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.

Genetic correlations of psychiatric traits with body composition and glycemic traits are sex- and age-dependent.

Body composition is often altered in psychiatric disorders. Using genome-wide common genetic variation data, we calculate sex-specific genetic correlations amongst body fat %, fat mass, fat-free mass, physical activity, glycemic traits and 17 psychiatric traits (up to N = 217,568). Two patterns emerge: (1) anorexia nervosa, schizophrenia, obsessive-compulsive disorder, and education years are negatively genetically correlated with body fat % and fat-free mass, whereas (2) attention-deficit/hyperactivity disorder (ADHD), alcohol dependence, insomnia, and heavy smoking are positively correlated. Anorexia nervosa shows a stronger genetic correlation with body fat % in females, whereas education years is more strongly correlated with fat mass in males. Education years and ADHD show genetic overlap with childhood obesity. Mendelian randomization identifies schizophrenia, anorexia nervosa, and higher education as causal for decreased fat mass, with higher body fat % possibly being a causal risk factor for ADHD and heavy smoking. These results suggest new possibilities for targeted preventive strategies.

Genome-wide meta-analysis of 241,258 adults accounting for smoking behaviour identifies novel loci for obesity traits.

Few genome-wide association studies (GWAS) account for environmental exposures, like smoking, potentially impacting the overall trait variance when investigating the genetic contribution to obesity-related traits. Here, we use GWAS data from 51,080 current smokers and 190,178 nonsmokers (87% European descent) to identify loci influencing BMI and central adiposity, measured as waist circumference and waist-to-hip ratio both adjusted for BMI. We identify 23 novel genetic loci, and 9 loci with convincing evidence of gene-smoking interaction (GxSMK) on obesity-related traits. We show consistent direction of effect for all identified loci and significance for 18 novel and for 5 interaction loci in an independent study sample. These loci highlight novel biological functions, including response to oxidative stress, addictive behaviour, and regulatory functions emphasizing the importance of accounting for environment in genetic analyses. Our results suggest that tobacco smoking may alter the genetic susceptibility to overall adiposity and body fat distribution.

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.

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.

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.

Genetic and clinical correlates of early-outgrowth colony-forming units.

BACKGROUND: Several bone marrow-derived cell populations may have angiogenic activity, including cells termed endothelial progenitor cells. Decreased numbers of circulating angiogenic cell populations have been associated with increased cardiovascular risk. However, few data exist from large, unselected samples, and the genetic determinants of these traits are unclear. METHODS AND RESULTS: We examined the clinical and genetic correlates of early-outgrowth colony-forming units (CFUs) in 1799 participants of the Framingham Heart Study (mean age, 66 years; 54% women). Among individuals without cardiovascular disease (n = 1612), CFU number was inversely related to advanced age (P = 0.004), female sex (P = 0.04), and triglycerides (P = 0.008) and positively related to hormone replacement (P = 0.008) and statin therapy (P = 0.027) in stepwise multivariable analyses. Overall, CFU number was inversely related to the Framingham risk score (P = 0.01) but not with prevalent cardiovascular disease. In genome-wide association analyses in the entire sample, polymorphisms were associated with CFUs at the MOSC1 locus (P = 3.3 × 10(-7)) and at the SLC22A3-LPAL2-LPA locus (P = 4.9 × 10(-7)), a previously replicated susceptibility locus for myocardial infarction. Furthermore, alleles at the SLC22A3-LPAL2-LPA locus that were associated with decreased CFUs were also related to increased risk of myocardial infarction (P = 1.1 × 10(-4)). CONCLUSIONS: In a community-based sample, early-outgrowth CFUs are inversely associated with select cardiovascular risk factors. Furthermore, genetic variants at the SLC22A3-LPAL2-LPA locus are associated with both decreased CFUs and an increased risk of myocardial infarction. These findings are consistent with the hypothesis that decreased circulating angiogenic cell populations promote susceptibility to myocardial infarction.