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We used a bivariate (multivariate) linear mixed-effects model to estimate the narrow-sense heritability (h2) and heritability explained by the common SNPs (hg2) for several metabolic syndrome (MetS) traits and the genetic correlation between pairs of traits for the Atherosclerosis Risk in Communities (ARIC) genome-wide association study (GWAS) population. MetS traits included body-mass index (BMI), waist-to-hip ratio (WHR), systolic blood pressure (SBP), fasting glucose (GLU), fasting insulin (INS), fasting trigylcerides (TG), and fasting high-density lipoprotein (HDL). We found the percentage of h2 accounted for by common SNPs to be 58% of h2 for height, 41% for BMI, 46% for WHR, 30% for GLU, 39% for INS, 34% for TG, 25% for HDL, and 80% for SBP. We confirmed prior reports for height and BMI using the ARIC population and independently in the Framingham Heart Study (FHS) population. We demonstrated that the multivariate model supported large genetic correlations between BMI and WHR and between TG and HDL. We also showed that the genetic correlations between the MetS traits are directly proportional to the phenotypic correlations.

Author Summary

The narrow-sense heritability of a trait such as body-mass index is a measure of the variability of the trait between people that is accounted for by their additive genetic differences. Knowledge of these genetic differences provides insight into biological mechanisms and hence treatments for diseases. Genome-wide association studies (GWAS) survey a large set of genetic markers common to the population. They have identified several single markers that are associated with traits and diseases. However, these markers do not seem to account for all of the known narrow-sense heritability. Here we used a recently developed model to quantify the genetic information contained in GWAS for single traits and shared between traits. We specifically investigated metabolic syndrome traits that are associated with type 2 diabetes and heart disease, and we found that for the majority of these traits much of the previously unaccounted for heritability is contained within common markers surveyed in GWAS. We also computed the genetic correlation between traits, which is a measure of the genetic components shared by traits. We found that the genetic correlation between these traits could be predicted from their phenotypic correlation.

Background

Asian Indian immigrants have an increased risk for developing cardiovascular disease (CVD); however, there is very little data examining how the adipokines leptin and adiponectin relate to CVD risk factors such as body fat percentage (BF%), waist to hip ratio (WHR) and the apoB/apoA1 ratio in Asian Indian men and women living in Canada.

Subjects and methods

A cross-sectional study comparing leptin, adiponectin, lipoproteins and anthropometric parameters in Asian Indian men and women to Caucasian men and women (4 groups). Anthropometric data (BMI, BF%, WHR), circulating lipids (apoA1, apoB, total cholesterol, and HDL-cholesterol), leptin and adiponectin were measured.

Results

Asian Indian men and women had higher leptin and lower adiponectin concentrations then Caucasian men and women, respectively. Leptin (positively) and adiponectin (negatively) correlated with anthropometric parameters and lipoproteins in all four groups. Using stepwise forward multiple regression, a model including TC/HDL-C ratio, WHR, BF%, hip circumference and waist circumference predicted 74.2% of leptin concentration in men. In women, apoB, BF%, waist circumference and age predicted 77.5% of leptin concentration. Adiponectin concentrations in men were predicted (30.2%) by HDL-C, total cholesterol, hip circumference and BF% while in women 41.2% of adiponectin concentration was predicted by the apoB/apoA1 ratio, WHR and age.

Conclusion

As is evident from our data, there is a strong relationship between leptin, adiponectin, and abdominal obesity with increased CVD risk, as assessed by the apoB/apoA1 ratio. Dysregulation of these parameters may account for the increased risk of CVD in Asian Indians.

