Genome-wide meta-analysis identifies 11 new loci for anthropometric traits and provides insights into genetic architecture

The Relevance of Genomic Signatures at Adhesion GPCR Loci in Humans

Adhesion G protein-coupled receptors (aGPCRs) have a long evolutionary history dating back to very basal unicellular eukaryotes. Almost every vertebrate is equipped with a set of different aGPCRs. Genomic sequence data of several hundred extinct and extant species allows for reconstruction of aGPCR phylogeny in vertebrates and non-vertebrates in general but also provides a detailed view into the recent evolutionary history of human aGPCRs. Mining these sequence sources with bioinformatic tools can unveil many facets of formerly unappreciated aGPCR functions. In this review, we extracted such information from the literature and open public sources and provide insights into the history of aGPCR in humans. This includes comprehensive analyses of signatures of selection, variability of human aGPCR genes, and quantitative traits at human aGPCR loci. As indicated by a large number of genome-wide genotype-phenotype association studies, variations in aGPCR contribute to specific human phenotypes. Our survey demonstrates that aGPCRs are significantly involved in adaptation processes, phenotype variations, and diseases in humans.

Genome-wide association studies of human adiposity: Zooming in on synapses

The decade anniversary for genome-wide association studies (GWAS) is approaching, and this experimental approach has commenced a deeper understanding of the genetics underlying complex diseases. In obesity genetics the GIANT (Genetic Investigation of ANthropometric Traits) consortium has played a crucial role, recently with two comprehensive meta-analyses, one focusing on general obesity, analyzing body-mass index (BMI) and the other on fat distribution, focusing on waist-hip ratio adjusted for BMI. With the in silico methods applied in these two studies as the pivot, this review looks into some of the biological knowledge, beginning to emerge from the intricate genomic background behind the genetic determinants of human adiposity. These include synaptic dysfunction, where GWAS pinpoint potential new mechanisms in pathways already known to be linked with obesity.

Complete re-sequencing of a 2Mb topological domain encompassing the FTO/IRXB genes identifies a novel obesity-associated region upstream of IRX5

BACKGROUND

Association studies have identified a number of loci that contribute to an increased body mass index (BMI), the strongest of which is in the first intron of the FTO gene on human chromosome 16q12.2. However, this region is both non-coding and under strong linkage disequilibrium, making it recalcitrant to functional interpretation. Furthermore, the FTO gene is located within a complex cis-regulatory landscape defined by a topologically associated domain that includes the IRXB gene cluster, a trio of developmental regulators. Consequently, at least three genes in this interval have been implicated in the aetiology of obesity.

METHODS

Here, we sequence a 2 Mb region encompassing the FTO, RPGRIP1L and IRXB cluster genes in 284 individuals from a well-characterised study group of Danish men containing extremely overweight young adults and controls. We further replicate our findings both in an expanded male cohort and an independent female study group. Finally, we compare our variant data with a previous study describing IRX3 and FTO interactions in this region.

RESULTS

We obtain deep coverage across the entire region, allowing accurate and unequivocal determination of almost every single nucleotide polymorphism and short insertion/deletion. As well as confirming previous findings across the interval, we identify a further novel age-dependent association upstream of IRX5 that imposes a similar burden on BMI to the FTO locus.

CONCLUSIONS

Our findings are consistent with the hypothesis that chromatin architectures play a role in regulating gene expression levels across topological domains while our targeted sequence approach represents a widely applicable methodology for high-resolution analysis of regional variation across candidate genomic loci.

Sixteen new lung function signals identified through 1000 Genomes Project reference panel imputation

Lung function measures are used in the diagnosis of chronic obstructive pulmonary disease. In 38,199 European ancestry individuals, we studied genome-wide association of forced expiratory volume in 1 s (FEV1), forced vital capacity (FVC) and FEV1/FVC with 1000 Genomes Project (phase 1)-imputed genotypes and followed up top associations in 54,550 Europeans. We identify 14 novel loci (P<5 × 10(-8)) in or near ENSA, RNU5F-1, KCNS3, AK097794, ASTN2, LHX3, CCDC91, TBX3, TRIP11, RIN3, TEKT5, LTBP4, MN1 and AP1S2, and two novel signals at known loci NPNT and GPR126, providing a basis for new understanding of the genetic determinants of these traits and pulmonary diseases in which they are altered.

Adenylyl Cyclase 9 Polymorphisms Reveal Potential Link to HDL Function and Cardiovascular Events in Multiple Pathologies: Potential Implications in Sickle Cell Disease

Adenylyl cyclase 9 (ADCY9) mediates β2-adrenoceptor (β2-AR) signalling. Both proteins are associated with caveolae, specialized cholesterol-rich membrane substructures. Apolipoprotein A1 (ApoA1), the major protein component of high-density lipoprotein (HDL), removes cholesterol from cell membrane and caveolae and may thereby influence β2-AR signalling, shown in vitro to be modulated by cholesterol. Patients with Sickle Cell Disease (SCD) typically have low HDL and ApoA1 levels. In patients, mainly of African origin, with SCD, β2-AR activation may trigger adhesion of red blood cells to endothelial cells, leading to vascular occlusive events. Moreover, ADCY9 polymorphism is associated with risk of stroke in SCD. In recent clinical trials, ADCY9 polymorphism was found to be a discriminant factor associated with the risk of cardiovascular (CV) events in Caucasian patients treated with the HDL-raising compound dalcetrapib. We hypothesize that these seemingly disparate observations share a common mechanism related to interaction of HDL/ApoA1 and ADCY9 on β2-AR signalling. This review also raises the importance of characterizing polymorphisms that determine the response to HDL-raising and -mimicking agents in the non-Caucasian population at high risk of CV diseases and suffering from SCD. This may facilitate personalized CV treatments.

Recent Progress in the Understanding of Obesity: Contributions of Genome-Wide Association Studies

Since 2007, discovery of genetic variants associated with general obesity and fat distribution has advanced tremendously through genome-wide association studies (GWAS). Currently, the number of robustly associated loci is 190. Even though these loci explain <3 % of the variance, they have provided us a still emerging picture of genomic localization, frequency and effect size spectra, and hints of functional implications. The translation into biological knowledge has turned out to be an immense task. However, in silico enrichment analyses of genes involved in specific pathways or expressed in specific tissues have the power to suggest biological mechanisms underlying obesity. Inspired by this, we highlight genes in five loci potentially mechanistically linked to leptin-receptor trafficking and signaling in primary cilia. The clinical application of genetic knowledge as prediction, prevention, or treatment strategies is unfortunately still far from reality. Thus, despite major advances, further research is warranted to solve one of the greatest health problems in modern society.

The Genetics of Pediatric Obesity

Obesity among children and adults has notably escalated over recent decades and represents a global major health problem. We now know that both genetic and environmental factors contribute to its complex etiology. Genome-wide association studies (GWAS) have revealed compelling genetic signals influencing obesity risk in adults. Recent reports for childhood obesity revealed that many adult loci also play a role in the pediatric setting. Childhood GWAS have uncovered novel loci below the detection range in adult studies, suggesting that obesity genes may be more easily uncovered in the pediatric setting. Shedding light on the genetic architecture of childhood obesity will facilitate the prevention and treatment of pediatric cases, and will have fundamental implications for diseases that present later in life.

