A Bayesian hierarchical model for improving measurement of 5mC and 5hmC levels: Toward revealing associations between phenotypes and methylation states.

5-hydroxymethylcytosine (5hmC) is a methylation state linked with gene regulation, commonly found in cells of the central nervous system. 5hmC is associated with demethylation of cytosines from 5-methylcytosine (5mC) to the unmethylated state. The presence of 5hmC can be inferred by a paired experiment involving bisulfite and oxidation-bisulfite treatments on the same sample, followed by a methylation assay using a platform such as the Illumina Infinium MethylationEPIC BeadChip (EPIC). Existing methods for analysis of the resulting EPIC data are not ideal. Most approaches ignore the correlation between the two experiments and any imprecision associated with DNA damage from the additional treatment. Estimates of 5mC/5hmC levels free from these limitations are desirable to reveal associations between methylation states and phenotypes. We propose a hierarchical Bayesian method called Constrained HYdroxy Methylation Estimation (CHYME) to simultaneously estimate 5mC/5hmC signals as well as any associations between these signals and covariates or phenotypes, while accounting for the potential impact of DNA damage and dependencies induced by the experimental design. Simulations show that CHYME has valid type 1 error and better power than a range of alternative methods, including the popular OxyBS method and linear models on transformed proportions. Other methods we examined suffer from hugely inflated type 1 error for inference on 5hmC proportions. We use CHYME to explore genome-wide associations between 5mC/5hmC levels and cause of death in postmortem prefrontal cortex brain tissue samples. These analyses indicate that CHYME is a useful tool to reveal phenotypic associations with 5mC/5hmC levels.

Meta-genome-wide association studies identify a locus on chromosome 1 and multiple variants in the MHC region for serum C-peptide in type 1 diabetes.

AIMS/HYPOTHESIS: The aim of this study was to identify genetic variants associated with beta cell function in type 1 diabetes, as measured by serum C-peptide levels, through meta-genome-wide association studies (meta-GWAS). METHODS: We performed a meta-GWAS to combine the results from five studies in type 1 diabetes with cross-sectionally measured stimulated, fasting or random C-peptide levels, including 3479 European participants. The p values across studies were combined, taking into account sample size and direction of effect. We also performed separate meta-GWAS for stimulated (n = 1303), fasting (n = 2019) and random (n = 1497) C-peptide levels. RESULTS: In the meta-GWAS for stimulated/fasting/random C-peptide levels, a SNP on chromosome 1, rs559047 (Chr1:238753916, T>A, minor allele frequency [MAF] 0.24-0.26), was associated with C-peptide (p = 4.13 × 10-8), meeting the genome-wide significance threshold (p < 5 × 10-8). In the same meta-GWAS, a locus in the MHC region (rs9260151) was close to the genome-wide significance threshold (Chr6:29911030, C>T, MAF 0.07-0.10, p = 8.43 × 10-8). In the stimulated C-peptide meta-GWAS, rs61211515 (Chr6:30100975, T/-, MAF 0.17-0.19) in the MHC region was associated with stimulated C-peptide (ß [SE] = - 0.39 [0.07], p = 9.72 × 10-8). rs61211515 was also associated with the rate of stimulated C-peptide decline over time in a subset of individuals (n = 258) with annual repeated measures for up to 6 years (p = 0.02). In the meta-GWAS of random C-peptide, another MHC region, SNP rs3135002 (Chr6:32668439, C>A, MAF 0.02-0.06), was associated with C-peptide (p = 3.49 × 10-8). Conditional analyses suggested that the three identified variants in the MHC region were independent of each other. rs9260151 and rs3135002 have been associated with type 1 diabetes, whereas rs559047 and rs61211515 have not been associated with a risk of developing type 1 diabetes. CONCLUSIONS/INTERPRETATION: We identified a locus on chromosome 1 and multiple variants in the MHC region, at least some of which were distinct from type 1 diabetes risk loci, that were associated with C-peptide, suggesting partly non-overlapping mechanisms for the development and progression of type 1 diabetes. These associations need to be validated in independent populations. Further investigations could provide insights into mechanisms of beta cell loss and opportunities to preserve beta cell function.

Epigenetics, heritability and longitudinal analysis.

