Global Gene Expression Profiling of Body-Mass Index in Middle-Aged Danish Twins

Objective: The body mass index (BMI) measured as weight in relation to height is an important monitor for obesity and diabetes, with individual variation under control by genetic and environmental factors. In transcriptome-wide association studies on BMI, the genetic contribution calls for controlling of genetic confounding that affects both BMI and gene expression. We performed a global gene expression profiling of BMI on peripheral blood cells using monozygotic twins for efficient handling of genetic make-ups. Methods: We applied a generalized association method to genome-wide gene expression data on 229 pairs of monozygotic twins (age 56-80 years) for detecting diverse patterns of correlation between BMI and gene expression. Results: We detected seven probes associated with BMI with p<1e-04, among them two probes with p<1e-05 (p=2.83e-06 AAK1; p=7.83e-06 LILRA3). In total, the analysis found 1579 probes with nominal p<0.05. Biological pathway analysis of enriched pathways found 50 KEGG and 45 Reactome pathways (FDR<0.05). The identified top functional pathways included immune function, JAK-STAT signalling, insulin signalling and regulation of energy metabolism. Conclusion: This transcriptome-wide association study using monozygotic twins and generalized correlation identified differentially expressed genes and a broad spectrum of enriched biological pathways that may implicate metabolic health

Novel DNA methylation marker discovery by assumption-free genome-wide association analysis of cognitive function in twins

Privileged by rapid increase in available epigenomic data, epigenome-wide association studies (EWAS) are to make a profound contribution to understand the molecular mechanism of DNA methylation in cognitive aging. Current statistical methods used in EWAS are dominated by models based on multiple assumptions, for example, linear relationship between molecular profiles and phenotype, normal distribution for the methylation data and phenotype. In this study, we applied an assumption-free method, the generalized correlation coefficient (GCC), and compare it to linear models, namely the linear mixed model and kinship model. We use DNA methylation associated with a cognitive score in 400 and 206 twins as discovery and replication samples respectively. DNA methylation associated with cognitive function using GCC, linear mixed model, and kinship model, identified 65 CpGs (p < 1e-04) from discovery sample displaying both nonlinear and linear correlations. Replication analysis successfully replicated 9 of these top CpGs. When combining results of GCC and linear models to cover diverse patterns of relationships, we identified genes like KLHDC4, PAPSS2, and MRPS18B as well as pathways including focal adhesion, axon guidance, and some neurological signaling. Genomic region-based analysis found 15 methylated regions harboring 11 genes, with three verified in gene expression analysis, also the 11 genes were related to top functional clusters including neurohypophyseal hormone and maternal aggressive behaviors. The GCC approach detects valuable methylation sites missed by traditional linear models. A combination of methylation markers from GCC and linear models enriched biological pathways sensible in neurological function that could implicate cognitive performance and cognitive aging.Peer reviewe

Genetic and environmental determinants of O6-methylguanine DNA-methyltransferase (MGMT) gene methylation: a 10-year longitudinal study of Danish twins

Background: Epigenetic inactivation of O6-methylguanine DNA-methyltransferase (MGMT) is associated with increased sensitivity to alkylating chemotherapeutic agents in glioblastoma patients. The genetic background underlying MGMT gene methylation may explain individual differences in treatment response and provide a clue to a personalized treatment strategy. Making use of the longitudinal twin design, we aimed, for the first time, to estimate the genetic contributions to MGMT methylation in a Danish twin cohort. Methods: DNA-methylation from whole blood (18 monozygotic (MZ) and 25 dizygotic (DZ) twin pairs) repeated 10 years apart from the Longitudinal Study of Aging Danish Twins (LSADT) were used to search for genetic and environmental contributions to DNA-methylation at 170 CpG sites of across the MGMT gene. Both univariate and bivariate twin models were applied. The intraclass correlations, performed on cross-sectional data (246 MZ twin pairs) from an independent study population, the Middle-Aged Danish Twins (MADT), were used to assess the genetic influence at each CpG site of MGMT for replication. Results: Univariate twin model revealed twelve CpG sites showing significantly high heritability at intake (wave 1, h2 > 0.43), and seven CpG sites with significant heritability estimates at end of follow-up (wave 2, h2 > 0.5). There were six significant CpG sites, located at the gene body region, that overlapped among the two waves (h2 > 0.5), of which five remained significant in the bivariate twin model, which was applied to both waves. Within MZ pair correlation in these six CpGs from MADT demarks top level of genetic influence. There were 11 CpGs constantly have substantial common environmental component over the 10 years. Conclusions: We have identified 6 CpG sites linked to the MGMT gene with strong and persistent genetic control based on their DNA methylation levels. The genetic basis of MGMT gene methylation could help to explain individual differences in glioblastoma treatment response and most importantly, provide references for mapping the methylation Quantitative Trait Loci (meQTL) underlying the genetic regulation.Peer reviewe