A Comprehensive Analysis of Common and Rare Variants to Identify Adiposity Loci in Hispanic Americans: The IRAS Family Study (IRASFS)

Obesity is growing epidemic affecting 35% of adults in the United States. Previous genome-wide association studies (GWAS) have identified numerous loci associated with obesity. However, the majority of studies have been completed in Caucasians focusing on total body measures of adiposity. Here we report the results from genome-wide and exome chip association studies focusing on total body measures of adiposity including body mass index (BMI), percent body fat (PBF) and measures of fat deposition including waist circumference (WAIST), waist-hip ratio (WHR), subcutaneous adipose tissue (SAT), and visceral adipose tissue (VAT) in Hispanic Americans (n_max = 1263) from the Insulin Resistance Atherosclerosis Family Study (IRASFS). Five SNPs from two novel loci attained genome-wide significance (P<5.00x10^-8) in IRASFS. A missense SNP in the isocitrate dehydrogenase 1 gene (IDH1) was associated with WAIST (rs34218846, MAF = 6.8%, P_DOM = 1.62x10^-8). This protein is postulated to play an important role in fat and cholesterol biosynthesis as demonstrated in cell and knock-out animal models. Four correlated intronic SNPs in the Zinc finger, GRF-type containing 1 gene (ZGRF1; SNP rs1471880, MAF = 48.1%, P_DOM = 1.00x10^-8) were strongly associated with WHR. The exact biological function of ZGRF1 and the connection with adiposity remains unclear. SNPs with p-values less than 5.00x10^-6 from IRASFS were selected for replication. Meta-analysis was computed across seven independent Hispanic-American cohorts (n_max = 4156) and the strongest signal was rs1471880 (P_DOM = 8.38x10^-6) in ZGRF1 with WAIST. In conclusion, a genome-wide and exome chip association study was conducted that identified two novel loci (IDH1 and ZGRF1) associated with adiposity. While replication efforts were inconclusive, when taken together with the known biology, IDH1 and ZGRF1 warrant further evaluation.

The anatomical distribution of genetic associations

Deeper understanding of the anatomical intermediaries for disease and other complex genetic traits is essential to understanding mechanisms and developing new interventions. Existing ontology tools provide functional, curated annotations for many genes and can be used to develop mechanistic hypotheses; yet information about the spatial expression of genes may be equally useful in interpreting results and forming novel hypotheses for a trait. Therefore, we developed an approach for statistically testing the relationship between gene expression across the body and sets of candidate genes from across the genome. We validated this tool and tested its utility on three applications. First, we show that the expression of genes in associated loci from GWA studies implicates specific tissues for 57 out of 98 traits. Second, we tested the ability of the tool to identify novel relationships between gene expression and phenotypes. Specifically, we experimentally confirmed an underappreciated prediction highlighted by our tool: that white blood cell count--a quantitative trait of the immune system--is genetically modulated by genes expressed in the skin. Finally, using gene lists derived from exome sequencing data, we show that human genes under selective constraint are disproportionately expressed in nervous system tissues.

The center for causal discovery of biomedical knowledge from big data

The Big Data to Knowledge (BD2K) Center for Causal Discovery is developing and disseminating an integrated set of open source tools that support causal modeling and discovery of biomedical knowledge from large and complex biomedical datasets. The Center integrates teams of biomedical and data scientists focused on the refinement of existing and the development of new constraint-based and Bayesian algorithms based on causal Bayesian networks, the optimization of software for efficient operation in a supercomputing environment, and the testing of algorithms and software developed using real data from 3 representative driving biomedical projects: cancer driver mutations, lung disease, and the functional connectome of the human brain. Associated training activities provide both biomedical and data scientists with the knowledge and skills needed to apply and extend these tools. Collaborative activities with the BD2K Consortium further advance causal discovery tools and integrate tools and resources developed by other centers.

An Adaptive Association Test for Multiple Phenotypes with GWAS Summary Statistics

We study the problem of testing for single marker-multiple phenotype associations based on genome-wide association study (GWAS) summary statistics without access to individual-level genotype and phenotype data. For most published GWASs, because obtaining summary data is substantially easier than accessing individual-level phenotype and genotype data, while often multiple correlated traits have been collected, the problem studied here has become increasingly important. We propose a powerful adaptive test and compare its performance with some existing tests. We illustrate its applications to analyses of a meta-analyzed GWAS dataset with three blood lipid traits and another with sex-stratified anthropometric traits, and further demonstrate its potential power gain over some existing methods through realistic simulation studies. We start from the situation with only one set of (possibly meta-analyzed) genome-wide summary statistics, then extend the method to meta-analysis of multiple sets of genome-wide summary statistics, each from one GWAS. We expect the proposed test to be useful in practice as more powerful than or complementary to existing methods.

Association of angiotensin-converting enzyme I/D and α-actinin-3 R577X genotypes with metabolic syndrome risk factors in Korean children

OBJECTIVE

This study analysed the risk factors associated with metabolic syndrome through the interaction between ACTN3 and ACE gene polymorphism in Korean children.

METHODS

The subjects of the study consisted of elementary school students (n=788, age 10.10±0.07 yr). The anthropometric parameters, blood lipid profiles, and metabolic markers were compared among groups of the ACE I/D or the ACTN3 R577X polymorphisms.

RESULTS

The subjects with the DD genotype showed significantly higher systolic blood pressure than the subjects with the II and ID genotype of the ACE gene polymorphism. XX genotype had significantly lower waist-hip ratio than those with RR genotype of the ACTN3 gene polymorphism. Also, the subjects with XX genotype exhibited significantly higher blood HDL cholesterol level than those with RR or RX genotype. The interaction of ACTN3 and ACE gene polymorphism in subjects having both ACE DD and ACTN3 RR genotypes demonstrated a significantly higher metabolic syndrome score than any other groups.

CONCLUSION

The children having both ACTN3 RR or RX genotype and ACE DD genotype showed high systolic blood pressure and low blood HDL cholesterol level, which may be considered a high-risk in metabolic syndrome.

Insights into the Genetic Susceptibility to Type 2 Diabetes from Genome-Wide Association Studies of Obesity-Related Traits

Obesity and type 2 diabetes (T2D) are common and complex metabolic diseases, which are caused by an interchange between environmental and genetic factors. Recently, a number of large-scale genome-wide association studies (GWAS) have improved our knowledge of the genetic architecture and biological mechanisms of these diseases. Currently, more than ~250 genetic loci have been found for monogenic, syndromic, or common forms of T2D and/or obesity-related traits. In this review, we discuss the implications of these GWAS for obesity and T2D, and investigate the overlap of loci for obesity-related traits and T2D, highlighting potential mechanisms that affect T2D susceptibility.