BACKGROUND: Longitudinal data and repeated measurements in epigenome-wide association studies (EWAS) provide a rich resource for understanding epigenetics. We summarize 7 analytical approaches to the GAW20 data sets that addressed challenges and potential applications of phenotypic and epigenetic data. All contributions used the GAW20 real data set and employed either linear mixed effect (LME) models or marginal models through generalized estimating equations (GEE). These contributions were subdivided into 3 categories: (a) quality control (QC) methods for DNA methylation data; (b) heritability estimates pretreatment and posttreatment with fenofibrate; and (c) impact of drug response pretreatment and posttreatment with fenofibrate on DNA methylation and blood lipids. RESULTS: Two contributions addressed QC and identified large statistical differences with pretreatment and posttreatment DNA methylation, possibly a result of batch effects. Two contributions compared epigenome-wide heritability estimates pretreatment and posttreatment, with one employing a Bayesian LME and the other using a variance-component LME. Density curves comparing these studies indicated these heritability estimates were similar. Another contribution used a variance-component LME to depict the proportion of heritability resulting from a genetic and shared environment. By including environmental exposures as random effects, the authors found heritability estimates became more stable but not significantly different. Two contributions investigated treatment response. One estimated drug-associated methylation effects on triglyceride levels as the response, and identified 11 significant cytosine-phosphate-guanine (CpG) sites with or without adjusting for high-density lipoprotein. The second contribution performed weighted gene coexpression network analysis and identified 6 significant modules of at least 30 CpG sites, including 3 modules with topological differences pretreatment and posttreatment. CONCLUSIONS: Four conclusions from this GAW20 working group are: (a) QC measures are an important consideration for EWAS studies that are investigating multiple time points or repeated measurements; (b) application of heritability estimates between time points for individual CpG sites is a useful QC measure for DNA methylation studies; (c) drug intervention demonstrated strong epigenome-wide DNA methylation patterns across the 2 time points; and (d) new statistical methods are required to account for the environmental contributions of DNA methylation across time. These contributions demonstrate numerous opportunities exist for the analysis of longitudinal data in future epigenetic studies.

Polymorphism in the innate immune receptor SIRPα controls CD47 binding and autoimmunity in the nonobese diabetic mouse.

The signal regulatory protein (SIRP) locus encodes a family of paired receptors that mediate both activating and inhibitory signals and is associated with type 1 diabetes (T1D) risk. The NOD mouse model recapitulates multiple features of human T1D and enables mechanistic analysis of the impact of genetic variations on disease. In this study, we identify Sirpa encoding an inhibitory receptor on myeloid cells as a gene in the insulin-dependent diabetes locus 13.2 (Idd13.2) that drives islet inflammation and T1D. Compared to T1D-resistant strains, the NOD variant of SIRPα displayed greater binding to its ligand CD47, as well as enhanced T cell proliferation and diabetogenic potency. Myeloid cell-restricted expression of a Sirpa transgene accelerated disease in a dose-dependent manner and displayed genetic and functional interaction with the Idd5 locus to potentiate insulitis progression. Our study demonstrates that variations in both SIRPα sequence and expression level modulate T1D immunopathogenesis. Thus, we identify Sirpa as a T1D risk gene and provide insight into the complex mechanisms by which disease-associated variants act in concert to drive defined stages in disease progression.

The association of previously reported polymorphisms for microvascular complications in a meta-analysis of diabetic retinopathy.

We investigated the association of signals from previous GWAS and candidate gene meta-analyses for diabetic retinopathy (DR) or nephropathy (DN), as well as an EPO variant in meta-analyses of severe (SDR) and mild diabetic retinopathy (MDR). Meta-analyses of SDR (≥severe non-proliferative diabetic retinopathy (NPDR) or history of panretinal photocoagulation) and MDR (≥mild NPDR), defined based on seven-field stereoscopic fundus photographs, were performed in two well-characterized type 1 diabetes (T1D) cohorts: the Diabetes Control and Complications Trial/Epidemiology of Diabetes Interventions and Complications (DCCT/EDIC, n = 1,304) and Wisconsin Epidemiologic Study of Diabetic Retinopathy (WESDR, n = 603). Among 34 previous signals for DR, after controlling for multiple testing, no association was replicated in our meta-analyses. rs1571942 and rs12219125 at PLXDC2 locus showed nominally significant (<0.05) association with SDR in the same direction as previous report, as did rs1801282 in PPARG gene with MDR. Among 55 loci previously associated with DN, three showed suggestive associations with SDR in our study without maintaining significance after correction for multiple testing. Of particular interest, rs1617640 (EPO) was not significantly associated with DR status, combined SDR-DN phenotype, time to SDR or time to DN (all P > 0.05). Lack of replication of previous DR hits and EPO despite reasonable statistical power implies that many of these may be false positives. Consistent with pleiotropy, we provide suggestive collective evidence for association between DR and variants previously associated with DN without reaching statistical significance at any single locus.

Genetic Risk Factors for CVD in Type 1 Diabetes: The DCCT/EDIC Study.

