We carried out a trans-ancestry genome-wide association and replication study of blood pressure phenotypes among up to 320,251 individuals of East Asian, European and South Asian ancestry. We find genetic variants at 12 new loci to be associated with blood pressure (P = 3.9 × 10−11 to 5.0 × 10−21). The sentinel blood pressure SNPs are enriched for association with DNA methylation at multiple nearby CpG sites, suggesting that, at some of the loci identified, DNA methylation may lie on the regulatory pathway linking sequence variation to blood pressure. The sentinel SNPs at the 12 new loci point to genes involved in vascular smooth muscle (IGFBP3, KCNK3, PDE3A and PRDM6) and renal (ARHGAP24, OSR1, SLC22A7 and TBX2) function. The new and known genetic variants predict increased left ventricular mass, circulating levels of NT-proBNP, and cardiovascular and all-cause mortality (P = 0.04 to 8.6 × 10−6). Our results provide new evidence for the role of DNA methylation in blood pressure regulation.
CpG methylation variation is involved in human trait formation and disease susceptibility. Analyses within populations have been biased towards CpG-dense regions through the application of targeted arrays. We generate whole-genome bisulfite sequencing data for approximately 30 adipose and blood samples from monozygotic and dizygotic twins for the characterization of non-genetic and genetic effects at single-site resolution.
Purely invariable CpGs display a bimodal distribution with enrichment of unmethylated CpGs and depletion of fully methylated CpGs in promoter and enhancer regions. Population-variable CpGs account for approximately 15–20 % of total CpGs per tissue, are enriched in enhancer-associated regions and depleted in promoters, and single nucleotide polymorphisms at CpGs are a frequent confounder of extreme methylation variation. Differential methylation is primarily non-genetic in origin, with non-shared environment accounting for most of the variance. These non-genetic effects are mainly tissue-specific. Tobacco smoking is associated with differential methylation in blood with no evidence of this exposure impacting cell counts. Opposite to non-genetic effects, genetic effects of CpG methylation are shared across tissues and thus limit inter-tissue epigenetic drift. CpH methylation is rare, and shows similar characteristics of variation patterns as CpGs.
Our study highlights the utility of low pass whole-genome bisulfite sequencing in identifying methylome variation beyond promoter regions, and suggests that targeting the population dynamic methylome of tissues requires assessment of understudied intergenic CpGs distal to gene promoters to reveal the full extent of inter-individual variation.
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The online version of this article (doi:10.1186/s13059-015-0856-1) contains supplementary material, which is available to authorized users.
Adipose; Blood; CpH methylation; Differentially methylated region (DMR); DNA methylation; Enhancer; Environment; Population; Twins; Whole-genome bisulfite sequencing (WGBS)
Patients with established type 2 diabetes display both β-cell dysfunction and insulin resistance. To define fundamental processes leading to the diabetic state, we examined the relationship between type 2 diabetes risk variants at 37 established susceptibility loci, and indices of proinsulin processing, insulin secretion, and insulin sensitivity. We included data from up to 58,614 nondiabetic subjects with basal measures and 17,327 with dynamic measures. We used additive genetic models with adjustment for sex, age, and BMI, followed by fixed-effects, inverse-variance meta-analyses. Cluster analyses grouped risk loci into five major categories based on their relationship to these continuous glycemic phenotypes. The first cluster (PPARG, KLF14, IRS1, GCKR) was characterized by primary effects on insulin sensitivity. The second cluster (MTNR1B, GCK) featured risk alleles associated with reduced insulin secretion and fasting hyperglycemia. ARAP1 constituted a third cluster characterized by defects in insulin processing. A fourth cluster (TCF7L2, SLC30A8, HHEX/IDE, CDKAL1, CDKN2A/2B) was defined by loci influencing insulin processing and secretion without a detectable change in fasting glucose levels. The final group contained 20 risk loci with no clear-cut associations to continuous glycemic traits. By assembling extensive data on continuous glycemic traits, we have exposed the diverse mechanisms whereby type 2 diabetes risk variants impact disease predisposition.
Polycystic ovary syndrome (PCOS) is associated with dyslipidaemia and obesity. It is not clear whether the dyslipidaemia of PCOS is attributable to PCOS itself, obesity, or a combination of both. Our objective was to assess the importance of familial dyslipidaemia in PCOS by comparing fasting lipids between probands and their (affected and nonaffected) sisters.
Retrospective data set analyses.
Family study; 157 probands, 214 sisters and 76 control women (normal ovaries and regular cycles). All probands had PCOS, defined by symptoms of anovulation and/or hyperandrogenism with polycystic ovaries on ultrasound. Affected or unaffected status of sisters was defined by ovarian morphology.
Serum concentrations of triglycerides, total cholesterol, high-density lipoprotein (HDL)-cholesterol and low-density lipoprotein (LDL)-cholesterol.
