Secondary analyses for genome-wide association studies using expression quantitative trait loci.

Genome-wide association studies (GWAS) have successfully identified thousands of single nucleotide polymorphisms (SNPs) associated with complex traits; however, the identified SNPs account for a fraction of trait heritability, and identifying the functional elements through which genetic variants exert their effects remains a challenge. Recent evidence suggests that SNPs associated with complex traits are more likely to be expression quantitative trait loci (eQTL). Thus, incorporating eQTL information can potentially improve power to detect causal variants missed by traditional GWAS approaches. Using genomic, transcriptomic, and platelet phenotype data from the Genetic Study of Atherosclerosis Risk family-based study, we investigated the potential to detect novel genomic risk loci by incorporating information from eQTL in the relevant target tissues (i.e., platelets and megakaryocytes) using established statistical principles in a novel way. Permutation analyses were performed to obtain family-wise error rates for eQTL associations, substantially lowering the genome-wide significance threshold for SNP-phenotype associations. In addition to confirming the well known association between PEAR1 and platelet aggregation, our eQTL-focused approach identified a novel locus (rs1354034) and gene (ARHGEF3) not previously identified in a GWAS of platelet aggregation phenotypes. A colocalization analysis showed strong evidence for a functional role of this eQTL.

Systolic Blood Pressure Is Associated with Increased Brain Amyloid Load in Mild Cognitively Impaired Participants: Alzheimer's Disease Neuroimaging Initiatives Study.

BACKGROUND: Cardiovascular disease (CVD), including elevated blood pressure (BP), is known to promote Alzheimer's disease (AD) risk. Although brain amyloid load is a recognized hallmark of pre-symptomatic AD, its relationship to increased BP is less known. The objective of this study was to examine the relationship of BP to brain estimates of amyloid-ß (Aß) and standard uptake ratio (SUVr). We hypothesized that increased BP is associated with increased SUVr. METHODS: Using data from the Alzheimer's Disease Neuroimaging Initiative (ADNI), we stratified BP according to the Seventh Joint National Committee (JNC) on Prevention, Detection, Evaluation, and Treatment of High Blood Pressure Classification (JNC VII). Florbetapir (AV-45) SUVr was derived from the averaged frontal, anterior cingulate, precuneus, and parietal cortex relative to the cerebellum. A linear mixed-effects model enabled the elucidation of amyloid SUVr relationships to BP. The model discounted the effects of demographics, biologics, and diagnosis at baseline within APOE genotype groups. The least squares means procedure was used to estimate the fixed-effect means. All analyses were performed using the Statistical Analysis System (SAS). RESULTS: In non-ɛ4 carrier MCI subjects, escalating JNC categories of BP was associated with increasing mean SUVr using JNC-4 as a reference point (low-normal (JNC1) p = 0.018; normal (JNC-1) p = 0.039; JNC-2 p = 0.018 and JNC-3 p = 0.04). A significantly higher brain SUVr was associated with increasing BP despite adjustment for demographics and biological variables in non-ɛ4 carriers but not in ɛ4-carriers. This observation supports the view that CVD risk may promote increased brain amyloid load, and potentially, amyloid-mediated cognitive decline. CONCLUSION: Increasing levels of JNC classification of BP is dynamically associated with significant changes in brain amyloid burden in non-ɛ4 carriers but not in ɛ4-carrier MCI subjects. Though not statistically significant, amyloid burden tended to decrease with increasing BP in ɛ4 homozygote, perhaps motivated by increased vascular resistance and the need for higher brain perfusion pressure.

Revisiting methods for modeling longitudinal and survival data: Framingham Heart Study.