OBJECTIVE: The role of genetic factors in the risk of cardiovascular disease (CVD) for patients with type 1 diabetes (T1D) remains unknown. We therefore examined whether previously identified genetic factors for coronary artery disease (CAD) are associated with the risk of CVD above and beyond established demographic and clinical factors in the Diabetes Control and Complications Trial (DCCT)/Epidemiology of Diabetes Interventions and Complications (EDIC) study. RESEARCH DESIGN AND METHODS: Polygenic risk scores (PRS) and individual genetic variants identified in previous studies were obtained from genome-wide genotyping performed in 1,371 DCCT/EDIC participants. Two composite CVD outcomes were considered: major adverse cardiovascular events (MACE) (CVD death or nonfatal myocardial infarction [MI] or stroke) and any CVD (MACE plus confirmed angina, silent MI, revascularization, or congestive heart failure). Cox proportional hazards models assessed the association between the genetic factors and the risk of CVD with adjustment for other factors (including age, lipids, blood pressure, and glycemia). RESULTS: CAD PRS was strongly associated with the subsequent risk of any CVD (42% and 38% higher risk per 1-SD increase in unadjusted and fully adjusted models, respectively; P < 0.0001) and with the risk of MACE (50% and 40% higher risk per 1-SD increase in unadjusted and fully adjusted models, respectively; P < 0.0001). Several individual single nucleotide polymorphisms were also nominally associated with the risk of any CVD and MACE. CONCLUSIONS: Genetic factors are associated with the risk of subsequent CVD in individuals with T1D above and beyond the effect of established risk factors such as age, lipids, blood pressure, and glycemia.

Lunatic Fringe-mediated Notch signaling is required for lung alveogenesis.

Distal lung development occurs through coordinated induction of myofibroblasts, epithelial cells, and capillaries. Lunatic Fringe (Lfng) is a beta(1-3) N-acetylglucosamine transferase that modifies Notch receptors to facilitate their activation by Delta-like (Dll1/4) ligands. Lfng is expressed in the distal lung during saccular development, and deletion of this gene impairs myofibroblast differentiation and alveogenesis in this context. A similar defect was observed in Notch2(beta-geo/+)Notch3(beta-geo/beta-geo) compound mutant mice but not in Notch2(beta-geo/+) or Notch3(beta-geo/beta-geo) single mutants. Finally, to directly test for the role of Notch signaling in myofibroblast differentiation in vivo, we used ROSA26-rtTA(/+);tetO-CRE(/+);RBPJkappa(flox/flox) inducible mutant mice to show that disruption of canonical Notch signaling during late embryonic development prevents induction of smooth muscle actin in mesenchymal cells of the distal lung. In sum, these results demonstrate that Lfng functions to enhance Notch signaling in myofibroblast precursor cells and thereby to coordinate differentiation and mobilization of myofibroblasts required for alveolar septation.

DNA methylation age calculators reveal association with diabetic neuropathy in type 1 diabetes.

BACKGROUND: Many CpGs become hyper or hypo-methylated with age. Multiple methods have been developed by Horvath et al. to estimate DNA methylation (DNAm) age including Pan-tissue, Skin & Blood, PhenoAge, and GrimAge. Pan-tissue and Skin & Blood try to estimate chronological age in the normal population whereas PhenoAge and GrimAge use surrogate markers associated with mortality to estimate biological age and its departure from chronological age. Here, we applied Horvath's four methods to calculate and compare DNAm age in 499 subjects with type 1 diabetes (T1D) from the Diabetes Control and Complications Trial/Epidemiology of Diabetes Interventions and Complications (DCCT/EDIC) study using DNAm data measured by Illumina EPIC array in the whole blood. Association of the four DNAm ages with development of diabetic complications including cardiovascular diseases (CVD), nephropathy, retinopathy, and neuropathy, and their risk factors were investigated. RESULTS: Pan-tissue and GrimAge were higher whereas Skin & Blood and PhenoAge were lower than chronological age (p < 0.0001). DNAm age was not associated with the risk of CVD or retinopathy over 18-20 years after DNAm measurement. However, higher PhenoAge (ß = 0.023, p = 0.007) and GrimAge (ß = 0.029, p = 0.002) were associated with higher albumin excretion rate (AER), an indicator of diabetic renal disease, measured over time. GrimAge was also associated with development of both diabetic peripheral neuropathy (OR = 1.07, p = 9.24E-3) and cardiovascular autonomic neuropathy (OR = 1.06, p = 0.011). Both HbA1c (ß = 0.38, p = 0.026) and T1D duration (ß = 0.01, p = 0.043) were associated with higher PhenoAge. Employment (ß = - 1.99, p = 0.045) and leisure time (ß = - 0.81, p = 0.022) physical activity were associated with lower Pan-tissue and Skin & Blood, respectively. BMI (ß = 0.09, p = 0.048) and current smoking (ß = 7.13, p = 9.03E-50) were positively associated with Skin & Blood and GrimAge, respectively. Blood pressure, lipid levels, pulse rate, and alcohol consumption were not associated with DNAm age regardless of the method used. CONCLUSIONS: Various methods of measuring DNAm age are sub-optimal in detecting people at higher risk of developing diabetic complications although some work better than the others.