Triglyceride levels and body mass index (BMI) were higher and HDL cholesterol levels were lower in the probands than affected sisters, unaffected sisters and controls. These differences in lipid profiles between the groups disappeared after adjustment for BMI. No differences in lipids were seen between affected and unaffected sisters.
These data are consistent with heritability of lipid levels in sisters but strongly suggest that the predominant influence on the manifestation of dyslipidaemia in PCOS is body weight.
The purpose of this study was to test whether elevated nonfasting glucose levels associate with and cause ischemic heart disease (IHD) and myocardial infarction (MI).
Elevated fasting plasma glucose levels associate with increased risk of IHD, but whether this is also true for nonfasting levels and whether this is a causal relationship is unknown.
Using a Mendelian randomization approach, we studied 80,522 persons from Copenhagen, Denmark. Of those, IHD developed in 14,155, and MI developed in 6,257. Subjects were genotyped for variants in GCK (rs4607517), G6PC2 (rs560887), ADCY5 (rs11708067), DGKB (rs2191349), and ADRA2A (rs10885122) associated with elevated fasting glucose levels in genome-wide association studies.
Risk of IHD and MI increased stepwise with increasing nonfasting glucose levels. The hazard ratio for IHD in subjects with nonfasting glucose levels ≥11 mmol/l (≥198 mg/dl) versus <5 mmol/l (<90 mg/dl) was 6.9 (95% confidence interval [CI]: 4.2 to 11.2) adjusted for age and sex, and 2.3 (95% CI: 1.3 to 4.2) adjusted multifactorially; corresponding values for MI were 9.2 (95% CI: 4.6 to 18.2) and 4.8 (95% CI: 2.1 to 11.2). Increasing number of glucose-increasing alleles was associated with increasing nonfasting glucose levels and with increased risk of IHD and MI. The estimated causal odds ratio for IHD and MI by instrumental variable analysis for a 1-mmol/l (18-mg/dl) increase in nonfasting glucose levels due to genotypes combined were 1.25 (95% CI: 1.03 to 1.52) and 1.69 (95% CI: 1.28 to 2.23), and the corresponding observed hazard ratio for IHD and MI by Cox regression was 1.18 (95% CI: 1.15 to 1.22) and 1.09 (95% CI: 1.07 to 1.11), respectively.
Like common nonfasting glucose elevation, plasma glucose-increasing polymorphisms associate with increased risk of IHD and MI. These data are compatible with a causal association.
cardiovascular disease; diabetes mellitus; epidemiology; nonfasting; plasma glucose; post-prandial
Low plasma 25-hydroxyvitamin D (25[OH]D) concentration is associated with high arterial blood pressure and hypertension risk, but whether this association is causal is unknown. We used a mendelian randomisation approach to test whether 25(OH)D concentration is causally associated with blood pressure and hypertension risk.
In this mendelian randomisation study, we generated an allele score (25[OH]D synthesis score) based on variants of genes that affect 25(OH)D synthesis or substrate availability (CYP2R1 and DHCR7), which we used as a proxy for 25(OH)D concentration. We meta-analysed data for up to 108 173 individuals from 35 studies in the D-CarDia collaboration to investigate associations between the allele score and blood pressure measurements. We complemented these analyses with previously published summary statistics from the International Consortium on Blood Pressure (ICBP), the Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) consortium, and the Global Blood Pressure Genetics (Global BPGen) consortium.
In phenotypic analyses (up to n=49 363), increased 25(OH)D concentration was associated with decreased systolic blood pressure (β per 10% increase, −0·12 mm Hg, 95% CI −0·20 to −0·04; p=0·003) and reduced odds of hypertension (odds ratio [OR] 0·98, 95% CI 0·97−0·99; p=0·0003), but not with decreased diastolic blood pressure (β per 10% increase, −0·02 mm Hg, −0·08 to 0·03; p=0·37). In meta-analyses in which we combined data from D-CarDia and the ICBP (n=146 581, after exclusion of overlapping studies), each 25(OH)D-increasing allele of the synthesis score was associated with a change of −0·10 mm Hg in systolic blood pressure (−0·21 to −0·0001; p=0·0498) and a change of −0·08 mm Hg in diastolic blood pressure (−0·15 to −0·02; p=0·01). When D-CarDia and consortia data for hypertension were meta-analysed together (n=142 255), the synthesis score was associated with a reduced odds of hypertension (OR per allele, 0·98, 0·96−0·99; p=0·001). In instrumental variable analysis, each 10% increase in genetically instrumented 25(OH)D concentration was associated with a change of −0·29 mm Hg in diastolic blood pressure (−0·52 to −0·07; p=0·01), a change of −0·37 mm Hg in systolic blood pressure (−0·73 to 0·003; p=0·052), and an 8·1% decreased odds of hypertension (OR 0·92, 0·87–0·97; p=0·002).