A Bayesian Approach to the Overlap Analysis of Epidemiologically Linked Traits

Diseases often cooccur in individuals more often than expected by chance, and may be explained by shared underlying genetic etiology. A common approach to genetic overlap analyses is to use summary genome‐wide association study data to identify single‐nucleotide polymorphisms (SNPs) that are associated with multiple traits at a selected P‐value threshold. However, P‐values do not account for differences in power, whereas Bayes’ factors (BFs) do, and may be approximated using summary statistics. We use simulation studies to compare the power of frequentist and Bayesian approaches with overlap analyses, and to decide on appropriate thresholds for comparison between the two methods. It is empirically illustrated that BFs have the advantage over P‐values of a decreasing type I error rate as study size increases for single‐disease associations. Consequently, the overlap analysis of traits from different‐sized studies encounters issues in fair P‐value threshold selection, whereas BFs are adjusted automatically. Extensive simulations show that Bayesian overlap analyses tend to have higher power than those that assess association strength with P‐values, particularly in low‐power scenarios. Calibration tables between BFs and P‐values are provided for a range of sample sizes, as well as an approximation approach for sample sizes that are not in the calibration table. Although P‐values are sometimes thought more intuitive, these tables assist in removing the opaqueness of Bayesian thresholds and may also be used in the selection of a BF threshold to meet a certain type I error rate. An application of our methods is used to identify variants associated with both obesity and osteoarthritis.

Rodent models for resolving extremes of exercise and health

The extremes of exercise capacity and health are considered a complex interplay between genes and the environment. In general, the study of animal models has proven critical for deep mechanistic exploration that provides guidance for focused and hypothesis-driven discovery in humans. Hypotheses underlying molecular mechanisms of disease and gene/tissue function can be tested in rodents to generate sufficient evidence to resolve and progress our understanding of human biology. Here we provide examples of three alternative uses of rodent models that have been applied successfully to advance knowledge that bridges our understanding of the connection between exercise capacity and health status. First we review the strong association between exercise capacity and all-cause morbidity and mortality in humans through artificial selection on low and high exercise performance in the rat and the consequent generation of the "energy transfer hypothesis." Second we review specific transgenic and knockout mouse models that replicate the human disease condition and performance. This includes human glycogen storage diseases (McArdle and Pompe) and α-actinin-3 deficiency. Together these rodent models provide an overview of the advancements of molecular knowledge required for clinical translation. Continued study of these models in conjunction with human association studies will be critical to resolving the complex gene-environment interplay linking exercise capacity, health, and disease.

Perspectives on pharmacogenomics of antiretroviral medications and HIV-associated comorbidities

PURPOSE OF REVIEW

To summarize current knowledge and provide perspective on relationships between human genetic variants, antiretroviral medications, and aging-related complications of HIV-1 infection.

RECENT FINDINGS

Human genetic variants have been convincingly associated with interindividual variability in antiretroviral toxicities, drug disposition, and aging-associated complications in HIV-1 infection. Screening for HLA-B5701 to avoid abacavir hypersensitivity reactions has become a routine part of clinical care, and has markedly improved drug safety. There are well established pharmacogenetic associations with other agents (efavirenz, nevirapine, atazanavir, dolutegravir, and others), but this knowledge has yet to have substantial impact on HIV-1 clinical care. As metabolic complications including diabetes mellitus, dyslipidemia, osteoporosis, and cardiovascular disease are becoming an increasing concern among individuals who are aging with well controlled HIV-1 infection, human genetic variants that predispose to these complications also become more relevant in this population.

SUMMARY

Pharmacogenetic knowledge has already had considerable impact on antiretroviral prescribing. With continued advances in the field of human genomics, the impact of pharmacogenomics on HIV-1 clinical care and research is likely to continue to grow in importance and scope.

Automated anthropometric phenotyping with novel Kinect-based three-dimensional imaging method: comparison with a reference laser imaging system

BACKGROUND/OBJECTIVES

Anthropometry for measuring body composition, shape, surface area and volume is important for human clinical research and practice. Although training and technical skills are required for traditional tape and caliper anthropometry, a new opportunity exists for automated measurement using newly developed relatively low-cost three-dimensional (3D) imaging devices. The aim of this study was to compare results provided by a Kinect-based device to a traditional laser 3D reference system.

SUBJECTS/METHODS

Measurements made by the evaluated device, a hybrid of commercially purchased hardware (KX-16; TC(2), Cary, NC, USA) with our additional added software, were compared with those derived by a high-resolution laser scanner (Vitus Smart XXL; Human Solutions North America, Cary, NC, USA). Both imaging systems were compared with additional linear (stadiometer-derived height) and volumetric (total volume, air-displacement plethysmography) measurements. Subjects (n=101) were healthy children (age ≥5 years) and adults varying in body mass index.

RESULTS

Representative linear (4), circumferential (6), volumetric (3) and surface area (1) measurements made by the Kinect-based device showed a consistent pattern relative to the laser system: high correlations (R(2)s= 0.70-0.99, all P<0.001); 1-3% differences for large linear (for example, height, X±s.d., -1.4±0.5%), circumferential (for example, waist circumference, -2.1±1.8%), volume (for example, total body, -0.8±2.2%) and surface area (whole-body, -1.7±2.0%) estimates. By contrast, mean measurement differences were substantially larger for small structures (for example, forearm volume, 31.3±31.4%).

CONCLUSIONS

Low-cost 3D Kinect-based imaging systems have the potential for providing automated accurate anthropometric and related body measurements for relatively large components; further hardware and software developments may be able to improve system small-component resolution.

Comparative Analyses of QTLs Influencing Obesity and Metabolic Phenotypes in Pigs and Humans

The pig is a well-known animal model used to investigate genetic and mechanistic aspects of human disease biology. They are particularly useful in the context of obesity and metabolic diseases because other widely used models (e.g. mice) do not completely recapitulate key pathophysiological features associated with these diseases in humans. Therefore, we established a F2 pig resource population (n = 564) designed to elucidate the genetics underlying obesity and metabolic phenotypes. Segregation of obesity traits was ensured by using breeds highly divergent with respect to obesity traits in the parental generation. Several obesity and metabolic phenotypes were recorded (n = 35) from birth to slaughter (242 ± 48 days), including body composition determined at about two months of age (63 ± 10 days) via dual-energy x-ray absorptiometry (DXA) scanning. All pigs were genotyped using Illumina Porcine 60k SNP Beadchip and a combined linkage disequilibrium-linkage analysis was used to identify genome-wide significant associations for collected phenotypes. We identified 229 QTLs which associated with adiposity- and metabolic phenotypes at genome-wide significant levels. Subsequently comparative analyses were performed to identify the extent of overlap between previously identified QTLs in both humans and pigs. The combined analysis of a large number of obesity phenotypes has provided insight in the genetic architecture of the molecular mechanisms underlying these traits indicating that QTLs underlying similar phenotypes are clustered in the genome. Our analyses have further confirmed that genetic heterogeneity is an inherent characteristic of obesity traits most likely caused by segregation or fixation of different variants of the individual components belonging to cellular pathways in different populations. Several important genes previously associated to obesity in human studies, along with novel genes were identified. Altogether, this study provides novel insight that may further the current understanding of the molecular mechanisms underlying human obesity.

Two further patients with the 1q24 deletion syndrome expand the phenotype: A possible role for the miR199-214 cluster in the skeletal features of the condition

Submicroscopic deletions within chromosome 1q24q25 are associated with a syndromic phenotype of short stature, brachydactyly, learning difficulties, and facial dysmorphism. The critical region for the deletion phenotype has previously been narrowed to a 1.9 Mb segment containing 13 genes. We describe two further patients with 1q24 microdeletions and the skeletal phenotype, the first of whom has normal intellect, whereas the second has only mild learning impairment. The deletion in the first patient is very small and further narrows the critical interval for the striking skeletal aspects of this condition to a region containing only Dynamin 3 (DNM3) and two microRNAs that are harbored within intron 14 of this gene: miR199 and miR214. Mouse studies raise the possibility that these microRNAs may be implicated in the short stature and skeletal abnormalities of this microdeletion condition. The deletion in the second patient spans the previously reported critical region and indicates that the cognitive impairment may not always be as severe as previous reports suggest.

