Host traits, lifestyle and environment are associated with human skin bacteria.

BACKGROUND: The human skin offers diverse ecosystems for microbial symbionts. However, the factors shaping skin-microbiome interactions are still insufficiently characterized. This contrasts with the broader knowledge about factors influencing gut microbiota. OBJECTIVES: We aimed to investigate major patterns of association of host traits, lifestyle and environmental factors with skin bacteria in two German populations. METHODS: This is a cross-sectional study with 647 participants from two population-based German cohorts, PopGen (n = 294) and KORA FF4 (n = 353), totalling 1794 skin samples. The V1-V2 regions of the 16S ribosomal RNA (rRNA) gene were sequenced. Associations were tested with two bacterial levels, community (beta diversity) and 16S rRNA gene amplicon sequence variants (ASVs). RESULTS: We validated known associations of the skin microbiota with skin microenvironment, age, body mass index and sex. These factors were associated with beta diversity and abundance of ASVs in PopGen, which was largely replicated in KORA FF4. Most intriguingly, dietary macronutrients and total dietary energy were associated with several ASVs. ASVs were also associated with smoking, alcohol consumption, skin pH, skin type, transepidermal water loss, education and several environmental exposures, including hours spent outdoors. Associated ASVs included members of the genera Propionibacterium, Corynebacterium and Staphylococcus. CONCLUSIONS: We expand the current understanding of factors associated with the skin bacterial community. We show the association of diet with skin bacteria. Finally, we hypothesize that the skin microenvironment and host physiology would shape the skin bacterial community to a greater extent compared with a single skin physiological feature, lifestyle and environmental exposure.

A network-based conditional genetic association analysis of the human metabolome.

Background: Genome-wide association studies have identified hundreds of loci that influence a wide variety of complex human traits; however, little is known regarding the biological mechanism of action of these loci. The recent accumulation of functional genomics ("omics"), including metabolomics data, has created new opportunities for studying the functional role of specific changes in the genome. Functional genomic data are characterized by their high dimensionality, the presence of (strong) statistical dependency between traits, and, potentially, complex genetic control. Therefore, the analysis of such data requires specific statistical genetics methods. Results: To facilitate our understanding of the genetic control of omics phenotypes, we propose a trait-centered, network-based conditional genetic association (cGAS) approach for identifying the direct effects of genetic variants on omics-based traits. For each trait of interest, we selected from a biological network a set of other traits to be used as covariates in the cGAS. The network can be reconstructed either from biological pathway databases (a mechanistic approach) or directly from the data, using a Gaussian graphical model applied to the metabolome (a data-driven approach). We derived mathematical expressions that allow comparison of the power of univariate analyses with conditional genetic association analyses. We then tested our approach using data from a population-based Cooperative Health Research in the region of Augsburg (KORA) study (n = 1,784 subjects, 1.7 million single-nucleotide polymorphisms) with measured data for 151 metabolites. Conclusions: We found that compared to single-trait analysis, performing a genetic association analysis that includes biologically relevant covariates can either gain or lose power, depending on specific pleiotropic scenarios, for which we provide empirical examples. In the context of analyzed metabolomics data, the mechanistic network approach had more power compared to the data-driven approach. Nevertheless, we believe that our analysis shows that neither a prior-knowledge-only approach nor a phenotypic-data-only approach is optimal, and we discuss possibilities for improvement.

An epigenome-wide association study meta-analysis of educational attainment.

The epigenome is associated with biological factors, such as disease status, and environmental factors, such as smoking, alcohol consumption and body mass index. Although there is a widespread perception that environmental influences on the epigenome are pervasive and profound, there has been little evidence to date in humans with respect to environmental factors that are biologically distal. Here we provide evidence on the associations between epigenetic modifications-in our case, CpG methylation-and educational attainment (EA), a biologically distal environmental factor that is arguably among the most important life-shaping experiences for individuals. Specifically, we report the results of an epigenome-wide association study meta-analysis of EA based on data from 27 cohort studies with a total of 10 767 individuals. We find nine CpG probes significantly associated with EA. However, robustness analyses show that all nine probes have previously been found to be associated with smoking. Only two associations remain when we perform a sensitivity analysis in the subset of never-smokers, and these two probes are known to be strongly associated with maternal smoking during pregnancy, and thus their association with EA could be due to correlation between EA and maternal smoking. Moreover, the effect sizes of the associations with EA are far smaller than the known associations with the biologically proximal environmental factors alcohol consumption, body mass index, smoking and maternal smoking during pregnancy. Follow-up analyses that combine the effects of many probes also point to small methylation associations with EA that are highly correlated with the combined effects of smoking. If our findings regarding EA can be generalized to other biologically distal environmental factors, then they cast doubt on the hypothesis that such factors have large effects on the epigenome.