Increased plasma concentrations of 25(OH)D might reduce the risk of hypertension. This finding warrants further investigation in an independent, similarly powered study.
Objective: The study investigated whether childhood cognitive ability moderates Type 2 diabetes polygenic risk manifestation in older age. Method: In 940 relatively healthy people (mean age 69.55 ± 0.85), we tested whether self-reported diabetes and hemoglobin HbA1c (HbA1c) levels were predicted by diabetes polygenic risk, cognitive ability measured about 60 years earlier, and their interaction. Polygenic risk scores aggregated the small effects of up to nearly 121,000 single-nucleotide polymorphisms (SNPs). Participants’ cognitive ability was measured at age 11. Results: Both polygenic risk and low childhood cognitive ability significantly predicted diabetes diagnosis. Polygenic risk interacted with cognitive ability (p = .02), predicting HbA1c levels more strongly in people with below-median cognitive ability (effect r = .21) than in people with above-median cognitive ability (effect r = .10). The interaction term was not significant for self-reported diabetes (p = .34), although the genetic risk-diabetes association showed a tendency of being stronger among those with below-median cognitive ability. Conclusions: Higher premorbid cognitive ability may provide some environmental protection against the manifestation of Type 2 diabetes genetic risk. This information may improve early identification of diabetes risk and inform intervention development.
cognitive ability; intelligence; diabetes; HbA11c; genetic risk
Polycystic ovary syndrome (PCOS) is a common, highly heritable complex disorder of unknown aetiology characterized by hyperandrogenism, chronic anovulation and defects in glucose homeostasis. Increased luteinizing hormone relative to follicle-stimulating hormone secretion, insulin resistance and developmental exposure to androgens are hypothesized to play a causal role in PCOS. Here we map common genetic susceptibility loci in European ancestry women for the National Institutes of Health PCOS phenotype, which confers the highest risk for metabolic morbidities, as well as reproductive hormone levels. Three loci reach genome-wide significance in the case–control meta-analysis, two novel loci mapping to chr 8p32.1 and chr 11p14.1, and a chr 9q22.32 locus previously found in Chinese PCOS. The same chr 11p14.1 SNP, rs11031006, in the region of the follicle-stimulating hormone B polypeptide (FSHB) gene strongly associates with PCOS diagnosis and luteinizing hormone levels. These findings implicate neuroendocrine changes in disease pathogenesis.
Polycystic Ovary Sydrome is a highly heritable, complex reproductive disorder with unknown underlying genetic factors. Here Hayes and Urbanek et al. identify three loci in European women strongly associated with neuroendocrine changes and disease susceptibility.
Little is known about genes regulating male puberty. Further, while many identified pubertal timing variants associate with age at menarche, a late manifestation of puberty, and body mass, little is known about these variants' relationship to pubertal initiation or tempo. To address these questions, we performed genome-wide association meta-analysis in over 11 000 European samples with data on early pubertal traits, male genital and female breast development, measured by the Tanner scale. We report the first genome-wide significant locus for male sexual development upstream of myocardin-like 2 (MKL2) (P = 8.9 × 10−9), a menarche locus tagging a developmental pathway linking earlier puberty with reduced pubertal growth (P = 4.6 × 10−5) and short adult stature (p = 7.5 × 10−6) in both males and females. Furthermore, our results indicate that a proportion of menarche loci are important for pubertal initiation in both sexes. Consistent with epidemiological correlations between increased prepubertal body mass and earlier pubertal timing in girls, body mass index (BMI)-increasing alleles correlated with earlier breast development. In boys, some BMI-increasing alleles associated with earlier, and others with delayed, sexual development; these genetic results mimic the controversy in epidemiological studies, some of which show opposing correlations between prepubertal BMI and male puberty. Our results contribute to our understanding of the pubertal initiation program in both sexes and indicate that although mechanisms regulating pubertal onset in males and females may largely be shared, the relationship between body mass and pubertal timing in boys may be complex and requires further genetic studies.
Statins effectively lower LDL cholesterol levels in large studies and the observed interindividual response variability may be partially explained by genetic variation. Here we perform a pharmacogenetic meta-analysis of genome-wide association studies (GWAS) in studies addressing the LDL cholesterol response to statins, including up to 18,596 statin-treated subjects. We validate the most promising signals in a further 22,318 statin recipients and identify two loci, SORT1/CELSR2/PSRC1 and SLCO1B1, not previously identified in GWAS. Moreover, we confirm the previously described associations with APOE and LPA. Our findings advance the understanding of the pharmacogenetic architecture of statin response.
Statins are effectively used to prevent and manage cardiovascular disease, but patient response to these drugs is highly variable. Here, the authors identify two new genes associated with the response of LDL cholesterol to statins and advance our understanding of the genetic basis of drug response.