Sequence Kernel Association Analysis of Rare Variant Set Based on the Marginal Regression Model for Binary Traits

Recent sequencing efforts have focused on exploring the influence of rare variants on the complex diseases. Gene level based tests by aggregating information across rare variants within a gene have become attractive to enrich the rare variant association signal. Among them, the sequence kernel association test (SKAT) has proved to be a very powerful method for jointly testing multiple rare variants within a gene. In this article, we explore an alternative SKAT. We propose to use the univariate likelihood ratio statistics from the marginal model for individual variants as input into the kernel association test. We show how to compute its significance P-value efficiently based on the asymptotic chi-square mixture distribution. We demonstrate through extensive numerical studies that the proposed method has competitive performance. Its usefulness is further illustrated with application to associations between rare exonic variants and type 2 diabetes (T2D) in the Atherosclerosis Risk in Communities (ARIC) study. We identified an exome-wide significant rare variant set in the gene ZZZ3 worthy of further investigations.

Pathway analysis of body mass index genome-wide association study highlights risk pathways in cardiovascular disease

Cardiovascular disease (CVD) is a class of diseases that involve the heart or blood vessels. It is reported that body mass index (BMI) is risk factor for CVD. Genome-wide association studies (GWAS) have recently provided rapid insights into genetics of CVD and its risk factors. However, the specific mechanisms how BMI influences CVD risk are largely unknown. We think that BMI may influences CVD risk by shared genetic pathways. In order to confirm this view, we conducted a pathway analysis of BMI GWAS, which examined approximately 329,091 single nucleotide polymorphisms from 4763 samples. We identified 31 significant KEGG pathways. There is literature evidence supporting the involvement of GnRH signaling, vascular smooth muscle contraction, dilated cardiomyopathy, Gap junction, Wnt signaling, Calcium signaling and Chemokine signaling in CVD. Collectively, our study supports the potential role of the CVD risk pathways in BMI. BMI may influence CVD risk by the shared genetic pathways. We believe that our results may advance our understanding of BMI mechanisms in CVD.

New insights from monogenic diabetes for "common" type 2 diabetes

Boundaries between monogenic and complex genetic diseases are becoming increasingly blurred, as a result of better understanding of phenotypes and their genetic determinants. This had a large impact on the way complex disease genetics is now being investigated. Starting with conventional approaches like familial linkage, positional cloning and candidate genes strategies, the scope of complex disease genetics has grown exponentially with scientific and technological advances in recent times. Despite identification of multiple loci harboring common and rare variants associated with complex diseases, interpreting and evaluating their functional role has proven to be difficult. Information from monogenic diseases, especially related to the intermediate traits associated with complex diseases comes handy. The significant overlap between traits and phenotypes of monogenic diseases with related complex diseases provides a platform to understand the disease biology better. In this review, we would discuss about one such complex disease, type 2 diabetes, which shares marked similarity of intermediate traits with different forms of monogenic diabetes.

Increased genetic risk for obesity in premature coronary artery disease

There is ongoing controversy as to whether obesity confers risk for CAD independently of associated risk factors including diabetes mellitus. We have carried out a Mendelian randomization study using a genetic risk score (GRS) for body mass index (BMI) based on 35 risk alleles to investigate this question in a population of 5831 early onset CAD cases without diabetes mellitus and 3832 elderly healthy control subjects, all of strictly European ancestry, with adjustment for traditional risk factors (TRFs). We then estimated the genetic correlation between these BMI and CAD (rg) by relating the pairwise genetic similarity matrix to a phenotypic covariance matrix between these two traits. GRS_BMI significantly (P=2.12 × 10⁻¹²) associated with CAD status in a multivariate model adjusted for TRFs, with a per allele odds ratio (OR) of 1.06 (95% CI 1.042–1.076). The addition of GRS_BMI to TRFs explained 0.75% of CAD variance and yielded a continuous net recombination index of 16.54% (95% CI=11.82–21.26%, P<0.0001). To test whether GRS_BMI explained CAD status when adjusted for measured BMI, separate models were constructed in which the score and BMI were either included as covariates or not. The addition of BMI explained ~1.9% of CAD variance and GRS_BMI plus BMI explained 2.65% of CAD variance. Finally, using bivariate restricted maximum likelihood analysis, we provide strong evidence of genome-wide pleiotropy between obesity and CAD. This analysis supports the hypothesis that obesity is a causal risk factor for CAD.

Leveraging Epidemiologic and Clinical Collections for Genomic Studies of Complex Traits

BACKGROUND/AIMS

Present-day limited resources demand DNA and phenotyping alternatives to the traditional prospective population-based epidemiologic collections.

METHODS

To accelerate genomic discovery with an emphasis on diverse populations, we--as part of the Epidemiologic Architecture for Genes Linked to Environment (EAGLE) study--accessed all non-European American samples (n = 15,863) available in BioVU, the Vanderbilt University biorepository linked to de-identified electronic medical records, for genomic studies as part of the larger Population Architecture using Genomics and Epidemiology (PAGE) I study. Given previous studies have cautioned against the secondary use of clinically collected data compared with epidemiologically collected data, we present here a characterization of EAGLE BioVU, including the billing and diagnostic (ICD-9) code distributions for adult and pediatric patients as well as comparisons made for select health metrics (body mass index, glucose, HbA1c, HDL-C, LDL-C, and triglycerides) with the population-based National Health and Nutrition Examination Surveys (NHANES) linked to DNA samples (NHANES III, n = 7,159; NHANES 1999-2002, n = 7,839).

RESULTS

Overall, the distributions of billing and diagnostic codes suggest this clinical sample is a mixture of healthy and sick patients like that expected for a contemporary American population.

CONCLUSION

Little bias is observed among health metrics, suggesting this clinical collection is suitable for genomic studies along with traditional epidemiologic cohorts.

Differences in motor performance between children and adolescents in Mozambique and Portugal: impact of allometric scaling

BACKGROUND

Children from developed and developing countries have different anthropometric characteristics which may affect their motor performance (MP).

AIM

To use the allometric approach to model the relationship between body size and MP in youth from two countries differing in socio-economic status-Portugal and Mozambique.

SUBJECTS AND METHODS

A total of 2946 subjects, 1280 Mozambicans (688 girls) and 1666 Portuguese (826 girls), aged 10-15 years were sampled. Height and weight were measured and the reciprocal ponderal index (RPI) was computed. MP included handgrip strength, 1-mile run/walk, curl-ups and standing long jump tests. A multiplicative allometric model was adopted to adjust for body size differences across countries.

RESULTS

Differences in MP between Mozambican and Portuguese children exist, invariably favouring the latter. The allometric models used to adjust MP for differences in body size identified the optimal body shape to be either the RPI or even more linear, i.e. approximately (height/mass(0.25)). Having adjusted the MP variables for differences in body size, the differences between Mozambican and Portuguese children were invariably reduced and, in the case of grip strength, reversed.