To identify genetic loci influencing central obesity and fat distribution, we performed a meta-analysis of 16 genome-wide association studies (GWAS, N = 38,580) informative for adult waist circumference (WC) and waist–hip ratio (WHR). We selected 26 SNPs for follow-up, for which the evidence of association with measures of central adiposity (WC and/or WHR) was strong and disproportionate to that for overall adiposity or height. Follow-up studies in a maximum of 70,689 individuals identified two loci strongly associated with measures of central adiposity; these map near TFAP2B (WC, P = 1.9×10−11) and MSRA (WC, P = 8.9×10−9). A third locus, near LYPLAL1, was associated with WHR in women only (P = 2.6×10−8). The variants near TFAP2B appear to influence central adiposity through an effect on overall obesity/fat-mass, whereas LYPLAL1 displays a strong female-only association with fat distribution. By focusing on anthropometric measures of central obesity and fat distribution, we have identified three loci implicated in the regulation of human adiposity.

Author Summary

Here, we describe a meta-analysis of genome-wide association data from 38,580 individuals, followed by large-scale replication (in up to 70,689 individuals) designed to uncover variants influencing anthropometric measures of central obesity and fat distribution, namely waist circumference (WC) and waist–hip ratio (WHR). This work complements parallel efforts that have been successful in defining variants impacting overall adiposity and focuses on the visceral fat accumulation which has particularly strong relationships to metabolic and cardiovascular disease. Our analyses have identified two loci (TFAP2B and MSRA) associated with WC, and a further locus, near LYPLAL1, which shows gender-specific relationships with WHR (all to levels of genome-wide significance). These loci vary in the strength of their associations with overall adiposity, and LYPLAL1 in particular appears to have a specific effect on patterns of fat distribution. All in all, these three loci provide novel insights into human physiology and the development of obesity.

Waist-hip ratio (WHR) is a measure of body fat distribution and a predictor of metabolic consequences independent of overall adiposity. WHR is heritable, but few genetic variants influencing this trait have been identified. We conducted a meta-analysis of 32 genome-wide association studies for WHR adjusted for body-mass-index (up to 77,167 participants), following up 16 loci in an additional 29 studies (up to 113,636 subjects). We identified 13 novel loci in or near RSPO3, VEGFA, TBX15-WARS2, NFE2L3, GRB14, DNM3-PIGC, ITPR2-SSPN, LY86, HOXC13, ADAMTS9, ZNRF3-KREMEN1, NISCH-STAB1, and CPEB4 (P 1.9 × 10−9 to 1.8 × 10−40), and the known signal at LYPLAL1. Seven of these loci exhibited marked sexual dimorphism, all with a stronger effect on WHR in women than men (P for sex-difference 1.9 × 10−3 to 1.2 × 10−13). These findings provide evidence for multiple loci that modulate body fat distribution, independent of overall adiposity, and reveal powerful gene-by-sex interactions.

Waist-hip ratio (WHR) is a measure of body fat distribution and a predictor of metabolic consequences independent of overall adiposity. WHR is heritable, but few genetic variants influencing this trait have been identified. We conducted a meta-analysis of 32 genome-wide association studies for WHR adjusted for body mass index (comprising up to 77,167 participants), following up 16 loci in an additional 29 studies (comprising up to 113,636 subjects). We identified 13 new loci in or near RSPO3, VEGFA, TBX15-WARS2, NFE2L3, GRB14, DNM3-PIGC, ITPR2-SSPN, LY86, HOXC13, ADAMTS9, ZNRF3-KREMEN1, NISCH-STAB1 and CPEB4 (P = 1.9 × 10−9 to P = 1.8 × 10−40) and the known signal at LYPLAL1. Seven of these loci exhibited marked sexual dimorphism, all with a stronger effect on WHR in women than men (P for sex difference = 1.9 × 10−3 to P = 1.2 × 10−13). These findings provide evidence for multiple loci that modulate body fat distribution independent of overall adiposity and reveal strong gene-by-sex interactions.