BACKGROUND: Statistical methods for modeling longitudinal and time-to-event data has received much attention in medical research and is becoming increasingly useful. In clinical studies, such as cancer and AIDS, longitudinal biomarkers are used to monitor disease progression and to predict survival. These longitudinal measures are often missing at failure times and may be prone to measurement errors. More importantly, time-dependent survival models that include the raw longitudinal measurements may lead to biased results. In previous studies these two types of data are frequently analyzed separately where a mixed effects model is used for the longitudinal data and a survival model is applied to the event outcome. METHODS: In this paper we compare joint maximum likelihood methods, a two-step approach and a time dependent covariate method that link longitudinal data to survival data with emphasis on using longitudinal measures to predict survival. We apply a Bayesian semi-parametric joint method and maximum likelihood joint method that maximizes the joint likelihood of the time-to-event and longitudinal measures. We also implement the Two-Step approach, which estimates random effects separately, and a classic Time Dependent Covariate Model. We use simulation studies to assess bias, accuracy, and coverage probabilities for the estimates of the link parameter that connects the longitudinal measures to survival times. RESULTS: Simulation results demonstrate that the Two-Step approach performed best at estimating the link parameter when variability in the longitudinal measure is low but is somewhat biased downwards when the variability is high. Bayesian semi-parametric and maximum likelihood joint methods yield higher link parameter estimates with low and high variability in the longitudinal measure. The Time Dependent Covariate method resulted in consistent underestimation of the link parameter. We illustrate these methods using data from the Framingham Heart Study in which lipid measurements and Myocardial Infarction data were collected over a period of 26 years. CONCLUSIONS: Traditional methods for modeling longitudinal and survival data, such as the time dependent covariate method, that use the observed longitudinal data, tend to provide downwardly biased estimates. The two-step approach and joint models provide better estimates, although a comparison of these methods may depend on the underlying residual variance.

Genome-wide meta-analysis of macronutrient intake of 91,114 European ancestry participants from the cohorts for heart and aging research in genomic epidemiology consortium.

Macronutrient intake, the proportion of calories consumed from carbohydrate, fat, and protein, is an important risk factor for metabolic diseases with significant familial aggregation. Previous studies have identified two genetic loci for macronutrient intake, but incomplete coverage of genetic variation and modest sample sizes have hindered the discovery of additional loci. Here, we expanded the genetic landscape of macronutrient intake, identifying 12 suggestively significant loci (P < 1 × 10-6) associated with intake of any macronutrient in 91,114 European ancestry participants. Four loci replicated and reached genome-wide significance in a combined meta-analysis including 123,659 European descent participants, unraveling two novel loci; a common variant in RARB locus for carbohydrate intake and a rare variant in DRAM1 locus for protein intake, and corroborating earlier FGF21 and FTO findings. In additional analysis of 144,770 participants from the UK Biobank, all identified associations from the two-stage analysis were confirmed except for DRAM1. Identified loci might have implications in brain and adipose tissue biology and have clinical impact in obesity-related phenotypes. Our findings provide new insight into biological functions related to macronutrient intake.

Correction: Genome-wide physical activity interactions in adiposity - A meta-analysis of 200,452 adults.

[This corrects the article DOI: 10.1371/journal.pgen.1006528.].

Genome-wide physical activity interactions in adiposity - A meta-analysis of 200,452 adults.

Physical activity (PA) may modify the genetic effects that give rise to increased risk of obesity. To identify adiposity loci whose effects are modified by PA, we performed genome-wide interaction meta-analyses of BMI and BMI-adjusted waist circumference and waist-hip ratio from up to 200,452 adults of European (n = 180,423) or other ancestry (n = 20,029). We standardized PA by categorizing it into a dichotomous variable where, on average, 23% of participants were categorized as inactive and 77% as physically active. While we replicate the interaction with PA for the strongest known obesity-risk locus in the FTO gene, of which the effect is attenuated by ~30% in physically active individuals compared to inactive individuals, we do not identify additional loci that are sensitive to PA. In additional genome-wide meta-analyses adjusting for PA and interaction with PA, we identify 11 novel adiposity loci, suggesting that accounting for PA or other environmental factors that contribute to variation in adiposity may facilitate gene discovery.

Genomics of body fat percentage may contribute to sex bias in anorexia nervosa.

