Marginal Regression Models with Varying Coefficients for Correlated Ordinal Data

This paper discusses marginal regression models for repeated or clustered ordinal measurements in which the coefficients of explanatory variables are allowed to vary as smooth functions of other covariates. We model the marginal response probabilities and the marginal pairwise association structure by two semiparametric regressions. To estimate the fixed parameters and varying coefficients in both models we derive an algorithm that is based on penalized generalized estimating equations. This allows to estimate the marginal model without specifying the entire distribution of the correlated categorical response variables. Our implementation of the estimation algorithm uses an orthonormal cubic spline basis that separates the estimated varying coefficients into a linear part and a smooth curvature part. By avoiding an additional backfitting step in the optimization procedure we are able to compute a robust approximation for the covariance matrix of the final estimate. We illustrate our method by an application to longitudinal data from a forest damage survey. We show how to model the dependence of damage state of beeches on non-linear trend functions and time-varying effects of age

Non- and semiparametric marginal regression models for ordinal response

We present a class of multivariate regression models for ordinal response variables in which the coefficients of the explanatory variables are allowed to vary as smooth functions of other variables. In the first part of the paper we consider a semiparametric cumulative regression model for a single ordinal outcome variable. A penalized maximum likelihood approach for estimating functions and parameters of interest is described. In the second part we explore a semiparametric marginal modeling framework appropriate for correlated ordinal responses. We model the marginal response probabilities and pairwise association structure by two semiparametric regressions. To estimate the model we derive an algorithm which is based on penalized generalized estimating equations. This nonparametric approach allows to estimate the marginal model without specifying the entire distribution of the correlated response. The methods are illustrated by two applications concerning the attitude toward smoking restrictions in the workplace and the state of damage in a Bavarian forest district

An application of isotonic longitudinal marginal regression to monitoring the healing process

This paper discusses marginal regression for repeated ordinal measurements that are isotonic over time. Such data are often observed in longitudinal studies on healing processes where, due to recovery, the status of patients only improves or stays the same. We show how this prior information can be used to construct appropriate and parsimoniously parametrized marginal models. As a second aspect, we also incorporate nonparametric fitting of covariate effects via a penalized quasi-likelihood or GEE approach. We illustrate our methods by an application to injuries from sporting activities

Additive, Dynamic and Multiplicative Regression

We survey and compare model-based approaches to regression for cross-sectional and longitudinal data which extend the classical parametric linear model for Gaussian responses in several aspects and for a variety of settings. Additive models replace the sum of linear functions of regressors by a sum of smooth functions. In dynamic or state space models, still linear in the regressors, coefficients are allowed to vary smoothly with time according to a Bayesian smoothness prior. We show that this is equivalent to imposing a roughness penalty on time-varying coefficients. Admitting the coefficients to vary with the values of other covariates, one obtains a class of varying-coefficient models (Hastie and Tibshirani, 1993), or in another interpretation, multiplicative models. The roughness penalty approach to non- and semiparametric modelling, together with Bayesian justifications, is used as a unifying and general framework for estimation. The methodological discussion is illustrated by some real data applications

Validation of the 30-Year Framingham Risk Score in a German Population-Based Cohort

The Framingham Risk Score to predict 30-year risk (FRS30y) of cardiovascular disease (CVD) constitutes an important tool for long-term risk prediction. However, due to its complex statistical properties and the paucity of large population-based cohorts with appropriate data, validation of the FRS30y is lacking. A population-based cohort from Southern Germany (N = 3110, 1516 (48.7%) women) was followed up for a median time of 29.5 [18.7, 31.2] years. Discrimination and calibration were assessed for the original, recalibrated and refitted FRS30y version. During follow up, 620 incident CVD events (214 in women) occurred. The FRS30y showed adequate discrimination (original and recalibrated version: Area under the curve (AUC): 78.4 for women and 74.9 for men) but overestimated actual CVD risk (original version: discordance 45.4% for women and 37.3% for men, recalibrated version: 37.6% and 28.6%, respectively). Refitting showed substantial improvement in neither discrimination nor calibration. The performance of FRS30y is adequate for long-term CVD risk prediction and could serve as an important tool in risk communication, especially for younger audiences