Serum concentrations of low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C), triglycerides (TGs) and total cholesterol (TC) are important heritable risk factors for cardiovascular disease. Although genome-wide association studies (GWASs) of circulating lipid levels have identified numerous loci, a substantial portion of the heritability of these traits remains unexplained. Evidence of unexplained genetic variance can be detected by combining multiple independent markers into additive genetic risk scores. Such polygenic scores, constructed using results from the ENGAGE Consortium GWAS on serum lipids, were applied to predict lipid levels in an independent population-based study, the Rotterdam Study-II (RS-II). We additionally tested for evidence of a shared genetic basis for different lipid phenotypes. Finally, the polygenic score approach was used to identify an alternative genome-wide significance threshold before pathway analysis and those results were compared with those based on the classical genome-wide significance threshold. Our study provides evidence suggesting that many loci influencing circulating lipid levels remain undiscovered. Cross-prediction models suggested a small overlap between the polygenic backgrounds involved in determining LDL-C, HDL-C and TG levels. Pathway analysis utilizing the best polygenic score for TC uncovered extra information compared with using only genome-wide significant loci. These results suggest that the genetic architecture of circulating lipids involves a number of undiscovered variants with very small effects, and that increasing GWAS sample sizes will enable the identification of novel variants that regulate lipid levels.

Background

The discovery of genetic associations is an important factor in the understanding of human illness to derive disease pathways. Identifying multiple interacting genetic mutations associated with disease remains challenging in studying the etiology of complex diseases. And although recently new single nucleotide polymorphisms (SNPs) at genes implicated in immune response, cholesterol/lipid metabolism, and cell membrane processes have been confirmed by genome-wide association studies (GWAS) to be associated with late-onset Alzheimer's disease (LOAD), a percentage of AD heritability continues to be unexplained. We try to find other genetic variants that may influence LOAD risk utilizing data mining methods.

Methods

Two different approaches were devised to select SNPs associated with LOAD in a publicly available GWAS data set consisting of three cohorts. In both approaches, single-locus analysis (logistic regression) was conducted to filter the data with a less conservative p-value than the Bonferroni threshold; this resulted in a subset of SNPs used next in multi-locus analysis (random forest (RF)). In the second approach, we took into account prior biological knowledge, and performed sample stratification and linkage disequilibrium (LD) in addition to logistic regression analysis to preselect loci to input into the RF classifier construction step.

Results

The first approach gave 199 SNPs mostly associated with genes in calcium signaling, cell adhesion, endocytosis, immune response, and synaptic function. These SNPs together with APOE and GAB2 SNPs formed a predictive subset for LOAD status with an average error of 9.8% using 10-fold cross validation (CV) in RF modeling. Nineteen variants in LD with ST5, TRPC1, ATG10, ANO3, NDUFA12, and NISCH respectively, genes linked directly or indirectly with neurobiology, were identified with the second approach. These variants were part of a model that included APOE and GAB2 SNPs to predict LOAD risk which produced a 10-fold CV average error of 17.5% in the classification modeling.

Conclusions

With the two proposed approaches, we identified a large subset of SNPs in genes mostly clustered around specific pathways/functions and a smaller set of SNPs, within or in proximity to five genes not previously reported, that may be relevant for the prediction/understanding of AD.

Genome-wide association studies (GWAS) have identified 38 larger genetic regions affecting classical blood lipid levels without adjusting for important environmental influences. We modeled diet and physical activity in a GWAS in order to identify novel loci affecting total cholesterol, LDL cholesterol, HDL cholesterol, and triglyceride levels. The Swedish (SE) EUROSPAN cohort (NSE = 656) was screened for candidate genes and the non-Swedish (NS) EUROSPAN cohorts (NNS = 3,282) were used for replication. In total, 3 SNPs were associated in the Swedish sample and were replicated in the non-Swedish cohorts. While SNP rs1532624 was a replication of the previously published association between CETP and HDL cholesterol, the other two were novel findings. For the latter SNPs, the p-value for association was substantially improved by inclusion of environmental covariates: SNP rs5400 (pSE,unadjusted = 3.6×10−5, pSE,adjusted = 2.2×10−6, pNS,unadjusted = 0.047) in the SLC2A2 (Glucose transporter type 2) and rs2000999 (pSE,unadjusted = 1.1×10−3, pSE,adjusted = 3.8×10−4, pNS,unadjusted = 0.035) in the HP gene (Haptoglobin-related protein precursor). Both showed evidence of association with total cholesterol. These results demonstrate that inclusion of important environmental factors in the analysis model can reveal new genetic susceptibility loci.