Anorexia nervosa (AN) occurs nine times more often in females than in males. Although environmental factors likely play a role, the reasons for this imbalanced sex ratio remain unresolved. AN displays high genetic correlations with anthropometric and metabolic traits. Given sex differences in body composition, we investigated the possible metabolic underpinnings of female propensity for AN. We conducted sex-specific GWAS in a healthy and medication-free subsample of the UK Biobank (n = 155,961), identifying 77 genome-wide significant loci associated with body fat percentage (BF%) and 174 with fat-free mass (FFM). Partitioned heritability analysis showed an enrichment for central nervous tissue-associated genes for BF%, which was more prominent in females than males. Genetic correlations of BF% and FFM with the largest GWAS of AN by the Psychiatric Genomics Consortium were estimated to explore shared genomics. The genetic correlations of BF%male and BF%female with AN differed significantly from each other (p < .0001, δ = -0.17), suggesting that the female preponderance in AN may, in part, be explained by sex-specific anthropometric and metabolic genetic factors increasing liability to AN.

Associations of Pulse and Blood Pressure with Hippocampal Volume by APOE and Cognitive Phenotype: The Alzheimer's Disease Neuroimaging Initiative (ADNI).

BACKGROUND: It is increasingly evident that high blood pressure can promote reduction in global and regional brain volumes. While these effects may preferentially affect the hippocampus, reports are inconsistent. METHODS: Using data from the Alzheimer's Disease Neuroimaging Initiative (ADNI), we examined the relationships of hippocampal volume to pulse pressure (PPR) and systolic (SBP) and diastolic (DBP) blood pressure according to apolipoprotein (APOE) ɛ4 positivity and cognitive status. The ADNI data included 1,308 participants: Alzheimer disease (AD = 237), late mild cognitive impairment (LMCI = 454), early mild cognitive impairment (EMCI = 254), and cognitively normal (CN = 365), with up to 24 months of follow-up. RESULTS: Higher quartiles of PPR were significantly associated with lower hippocampal volumes (Q1 vs. Q4, p = 0.034) in the CN and AD groups, but with increasing hippocampal volume (Q1, p = 0.008; Q2, p = 0.020; Q3, p = 0.017; Q4 = reference) in the MCI groups. In adjusted stratified analyses among non-APOE ɛ4 carriers, the effects in the CN (Q1 vs. Q4, p = 0.006) and EMCI groups (Q1, p = 0.002; Q2, p = 0.013; Q3, p = 0.002; Q4 = reference) remained statistically significant. Also, higher DBP was significantly associated with higher hippocampal volume (p = 0.002) while higher SBP was significantly associated with decreasing hippocampal volume in the EMCI group (p = 0.015). CONCLUSION: Changes in PPR, SBP, and DBP differentially influenced hippocampal volumes depending on the cognitive and APOE genotypic categories.

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.

FTO genetic variants, dietary intake and body mass index: insights from 177,330 individuals.

FTO is the strongest known genetic susceptibility locus for obesity. Experimental studies in animals suggest the potential roles of FTO in regulating food intake. The interactive relation among FTO variants, dietary intake and body mass index (BMI) is complex and results from previous often small-scale studies in humans are highly inconsistent. We performed large-scale analyses based on data from 177,330 adults (154 439 Whites, 5776 African Americans and 17 115 Asians) from 40 studies to examine: (i) the association between the FTO-rs9939609 variant (or a proxy single-nucleotide polymorphism) and total energy and macronutrient intake and (ii) the interaction between the FTO variant and dietary intake on BMI. The minor allele (A-allele) of the FTO-rs9939609 variant was associated with higher BMI in Whites (effect per allele = 0.34 [0.31, 0.37] kg/m(2), P = 1.9 × 10(-105)), and all participants (0.30 [0.30, 0.35] kg/m(2), P = 3.6 × 10(-107)). The BMI-increasing allele of the FTO variant showed a significant association with higher dietary protein intake (effect per allele = 0.08 [0.06, 0.10] %, P = 2.4 × 10(-16)), and relative weak associations with lower total energy intake (-6.4 [-10.1, -2.6] kcal/day, P = 0.001) and lower dietary carbohydrate intake (-0.07 [-0.11, -0.02] %, P = 0.004). The associations with protein (P = 7.5 × 10(-9)) and total energy (P = 0.002) were attenuated but remained significant after adjustment for BMI. We did not find significant interactions between the FTO variant and dietary intake of total energy, protein, carbohydrate or fat on BMI. Our findings suggest a positive association between the BMI-increasing allele of FTO variant and higher dietary protein intake and offer insight into potential link between FTO, dietary protein intake and adiposity.