Reference panels from the 1000 Genomes (1000G) Project Consortium provide near complete coverage of common and low-frequency genetic variation with minor allele frequency ≥0.5% across European ancestry populations. Within the European Network for Genetic and Genomic Epidemiology (ENGAGE) Consortium, we have undertaken the first large-scale meta-analysis of genome-wide association studies (GWAS), supplemented by 1000G imputation, for four quantitative glycaemic and obesity-related traits, in up to 87,048 individuals of European ancestry. We identified two loci for body mass index (BMI) at genome-wide significance, and two for fasting glucose (FG), none of which has been previously reported in larger meta-analysis efforts to combine GWAS of European ancestry. Through conditional analysis, we also detected multiple distinct signals of association mapping to established loci for waist-hip ratio adjusted for BMI (RSPO3) and FG (GCK and G6PC2). The index variant for one association signal at the G6PC2 locus is a low-frequency coding allele, H177Y, which has recently been demonstrated to have a functional role in glucose regulation. Fine-mapping analyses revealed that the non-coding variants most likely to drive association signals at established and novel loci were enriched for overlap with enhancer elements, which for FG mapped to promoter and transcription factor binding sites in pancreatic islets, in particular. Our study demonstrates that 1000G imputation and genetic fine-mapping of common and low-frequency variant association signals at GWAS loci, integrated with genomic annotation in relevant tissues, can provide insight into the functional and regulatory mechanisms through which their effects on glycaemic and obesity-related traits are mediated.
Human genetic studies have demonstrated that quantitative human anthropometric and metabolic traits, including body mass index, waist-hip ratio, and plasma concentrations of glucose and insulin, are highly heritable, and are established risk factors for type 2 diabetes and cardiovascular diseases. Although many regions of the genome have been associated with these traits, the specific genes responsible have not yet been identified. By making use of advanced statistical “imputation” techniques applied to more than 87,000 individuals of European ancestry, and publicly available “reference panels” of more than 37 million genetic variants, we have been able to identify novel regions of the genome associated with these glycaemic and obesity-related traits and localise genes within these regions that are most likely to be causal. This improved understanding of the biological mechanisms underlying glycaemic and obesity-related traits is extremely important because it may advance drug development for downstream disease endpoints, ultimately leading to public health benefits.
The incidence of hematologic cancers increases with age. These cancers are associated with recurrent somatic mutations in specific genes. We hypothesized that such mutations would be detectable in the blood of some persons who are not known to have hematologic disorders.
We analyzed whole-exome sequencing data from DNA in the peripheral-blood cells of 17,182 persons who were unselected for hematologic phenotypes. We looked for somatic mutations by identifying previously characterized single-nucleotide variants and small insertions or deletions in 160 genes that are recurrently mutated in hematologic cancers. The presence of mutations was analyzed for an association with hematologic phenotypes, survival, and cardiovascular events.
Detectable somatic mutations were rare in persons younger than 40 years of age but rose appreciably in frequency with age. Among persons 70 to 79 years of age, 80 to 89 years of age, and 90 to 108 years of age, these clonal mutations were observed in 9.5% (219 of 2300 persons), 11.7% (37 of 317), and 18.4% (19 of 103), respectively. The majority of the variants occurred in three genes: DNMT3A, TET2, and ASXL1. The presence of a somatic mutation was associated with an increase in the risk of hematologic cancer (hazard ratio, 11.1; 95% confidence interval [CI], 3.9 to 32.6), an increase in all-cause mortality (hazard ratio, 1.4; 95% CI, 1.1 to 1.8), and increases in the risks of incident coronary heart disease (hazard ratio, 2.0; 95% CI, 1.2 to 3.4) and ischemic stroke (hazard ratio, 2.6; 95% CI, 1.4 to 4.8).
Age-related clonal hematopoiesis is a common condition that is associated with increases in the risk of hematologic cancer and in all-cause mortality, with the latter possibly due to an increased risk of cardiovascular disease. (Funded by the National Institutes of Health and others.)
MicroRNAs play a crucial role in the regulation of cell growth and differentiation. Mice with genetic deletion of miR-375 exhibit impaired glycemic control due to decreased β-cell and increased α-cell mass and function. The relative importance of these processes for the overall phenotype of miR-375KO mice is unknown. Here, we show that mice overexpressing miR-375 exhibit normal β-cell mass and function. Selective re-expression of miR-375 in β-cells of miR-375KO mice normalizes both, α- and β-cell phenotypes as well as glucose metabolism. Using this model, we also analyzed the contribution of β-cells to the total plasma miR-375 levels. Only a small proportion (≈1 %) of circulating miR-375 originates from β-cells. Furthermore, acute and profound β-cell destruction is sufficient to detect elevations of miR-375 levels in the blood. These findings are supported by higher miR-375 levels in the circulation of type 1 diabetes (T1D) subjects but not mature onset diabetes of the young (MODY) and type 2 diabetes (T2D) patients. Together, our data support an essential role for miR-375 in the maintenance of β-cell mass and provide in vivo evidence for release of miRNAs from pancreatic β-cells. The small contribution of β-cells to total plasma miR-375 levels make this miRNA an unlikely biomarker for β-cell function but suggests a utility for the detection of acute β-cell death for autoimmune diabetes.