CONCLUSION

These results reinforce the notion that significant differences exist in MP across countries, even after adjusting for differences in body size.

Coexpression networks identify brain region-specific enhancer RNAs in the human brain

Despite major progress in identifying enhancer regions on a genome-wide scale, the majority of available data are limited to model organisms and human transformed cell lines. We have identified a robust set of enhancer RNAs (eRNAs) expressed in the human brain and constructed networks assessing eRNA-gene coexpression interactions across human fetal brain and multiple adult brain regions. Our data identify brain region-specific eRNAs and show that enhancer regions expressing eRNAs are enriched for genetic variants associated with autism spectrum disorders.

Genetic Determinants of Metabolism and Benign Prostate Enlargement: Associations with Prostate Volume

Prostate enlargement leading to clinical benign prostatic hyperplasia (BPH) is associated with metabolic dysregulation and obesity. The genetic basis of this association is unclear. Our objective was to evaluate whether single nucleotide polymorphisms (SNPs) previously associated with metabolic disorders are also associated with prostate volume (PV). Participants included 876 men referred for prostate biopsy and found to be prostate cancer free. PV was measured by transrectal ultrasound. Samples were genotyped using the Illumina Cardio-MetaboChip platform. Multivariable adjusted linear regression models were used to evaluate SNPs (additive coding) in relation to natural-log transformed (log) PV. We compared SNP-PV results from biopsy-negative men to 442 men with low-grade prostate cancer with similar levels of obesity and PV. Beta-coefficients from the discovery and replication samples were then aggregated with fixed effects inverse variance weighted meta-analysis. SNP rs11736129 (near the pseudo-gene LOC100131429) was significantly associated with log-PV (beta: 0.16, p-value 1.16x10^-8) after adjusting for multiple testing. Other noteworthy SNPs that were nominally associated (p-value < 1x10^-4) with log-PV included rs9583484 (intronic SNP in COL4A2), rs10146527 (intronic SNP in NRXN3), rs9909466 (SNP near RPL32P31), and rs2241606 (synonymous SNP in SLC12A7). We found several SNPs in metabolic loci associated with PV. Further studies are needed to confirm our results and elucidate the mechanism between these genetic loci, PV, and clinical BPH.

Pharmacogenomic variants have larger effect sizes than genetic variants associated with other dichotomous complex traits

It has been suggested that pharmacogenomic phenotypes are influenced by genetic variants with larger effect sizes than other phenotypes, such as complex disease risk. This is presumed to reflect the fact that relevant environmental factors (drug exposure) are appropriately measured and taken into account. To test this hypothesis, we performed a systematic comparison of effect sizes between pharmacogenomic and non-pharmacogenomic phenotypes across all genome-wide association studies (GWAS) reported in the NHGRI GWAS catalog. We found significantly larger effect sizes for studies focused on pharmacogenomic phenotypes, as compared to complex disease risk, morphological phenotypes, and endophenotypes. We found no significant differences in effect sizes between pharmacogenomic studies focused on adverse events versus those focused on drug efficacy. Furthermore, we found that this pattern persists among sample size-matched studies, suggesting that this pattern does not reflect over-estimation of effect sizes due to smaller sample sizes in pharmacogenomic studies.

Low Copy Number of the AMY1 Locus Is Associated with Early-Onset Female Obesity in Finland

Background

The salivary α-amylase locus (AMY1) is located in a highly polymorphic multi allelic copy number variable chromosomal region. A recent report identified an association between AMY1 copy numbers and BMI in common obesity. The present study investigated the relationship between AMY1 copy number, BMI and serum amylase in childhood-onset obesity.

Patients

Sixty-one subjects with a history of childhood-onset obesity (mean age 19.1 years, 54% males) and 71 matched controls (19.8 yrs, 45% males) were included. All anthropometric measures were greater in the obese; their mean BMI was 40 kg/m² (range 25-62 kg/m²) compared with 23 kg/m² in the controls (15-32 kg/m²).

Results

Mean AMY1 copy numbers did not differ between the obese and control subjects, but gender differences were observed; obese men showed the highest and obese women the lowest number of AMY1 copies (p=0.045). Further, only in affected females, AMY1 copy number correlated significantly with whole body fat percent (r=-0.512, p=0.013) and BMI (r=-0.416, p=0.025). Finally, a clear linear association between AMY1 copy number and serum salivary amylase was observed in all subgroups but again differences existed between obese males and females.

Conclusions

In conclusion, our findings suggest that AMY1 copy number differences play a role in childhood-onset obesity but the effect differs between males and females. Further studies in larger cohorts are needed to confirm these observations.

The European Genome-phenome Archive of human data consented for biomedical research

The European Genome-phenome Archive (EGA) is a permanent archive that promotes distribution and sharing of genetic and phenotype data consented for specific approved uses, but not fully open public distribution. The EGA follows strict protocols for information management, data storage, security and dissemination. Authorized access to the data is managed in partnership with the data providing organizations. The EGA includes major reference data collections for human genetics research.

Discovery and Fine-Mapping of Glycaemic and Obesity-Related Trait Loci Using High-Density Imputation

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.

Genetic variation within transcriptional regulatory elements and its implications for human disease

Common human pathologies have a complicated etiology involving both genetic and environmental risk factors. Moreover, the genetic basis of these disorders is also complex, with multiple and weak genetic variants contributing to disease susceptibility. In addition, most of these risk genetic variants occur outside genes, within the vast non-coding human genomic space. In this review I first illustrate how large-scale genomic studies aimed at mapping cis-regulatory elements in the human genome are facilitating the identification of disease-causative non-coding genetic variation. I then discuss some of the challenges that remain to be solved before the pathological consequences of non-coding genetic variation can be fully appreciated. Ultimately, revealing the genetics of human complex disease can be a critical step towards more personalized and effective diagnosis and treatments.

Structural forms of the human amylase locus and their relationships to SNPs, haplotypes and obesity

Hundreds of genes reside in structurally complex, poorly understood regions of the human genome^1-3. One such region contains the three amylase genes (AMY2B, AMY2A, and AMY1) responsible for digesting starch into sugar. The copy number of AMY1 is reported to be the genome’s largest influence on obesity⁴, though genome-wide association studies for obesity have found this locus unremarkable. Using whole genome sequence analysis^3,5, droplet digital PCR⁶, and genome mapping⁷, we identified eight common structural haplotypes of the amylase locus that suggest its mutational history. We found that AMY1 copy number in individuals’ genomes is generally even (rather than odd) and partially correlates to nearby SNPs, which do not associate with BMI. We measured amylase gene copy number in 1,000 obese or lean Estonians and in two other cohorts totaling ~3,500 individuals. We had 99% power to detect the lower bound of the reported effects on BMI⁴, yet found no association.