Evidence of batch effects masking treatment effect in GAW20 methylation data.

Using the real data set from GAW20, we examined changes in the distribution of DNA methylation before and after treatment. Paired analysis of differences in both mean and variance had grossly inflated type 1 error, suggesting either a very large number of changes across the entire epigenome or major non-biological issues, such as batch effects. Separate analysis of Infinium I and II probes indicated differences in the paired t-test statistics between these two types of probes. Examination of combined principal components showed that the first and fourth principal components discriminate between the before and after treatment measurements, further evidencing the presence of batch effects that make any conclusions about treatment effect suspect.

Investigating potential causal relationships between SNPs, DNA methylation and HDL.

Using data on 680 patients from the GAW20 real data set, we conducted Mendelian randomization (MR) studies to explore the causal relationships between methylation levels at selected probes (cytosine-phosphate-guanine sites [CpGs]) and high-density lipoprotein (HDL) changes (ΔHDL) using single-nucleotide polymorphisms (SNPs) as instrumental variables. Several methods were used to estimate the causal effects at CpGs of interest on ΔHDL, including a newly developed method that we call constrained instrumental variables (CIV). CIV performs automatic SNP selection while providing estimates of causal effects adjusted for possible pleiotropy, when the potentially-pleiotropic phenotypes are measured. For CpGs in or near the 10 genes identified as associated with ΔHDL using a family-based VC-score test, we compared CIV to Egger regression and the two-stage least squares (TSLS) method. All 3 approaches selected at least 1CpG in 2 genes-RNMT;C18orf19 and C6orf141-as showing a causal relationship with ΔHDL.

Factors associated with heterogeneity in microarray gene expression in peripheral blood mononuclear cells from large pedigrees.

BACKGROUND: Genome-wide microarray expression is a rich source of functional genomic data. We examined evidence for differences in expression from peripheral blood mononuclear cells between individuals, examined some of factors that may be responsible and provide recommendations for analysis. METHODS: A total of 643 individuals from 17 large Mexican American pedigrees had microarray gene expression data generated from peripheral blood mononuclear cells. This data has previously been used to map cis- and trans-expression quantitative trait loci using genome-wide linkage analysis. We estimated both principal components and cell proportions in these data, and tested them for association with clinical factors to provide insight into causes of variation in gene expression between individuals. RESULTS: We identified that there were highly significant differences in the second principal component of gene expression between pedigrees, with 3 pedigrees being outliers. The estimated cell proportions identified 1 individual who was a gross outlier, as well as pedigrees that differed from others in their estimated proportions of helper and cytotoxic T cells. CONCLUSIONS: These phenomena could be from either pedigree-specific genetic variation, technical artefacts, or clinical factors. Incorporating factors that influence gene expression into genetic analysis, and exclusion of outliers could improve the power of genetic mapping of expression traits.

New Locus for Skin Intrinsic Fluorescence in Type 1 Diabetes Also Associated With Blood and Skin Glycated Proteins.

Skin fluorescence (SF) noninvasively measures advanced glycation end products (AGEs) in the skin and is a risk indicator for diabetes complications. N-acetyltransferase 2 (NAT2) is the only known locus influencing SF. We aimed to identify additional genetic loci influencing SF in type 1 diabetes (T1D) through a meta-analysis of genome-wide association studies (N = 1,359) including Diabetes Control and Complications Trial/Epidemiology of Diabetes Interventions and Complications (DCCT/EDIC) and Wisconsin Epidemiologic Study of Diabetic Retinopathy (WESDR). A locus on chromosome 1, rs7533564 (P = 1.9 × 10(-9)), was associated with skin intrinsic fluorescence measured by SCOUT DS (excitation 375 nm, emission 435-655 nm), which remained significant after adjustment for time-weighted HbA1c (P = 1.7 × 10(-8)). rs7533564 was associated with mean HbA1c in meta-analysis (P = 0.0225), mean glycated albumin (P = 0.0029), and glyoxal hydroimidazolones (P = 0.049), an AGE measured in skin biopsy collagen, in DCCT. rs7533564 was not associated with diabetes complications in DCCT/EDIC or with SF in subjects without diabetes (nondiabetic [ND]) (N = 8,721). In conclusion, we identified a new locus associated with SF in T1D subjects that did not show similar effect in ND subjects, suggesting a diabetes-specific effect. This association needs to be investigated in type 2 diabetes.