Author Summary

In this article we report a genome-wide association study on cholesterol levels in the human blood. We used a Swedish cohort to select genetic polymorphisms that showed the strongest association with cholesterol levels adjusted for diet and physical activity. We replicated several genetic loci in other European cohorts. This approach extends present genome-wide association studies on lipid levels, which did not take these lifestyle factors into account, to improve statistical results and discover novel genes. In our analysis, we could identify two genetic loci in the SLC2A2 (Glucose transporter type 2) and the HP (Haptoglobin-related protein precursor) gene whose effects on total cholesterol have not been reported yet. The results show that inclusion of important environmental factors in the analysis model can reveal new insights into genetic determinants of clinical parameters relevant for metabolic and cardiovascular disease.

Background

Sagittal abdominal diameter (SAD) is a novel anthropometric measure hypothesized to be a surrogate measure of visceral abdominal obesity in adults. This study aims to determine whether SAD is superior to other anthropometric measures such as body mass index (BMI) and waist to hip ratio (WHR) in terms of association to cardiometabolic risk and circulating adipocytokine concentrations in a cohort of Saudi children and adolescents.

Methods

A total of 948 (495 boys and 453 girls) apparently healthy children with varying BMI, aged 10–17 years, were included in this cross sectional study. Fasting glucose, lipid profile, leptin, adiponectin, resistin, insulin, TNF-α and aPAI-1 were measured in serum and HOMA-IR was calculated. MetS components were defined according to the International Diabetes Federation (IDF) criteria.

Results

BMI was superior to SAD as well as WHR, and had the highest number of significant associations to MetS components and adipocytokines even after adjustment for age and gender, including blood pressure, lipids, glucose and leptin.

Conclusion

In conclusion, while SAD is significantly associated with components of MetS among children and adolescents, it is not superior to BMI. The use of SAD therefore may not be practical for use in the pediatric clinical setting. Follow-up studies are needed to determine whether SAD has clinical significance in terms of harder outcomes such as predicting diabetes mellitus or cardiovascular diseases.

For the past five years, genome-wide association studies (GWAS) have identified hundreds of common variants associated with human diseases and traits, including high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), and triglyceride (TG) levels. Approximately 95 loci associated with lipid levels have been identified primarily among populations of European ancestry. The Population Architecture using Genomics and Epidemiology (PAGE) study was established in 2008 to characterize GWAS–identified variants in diverse population-based studies. We genotyped 49 GWAS–identified SNPs associated with one or more lipid traits in at least two PAGE studies and across six racial/ethnic groups. We performed a meta-analysis testing for SNP associations with fasting HDL-C, LDL-C, and ln(TG) levels in self-identified European American (∼20,000), African American (∼9,000), American Indian (∼6,000), Mexican American/Hispanic (∼2,500), Japanese/East Asian (∼690), and Pacific Islander/Native Hawaiian (∼175) adults, regardless of lipid-lowering medication use. We replicated 55 of 60 (92%) SNP associations tested in European Americans at p<0.05. Despite sufficient power, we were unable to replicate ABCA1 rs4149268 and rs1883025, CETP rs1864163, and TTC39B rs471364 previously associated with HDL-C and MAFB rs6102059 previously associated with LDL-C. Based on significance (p<0.05) and consistent direction of effect, a majority of replicated genotype-phentoype associations for HDL-C, LDL-C, and ln(TG) in European Americans generalized to African Americans (48%, 61%, and 57%), American Indians (45%, 64%, and 77%), and Mexican Americans/Hispanics (57%, 56%, and 86%). Overall, 16 associations generalized across all three populations. For the associations that did not generalize, differences in effect sizes, allele frequencies, and linkage disequilibrium offer clues to the next generation of association studies for these traits.