Role of the natriuretic peptide system in normal growth and growth disorders

The C-type natriuretic peptide (CNP) and its receptor (NPR-B) are recognized as important regulators of longitudinal growth. Animal models involving CNP or NPR-B genes (Nppc or Npr2) support the fundamental role of CNP/NPR-B for endochondral ossification. Studies with these animals allow the development of potential drug therapies for dwarfism. Polymorphisms in two genes related to the CNP pathway have been implicated in height variability in healthy individuals. Biallelic loss-of-function mutations in NPR-B gene (NPR2) cause acromesomelic dysplasia type Maroteux, a skeletal dysplasia with extremely short stature. Heterozygous mutations in NPR2 are responsible for nonsyndromic familial short stature. Conversely, heterozygous gain-of-function mutations in NPR2 cause tall stature, with a variable phenotype. A phase 2 multicenter and multinational trial is being developed to evaluate a CNP analog treatment for achondroplasia. Pediatricians and endocrinologists must be aware of growth disorders related to natriuretic peptides, although there is still much to be learned about its diagnostic and therapeutic use.

Genome-wide association study identifies novel genetic variants contributing to variation in blood metabolite levels

Metabolites are small molecules involved in cellular metabolism, which can be detected in biological samples using metabolomic techniques. Here we present the results of genome-wide association and meta-analyses for variation in the blood serum levels of 129 metabolites as measured by the Biocrates metabolomic platform. In a discovery sample of 7,478 individuals of European descent, we find 4,068 genome- and metabolome-wide significant (Z-test, P < 1.09 × 10(-9)) associations between single-nucleotide polymorphisms (SNPs) and metabolites, involving 59 independent SNPs and 85 metabolites. Five of the fifty-nine independent SNPs are new for serum metabolite levels, and were followed-up for replication in an independent sample (N = 1,182). The novel SNPs are located in or near genes encoding metabolite transporter proteins or enzymes (SLC22A16, ARG1, AGPS and ACSL1) that have demonstrated biomedical or pharmaceutical importance. The further characterization of genetic influences on metabolic phenotypes is important for progress in biological and medical research.

Adipose Tissue in Metabolic Syndrome: Onset and Progression of Atherosclerosis

Metabolic syndrome (MetS) should be considered a clinical entity when its different symptoms share a common etiology: obesity/insulin resistance as a result of a multi-organ dysfunction. The main interest in treating MetS as a clinical entity is that the addition of its components drastically increases the risk of atherosclerosis. In MetS, the adipose tissue plays a central role along with an unbalanced gut microbiome, which has become relevant in recent years. Once visceral adipose tissue (VAT) increases, dyslipidemia and endothelial dysfunction follow as additive risk factors. However, when the nonalcoholic fatty liver is present, risk of a cardiovascular event is highly augmented. Epicardial adipose tissue (EAT) seems to increase simultaneously with the VAT. In this context, the former may play a more important role in the development of the atherosclerotic plaque than the latter. Hence, EAT may act as a paracrine tissue vis-à-vis the coronary arteries favoring the local inflammation and the atheroma calcification.

Potential Signal Transduction Regulation by HDL of the β2-Adrenergic Receptor Pathway. Implications in Selected Pathological Situations

The main atheroprotective mechanism of high-density lipoprotein (HDL) has been regarded as reverse cholesterol transport, whereby cholesterol from peripheral tissues is removed and transported to the liver for elimination. Although numerous additional atheroprotective mechanisms have been suggested, the role of HDL in modulating signal transduction of cell membrane-bound receptors has received little attention to date. This potential was recently highlighted following the identification of a polymorphism in the adenylyl cyclase 9 gene (ADCY9) that was shown to be a determining factor in the risk of cardiovascular (CV) events in patients treated with the HDL-raising compound dalcetrapib. Indeed, ADCY9 is part of the signaling pathway of the β2-adrenergic receptor (β2-AR) and both are membrane-bound proteins affected by changes in membrane-rich cholesterol plasma membrane domains (caveolae). Numerous G-protein-coupled receptors (GPCRs) and ion channels are affected by caveolae, with caveolae composition acting as a 'signalosome'. Polymorphisms in the genes encoding ADCY9 and β2-AR are associated with response to β2-agonist drugs in patients with asthma, malaria and with sickle cell disease. Crystallization of the β2-AR has found cholesterol tightly bound to transmembrane structures of the receptor. Cholesterol has also been shown to modulate the activity of this receptor. Apolipoprotein A1 (ApoA1), the major protein component of HDL, destabilizes and removes cholesterol from caveolae with high affinity through interaction with ATP-binding cassette transporter. Furthermore, β2-AR activity may be affected by ApoA1/HDL-targeted therapies. Taken together, these observations suggest a common pathway that potentially links a primary HDL function to the regulation of signal transduction.

Olfactory receptor genes cooperate with protocadherin genes in human extreme obesity

Worldwide, the incidence of obesity has increased dramatically over the past decades. More knowledge about the complex etiology of obesity is needed in order to find additional approaches for treatment and prevention. Investigating the exome sequencing data of 30 extremely obese subjects (BMI 45-65 kg/m(2)) shows that predicted damaging missense variants in olfactory receptor genes on chromosome 1q and rare predicted damaging variants in the protocadherin (PCDH) beta-cluster genes on chromosome 5q31, reported in our previous work, co-localize in subjects with extreme obesity. This implies a synergistic effect between genetic variation in these gene clusters in the predisposition to extreme obesity. Evidence for a general involvement of the olfactory transduction pathway on itself could not be found. Bioinformatic analysis indicates a specific involvement of the PCDH beta-cluster genes in controlling tissue development. Further mechanistic insight needs to await the identification of the ligands of the 1q olfactory receptors. Eventually, this may provide the possibility to manipulate food flavor in a way to reduce the risk of overeating and of extreme obesity in genetically predisposed subjects.

The relationship between NR2E1 and subclinical inflammation in newly diagnosed type 2 diabetic patients

AIMS

To evaluate the expression level of NR2E1 (nuclear receptor subfamily 2,group E,member 1) and its correlation with type 2 diabetes (T2DM).

METHODS

Plasma and peripheral blood mononuclear cells (PBMCs) were collected from 54 T2DM and 88 healthy individuals. The levels of free fatty acids (FFAs), total cholesterol (TC), triglyceride (TG), high density lipoprotein (HDL-c), low density lipoprotein (LDL-c), fasting insulin (FIN), and fasting blood glucose (FBG) were measured. The insulin resistance index was calculated using the homeostasis model assessment (HOMA). NR2E1 in PBMCs were analyzed using real-time RT-PCR and Western blots. Tumor necrosis factor α (TNF-α) and interleukin-6 (IL-6) in the plasma were measured by enzyme-linked immunosorbent assay (ELISA). PBMCs isolated from healthy volunteers were treated with glucose or palmitate for 24h, followed by analysis for the expression level of NR2E1.The amount of TNF-α and IL-6 secreted into the supernatant were measured by ELISA.

RESULTS

FIN, FBG, HOMA and TNF-α, IL-6 were significantly higher in diabetic patients, compared to the control group. Levels of NR2E1 were significantly higher in the PBMCs isolated from the diabetic group, compared to the control group. NR2E1 expression was positively correlated with FBG, FIN, HOMA, FFAs, TNF-α and IL-6. Glucose and palmitate treatment significantly increased NR2E1 gene expression and inflammatory cytokines production in PBMCs in vitro.

CONCLUSIONS

Increased NR2E1 level may be closely associated with inflammation and disorder of lipid and glucose metabolism in diabetic patients.