Generation and analysis of a mouse intestinal metatranscriptome through Illumina based RNA-sequencing.

With the advent of high through-put sequencing (HTS), the emerging science of metagenomics is transforming our understanding of the relationships of microbial communities with their environments. While metagenomics aims to catalogue the genes present in a sample through assessing which genes are actively expressed, metatranscriptomics can provide a mechanistic understanding of community inter-relationships. To achieve these goals, several challenges need to be addressed from sample preparation to sequence processing, statistical analysis and functional annotation. Here we use an inbred non-obese diabetic (NOD) mouse model in which germ-free animals were colonized with a defined mixture of eight commensal bacteria, to explore methods of RNA extraction and to develop a pipeline for the generation and analysis of metatranscriptomic data. Applying the Illumina HTS platform, we sequenced 12 NOD cecal samples prepared using multiple RNA-extraction protocols. The absence of a complete set of reference genomes necessitated a peptide-based search strategy. Up to 16% of sequence reads could be matched to a known bacterial gene. Phylogenetic analysis of the mapped ORFs revealed a distribution consistent with ribosomal RNA, the majority from Bacteroides or Clostridium species. To place these HTS data within a systems context, we mapped the relative abundance of corresponding Escherichia coli homologs onto metabolic and protein-protein interaction networks. These maps identified bacterial processes with components that were well-represented in the datasets. In summary this study highlights the potential of exploiting the economy of HTS platforms for metatranscriptomics.

Stem cell gene expression programs influence clinical outcome in human leukemia.

Xenograft studies indicate that some solid tumors and leukemias are organized as cellular hierarchies sustained by cancer stem cells (CSCs). Despite the promise of the CSC model, its relevance in humans remains uncertain. Here we show that acute myeloid leukemia (AML) follows a CSC model on the basis of sorting multiple populations from each of 16 primary human AML samples and identifying which contain leukemia stem cells (LSCs) using a sensitive xenograft assay. Analysis of gene expression from all functionally validated populations yielded an LSC-specific signature. Similarly, a hematopoietic stem cell (HSC) gene signature was established. Bioinformatic analysis identified a core transcriptional program shared by LSCs and HSCs, revealing the molecular machinery underlying 'stemness' properties. Both stem cell programs were highly significant independent predictors of patient survival and were found in existing prognostic signatures. Thus, determinants of stemness influence the clinical outcome of AML, establishing that LSCs are clinically relevant and not artifacts of xenotransplantation.

A genome-wide association study identifies a novel major locus for glycemic control in type 1 diabetes, as measured by both A1C and glucose.

OBJECTIVE: Glycemia is a major risk factor for the development of long-term complications in type 1 diabetes; however, no specific genetic loci have been identified for glycemic control in individuals with type 1 diabetes. To identify such loci in type 1 diabetes, we analyzed longitudinal repeated measures of A1C from the Diabetes Control and Complications Trial. RESEARCH DESIGN AND METHODS: We performed a genome-wide association study using the mean of quarterly A1C values measured over 6.5 years, separately in the conventional (n = 667) and intensive (n = 637) treatment groups of the DCCT. At loci of interest, linear mixed models were used to take advantage of all the repeated measures. We then assessed the association of these loci with capillary glucose and repeated measures of multiple complications of diabetes. RESULTS: We identified a major locus for A1C levels in the conventional treatment group near SORCS1 (10q25.1, P = 7 x 10(-10)), which was also associated with mean glucose (P = 2 x 10(-5)). This was confirmed using A1C in the intensive treatment group (P = 0.01). Other loci achieved evidence close to genome-wide significance: 14q32.13 (GSC) and 9p22 (BNC2) in the combined treatment groups and 15q21.3 (WDR72) in the intensive group. Further, these loci gave evidence for association with diabetic complications, specifically SORCS1 with hypoglycemia and BNC2 with renal and retinal complications. We replicated the SORCS1 association in Genetics of Diabetes in Kidneys (GoKinD) study control subjects (P = 0.01) and the BNC2 association with A1C in nondiabetic individuals. CONCLUSIONS: A major locus for A1C and glucose in individuals with diabetes is near SORCS1. This may influence the design and analysis of genetic studies attempting to identify risk factors for long-term diabetic complications.