Author Summary

Low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C), and triglyceride (TG) levels are well known independent risk factors for cardiovascular disease. Lipid-associated genetic variants are being discovered in genome-wide association studies (GWAS) in samples of European descent, but an insufficient amount of data exist in other populations. Therefore, there is a strong need to characterize the effect of these GWAS–identified variants in more diverse cohorts. In this study, we selected over forty genetic loci previously associated with lipid levels and tested for replication in a large European American cohort. We also investigated if the effect of these variants generalizes to non-European descent populations, including African Americans, American Indians, and Mexican Americans/Hispanics. A majority of these GWAS–identified associations replicated in our European American cohort. However, the ability of associations to generalize across other racial/ethnic populations varied greatly, indicating that some of these GWAS–identified variants may not be functional and are more likely to be in linkage disequilibrium with the functional variant(s).

Genome-wide association studies (GWAS) have replicably identified multiple loci associated with population-based plasma lipid concentrations1-5. Common genetic variants at these loci together explain <10% of the total variation of each lipid trait4,5. Rare variants of individually large effect may contribute additionally to the “missing heritability” of lipid traits6,7, however it remains to be shown to what extent rare variants will affect lipid phenotypes. Here, we demonstrate a significant accumulation of rare variants in GWAS-identified genes in patients with an extreme phenotype of abnormal plasma triglyceride (TG) metabolism. A GWAS of hypertriglyceridemia (HTG) patients revealed that common variants in APOA5, GCKR, LPL and APOB genes were associated with the HTG phenotype at genome-wide significance. We subsequently resequenced protein coding regions of these genes and found a significant burden of 154 rare missense or nonsense variants in 438 HTG patients, in contrast to 53 variants in 327 controls (P=6.2X10-8); this corresponds to a carrier frequency of 28.1% of HTG patients and 15.3% of controls (P=2.6X10-5). Many rare variants were predicted in silico to have compromised function; additionally some had previously demonstrated dysfunctionality in vitro. Rare variants in these 4 genes explained 1.1% of total variation in HTG diagnoses. Our study demonstrates a marked mutation skew that likely contributes to disease pathophysiology in patients with HTG.

Background and Aims

Genome-wide association studies (GWAS) have identified several loci that are associated with body mass index (BMI = kg/m2). However, little is known regarding whether the genetic basis of BMI differs among children of diverse racial/ethnic backgrounds, how the cumulative effect of these genes influences weight, or the contribution of these variants to body composition. This study examined the association between 17 GWAS-identified loci located in 16 genes and body-composition phenotypes in a multiethnic pediatric sample and evaluated the association of a composite genetic risk score with these phenotypes.

Methods

Anthropometric measures of BMI, waist circumference and waist-to-hip ratio were obtained in a sample of 298 children. Lean and fat mass were obtained from dual-energy X-ray absorptiometry (DXA). Genotypes of 17 single nucleotide polymorphisms (SNPs) were tested for association with the phenotypic measures, adjusted by standard covariates and estimates of genetic admixture.

Results

Both SNPs rs8050136 and rs9939609 in FTO were associated with BMI and waist circumference in a direction opposite to that observed among adults, and an inverse association was detected between the risk variant in MC4R and total lean body mass. Lean body mass mediated the association between TMEM18 and BMI. The association between the genetic risk score and body composition differed according to ethnic/racial classification.

Conclusions

Our findings suggest that genetic associations with BMI among children are different from those in adults, that some loci may operate through lean body mass, and that genetic risk scores will not have universal applicability across ethnic/racial groups.