Genome-wide meta-analysis identifies six novel loci associated with habitual coffee consumption.

Coffee, a major dietary source of caffeine, is among the most widely consumed beverages in the world and has received considerable attention regarding health risks and benefits. We conducted a genome-wide (GW) meta-analysis of predominately regular-type coffee consumption (cups per day) among up to 91,462 coffee consumers of European ancestry with top single-nucleotide polymorphisms (SNPs) followed-up in ~30 062 and 7964 coffee consumers of European and African-American ancestry, respectively. Studies from both stages were combined in a trans-ethnic meta-analysis. Confirmed loci were examined for putative functional and biological relevance. Eight loci, including six novel loci, met GW significance (log10Bayes factor (BF)>5.64) with per-allele effect sizes of 0.03-0.14 cups per day. Six are located in or near genes potentially involved in pharmacokinetics (ABCG2, AHR, POR and CYP1A2) and pharmacodynamics (BDNF and SLC6A4) of caffeine. Two map to GCKR and MLXIPL genes related to metabolic traits but lacking known roles in coffee consumption. Enhancer and promoter histone marks populate the regions of many confirmed loci and several potential regulatory SNPs are highly correlated with the lead SNP of each. SNP alleles near GCKR, MLXIPL, BDNF and CYP1A2 that were associated with higher coffee consumption have previously been associated with smoking initiation, higher adiposity and fasting insulin and glucose but lower blood pressure and favorable lipid, inflammatory and liver enzyme profiles (P<5 × 10(-8)).Our genetic findings among European and African-American adults reinforce the role of caffeine in mediating habitual coffee consumption and may point to molecular mechanisms underlying inter-individual variability in pharmacological and health effects of coffee.

A comparison of time dependent Cox regression, pooled logistic regression and cross sectional pooling with simulations and an application to the Framingham Heart Study.

BACKGROUND: Typical survival studies follow individuals to an event and measure explanatory variables for that event, sometimes repeatedly over the course of follow up. The Cox regression model has been used widely in the analyses of time to diagnosis or death from disease. The associations between the survival outcome and time dependent measures may be biased unless they are modeled appropriately. METHODS: In this paper we explore the Time Dependent Cox Regression Model (TDCM), which quantifies the effect of repeated measures of covariates in the analysis of time to event data. This model is commonly used in biomedical research but sometimes does not explicitly adjust for the times at which time dependent explanatory variables are measured. This approach can yield different estimates of association compared to a model that adjusts for these times. In order to address the question of how different these estimates are from a statistical perspective, we compare the TDCM to Pooled Logistic Regression (PLR) and Cross Sectional Pooling (CSP), considering models that adjust and do not adjust for time in PLR and CSP. RESULTS: In a series of simulations we found that time adjusted CSP provided identical results to the TDCM while the PLR showed larger parameter estimates compared to the time adjusted CSP and the TDCM in scenarios with high event rates. We also observed upwardly biased estimates in the unadjusted CSP and unadjusted PLR methods. The time adjusted PLR had a positive bias in the time dependent Age effect with reduced bias when the event rate is low. The PLR methods showed a negative bias in the Sex effect, a subject level covariate, when compared to the other methods. The Cox models yielded reliable estimates for the Sex effect in all scenarios considered. CONCLUSIONS: We conclude that survival analyses that explicitly account in the statistical model for the times at which time dependent covariates are measured provide more reliable estimates compared to unadjusted analyses. We present results from the Framingham Heart Study in which lipid measurements and myocardial infarction data events were collected over a period of 26 years.

Novel locus including FGF21 is associated with dietary macronutrient intake.