An update on the genetic architecture of hyperuricemia and gout

Genome-wide association studies that scan the genome for common genetic variants associated with phenotype have greatly advanced medical knowledge. Hyperuricemia is no exception, with 28 loci identified. However, genetic control of pathways determining gout in the presence of hyperuricemia is still poorly understood. Two important pathways determining hyperuricemia have been confirmed (renal and gut excretion of uric acid with glycolysis now firmly implicated). Major urate loci are SLC2A9 and ABCG2. Recent studies show that SLC2A9 is involved in renal and gut excretion of uric acid and is implicated in antioxidant defense. Although etiological variants at SLC2A9 are yet to be identified, it is clear that considerable genetic complexity exists at the SLC2A9 locus, with multiple statistically independent genetic variants and local epistatic interactions. The positions of implicated genetic variants within or near chromatin regions involved in transcriptional control suggest that this mechanism (rather than structural changes in SLC2A9) is important in regulating the activity of SLC2A9. ABCG2 is involved primarily in extra-renal uric acid under-excretion with the etiological variant influencing expression. At the other 26 loci, probable causal genes can be identified at three (PDZK1, SLC22A11, and INHBB) with strong candidates at a further 10 loci. Confirmation of the causal gene will require a combination of re-sequencing, trans-ancestral mapping, and correlation of genetic association data with expression data. As expected, the urate loci associate with gout, although inconsistent effect sizes for gout require investigation. Finally, there has been no genome-wide association study using clinically ascertained cases to investigate the causes of gout in the presence of hyperuricemia. In such a study, use of asymptomatic hyperurcemic controls would be expected to increase the ability to detect genetic associations with gout.

Contribution of large region joint associations to complex traits genetics

A polygenic model of inheritance, whereby hundreds or thousands of weakly associated variants contribute to a trait’s heritability, has been proposed to underlie the genetic architecture of complex traits. However, relatively few genetic variants have been positively identified so far and they collectively explain only a small fraction of the predicted heritability. We hypothesized that joint association of multiple weakly associated variants over large chromosomal regions contributes to complex traits variance. Confirmation of such regional associations can help identify new loci and lead to a better understanding of known ones. To test this hypothesis, we first characterized the ability of commonly used genetic association models to identify large region joint associations. Through theoretical derivation and simulation, we showed that multivariate linear models where multiple SNPs are included as independent predictors have the most favorable association profile. Based on these results, we tested for large region association with height in 3,740 European participants from the Health and Retirement Study (HRS) study. Adjusting for SNPs with known association with height, we demonstrated clustering of weak associations (p = 2x10^-4) in regions extending up to 433.0 Kb from known height loci. The contribution of regional associations to phenotypic variance was estimated at 0.172 (95% CI 0.063-0.279; p < 0.001), which compared favorably to 0.129 explained by known height variants. Conversely, we showed that suggestively associated regions are enriched for known height loci. To extend our findings to other traits, we also tested BMI, HDLc and CRP for large region associations, with consistent results for CRP. Our results demonstrate the presence of large region joint associations and suggest these can be used to pinpoint weakly associated SNPs.

It is widely accepted that genetics influences a broad range of human traits and diseases, yet only a few genetic variants are known to determine these traits and their impact is modest. In this report, we made the hypothesis that combining information from a large number of genetic variants would help better explain how they together contribute to traits such as height. To do so, we first had to select a proper method to integrate large numbers of genetic variants in a single test, here named “large region joint association”. Next, we tested our method on height in 3,740 European participants from the Health and Retirement Study. We showed that the contribution of regional associations to variation in height was 17.2%, as compared to the 12.9% explained by known genetic determinants of height. In other words, the joint effect of multiple genetic variants integrated together contributed to a substantial fraction of the genetics of height. These results are significant because they can help identify new genes or genetic regions associated with human traits or diseases. Conversely, these results can be used to better understand genes that we already know are associated. Furthermore, our results provide insights on how traits are genetically determined.

Genome-wide blood DNA methylation alterations at regulatory elements and heterochromatic regions in monozygotic twins discordant for obesity and liver fat

Background

The current epidemic of obesity and associated diseases calls for swift actions to better understand the mechanisms by which genetics and environmental factors affect metabolic health in humans. Monozygotic (MZ) twin pairs showing discordance for obesity suggest that epigenetic influences represent one such mechanism. We studied genome-wide leukocyte DNA methylation variation in 30 clinically healthy young adult MZ twin pairs discordant for body mass index (BMI; average within-pair BMI difference: 5.4 ± 2.0 kg/m²).

Results

There were no differentially methylated cytosine-guanine (CpG) sites between the co-twins discordant for BMI. However, stratification of the twin pairs based on the level of liver fat accumulation revealed two epigenetically highly different groups. Significant DNA methylation differences (n = 1,236 CpG sites (CpGs)) between the co-twins were only observed if the heavier co-twins had excessive liver fat (n = 13 twin pairs). This unhealthy pattern of obesity was coupled with insulin resistance and low-grade inflammation. The differentially methylated CpGs included 23 genes known to be associated with obesity, liver fat, type 2 diabetes mellitus (T2DM) and metabolic syndrome, and potential novel metabolic genes. Differentially methylated CpG sites were overrepresented at promoters, insulators, and heterochromatic and repressed regions. Based on predictions by overlapping histone marks, repressed and weakly transcribed sites were significantly more often hypomethylated, whereas sites with strong enhancers and active promoters were hypermethylated. Further, significant clustering of differentially methylated genes in vitamin, amino acid, fatty acid, sulfur, and renin-angiotensin metabolism pathways was observed.

Conclusions

The methylome in leukocytes is altered in obesity associated with metabolic disturbances, and our findings indicate several novel candidate genes and pathways in obesity and obesity-related complications.

Electronic supplementary material

The online version of this article (doi:10.1186/s13148-015-0073-5) contains supplementary material, which is available to authorized users.

The genetics of diabetic complications

The rising global prevalence of diabetes mellitus is accompanied by an increasing burden of morbidity and mortality that is attributable to the complications of chronic hyperglycaemia. These complications include blindness, renal failure and cardiovascular disease. Current therapeutic options for chronic hyperglycaemia reduce, but do not eradicate, the risk of these complications. Success in defining new preventative and therapeutic strategies hinges on an improved understanding of the molecular processes involved in the development of these complications. This Review explores the role of human genetics in delivering such insights, and describes progress in characterizing the sequence variants that influence individual predisposition to diabetic kidney disease, retinopathy, neuropathy and accelerated cardiovascular disease. Numerous risk variants for microvascular complications of diabetes have been reported, but very few have shown robust replication. Furthermore, only limited evidence exists of a difference in the repertoire of risk variants influencing macrovascular disease between those with and those without diabetes. Here, we outline the challenges associated with the genetic analysis of diabetic complications and highlight ongoing efforts to deliver biological insights that can drive translational benefits.

DISSCO: direct imputation of summary statistics allowing covariates

BACKGROUND

Imputation of individual level genotypes at untyped markers using an external reference panel of genotyped or sequenced individuals has become standard practice in genetic association studies. Direct imputation of summary statistics can also be valuable, for example in meta-analyses where individual level genotype data are not available. Two methods (DIST and ImpG-Summary/LD), that assume a multivariate Gaussian distribution for the association summary statistics, have been proposed for imputing association summary statistics. However, both methods assume that the correlations between association summary statistics are the same as the correlations between the corresponding genotypes. This assumption can be violated in the presence of confounding covariates.