The field of genomics has entered a new era in which the ability to identify genetic variants that impact complex human traits and disease in an unbiased fashion using genome-wide approaches is widely accessible. To date, the workhorse of these efforts has been the genome-wide association study (GWAS), which has quickly moved from novel to routine, and has provided key insights into aspects of the underlying allelic architecture of complex traits. The main lesson learned from the early GWAS efforts is that though many disease-associated variants are often discovered, most have only a minor effect on disease, and in total explain only a small amount of the apparent heritability. Here we provide a brief overview of the genetic variation classes that may harbor the heritability missing from GWAS, and touch on approaches that will be leveraged in the coming years as genomics—and by extension medicine—becomes increasingly personalized.

The genome-wide association study by Herbert and colleagues identified the INSIG2 single nucleotide polymorphism (SNP) rs7566605 as contributing to increased BMI in ethnically distinct cohorts. The present study sought to further clarify by testing whether SNPs of INSIG2 influenced quantitative adiposity or glucose homeostasis traits in Hispanics of the Insulin Resistance Atherosclerosis Family Study (IRASFS). Using a tagging SNP approach, rs7566605 and 31 additional SNPs were genotyped in 1425 IRASFS Hispanics. SNPs were tested for association with six adiposity measures: BMI, waist circumference (WAIST), waist to hip ratio (WHR), subcutaneous adipose tissue (SAT), visceral adipose tissue (VAT), and VAT to SAT ratio (VSR). SNPs were also tested for association with fasting glucose (GFAST), fasting insulin (FINS), and three measures obtained from the frequently sampled intravenous glucose tolerance test: insulin sensitivity (SI), acute insulin response (AIR), and disposition index (DI). Most prominent association was observed with direct CT-measured adiposity phenotypes, including VAT, SAT, and VSR (P-values range from 0.007 to 0.044 for rs17586756, rs17047718, rs17047731, rs9308762, rs12623648, and rs11673900). Multiple SNP associations were observed with all glucose homeostasis traits (P-values range from 0.001 to 0.031 for rs17047718, rs17047731, rs2161829, rs10490625, rs889904, and rs12623648). Using BMI as a covariate in evaluation of glucose homeostasis traits slightly reduced their association. However, association with adiposity and glucose homeostasis phenotypes is not significant following multiple comparisons adjustment. Trending association after multiple comparisons adjustment remains suggestive of a role for genetic variation of INSIG2 in obesity, but these results require validation.

Most pathway and gene-set enrichment methods prioritize genes by their main effect and do not account for variation due to interactions in the pathway. A portion of the presumed missing heritability in genome-wide association studies (GWAS) may be accounted for through gene–gene interactions and additive genetic variability. In this study, we prioritize genes for pathway enrichment in GWAS of bipolar disorder (BD) by aggregating gene–gene interaction information with main effect associations through a machine learning (evaporative cooling) feature selection and epistasis network centrality analysis. We validate this approach in a two-stage (discovery/replication) pathway analysis of GWAS of BD. The discovery cohort comes from the Wellcome Trust Case Control Consortium (WTCCC) GWAS of BD, and the replication cohort comes from the National Institute of Mental Health (NIMH) GWAS of BD in European Ancestry individuals. Epistasis network centrality yields replicated enrichment of Cadherin signaling pathway, whose genes have been hypothesized to have an important role in BD pathophysiology but have not demonstrated enrichment in previous analysis. Other enriched pathways include Wnt signaling, circadian rhythm pathway, axon guidance and neuroactive ligand-receptor interaction. In addition to pathway enrichment, the collective network approach elevates the importance of ANK3, DGKH and ODZ4 for BD susceptibility in the WTCCC GWAS, despite their weak single-locus effect in the data. These results provide evidence that numerous small interactions among common alleles may contribute to the diathesis for BD and demonstrate the importance of including information from the network of gene–gene interactions as well as main effects when prioritizing genes for pathway analysis.