Dietary intake of macronutrients (carbohydrate, protein, and fat) has been associated with risk of chronic conditions such as obesity and diabetes. Family studies have reported a moderate contribution of genetics to variation in macronutrient intake. In a genome-wide meta-analysis of a population-based discovery cohort (n = 33 533), rs838133 in FGF21 (19q13.33), rs197273 near TRAF family member-associated NF-kappa-B activator (TANK) (2p24.2), and rs10163409 in FTO (16q12.2) were among the top associations (P < 10(-5)) for percentage of total caloric intake from protein and carbohydrate. rs838133 was replicated in silico in an independent sample from the Cohorts for Heart and Aging Research in Genomic Epidemiology Consortium (CHARGE) Nutrition Working Group (n = 38 360) and attained genome-wide significance in combined analysis (Pjoint = 7.9 × 10(-9)). A cytokine involved in cellular metabolism, FGF21 is a potential susceptibility gene for obesity and type 2 diabetes. Our results highlight the potential of genetic variation for determining dietary macronutrient intake.

Genome-wide analysis of BMI in adolescents and young adults reveals additional insight into the effects of genetic loci over the life course.

Genetic loci for body mass index (BMI) in adolescence and young adulthood, a period of high risk for weight gain, are understudied, yet may yield important insight into the etiology of obesity and early intervention. To identify novel genetic loci and examine the influence of known loci on BMI during this critical time period in late adolescence and early adulthood, we performed a two-stage meta-analysis using 14 genome-wide association studies in populations of European ancestry with data on BMI between ages 16 and 25 in up to 29 880 individuals. We identified seven independent loci (P < 5.0 × 10⁻8) near FTO (P = 3.72 × 10⁻²³), TMEM18 (P = 3.24 × 10⁻¹7), MC4R (P = 4.41 × 10⁻¹7), TNNI3K (P = 4.32 × 10⁻¹¹), SEC16B (P = 6.24 × 10⁻9), GNPDA2 (P = 1.11 × 10⁻8) and POMC (P = 4.94 × 10⁻8) as well as a potential secondary signal at the POMC locus (rs2118404, P = 2.4 × 10⁻5 after conditioning on the established single-nucleotide polymorphism at this locus) in adolescents and young adults. To evaluate the impact of the established genetic loci on BMI at these young ages, we examined differences between the effect sizes of 32 published BMI loci in European adult populations (aged 18-90) and those observed in our adolescent and young adult meta-analysis. Four loci (near PRKD1, TNNI3K, SEC16B and CADM2) had larger effects and one locus (near SH2B1) had a smaller effect on BMI during adolescence and young adulthood compared with older adults (P < 0.05). These results suggest that genetic loci for BMI can vary in their effects across the life course, underlying the importance of evaluating BMI at different ages.

Discovery and fine mapping of serum protein loci through transethnic meta-analysis.

Many disorders are associated with altered serum protein concentrations, including malnutrition, cancer, and cardiovascular, kidney, and inflammatory diseases. Although these protein concentrations are highly heritable, relatively little is known about their underlying genetic determinants. Through transethnic meta-analysis of European-ancestry and Japanese genome-wide association studies, we identified six loci at genome-wide significance (p < 5 × 10(-8)) for serum albumin (HPN-SCN1B, GCKR-FNDC4, SERPINF2-WDR81, TNFRSF11A-ZCCHC2, FRMD5-WDR76, and RPS11-FCGRT, in up to 53,190 European-ancestry and 9,380 Japanese individuals) and three loci for total protein (TNFRS13B, 6q21.3, and ELL2, in up to 25,539 European-ancestry and 10,168 Japanese individuals). We observed little evidence of heterogeneity in allelic effects at these loci between groups of European and Japanese ancestry but obtained substantial improvements in the resolution of fine mapping of potential causal variants by leveraging transethnic differences in the distribution of linkage disequilibrium. We demonstrated a functional role for the most strongly associated serum albumin locus, HPN, for which Hpn knockout mice manifest low plasma albumin concentrations. Other loci associated with serum albumin harbor genes related to ribosome function, protein translation, and proteasomal degradation, whereas those associated with serum total protein include genes related to immune function. Our results highlight the advantages of transethnic meta-analysis for the discovery and fine mapping of complex trait loci and have provided initial insights into the underlying genetic architecture of serum protein concentrations and their association with human disease.

Circulating CD34(+) progenitor cell frequency is associated with clinical and genetic factors.