METHODS

We analytically show that in the absence of covariates, correlation among association summary statistics is indeed the same as that among the corresponding genotypes, thus serving as a theoretical justification for the recently proposed methods. We continue to prove that in the presence of covariates, correlation among association summary statistics becomes the partial correlation of the corresponding genotypes controlling for covariates. We therefore develop direct imputation of summary statistics allowing covariates (DISSCO).

RESULTS

We consider two real-life scenarios where the correlation and partial correlation likely make practical difference: (i) association studies in admixed populations; (ii) association studies in presence of other confounding covariate(s). Application of DISSCO to real datasets under both scenarios shows at least comparable, if not better, performance compared with existing correlation-based methods, particularly for lower frequency variants. For example, DISSCO can reduce the absolute deviation from the truth by 3.9-15.2% for variants with minor allele frequency <5%.

Obesity genetics in mouse and human: back and forth, and back again

Obesity is a major public health concern. This condition results from a constant and complex interplay between predisposing genes and environmental stimuli. Current attempts to manage obesity have been moderately effective and a better understanding of the etiology of obesity is required for the development of more successful and personalized prevention and treatment options. To that effect, mouse models have been an essential tool in expanding our understanding of obesity, due to the availability of their complete genome sequence, genetically identified and defined strains, various tools for genetic manipulation and the accessibility of target tissues for obesity that are not easily attainable from humans. Our knowledge of monogenic obesity in humans greatly benefited from the mouse obesity genetics field. Genes underlying highly penetrant forms of monogenic obesity are part of the leptin-melanocortin pathway in the hypothalamus. Recently, hypothesis-generating genome-wide association studies for polygenic obesity traits in humans have led to the identification of 119 common gene variants with modest effect, most of them having an unknown function. These discoveries have led to novel animal models and have illuminated new biologic pathways. Integrated mouse-human genetic approaches have firmly established new obesity candidate genes. Innovative strategies recently developed by scientists are described in this review to accelerate the identification of causal genes and deepen our understanding of obesity etiology. An exhaustive dissection of the molecular roots of obesity may ultimately help to tackle the growing obesity epidemic worldwide.

Polymorphisms in endothelin system genes, arsenic levels and obesity risk

BACKGROUND/OBJECTIVES

Obesity has been linked to morbidity and mortality through increased risk for many chronic diseases. Endothelin (EDN) system has been related to endothelial function but it can be involved in lipid metabolism regulation: Receptor type A (EDNRA) activates lipolysis in adipocytes, the two endothelin receptors mediate arsenic-stimulated adipocyte dysfunction, and endothelin system can regulate adiposity by modulating adiponectin activity in different situations and, therefore, influence obesity development. The aim of the present study was to analyze if single nucleotide polymorphisms (SNPs) in the EDN system could be associated with human obesity.

SUBJECTS/METHODS

We analyzed two samples of general-population-based studies from two different regions of Spain: the VALCAR Study, 468 subjects from the area of Valencia, and the Hortega Study, 1502 subjects from the area of Valladolid. Eighteen SNPs throughout five genes were analyzed using SNPlex.

RESULTS

We found associations for two polymorphisms of the EDNRB gene which codifies for EDN receptor type B. Genotypes AG and AA of the rs5351 were associated with a lower risk for obesity in the VALCAR sample (p=0.048, OR=0.63) and in the Hortega sample (p=0.001, OR=0.62). Moreover, in the rs3759475 polymorphism, genotypes CT and TT were also associated with lower risk for obesity in the Hortega sample (p=0.0037, OR=0.66) and in the VALCAR sample we found the same tendency (p=0.12, OR=0.70). Furthermore, upon studying the pooled population, we found a stronger association with obesity (p=0.0001, OR=0.61 and p=0.0008, OR=0.66 for rs5351 and rs3759475, respectively). Regarding plasma arsenic levels, we have found a positive association for the two SNPs studied with obesity risk in individuals with higher arsenic levels in plasma: rs5351 (p=0.0054, OR=0.51) and rs3759475 (p=0.009, OR=0.53).

CONCLUSIONS

Our results support the hypothesis that polymorphisms of the EDNRB gene may influence the susceptibility to obesity and can interact with plasma arsenic levels.

Indications for potential parent-of-origin effects within the FTO gene

Genome-Wide Association Studies (GWAS) were successfully applied to discover associations with obesity. However, the GWAS design is usually based on unrelated individuals and inheritance information on the parental origin of the alleles is missing. Taking into account parent-of-origin may provide further insights into the genetic mechanisms contributing to obesity. We hypothesized that there may be variants within the robustly replicated fat mass and obesity associated (FTO) gene that may confer different risk for obesity depending on transmission from mother or father. Genome-wide genotypes and pedigree information from the Sorbs population were used. Phased genotypes among 525 individuals were generated by AlphaImpute. Subsequently, 22 SNPs within FTO introns 1 to 3 were selected and parent-of-origin specific association analyses were performed using PLINK. Interestingly, we identified several SNPs conferring different genetic effects (P≤0.05) depending on parental origin—among them, rs1861868, rs1121980 and rs9939973 (all in intron 1). To confirm our findings, we investigated the selected variants in 705 German trios comprising an (extremely) obese child or adolescent and both parents. Again, we observed evidence for POE effects in intron 2 and 3 (P≤0.05) as indicated by the parental asymmetry test. Our results suggest that the obesity risk transmitted by several FTO variants may depend on the parental origin of the allele. Larger family-based studies are warranted to replicate our findings.

Contribution of common non-synonymous variants in PCSK1 to body mass index variation and risk of obesity: a systematic review and meta-analysis with evidence from up to 331 175 individuals

Polymorphisms rs6232 and rs6234/rs6235 in PCSK1 have been associated with extreme obesity [e.g. body mass index (BMI) ≥ 40 kg/m(2)], but their contribution to common obesity (BMI ≥ 30 kg/m(2)) and BMI variation in a multi-ethnic context is unclear. To fill this gap, we collected phenotypic and genetic data in up to 331 175 individuals from diverse ethnic groups. This process involved a systematic review of the literature in PubMed, Web of Science, Embase and the NIH GWAS catalog complemented by data extraction from pre-existing GWAS or custom-arrays in consortia and single studies. We employed recently developed global meta-analytic random-effects methods to calculate summary odds ratios (OR) and 95% confidence intervals (CIs) or beta estimates and standard errors (SE) for the obesity status and BMI analyses, respectively. Significant associations were found with binary obesity status for rs6232 (OR = 1.15, 95% CI 1.06-1.24, P = 6.08 × 10(-6)) and rs6234/rs6235 (OR = 1.07, 95% CI 1.04-1.10, P = 3.00 × 10(-7)). Similarly, significant associations were found with continuous BMI for rs6232 (β = 0.03, 95% CI 0.00-0.07; P = 0.047) and rs6234/rs6235 (β = 0.02, 95% CI 0.00-0.03; P = 5.57 × 10(-4)). Ethnicity, age and study ascertainment significantly modulated the association of PCSK1 polymorphisms with obesity. In summary, we demonstrate evidence that common gene variation in PCSK1 contributes to BMI variation and susceptibility to common obesity in the largest known meta-analysis published to date in genetic epidemiology.