Heritability of young- and old-onset ischaemic stroke.

BACKGROUND AND PURPOSE: Although the genetic contribution to stroke risk is well known, it remains unclear if young-onset stroke has a stronger genetic contribution than old-onset stroke. This study aims to compare the heritability of ischaemic stroke risk between young and old, using common genetic variants from whole-genome array data in population-based samples. METHODS: This analysis included 4050 ischaemic stroke cases and 5765 controls from six study populations of European ancestry; 47% of cases were young-onset stroke (age < 55 years). To quantify the heritability for stroke risk in these unrelated individuals, the pairwise genetic relatedness was estimated between individuals based on their whole-genome array data using a mixed linear model. Heritability was estimated separately for young-onset stroke and old-onset stroke (age ≥ 55 years). RESULTS: Heritabilities for young-onset stroke and old-onset stroke were estimated at 42% (±8%, P < 0.001) and 34% (±10%, P < 0.001), respectively. CONCLUSIONS: Our data suggest that the genetic contribution to the risk of stroke may be higher in young-onset ischaemic stroke, although the difference was not statistically significant.

Genetic Geostatistical Framework for Spatial Analysis of Fine-Scale Genetic Heterogeneity in Modern Populations: Results from the KORA Study.

Aiming to investigate fine-scale patterns of genetic heterogeneity in modern humans from a geographic perspective, a genetic geostatistical approach framed within a geographic information system is presented. A sample collected for prospective studies in a small area of southern Germany was analyzed. None indication of genetic heterogeneity was detected in previous analysis. Socio-demographic and genotypic data of German citizens were analyzed (212 SNPs; n = 728). Genetic heterogeneity was evaluated with observed heterozygosity (H O ). Best-fitting spatial autoregressive models were identified, using socio-demographic variables as covariates. Spatial analysis included surface interpolation and geostatistics of observed and predicted patterns. Prediction accuracy was quantified. Spatial autocorrelation was detected for both socio-demographic and genetic variables. Augsburg City and eastern suburban areas showed higher H O values. The selected model gave best predictions in suburban areas. Fine-scale patterns of genetic heterogeneity were observed. In accordance to literature, more urbanized areas showed higher levels of admixture. This approach showed efficacy for detecting and analyzing subtle patterns of genetic heterogeneity within small areas. It is scalable in number of loci, even up to whole-genome analysis. It may be suggested that this approach may be applicable to investigate the underlying genetic history that is, at least partially, embedded in geographic data.

Increased amino acids levels and the risk of developing of hypertriglyceridemia in a 7-year follow-up.

BACKGROUND: Recently, five branched-chain and aromatic amino acids were shown to be associated with the risk of developing type 2 diabetes (T2D). AIM: We set out to examine whether amino acids are also associated with the development of hypertriglyceridemia. MATERIALS AND METHODS: We determined the serum amino acids concentrations of 1,125 individuals of the KORA S4 baseline study, for which follow-up data were available also at the KORA F4 7 years later. After exclusion for hypertriglyceridemia (defined as having a fasting triglyceride level above 1.70 mmol/L) and diabetes at baseline, 755 subjects remained for analyses. RESULTS: Increased levels of leucine, arginine, valine, proline, phenylalanine, isoleucine and lysine were significantly associated with an increased risk of hypertriglyceridemia. These associations remained significant when restricting to those individuals who did not develop T2D in the 7-year follow-up. The increase per standard deviation of amino acid level was between 26 and 40 %. CONCLUSIONS: Seven amino acids were associated with an increased risk of developing hypertriglyceridemia after 7 years. Further studies are necessary to elucidate the complex role of these amino acids in the pathogenesis of metabolic disorders.

Integrative genetic and metabolite profiling analysis suggests altered phosphatidylcholine metabolism in asthma.

BACKGROUND: Genome-wide association studies (GWAS) have identified many risk loci for asthma, but effect sizes are small, and in most cases, the biological mechanisms are unclear. Targeted metabolite quantification that provides information about a whole range of pathways of intermediary metabolism can help to identify biomarkers and investigate disease mechanisms. Combining genetic and metabolic information can aid in characterizing genetic association signals with high resolution. This work aimed to investigate the interrelation of current asthma, candidate asthma risk alleles and a panel of metabolites. METHODS: We investigated 151 metabolites, quantified by targeted mass spectrometry, in fasting serum of asthmatic and nonasthmatic individuals from the population-based KORA F4 study (N = 2925). In addition, we analysed effects of single-nucleotide polymorphisms (SNPs) at 24 asthma risk loci on these metabolites. RESULTS: Increased levels of various phosphatidylcholines and decreased levels of various lyso-phosphatidylcholines were associated with asthma. Likewise, asthma risk alleles from the PDED3 and MED24 genes at the asthma susceptibility locus 17q21 were associated with increased concentrations of various phosphatidylcholines with consistent effect directions. CONCLUSIONS: Our study demonstrated the potential of metabolomics to infer asthma-related biomarkers by the identification of potentially deregulated phospholipids that associate with asthma and asthma risk alleles.

