Telomere maintenance has emerged as an important molecular feature with impacts on adult glioma susceptibility and prognosis. Whether longer or shorter leukocyte telomere length (LTL) is associated with glioma risk remains elusive and is often confounded by the effects of age and patient treatment. We sought to determine if genotypically-estimated LTL is associated with glioma risk and if inherited single nucleotide polymorphisms (SNPs) that are associated with LTL are glioma risk factors. Using a Mendelian randomization approach, we assessed differences in genotypically-estimated relative LTL in two independent glioma case-control datasets from the UCSF Adult Glioma Study (652 patients and 3735 controls) and The Cancer Genome Atlas (478 non-overlapping patients and 2559 controls). LTL estimates were based on a weighted linear combination of subject genotype at eight SNPs, previously associated with LTL in the ENGAGE Consortium Telomere Project. Mean estimated LTL was 31bp (5.7%) longer in glioma patients than controls in discovery analyses (P = 7.82 × 10-8) and 27bp (5.0%) longer in glioma patients than controls in replication analyses (1.48 × 10-3). Glioma risk increased monotonically with each increasing septile of LTL (O.R.=1.12; P = 3.83 × 10-12). Four LTL-associated SNPs were significantly associated with glioma risk in pooled analyses, including those in the telomerase component genes TERC (O.R.=1.14; 95% C.I.=1.03-1.28) and TERT (O.R.=1.39; 95% C.I.=1.27-1.52), and those in the CST complex genes OBFC1 (O.R.=1.18; 95% C.I.=1.05-1.33) and CTC1 (O.R.=1.14; 95% C.I.=1.02-1.28). Future work is needed to characterize the role of the CST complex in gliomagenesis and further elucidate the complex balance between ageing, telomere length, and molecular carcinogenesis.

The majority of the heritability of coronary artery disease (CAD) remains unexplained, despite recent successes of genome-wide association studies (GWAS) in identifying novel susceptibility loci. Integrating functional genomic data from a variety of sources with a large-scale meta-analysis of CAD GWAS may facilitate the identification of novel biological processes and genes involved in CAD, as well as clarify the causal relationships of established processes. Towards this end, we integrated 14 GWAS from the CARDIoGRAM Consortium and two additional GWAS from the Ottawa Heart Institute (25,491 cases and 66,819 controls) with 1) genetics of gene expression studies of CAD-relevant tissues in humans, 2) metabolic and signaling pathways from public databases, and 3) data-driven, tissue-specific gene networks from a multitude of human and mouse experiments. We not only detected CAD-associated gene networks of lipid metabolism, coagulation, immunity, and additional networks with no clear functional annotation, but also revealed key driver genes for each CAD network based on the topology of the gene regulatory networks. In particular, we found a gene network involved in antigen processing to be strongly associated with CAD. The key driver genes of this network included glyoxalase I (GLO1) and peptidylprolyl isomerase I (PPIL1), which we verified as regulatory by siRNA experiments in human aortic endothelial cells. Our results suggest genetic influences on a diverse set of both known and novel biological processes that contribute to CAD risk. The key driver genes for these networks highlight potential novel targets for further mechanistic studies and therapeutic interventions. © 2014 Mäkinen et al.

© 2015 American Heart Association, Inc. RATIONALE:: Coronary Artery Disease (CAD) is a critical determinant of morbidity and mortality. Previous studies have identified several cardiovascular disease (CVD) risk factors, which may partly arise from a shared genetic basis with CAD, and thus be useful for discovery of CAD genes. OBJECTIVE:: We aimed to improve discovery of CAD genes, and inform the etiologic relationship between CAD and several CVD risk factors using a shared polygenic signal-informed statistical framework. METHODS AND RESULTS:: Using genome-wide association studies (GWAS) summary statistics and shared polygenic pleiotropy-informed conditional and conjunctional false discovery rate (FDR) methodology, we systematically investigated genetic overlap between CAD and 8 traits related to CVD risk factors: low density lipoprotein (LDL) cholesterol, high density lipoprotein (HDL) cholesterol, triglycerides (TG), type 2 diabetes (T2D), C-reactive protein (CRP), body mass index (BMI), systolic blood pressure (SBP) and type 1 diabetes (T1D). We found significant enrichment of single nucleotide polymorphisms (SNPs) associated with CAD as a function of their association with LDL, HDL, TG, T2D, CRP, BMI, SBP and T1D. Applying the conditional FDR method to the enriched phenotypes, we identified 67 novel loci associated with CAD (overall conditional FDR < 0.01). Further, we identified 53 loci with significant effects in both CAD and at least one of LDL, HDL, TG, T2D, CRP, SBP and T1D. CONCLUSIONS:: The observed polygenic overlap between CAD and cardio-metabolic risk factors indicates an etiological relation that warrants further investigation. The new gene loci identified implicate novel genetic mechanisms related to CAD.

© The Author 2017. Published by Oxford University Press. Resting heart rate is a heritable trait, and an increase in heart rate is associated with increased mortality risk. Genome-wide association study analyses have found loci associated with resting heart rate, at the time of our study these loci explained 0.9% of the variation. This study aims to discover new genetic loci associated with heart rate from Exome Chip meta-analyses.Heart rate was measured from either elecrtrocardiograms or pulse recordings. We meta-analysed heart rate association results from 104 452 European-ancestry individuals from 30 cohorts, genotyped using the Exome Chip. Twenty-four variants were selected for follow-up in an independent dataset (UK Biobank, N = 134 251). Conditional and gene-based testing was undertaken, and variants were investigated with bioinformatics methods.We discovered five novel heart rate loci, and one new independent low-frequency non-synonymous variant in an established heart rate locus (KIAA1755). Lead variants in four of the novel loci are non-synonymous variants in the genes C10orf71, DALDR3, TESK2 and SEC31B. The variant at SEC31B is significantly associated with SEC31B expression in heart and tibial nerve tissue. Further candidate genes were detected from long-range regulatory chromatin interactions in heart tissue (SCD, SLF2 and MAPK8). We observed significant enrichment in DNase I hypersensitive sites in fetal heart and lung. Moreover, enrichment was seen for the first time in human neuronal progenitor cells (derived from embryonic stem cells) and fetal muscle samples by including our novel variants.Our findings advance the knowledge of the genetic architecture of heart rate, and indicate new candidate genes for follow-up functional studies.