A recent meta-analysis of genome-wide association (GWA) studies identified 95 loci that influence lipid traits in the adult population and found that collectively these explained about 25–30% of heritability for each trait. Little is known about how these loci affect lipid levels in early life, but there is evidence that genetic effects on HDL- and LDL-cholesterol (HDL-C, LDL-C) and triglycerides vary with age. We studied Australian adults (N = 10,151) and adolescents (N = 2,363) who participated in twin and family studies and for whom we have lipid phenotypes and genotype information for 91 of the 95 genetic variants. Heterogeneity tests between effect sizes in adult and adolescent cohorts showed an excess of heterogeneity for HDL-C (pHet<0.05 at 5 out of 37 loci), but no more than expected by chance for LDL-C (1 out of 14 loci), or trigycerides (0 out 24). There were 2 (out of 5) with opposite direction of effect in adolescents compared to adults for HDL-C, but none for LDL-C. The biggest difference in effect size was for LDL-C at rs6511720 near LDLR, adolescents (0.021±0.033 mmol/L) and adults (0.157±0.023 mmol/L), pHet = 0.013; followed by ZNF664 (pHet = 0.018) and PABPC4 (pHet = 0.034) for HDL-C. Our findings suggest that some of the previously identified variants associate differently with lipid traits in adolescents compared to adults, either because of developmental changes or because of greater interactions with environmental differences in adults.

Objective

To compare the general adiposity index (BMI) with abdominal obesity indices (waist circumference (WC), waist-to-hip ratio (WHR) and waist-to-height ratio (WHtR)) in order to examine the best predictor of cardiometabolic risk factors among Hispanics living in Puerto Rico.

Design

Secondary analysis of measurements taken from a representative sample of adults. Logistic regression models (prevalence odds ratios (POR)), partial Pearson’s correlations (controlling for age and sex) and receiver-operating characteristic (ROC) curves were calculated between indices of obesity (BMI, WC, WHR and WHtR) and blood pressure, HDL cholesterol (HDL-C), LDL cholesterol (LDL-C), total cholesterol (TC):HDL-C, TAG, fasting blood glucose, glycosylated Hb, high-sensitivity C-reactive protein (hs-CRP), fibrinogen, plasminogen activator inhibitor-1 (PAI-1) and an aggregated measure of cardiometabolic risk.

Setting

Household study conducted between 2005 and 2007 in the San Juan Metropolitan Area in Puerto Rico.

Subjects

A representative sample of 858 non-institutionalized adults.

Results

All four obesity indices significantly correlated with the cardiometabolic risk factors. WHtR had the highest POR for high TC:HDL-C, blood pressure, hs-CRP, fibrinogen and PAI-1; WC had the highest POR for low HDL-C and high LDL-C and fasting blood glucose; WHR had the highest POR for overall cardiometabolic risk, TAG and glycosylated Hb. BMI had the lowest POR for most risk factors and smallest ROC curve for overall cardiometabolic risk.

Conclusions

The findings of the study suggest that general adiposity and abdominal adiposity are both associated with cardiometabolic risk in this population, although WC, WHR and WHtR appear to be slightly better predictors than BMI.

Background

Two meta-analyses of genome-wide association studies (GWAS) have suggested that four variants: rs2605100 in lysophospholipase-like 1 (LYPLAL1), rs10146997 in neuroxin 3 (NRXN3), rs545854 in methionine sulfoxide reductase A (MSRA), and rs987237 in transcription factor activating enhancer-binding protein 2 beta (TFAP2B) associate with measures of central obesity.

To elucidate potential underlying phenotypes we aimed to investigate whether these variants associated with: 1) quantitative metabolic traits, 2) anthropometric measures (waist circumference (WC), waist-hip ratio, and BMI), or 3) type 2 diabetes, and central and general overweight and obesity.

