower fat composition—and normal sleep duration should continue to be emphasized among individuals with the investigated circadian-related gene variants

Gene × dietary pattern interactions in obesity: Analysis of up to 68 317 adults of European ancestry

Obesity is highly heritable. Genetic variants showing robust associations with obesity traits have been identified through genome-wide association studies. We investigated whether a composite score representing healthy diet modifies associations of these variants with obesity traits. Totally, 32 body mass index (BMI)- and 14 waist-hip ratio (WHR)-associated single nucleotide polymorphisms were genotyped, and genetic risk scores (GRS) were calculated in 18 cohorts of European ancestry (n = 68 317). Diet score was calculated based on self-reported intakes of whole grains, fish, fruits, vegetables, nuts/seeds (favorable) and red/processed meats, sweets, sugar-sweetened beverages and fried potatoes (unfavorable). Multivariable adjusted, linear regression within each cohort followed by inverse variance-weighted, fixed-effects meta-analysis was used to characterize: (a) associations of each GR

Gene x dietary pattern interactions in obesity : analysis of up to 68 317 adults of European ancestry

Obesity is highly heritable. Genetic variants showing robust associationswith obesity traits have been identified through genome wide association studies. We investigated whether a composite score representing healthy diet modifies associations of these variants with obesity traits. Totally, 32 body mass index (BMI)- and 14 waist-hip ratio (WHR)-associated single nucleotide polymorphismswere genotyped, and genetic risk scores (GRS) were calculated in 18 cohorts of European ancestry (n = 68 317). Diet score was calculated based on self-reported intakes of whole grains, fish, fruits, vegetables, nuts/seeds (favorable) and red/processed meats, sweets, sugar-sweetened beverages and fried potatoes (unfavorable). Multivariable adjusted, linear regression within each cohort followed by inverse variance-weighted, fixed-effects meta-analysis was used to characterize: (a) associations of each GRS with BMI and BMI-adjustedWHR and (b) diet score modification of genetic associations with BMI and BMI-adjusted WHR. Nominally significant interactions (P = 0.006-0.04) were observed between the diet score and WHR-GRS (but not BMI-GRS), two WHR loci (GRB14 rs10195252; LYPLAL1 rs4846567) and two BMI loci (LRRN6C rs10968576; MTIF3 rs4771122), for the respective BMI-adjustedWHR or BMI outcomes. Although the magnitudes of these select interactions were small, our data indicated that associations between genetic predisposition and obesity traits were stronger with a healthier diet. Our findings generate interesting hypotheses; however, experimental and functional studies are needed to determine their clinical relevance.Peer reviewe

Gene × dietary pattern interactions in obesity: analysis of up to 68 317 adults of European ancestry

Obesity is highly heritable. Genetic variants showing robust associations with obesity traits have been identified through genome-wide association studies. We investigated whether a composite score representing healthy diet modifies associations of these variants with obesity traits. Totally, 32 body mass index (BMI)- and 14 waist–hip ratio (WHR)-associated single nucleotide polymorphisms were genotyped, and genetic risk scores (GRS) were calculated in 18 cohorts of European ancestry (n = 68 317). Diet score was calculated based on self-reported intakes of whole grains, fish, fruits, vegetables, nuts/seeds (favorable) and red/processed meats, sweets, sugar-sweetened beverages and fried potatoes (unfavorable). Multivariable adjusted, linear regression within each cohort followed by inverse variance-weighted, fixed-effects meta-analysis was used to characterize: (a) associations of each GRS with BMI and BMI-adjusted WHR and (b) diet score modification of genetic associations with BMI and BMI-adjusted WHR. Nominally significant interactions (P = 0.006–0.04) were observed between the diet score and WHR-GRS (but not BMI-GRS), two WHR loci (GRB14 rs10195252; LYPLAL1 rs4846567) and two BMI loci (LRRN6C rs10968576; MTIF3 rs4771122), for the respective BMI-adjusted WHR or BMI outcomes. Although the magnitudes of these select interactions were small, our data indicated that associations between genetic predisposition and obesity traits were stronger with a healthier diet. Our findings generate interesting hypotheses; however, experimental and functional studies are needed to determine their clinical relevance

Modeling Volatility Characteristics of Epileptic EEGs using GARCH Models

Objective: To determine if there was a difference in the volatility characteristics of seizure and non-seizure onset channels in the intracranial electroencephalogram (EEG) in a patient with temporal lobe epilepsy. Methods: The half-life of volatility for the different EEG channels was determined using Autoregressive Moving Average&ndash;Generalized Autoregressive Conditional Heteroscedasticity (ARMA&ndash;GARCH) models; confidence intervals were constructed using the delta method and an asymptotic method for comparing the half-lives. Results: Clinically determined seizure onsets occurred over strip electrodes named RAST (Right Anterior Subtemporal) and RMST (Right Mid Subtemporal), at locations 2, 3 and 4, on the strip electrodes. The half-lives of volatility for two of the three seizure channels, RAST3 and RAST4, were found to be significantly lower the rest of the channels for six one-minute EEG segments prior to seizure onset and nine one-minute EEG segments of an awake state. The half-lives of volatility for RAST3 and RAST4 were not significantly different to the non-seizure channels for ten one-minute segments of sleep and ten one-minute segments of sleep-to-awake states. The estimates for the half-lives were consistent for randomly selected one-minute EEG segments. Conclusions: The use of GARCH models may be a useful tool in determining hidden properties in epileptiform EEGs that may lead to better understanding of the seizure generating process

Using a PyMOL Activity to Reinforce the Connection between Genotype and Phenotype in an Undergraduate Genetics Laboratory

<div><p>With the purpose of developing an activity that would help clarify genetic concepts related to the connection between genotype and phenotype and the nature of mutations, we designed a three hour teaching module using the PyMol software. The activity starts with two pre-laboratory assignments, one to learn how to use PyMol and the other to read about a specific protein or protein family. During the laboratory students are given instructions where and how to find additional information on a specific disease and its causal mutations in order to prepare a 10-minute, in-class presentation. Using a post activity, anonymous quiz, we found a statistically significant different grade distribution in students that participated in the PyMol activity relative to a control group. We also found a significant improvement in the student’s comprehension when answering questions regarding the nature of mutations and protein structure. This demonstrates the utility of this simulation activity as a vehicle to improve student’s understanding of specific key genetic concepts.</p></div

Letter grade distribution (percentage) of the overall grade made on the post-quiz.

<p>Grades were defined as follows: A = 90–100%, B = 80–89%, C = 70–79%, D = 60–69%, F = equal or below 59%.</p

Overview of pre-lab and in-lab activities.

<p>For more details, please see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0114257#pone.0114257.s001" target="_blank">File S1</a> and <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0114257#pone.0114257.s002" target="_blank">File S2</a>.</p