DNA methylation and lipid metabolism: an EWAS of 226 metabolic measures

BACKGROUND The discovery of robust and trans-ethnically replicated DNA methylation markers of metabolic phenotypes, has hinted at a potential role of epigenetic mechanisms in lipid metabolism. However, DNA methylation and the lipid compositions and lipid concentrations of lipoprotein sizes have been scarcely studied. Here, we present an epigenome-wide association study (EWAS) (N = 5414 total) of mostly lipid-related metabolic measures, including a fine profiling of lipoproteins. As lipoproteins are the main players in the different stages of lipid metabolism, examination of epigenetic markers of detailed lipoprotein features might improve the diagnosis, prognosis, and treatment of metabolic disturbances. RESULTS We conducted an EWAS of leukocyte DNA methylation and 226 metabolic measurements determined by nuclear magnetic resonance spectroscopy in the population-based KORA F4 study (N = 1662) and replicated the results in the LOLIPOP, NFBC1966, and YFS cohorts (N = 3752). Follow-up analyses in the discovery cohort included investigations into gene transcripts, metabolic-measure ratios for pathway analysis, and disease endpoints. We identified 161 associations (p~value \textless 4.7 × 10-10), covering 16 CpG sites at 11 loci and 57 metabolic measures. Identified metabolic measures were primarily medium and small lipoproteins, and fatty acids. For apolipoprotein B-containing lipoproteins, the associations mainly involved triglyceride composition and concentrations of cholesterol esters, triglycerides, free cholesterol, and phospholipids. All associations for HDL lipoproteins involved triglyceride measures only. Associated metabolic measure ratios, proxies of enzymatic activity, highlight amino acid, glucose, and lipid pathways as being potentially epigenetically implicated. Five CpG sites in four genes were associated with differential expression of transcripts in blood or adipose tissue. CpG sites in ABCG1 and PHGDH showed associations with metabolic measures, gene transcription,~and metabolic measure ratios and were additionally linked to obesity or previous myocardial infarction, extending previously reported observations. CONCLUSION Our study provides evidence of a link between DNA methylation and the lipid compositions and lipid concentrations of different lipoprotein size subclasses, thus offering in-depth insights into well-known associations of DNA methylation with total serum lipids. The results support detailed profiling of lipid metabolism to improve the molecular understanding of dyslipidemia and related disease mechanisms

Human gene expression sensitivity according to large scale meta-analysis

<p>Abstract</p> <p>Background</p> <p>Genes show different sensitivities in expression corresponding to various biological conditions. Systematical study of this concept is required because of its important implications in microarray analysis etc. J.H. Ohn et al. first studied this gene property with yeast transcriptional profiling data.</p> <p>Results</p> <p>Here we propose a calculation framework for gene expression sensitivity analysis. We also compared the functions, centralities and transcriptional regulations of the sensitive and robust genes. We found that the robust genes tended to be involved in essential cellular processes. Oppositely, the sensitive genes perform their functions diversely. Moreover while genes from both groups show similar geometric centrality by coupling them onto integrated protein networks, the robust genes have higher vertex degree and betweenness than that of the sensitive genes. An interesting fact was also found that, not alike the sensitive genes, the robust genes shared less transcription factors as their regulators.</p> <p>Conclusion</p> <p>Our study reveals different propensities of gene expression to external perturbations, demonstrates different roles of sensitive genes and robust genes in the cell and proposes the necessity of combining the gene expression sensitivity in the microarray analysis.</p