Overexpression of miR-375 in β-cells does not influence β-cell mass and function.Increased α-cell mass in miR-375KO arises secondarily to loss of miR-375 in β-cells.Only a small proportion of circulating miR-375 levels originates from β-cells.Acute β-cell destruction results in measurable increases of miR-375 in the blood.Circulating miR-375 levels are not a biomarker for pancreatic β-cell function.
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MiRNA-375; Pancreatic β-cells; Biomarker; Diabetes; β-cell mass
There are potential advantages to the low-temperature (−196°C) banking of isolated islets, including the maintenance of viable islets for future research. We therefore assessed the in vitro and in vivo function of islets cryopreserved for nearly 20 years.
Human islets were cryopreserved from 1991 to 2001 and thawed between 2012 and 2014. These were characterised by immunostaining, patch-clamp electrophysiology, insulin secretion, transcriptome analysis and transplantation into a streptozotocin (STZ)-induced mouse model of diabetes.
The cryopreservation time was 17.6 ± 0.4 years (n = 43). The thawed islets stained positive with dithizone, contained insulin-positive and glucagon-positive cells, and displayed levels of apoptosis and transcriptome profiles similar to those of freshly isolated islets, although their insulin content was lower. The cryopreserved beta cells possessed ion channels and exocytotic responses identical to those of freshly isolated beta cells. Cells from a subset of five donors demonstrated similar perifusion insulin secretion profiles pre- and post-cryopreservation. The transplantation of cryopreserved islets into the diabetic mice improved their glucose tolerance but did not completely normalise their blood glucose levels. Circulating human insulin and insulin-positive grafts were detectable at 10 weeks post-transplantation.
We have demonstrated the potential for long-term banking of human islets for research, which could enable the use of tissue from a large number of donors with future technologies to gain new insight into diabetes.
Cryopreservation; Exocytosis; Human; Insulin; Ion channels; Islets; Secretion; Transplantation
Genome and exome sequencing in large cohorts enables characterization of the role of rare variation in complex diseases. Success in this endeavor, however, requires investigators to test a diverse array of genetic hypotheses which differ in the number, frequency and effect sizes of underlying causal variants. In this study, we evaluated the power of gene-based association methods to interrogate such hypotheses, and examined the implications for study design. We developed a flexible simulation approach, using 1000 Genomes data, to (a) generate sequence variation at human genes in up to 10K case-control samples, and (b) quantify the statistical power of a panel of widely used gene-based association tests under a variety of allelic architectures, locus effect sizes, and significance thresholds. For loci explaining ~1% of phenotypic variance underlying a common dichotomous trait, we find that all methods have low absolute power to achieve exome-wide significance (~5-20% power at α=2.5×10-6) in 3K individuals; even in 10K samples, power is modest (~60%). The combined application of multiple methods increases sensitivity, but does so at the expense of a higher false positive rate. MiST, SKAT-O, and KBAC have the highest individual mean power across simulated datasets, but we observe wide architecture-dependent variability in the individual loci detected by each test, suggesting that inferences about disease architecture from analysis of sequencing studies can differ depending on which methods are used. Our results imply that tens of thousands of individuals, extensive functional annotation, or highly targeted hypothesis testing will be required to confidently detect or exclude rare variant signals at complex disease loci.
Re-sequencing technologies allow for a more complete interrogation of the role of human variation in complex disease. The inadequate power of single variant methods to assess the role of less common variation has led to the development of numerous statistical methods for testing aggregate groups of variants for association with disease. Such endeavors pose substantial analytical challenges, however, due to the diverse array of genetic hypotheses that need to be considered. In this work, we systematically quantify and compare the performance of a panel of commonly used gene-based association methods under a range of allelic architectures, significance thresholds, locus effect sizes, sample sizes, and filters for neutral variation. We find that MiST, SKAT-O, and KBAC have the highest mean power across simulated datasets. Across all methods, however, the power to detect even loci of relatively large effect is very low at exome-wide significance thresholds for sample sizes comparable with those of ongoing sequencing studies; as such, the absence of signal in studies of a few thousand individuals does not exclude a role for rare variation in complex traits. Finally, we directly compare the results reported by different gene-based methods in order to identify their comparative advantages and disadvantages under distinct locus architectures. Our findings have implications for meaningful interpretation of both positive and negative findings in ongoing and future sequencing studies.