Circulating blood CD34(+) cells consist of hematopoietic stem/progenitor cells, angiogenic cells, and endothelial cells. In addition to their clinical use in hematopoietic stem cell transplantation, CD34(+) cells may also promote therapeutic neovascularization. Therefore, understanding the factors that influence circulating CD34(+) cell frequency has wide implications for vascular biology in addition to stem cell transplantation. In the present study, we examined the clinical and genetic characteristics associated with circulating CD34(+) cell frequency in a large, community-based sample of 1786 Framingham Heart Study participants.Among subjects without cardiovascular disease (n = 1595), CD34(+) frequency was inversely related to older age, female sex, and smoking. CD34(+) frequency was positively related to weight, serum total cholesterol, and statin therapy. Clinical covariates accounted for 6.3% of CD34(+) variability. CD34(+) frequency was highly heritable (h(2) = 54%; P < .0001). Genome-wide association analysis of CD34(+) frequency identified suggestive associations at several loci, including OR4C12 (chromosome 11; P = 6.7 × 10(-7)) and ENO1 and RERE (chromosome 1; P = 8.8 × 10(-7)). CD34(+) cell frequency is reduced in older subjects and is influenced by environmental factors including smoking and statin use. CD34(+) frequency is highly heritable. The results of the present study have implications for therapies that use CD34(+) cell populations and support efforts to better understand the genetic mechanisms that underlie CD34(+) frequency.

Meta-analysis investigating associations between healthy diet and fasting glucose and insulin levels and modification by loci associated with glucose homeostasis in data from 15 cohorts.

Whether loci that influence fasting glucose (FG) and fasting insulin (FI) levels, as identified by genome-wide association studies, modify associations of diet with FG or FI is unknown. We utilized data from 15 U.S. and European cohort studies comprising 51,289 persons without diabetes to test whether genotype and diet interact to influence FG or FI concentration. We constructed a diet score using study-specific quartile rankings for intakes of whole grains, fish, fruits, vegetables, and nuts/seeds (favorable) and red/processed meats, sweets, sugared beverages, and fried potatoes (unfavorable). We used linear regression within studies, followed by inverse-variance-weighted meta-analysis, to quantify 1) associations of diet score with FG and FI levels and 2) interactions of diet score with 16 FG-associated loci and 2 FI-associated loci. Diet score (per unit increase) was inversely associated with FG (ß = -0.004 mmol/L, 95% confidence interval: -0.005, -0.003) and FI (ß = -0.008 ln-pmol/L, 95% confidence interval: -0.009, -0.007) levels after adjustment for demographic factors, lifestyle, and body mass index. Genotype variation at the studied loci did not modify these associations. Healthier diets were associated with lower FG and FI concentrations regardless of genotype at previously replicated FG- and FI-associated loci. Studies focusing on genomic regions that do not yield highly statistically significant associations from main-effect genome-wide association studies may be more fruitful in identifying diet-gene interactions.

Higher magnesium intake is associated with lower fasting glucose and insulin, with no evidence of interaction with select genetic loci, in a meta-analysis of 15 CHARGE Consortium Studies.

Favorable associations between magnesium intake and glycemic traits, such as fasting glucose and insulin, are observed in observational and clinical studies, but whether genetic variation affects these associations is largely unknown. We hypothesized that single nucleotide polymorphisms (SNPs) associated with either glycemic traits or magnesium metabolism affect the association between magnesium intake and fasting glucose and insulin. Fifteen studies from the CHARGE (Cohorts for Heart and Aging Research in Genomic Epidemiology) Consortium provided data from up to 52,684 participants of European descent without known diabetes. In fixed-effects meta-analyses, we quantified 1) cross-sectional associations of dietary magnesium intake with fasting glucose (mmol/L) and insulin (ln-pmol/L) and 2) interactions between magnesium intake and SNPs related to fasting glucose (16 SNPs), insulin (2 SNPs), or magnesium (8 SNPs) on fasting glucose and insulin. After adjustment for age, sex, energy intake, BMI, and behavioral risk factors, magnesium (per 50-mg/d increment) was inversely associated with fasting glucose [ß = -0.009 mmol/L (95% CI: -0.013, -0.005), P < 0.0001] and insulin [-0.020 ln-pmol/L (95% CI: -0.024, -0.017), P < 0.0001]. No magnesium-related SNP or interaction between any SNP and magnesium reached significance after correction for multiple testing. However, rs2274924 in magnesium transporter-encoding TRPM6 showed a nominal association (uncorrected P = 0.03) with glucose, and rs11558471 in SLC30A8 and rs3740393 near CNNM2 showed a nominal interaction (uncorrected, both P = 0.02) with magnesium on glucose. Consistent with other studies, a higher magnesium intake was associated with lower fasting glucose and insulin. Nominal evidence of TRPM6 influence and magnesium interaction with select loci suggests that further investigation is warranted.