Gene-PUFA interactions and obesity risk.

Although there are indications for modulatory effects of PUFA on associations between SNP and obesity risk, scientific evidence in human subjects is still scarce. The present analyses investigated interaction effects between SNP in candidate genes for obesity and PUFA in erythrocyte membranes on obesity risk. Within the second Bavarian Food Consumption Survey (cross-sectional, population-based), 568 adults provided blood samples. Fatty acid composition of erythrocyte membranes was analysed by means of GC. Genotyping was performed for twenty-one genes, including cytokines, adipokines, neurotransmitters and transcription factors. In addition, plasma IL-6 concentrations were analysed. For the statistical analysis, a logistic regression model assuming additive genetic effects was chosen. About 20 % of the study participants were classified as obese (BMI ≥ 30 kg/m(2)). Several significant gene-PUFA interactions were found, indicating regulatory effects of PUFA by gene variants of IL-2, IL-6, IL-18, TNF receptor family member 1B and 21, leptin receptor and adiponectin on obesity risk. After stratification by genotype, the strongest effects were found for rs2069779 (IL-2) and all tested PUFA as well as for rs1800795 (IL-6) and linoleic or arachidonic acid. The obesity risk of minor allele carriers significantly decreased with increasing fatty acid content. The genetic PUFA-IL-6 interaction was also reflected in plasma IL-6 concentrations. If replicated in a prospective study with sufficient statistical power, the results would indicate a beneficial effect of high PUFA supply for a substantial proportion of the population with respect to obesity risk.

Polymorphisms in the receptor for GDNF (RET) are not associated with Parkinson's disease in Southern Germany.

The aetiology of the selective neurodegeneration in Parkinson's disease (PD) is still unknown. Neurotrophic factors, e.g. glial cell line-derived neurotrophic factor (GDNF), have been shown to promote survival of dopaminergic neurons. Interestingly, aged mice lacking GDNF-receptor (RET) in their dopaminergic neurons show a phenotype similar to presymptomatic PD. We therefore were interested whether polymorphisms in the RET gene were associated with increased PD risk. Analyzing 25 SNPs in the RET region in 340 Southern German PD patients and 340 age- and sex-matched controls from Southern Germany (KORA S4), we did not find any significant association with PD, suggesting that the equilibrium of trophic factors in PD might be disturbed on other levels than the genomic encoding.

Replication of restless legs syndrome loci in three European populations.

BACKGROUND: Restless legs syndrome (RLS) is associated with common variants in three intronic and intergenic regions in MEIS1, BTBD9, and MAP2K5/LBXCOR1 on chromosomes 2p, 6p and 15q. METHODS: Our study investigated these variants in 649 RLS patients and 1230 controls from the Czech Republic (290 cases and 450 controls), Austria (269 cases and 611 controls) and Finland (90 cases and 169 controls). Ten single nucleotide polymorphisms (SNPs) within the three genomic regions were selected according to the results of previous genome-wide scans. Samples were genotyped using Sequenom platforms. RESULTS: We replicated associations for all loci in the combined samples set (rs2300478 in MEIS1, p = 1.26 x 10(-5), odds ratio (OR) = 1.47, rs3923809 in BTBD9, p = 4.11 x 10(-5), OR = 1.58 and rs6494696 in MAP2K5/LBXCOR1, p = 0.04764, OR = 1.27). Analysing only familial cases against all controls, all three loci were significantly associated. Using sporadic cases only, we could confirm the association only with BTBD9. CONCLUSION: Our study shows that variants in these three loci confer consistent disease risks in patients of European descent. Among the known loci, BTBD9 seems to be the most consistent in its effect on RLS across populations and is also most independent of familial clustering.

Variants of the PPARG, IGF2BP2, CDKAL1, HHEX, and TCF7L2 genes confer risk of type 2 diabetes independently of BMI in the German KORA studies.