Age at menarche is a marker of timing of puberty in females. It varies widely between individuals, is a heritable trait and is associated with risks for obesity, type 2 diabetes, cardiovascular disease, breast cancer and all-cause mortality1. Studies of rare human disorders of puberty and animal models point to a complex hypothalamic-pituitary-hormonal regulation2,3, but the mechanisms that determine pubertal timing and underlie its links to disease risk remain unclear. Here, using genome-wide and custom-genotyping arrays in up to 182,416 women of European descent from 57 studies, we found robust evidence (P<5×10−8) for 123 signals at 106 genomic loci associated with age at menarche. Many loci were associated with other pubertal traits in both sexes, and there was substantial overlap with genes implicated in body mass index and various diseases, including rare disorders of puberty. Menarche signals were enriched in imprinted regions, with three loci (DLK1/WDR25, MKRN3/MAGEL2 and KCNK9) demonstrating parent-of-origin specific associations concordant with known parental expression patterns. Pathway analyses implicated nuclear hormone receptors, particularly retinoic acid and gamma-aminobutyric acid-B2 receptor signaling, among novel mechanisms that regulate pubertal timing in humans. Our findings suggest a genetic architecture involving at least hundreds of common variants in the coordinated timing of the pubertal transition.
Using an integrative approach in which genetic variation, gene expression, and clinical phenotypes are assessed in relevant tissues may help functionally characterize the contribution of genetics to disease susceptibility. We sought to identify genetic variation influencing skeletal muscle gene expression (expression quantitative trait loci [eQTLs]) as well as expression associated with measures of insulin sensitivity. We investigated associations of 3,799,401 genetic variants in expression of >7,000 genes from three cohorts (n = 104). We identified 287 genes with cis-acting eQTLs (false discovery rate [FDR] <5%; P < 1.96 × 10−5) and 49 expression–insulin sensitivity phenotype associations (i.e., fasting insulin, homeostasis model assessment–insulin resistance, and BMI) (FDR <5%; P = 1.34 × 10−4). One of these associations, fasting insulin/phosphofructokinase (PFKM), overlaps with an eQTL. Furthermore, the expression of PFKM, a rate-limiting enzyme in glycolysis, was nominally associated with glucose uptake in skeletal muscle (P = 0.026; n = 42) and overexpressed (Bonferroni-corrected P = 0.03) in skeletal muscle of patients with T2D (n = 102) compared with normoglycemic controls (n = 87). The PFKM eQTL (rs4547172; P = 7.69 × 10−6) was nominally associated with glucose uptake, glucose oxidation rate, intramuscular triglyceride content, and metabolic flexibility (P = 0.016–0.048; n = 178). We explored eQTL results using published data from genome-wide association studies (DIAGRAM and MAGIC), and a proxy for the PFKM eQTL (rs11168327; r2 = 0.75) was nominally associated with T2D (DIAGRAM P = 2.7 × 10−3). Taken together, our analysis highlights PFKM as a potential regulator of skeletal muscle insulin sensitivity.
DNA methylation plays a fundamental role in the regulation of the genome, but the optimal strategy for analysis of genome-wide DNA methylation data remains to be determined. We developed a comprehensive analysis pipeline for epigenome-wide association studies (EWAS) using the Illumina Infinium HumanMethylation450 BeadChip, based on 2,687 individuals, with 36 samples measured in duplicate. We propose new approaches to quality control, data normalisation and batch correction through control-probe adjustment and establish a null hypothesis for EWAS using permutation testing. Our analysis pipeline outperforms existing approaches, enabling accurate identification of methylation quantitative trait loci for hypothesis driven follow-up experiments.
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Common variants in WNT pathway genes have been associated with bone mass and fat distribution, the latter predicting diabetes and cardiovascular disease risk. Rare mutations in the WNT co-receptors LRP5 and LRP6 are similarly associated with bone and cardiometabolic disorders. We investigated the role of LRP5 in human adipose tissue. Subjects with gain-of-function LRP5 mutations and high bone mass had enhanced lower-body fat accumulation. Reciprocally, a low bone mineral density-associated common LRP5 allele correlated with increased abdominal adiposity. Ex vivo LRP5 expression was higher in abdominal versus gluteal adipocyte progenitors. Equivalent knockdown of LRP5 in both progenitor types dose-dependently impaired β-catenin signaling and led to distinct biological outcomes: diminished gluteal and enhanced abdominal adipogenesis. These data highlight how depot differences in WNT/β-catenin pathway activity modulate human fat distribution via effects on adipocyte progenitor biology. They also identify LRP5 as a potential pharmacologic target for the treatment of cardiometabolic disorders.