Global Gene Expression Profiling and Transcription Factor Network Analysis of Cognitive Aging in Monozygotic Twins

Cognitive aging is one of the major problems worldwide, especially as people get older. This study aimed to perform global gene expression profiling of cognitive function to identify associated genes and pathways and a novel transcriptional regulatory network analysis to identify important regulons. We performed single transcript analysis on 400 monozygotic twins using an assumption-free generalized correlation coefficient (GCC), linear mixed-effect model (LME) and kinship model and identified six probes (one significant at the standard FDR < 0.05 while the other results were suggestive with 0.18 ≤ FDR ≤ 0.28). We combined the GCC and linear model results to cover diverse patterns of relationships, and meaningful and novel genes like APOBEC3G, H6PD, SLC45A1, GRIN3B, and PDE4D were detected. Our exploratory study showed the downregulation of all these genes with increasing cognitive function or vice versa except the SLC45A1 gene, which was upregulated with increasing cognitive function. Linear models found only H6PD and SLC45A1, the other genes were captured by GCC. Significant functional pathways (FDR < 3.95e-10) such as focal adhesion, ribosome, cysteine and methionine metabolism, Huntington's disease, eukaryotic translation elongation, nervous system development, influenza infection, metabolism of RNA, and cell cycle were identified. A total of five regulons (FDR< 1.3e-4) were enriched in a transcriptional regulatory analysis in which CTCF and REST were activated and SP3, SRF, and XBP1 were repressed regulons. The genome-wide transcription analysis using both assumption-free GCC and linear models identified important genes and biological pathways implicated in cognitive performance, cognitive aging, and neurological diseases. Also, the regulatory network analysis revealed significant activated and repressed regulons on cognitive function.Peer reviewe

Popping pills in youth elite sports - fact or fiction?:A 36-week prospective cohort study of analgesic use in 1195 youth elite athletes and student controls

OBJECTIVE: To investigate analgesic use in a cohort of Danish youth elite athletes, and compare weekly analgesic use over 36 weeks to student controls. We also investigated and compared reasons for analgesic use and types of analgesics used. DESIGN: Prospective cohort study. METHODS: 690 youth elite athletes (44% females) and 505 student controls (59% females) (age 15-20 years) provided weekly reports on analgesic use over 36 weeks. We asked about number of days with analgesic use, reasons for use, and types of analgesics used. Prevalence and frequency of analgesic use was compared between youth elite athletes and student controls using mixed effects logistic regression and mixed effects Poisson regression models. Reasons for and types of analgesics used was compared between groups using Chi-square tests. Subgroup analyses were performed, stratified by sex. RESULTS: Overall, athletes had lower odds of analgesic use (OR 0.78, 95% CI 0.64 to 0.95) compared with student controls. The overall usage rate was similar between the groups (IRR 1.04, 95% CI 0.99 to 1.11). Subgroup analyses suggested no statistically significant differences in the odds of analgesic use. Significantly more athletes reported using analgesics to prevent or treat pain or injury in relation to sports participation and to use topical gels compared with student controls. CONCLUSION: Participating in youth elite sports was associated with lower odds of analgesic use compared to student controls, but usage rate was similar between the groups. Reasons for use and types of analgesics use differed between athletes and student controls. </p

Jllumina - A comprehensive Java-based API for statistical Illumina Infinium HumanMethylation450 and Infinium MethylationEPIC BeadChip data processing

Measuring differential methylation of the DNA is the nowadays most common approach to linking epigenetic modifications to diseases (called epigenome-wide association studies, EWAS). For its low cost, its efficiency and easy handling, the Illumina HumanMethylation450 BeadChip and its successor, the Infinium MethylationEPIC BeadChip, is the by far most popular techniques for conduction EWAS in large patient cohorts. Despite the popularity of this chip technology, raw data processing and statistical analysis of the array data remains far from trivial and still lacks dedicated software libraries enabling high quality and statistically sound downstream analyses. As of yet, only R-based solutions are freely available for low-level processing of the Illumina chip data. However, the lack of alternative libraries poses a hurdle for the development of new bioinformatic tools, in particular when it comes to web services or applications where run time and memory consumption matter, or EWAS data analysis is an integrative part of a bigger framework or data analysis pipeline. We have therefore developed and implemented Jllumina, an open-source Java library for raw data manipulation of Illumina Infinium HumanMethylation450 and Infinium MethylationEPIC BeadChip data, supporting the developer with Java functions covering reading and preprocessing the raw data, down to statistical assessment, permutation tests, and identification of differentially methylated loci. Jllumina is fully parallelizable and publicly available at http://dimmer.compbio.sdu.dk/download.htm