Aerobic Exercise Training-Induced Changes on DNA Methylation in Mild Cognitively Impaired Elderly African Americans: Gene, Exercise, and Memory Study - GEMS-I.

BACKGROUND: DNA methylation at CpG sites is a vital epigenetic modification of the human genome affecting gene expression, and potentially, health outcomes. However, evidence is just budding on the effects of aerobic exercise-induced adaptation on DNA methylation in older mild cognitively impaired (MCI) elderly African American (AAs). Therefore, we examined the effects of a 6-month aerobic exercise-intervention on genome-wide DNA methylation in elderly AA MCI volunteers. DESIGN: Elderly AA volunteers confirmed MCI assigned into a 6-month program of aerobic exercise (eleven participants) underwent a 40-min supervised-training 3-times/week and controls (eight participants) performed stretch training. Participants had maximal oxygen consumption (VO2max) test and Genome-wide methylation levels at CpG sites using the Infinium HumanMethylation450 BeadChip assay at baseline and after a 6-month exercise program. We computed false discovery rates (FDR) using Sidak to account for multiplicity of tests and performed quantitative real-time polymerase chain-reaction (qRT-PCR) to confirm the effects of DNA methylations on expression levels of the top 5 genes among the aerobic participants. CpG sites identified from aerobic-exercise participants were similarly analyzed by the stretch group to quantify the effects of exercise-induced methylation changes among the group of stretch participants. RESULTS: Eleven MCI participants (aerobic: 73% females; mean age 72.3 ± 6.6 years) and eight MCI participants (stretch: 75% female; mean age 70.6 ± 6.7 years) completed the training. Aerobic exercise-training was associated with increases in VO2max and with global hypo- and hypermethylation changes. The most notable finding was CpG hypomethylation within the body of the VPS52 gene (P = 5.4 × 10-26), a Golgi-associated protein, involved in intracellular protein trafficking including amyloid precursor protein. qRT-PCR confirmed a nearly twofold increased expression of VPS52. Other top findings with FDR q-value < 10-5, include hypomethylations of SCARB1 (8.8 × 10-25), ARTN (6.1 × 10-25), NR1H2 (2.1 × 10-18) and PPP2R5D (9.8 × 10-18). CONCLUSION: We conclude that genome-wide DNA methylation patterns is associated with exercise training-induced methylation changes. Identification of methylation changes around genes previously shown to interact with amyloid biology, intracellular protein trafficking, and lipoprotein regulations provide further support to the likely protective effect of exercise in MCI. Future studies in larger samples are needed to confirm our findings.

GENERATING SURVIVAL TIMES WITH TIME-VARYING COVARIATES USING THE LAMBERT W FUNCTION.

Simulation studies provide an important statistical tool in evaluating survival methods, requiring an appropriate data-generating process to simulate data for an underlying statistical model. Many studies with time-to-event outcomes use the Cox proportional hazard model. While methods for simulating such data with time-invariant predictors have been described, methods for simulating data with time-varying covariates are sorely needed. Here, we describe an approach for generating data for the Cox proportional hazard model with time-varying covariates when event times follow an Exponential or Weibull distribution. For each distribution, we derive a closed-form expression to generate survival times and link the time-varying covariates with the hazard function. We consider a continuous time-varying covariate measured at regular intervals over time, as well as time-invariant covariates, in generating time-to-event data under a number of scenarios. Our results suggest this method can lead to simulation studies with reliable and robust estimation of the association parameter in Cox-Weibull and Cox-Exponential models.