•Carriers of LRP5 variants display altered body fat distribution•LRP5 is more highly expressed in abdominal versus gluteal fat progenitor cells•LRP5 knockdown in both progenitor types leads to different biological responses•LRP5 modulates fat progenitor biology by controlling β-catenin signaling dosage
Loh et al. identify the WNT co-receptor LRP5 as a regulator of human body fat distribution, an independent predictor of diabetes and cardiovascular disease risk. Studying LRP5 gene variant carriers and human fat progenitors, they show that LRP5 differentially modulates regional adipose progenitor biology by titrating WNT/β-catenin signaling dosage.
Genome wide association studies (GWAS) for fasting glucose (FG) and insulin (FI) have identified common variant signals which explain 4.8% and 1.2% of trait variance, respectively. It is hypothesized that low-frequency and rare variants could contribute substantially to unexplained genetic variance. To test this, we analyzed exome-array data from up to 33,231 non-diabetic individuals of European ancestry. We found exome-wide significant (P<5×10-7) evidence for two loci not previously highlighted by common variant GWAS: GLP1R (p.Ala316Thr, minor allele frequency (MAF)=1.5%) influencing FG levels, and URB2 (p.Glu594Val, MAF = 0.1%) influencing FI levels. Coding variant associations can highlight potential effector genes at (non-coding) GWAS signals. At the G6PC2/ABCB11 locus, we identified multiple coding variants in G6PC2 (p.Val219Leu, p.His177Tyr, and p.Tyr207Ser) influencing FG levels, conditionally independent of each other and the non-coding GWAS signal. In vitro assays demonstrate that these associated coding alleles result in reduced protein abundance via proteasomal degradation, establishing G6PC2 as an effector gene at this locus. Reconciliation of single-variant associations and functional effects was only possible when haplotype phase was considered. In contrast to earlier reports suggesting that, paradoxically, glucose-raising alleles at this locus are protective against type 2 diabetes (T2D), the p.Val219Leu G6PC2 variant displayed a modest but directionally consistent association with T2D risk. Coding variant associations for glycemic traits in GWAS signals highlight PCSK1, RREB1, and ZHX3 as likely effector transcripts. These coding variant association signals do not have a major impact on the trait variance explained, but they do provide valuable biological insights.
Understanding how FI and FG levels are regulated is important because their derangement is a feature of T2D. Despite recent success from GWAS in identifying regions of the genome influencing glycemic traits, collectively these loci explain only a small proportion of trait variance. Unlocking the biological mechanisms driving these associations has been challenging because the vast majority of variants map to non-coding sequence, and the genes through which they exert their impact are largely unknown. In the current study, we sought to increase our understanding of the physiological pathways influencing both traits using exome-array genotyping in up to 33,231 non-diabetic individuals to identify coding variants and consequently genes associated with either FG or FI levels. We identified novel association signals for both traits including the receptor for GLP-1 agonists which are a widely used therapy for T2D. Furthermore, we identified coding variants at several GWAS loci which point to the genes underlying these association signals. Importantly, we found that multiple coding variants in G6PC2 result in a loss of protein function and lower fasting glucose levels.
DNA methylation has a great potential for understanding the aetiology of common complex traits such as Type 2 diabetes (T2D). Here we perform genome-wide methylated DNA immunoprecipitation sequencing (MeDIP-seq) in whole-blood-derived DNA from 27 monozygotic twin pairs and follow up results with replication and integrated omics analyses. We identify predominately hypermethylated T2D-related differentially methylated regions (DMRs) and replicate the top signals in 42 unrelated T2D cases and 221 controls. The strongest signal is in the promoter of the MALT1 gene, involved in insulin and glycaemic pathways, and related to taurocholate levels in blood. Integrating the DNA methylome findings with T2D GWAS meta-analysis results reveals a strong enrichment for DMRs in T2D-susceptibility loci. We also detect signals specific to T2D-discordant twins in the GPR61 and PRKCB genes. These replicated T2D associations reflect both likely causal and consequential pathways of the disease. The analysis indicates how an integrated genomics and epigenomics approach, utilizing an MZ twin design, can provide pathogenic insights as well as potential drug targets and biomarkers for T2D and other complex traits.
Type 2 diabetes (T2D) is a highly heterogeneous disease with a strong genetic component. Here the authors examine genome-wide methylation patterns in T2D-discordant, T2D-concordant and healthy concordant monozygotic twin pairs, and identify DNA methylation signals that may represent new biomarkers or drug targets for T2D.