Genome-wide association (GWA) studies identified novel gene variants that are associated with type 2 diabetes. However, results were not always consistent across different populations. Thus, the aims of this study were (i) to replicate findings from previous GWA studies in mainly Northern European populations using data from the German KORA 500 K diabetes project and (ii) to assess the impact of BMI on associations between single nucleotide polymorphisms (SNPs) and type 2 diabetes. The KORA 500 K diabetes project includes 433 cases with validated type 2 diabetes and 1 438 nondiabetic controls from two population-based KORA surveys. Genotyping was performed using the Affymetrix GeneChip Human Mapping 500 K Array Set. We investigated associations between SNPs and type 2 diabetes in 10 genes that have been reported to increase the risk of type 2 diabetes or were in complete or near-complete linkage disequilibrium with these variants. SNPs in the CDKAL1 gene showed the strongest association with type 2 diabetes [range of age and sex-adjusted odds ratios (OR): 1.30-1.39, p-values 0.0008-0.0004]. In addition, we found evidence for association of SNPs in the genes PPARG, IGF2BP2, HHEX, TCF7L2, and FTO with type 2 diabetes in the same directions as previously described (p<0.05), but not for WFS1, CDKN2A/B, KCNJ11, or EXT2. Adjustment for BMI slightly strengthened the link between CDKAL1 and type 2 diabetes, but had almost no impact on the other associations. We conclude that gene variants of CDKAL1, PPARG, IGF2BP2, HHEX, TCF7L2, and FTO predispose to type 2 diabetes in the German KORA 500 K study population. These associations appear to be independent of BMI.

Genetic variants in the leukemia-associated Rho guanine nucleotide exchange factor (ARHGEF12) gene are not associated with T2DM and related parameters in Caucasians (KORA study).

OBJECTIVE: The aim of our study was to determine the variant pattern of the leukemia-associated Rho guanine nucleotide exchange factor (LARG, or ARHGEF12) gene and investigate whether LARG variants are associated with diabetes mellitus type 2 (T2DM), the metabolic syndrome (MetS), or related parameters such as insulin sensitivity in German Caucasians. DESIGN: We analyzed single nucleotide polymorphisms (SNPs) in the LARG gene in the 55-74-year-old individuals of the population-based German Caucasian Cooperative Health Research in the region of Augsberg (KORA) survey 4 (S4). METHODS: Sequencing of Tyr1306Cys, which was of functional relevance in Pima Indians, in 48 randomly selected individuals and genotyping of 11 additional LARG SNPs in 1653 subjects were performed. Four linkage disequilibrium (LD) blocks (r(2)> or =0.8) were established and each block was statistically analyzed for association with metabolic traits. The association with T2DM and the MetS was analyzed by logistic regression in 1462 subjects, and HOMA-IR (homeostasis model assessment of insulin resistance) as a measure of insulin sensitivity was analyzed by the Kruskal-Wallis test in 1346 fasting subjects. RESULTS: The polymorphism Tyr1306Cys, which was significantly associated with insulin sensitivity in Pima Indians, was not found in the KORA S4 population. Statistical analysis yielded no significant associations (P>0.05) between the analyzed LARG variants and T2DM, the MetS, or related parameters such as insulin sensitivity. CONCLUSIONS: Caucasian individuals and Pima Indians differ in their genetic variance pattern in the LARG gene region. There is no evidence in the Caucasian KORA study that variants of the LARG gene confer susceptibility for T2DM, insulin sensitivity, or the MetS.

[Biobanks]. / Biobanken.

Biobanks are an important tool of genetic epidemiology, which investigates the role of genetic factors and their interaction with environmental factors (in a broad meaning) for the occurrence of diseases in human populations. Its aim is to understand the influence of genetics on the development of diseases, their course and the clinical implications, with the final goal to improve prevention, diagnostics and therapy. The extraordinary development of genetics in the last decades - with respect to the understanding of the meaning of genes for human health, as well as the availability of cost-effective high throughput methods in the lab, has opened enormous opportunities to study genetic factors. In addition, access to large samples of patients or from the population is needed. This can be realized via biobanks. Large biobanks with 500,000 or more participants are being established or planned in the UK, Japan, Australia, Sweden and the US. However, in Germany only two smaller activities are ongoing, KORA-gen in the south and POPGEN in the north. Possibilities to reach larger numbers for Germany, based on existing cohorts or disease networks, are discussed. For the implementation and use of biobanks, stringent ethical boundary conditions have to be taken into account. The opinion of the German National Ethics Council on Biobanks for Research as well as the new recommendations of the Telematic Platform (TMF), which has been developed in close collaboration with the Data Protection Officers, improve transparency and legal security.

KORA-gen--resource for population genetics, controls and a broad spectrum of disease phenotypes.

KORA-gen is a resource for genetic epidemiological research, based on the KORA platform (Cooperative Health Research in the Region of Augsburg). Biosamples and phenotypic characteristics as well as environmental parameters of 18,000 adults from Augsburg and the surrounding counties are available. The age range of the participants was 25 to 74 years of recruitment and is 30 to 90 years in 2005. KORA-gen can be used by external partners. Interested parties can inform themselves interactively via internet about the available data and the rules of access. The genotypic data base is a common resource of all partners.

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 in which, due to recovery, the status of patients only improves or remains 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 general estimating equation approach. We illustrate our methods by an application to sports-related injuries.