Discovery of Sexual Dimorphisms in Metabolic and Genetic Biomarkers

Metabolomic profiling and the integration of whole-genome genetic association data has proven to be a powerful tool to comprehensively explore gene regulatory networks and to investigate the effects of genetic variation at the molecular level. Serum metabolite concentrations allow a direct readout of biological processes, and association of specific metabolomic signatures with complex diseases such as Alzheimer's disease and cardiovascular and metabolic disorders has been shown. There are well-known correlations between sex and the incidence, prevalence, age of onset, symptoms, and severity of a disease, as well as the reaction to drugs. However, most of the studies published so far did not consider the role of sexual dimorphism and did not analyse their data stratified by gender. This study investigated sex-specific differences of serum metabolite concentrations and their underlying genetic determination. For discovery and replication we used more than 3,300 independent individuals from KORA F3 and F4 with metabolite measurements of 131 metabolites, including amino acids, phosphatidylcholines, sphingomyelins, acylcarnitines, and C6-sugars. A linear regression approach revealed significant concentration differences between males and females for 102 out of 131 metabolites (p-values<3.8 x 10(-4); Bonferroni-corrected threshold). Sex-specific genome-wide association studies (GWAS) showed genome-wide significant differences in beta-estimates for SNPs in the CPS1 locus (carbamoyl-phosphate synthase 1, significance level: p<3.8 x 10(-10); Bonferroni-corrected threshold) for glycine. We showed that the metabolite profiles of males and females are significantly different and, furthermore, that specific genetic variants in metabolism-related genes depict sexual dimorphism. Our study provides new important insights into sex-specific differences of cell regulatory processes and underscores that studies should consider sex-specific effects in design and interpretation

Mendelian inheritance of trimodal CpG methylation sites suggests distal cis-acting genetic effects.

Environmentally influenced phenotypes, such as obesity and insulin resistance, can be transmitted over multiple generations. Epigenetic modifications, such as methylation of DNA cytosine-guanine (CpG) pairs, may be carriers of inherited information. At the population level, the methylation state of such "heritable" CpG sites is expected to follow a trimodal distribution, and their mode of inheritance should be Mendelian. Using the Illumina Infinium 450 K DNA methylation array, we determined DNA CpG-methylation in blood cells from a family cohort 123 individuals of Arab ethnicity, including 18 elementary father-mother-child trios, we asked whether Mendelian inheritance of CpG methylation is observed, and most importantly, whether it is independent of any genetic signals. Using 40× whole genome sequencing, we therefore excluded all CpG sites with possibly confounding genetic variants (SNP) within the binding regions of the Illumina probes. We identified a total of 955 CpG sites that displayed a trimodal distribution and confirmed trimodality in a study of 1805 unrelated Caucasians. Of 955 CpG sites, 99.9% observed a strict Mendelian pattern of inheritance and had no SNP within +/-110 nucleotides of the CpG site by design. However, in 97% of these cases a distal cis-acting SNP within a +/-1 Mbp window was found that explained the observed CpG distribution, excluding the hypothesis of epigenetic inheritance for these clear-cut trimodal sites. Using power analysis, we showed that in 46% of all cases, the closest CpG-associated SNP was located more than 1000 bp from the CpG site. Our findings suggest that CpG methylation is maintained over larger genomic distances. Furthermore, nearly half of the SNPs associated with these trimodal sites were also associated with the expression of nearby genes (P = 4.08 × 10(-6)), implying a regulatory effect of these trimodal CpG sites

Higher Daily Air Temperature Is Associated with Shorter Leukocyte Telomere Length

[Image: see text] Higher air temperature is associated with increased age-related morbidity and mortality. To date, short-term effects of air temperature on leukocyte telomere length have not been investigated in an adult population. We aimed to examine the short-term associations between air temperature and leukocyte telomere length in an adult population-based setting, including two independent cohorts. This population-based study involved 5864 participants from the KORA F3 (2004–2005) and F4 (2006–2008) cohort studies conducted in Augsburg, Germany. Leukocyte telomere length was assessed by a quantitative PCR-based method. We estimated air temperature at each participant′s residential address through a highly resolved spatiotemporal model. We conducted cohort-specific generalized additive models to explore the short-term effects of air temperature on leukocyte telomere length at lags 0–1, 2–6, 0–6, and 0–13 days separately and pooled the estimates by fixed-effects meta-analysis. Our study found that between individuals, an interquartile range (IQR) increase in daily air temperature was associated with shorter leukocyte telomere length at lags 0–1, 2–6, 0–6, and 0–13 days (%change: −2.96 [−4.46; −1.43], −2.79 [−4.49; −1.07], −4.18 [−6.08; −2.25], and −6.69 [−9.04; −4.27], respectively). This meta-analysis of two cohort studies showed that between individuals, higher daily air temperature was associated with shorter leukocyte telomere length