The molecular mechanisms involved in the development of type 2 diabetes are poorly understood. Starting from genome-wide genotype data for 1,924 diabetic cases and 2,938 population controls generated by the Wellcome Trust Case Control Consortium, we set out to detect replicated diabetes association signals through analysis of 3,757 additional cases and 5,346 controls, and by integration of our findings with equivalent data from other international consortia. We detected diabetes susceptibility loci in and around the genes CDKAL1, CDKN2A/CDKN2B and IGF2BP2 and confirmed the recently described associations at HHEX/IDE and SLC30A8. Our findings provide insights into the genetic architecture of type 2 diabetes, emphasizing the contribution of multiple variants of modest effect. The regions identified underscore the importance of pathways influencing pancreatic beta cell development and function in the etiology of type 2 diabetes.
We aimed to validate genetic variants as instruments for insulin resistance and secretion, to characterise their association with intermediate phenotypes, and to investigate their role in T2D risk among normal-weight, overweight and obese individuals.We investigated the association of genetic scores with euglycaemic-hyperinsulinaemic clamp- and OGTT-based measures of insulin resistance and secretion, and a range of metabolic measures in up to 18,565 individuals. We also studied their association with T2D risk among normal-weight, overweight and obese individuals in up to 8,124 incident T2D cases. The insulin resistance score was associated with lower insulin sensitivity measured by M/I value (β in SDs-per-allele [95%CI]:−0.03[−0.04,−0.01];p=0.004). This score was associated with lower BMI (−0.01[−0.01,−0.0;p=0.02) and gluteofemoral fat-mass (−0.03[−0.05,−0.02;p=1.4×10−6), and with higher ALT (0.02[0.01,0.03];p=0.002) and gamma-GT (0.02[0.01,0.03];p=0.001). While the secretion score had a stronger association with T2D in leaner individuals (pinteraction=0.001), we saw no difference in the association of the insulin resistance score with T2D among BMI- or waist-strata(pinteraction>0.31). While insulin resistance is often considered secondary to obesity, the association of the insulin resistance score with lower BMI and adiposity and with incident T2D even among individuals of normal weight highlights the role of insulin resistance and ectopic fat distribution in T2D, independently of body size.
Genetics; type 2 diabetes; insulin resistance; insulin secretion; adipose expandability
Initial results from sequencing studies suggest that there are relatively few low-frequency (<5%) variants associated with large effects on common phenotypes. We performed low-pass whole-genome sequencing in 680 individuals from the InCHIANTI study to test two primary hypotheses: (i) that sequencing would detect single low-frequency–large effect variants that explained similar amounts of phenotypic variance as single common variants, and (ii) that some common variant associations could be explained by low-frequency variants. We tested two sets of disease-related common phenotypes for which we had statistical power to detect large numbers of common variant–common phenotype associations—11 132 cis-gene expression traits in 450 individuals and 93 circulating biomarkers in all 680 individuals. From a total of 11 657 229 high-quality variants of which 6 129 221 and 5 528 008 were common and low frequency (<5%), respectively, low frequency–large effect associations comprised 7% of detectable cis-gene expression traits [89 of 1314 cis-eQTLs at P < 1 × 10−06 (false discovery rate ∼5%)] and one of eight biomarker associations at P < 8 × 10−10. Very few (30 of 1232; 2%) common variant associations were fully explained by low-frequency variants. Our data show that whole-genome sequencing can identify low-frequency variants undetected by genotyping based approaches when sample sizes are sufficiently large to detect substantial numbers of common variant associations, and that common variant associations are rarely explained by single low-frequency variants of large effect.
Common genetic variants have been identified for adult height, but not much is known about the genetics of skeletal growth in early life. To identify common genetic variants that influence fetal skeletal growth, we meta-analyzed 22 genome-wide association studies (Stage 1; N = 28 459). We identified seven independent top single nucleotide polymorphisms (SNPs) (P < 1 × 10−6) for birth length, of which three were novel and four were in or near loci known to be associated with adult height (LCORL, PTCH1, GPR126 and HMGA2). The three novel SNPs were followed-up in nine replication studies (Stage 2; N = 11 995), with rs905938 in DC-STAMP domain containing 2 (DCST2) genome-wide significantly associated with birth length in a joint analysis (Stages 1 + 2; β = 0.046, SE = 0.008, P = 2.46 × 10−8, explained variance = 0.05%). Rs905938 was also associated with infant length (N = 28 228; P = 5.54 × 10−4) and adult height (N = 127 513; P = 1.45 × 10−5). DCST2 is a DC-STAMP-like protein family member and DC-STAMP is an osteoclast cell-fusion regulator. Polygenic scores based on 180 SNPs previously associated with human adult stature explained 0.13% of variance in birth length. The same SNPs explained 2.95% of the variance of infant length. Of the 180 known adult height loci, 11 were genome-wide significantly associated with infant length (SF3B4, LCORL, SPAG17, C6orf173, PTCH1, GDF5, ZNFX1, HHIP, ACAN, HLA locus and HMGA2). This study highlights that common variation in DCST2 influences variation in early growth and adult height.