Methodology/Principal Findings

The four variants were genotyped in Danish individuals using KASPar®. Quantitative metabolic traits were examined in a population-based sample (n = 6,038) and WC and BMI were furthermore analyzed in a combined study sample (n = 13,507). Case-control studies of diabetes and adiposity included 15,326 individuals. The major G-allele of LYPLAL1 rs2605100 associated with increased fasting serum triglyceride concentrations (per allele effect (β) = 3%(1;5(95%CI)), padditive = 2.7×10−3), an association driven by the male gender (pinteraction = 0.02). The same allele associated with increased fasting serum insulin concentrations (β = 3%(1;5), padditive = 2.5×10−3) and increased insulin resistance (HOMA-IR) (β = 4%(1;6), padditive = 1.5×10−3). The minor G-allele of rs10146997 in NRXN3 associated with increased WC among women (β = 0.55cm (0.20;0.89), padditive = 1.7×10−3, pinteraction = 1.0×10−3), but showed no associations with obesity related metabolic traits. The MSRA rs545854 and TFAP2B rs987237 showed nominal associations with central obesity; however, no underlying metabolic phenotypes became obvious, when investigating quantitative metabolic traits. None of the variants influenced the prevalence of type 2 diabetes.

Conclusion/Significance

We demonstrate that several of the central obesity-associated variants in LYPLAL1, NRXN3, MSRA, and TFAP2B associate with metabolic and anthropometric traits in Danish adults. However, analyses were made without adjusting for multiple testing, and further studies are needed to confirm the putative role of LYPLAL1, NRXN3, MSRA, and TFAP2B in the pathophysiology of obesity.

Total cholesterol, low-density lipoprotein cholesterol, triglyceride, and high-density lipoprotein cholesterol (HDL-C) levels are among the most important risk factors for coronary artery disease. We tested for gene–gene interactions affecting the level of these four lipids based on prior knowledge of established genome-wide association study (GWAS) hits, protein–protein interactions, and pathway information. Using genotype data from 9,713 European Americans from the Atherosclerosis Risk in Communities (ARIC) study, we identified an interaction between HMGCR and a locus near LIPC in their effect on HDL-C levels (Bonferroni corrected Pc = 0.002). Using an adaptive locus-based validation procedure, we successfully validated this gene–gene interaction in the European American cohorts from the Framingham Heart Study (Pc = 0.002) and the Multi-Ethnic Study of Atherosclerosis (MESA; Pc = 0.006). The interaction between these two loci is also significant in the African American sample from ARIC (Pc = 0.004) and in the Hispanic American sample from MESA (Pc = 0.04). Both HMGCR and LIPC are involved in the metabolism of lipids, and genome-wide association studies have previously identified LIPC as associated with levels of HDL-C. However, the effect on HDL-C of the novel gene–gene interaction reported here is twice as pronounced as that predicted by the sum of the marginal effects of the two loci. In conclusion, based on a knowledge-driven analysis of epistasis, together with a new locus-based validation method, we successfully identified and validated an interaction affecting a complex trait in multi-ethnic populations.

Author Summary

Genome-wide association studies (GWAS) have identified many loci associated with complex human traits or diseases. However, the fraction of heritable variation explained by these loci is often relatively low. Gene–gene interactions might play a significant role in complex traits or diseases and are one of the many possible factors contributing to the missing heritability. However, to date only a few interactions have been found and validated in GWAS due to the limited power caused by the need for multiple-testing correction for the very large number of tests conducted. Here, we used three types of prior knowledge, known GWAS hits, protein–protein interactions, and pathway information, to guide our search for gene–gene interactions affecting four lipid levels. We identified an interaction between HMGCR and a locus near LIPC in their effect on high-density lipoprotein cholesterol (HDL-C) and another pair of loci that interact in their effect on low-density lipoprotein cholesterol (LDL-C). We validated the interaction on HDL-C in a number of independent multiple-ethnic populations, while the interaction underlying LDL-C did not validate. The prior knowledge-driven searching approach and a locus-based validation procedure show the potential for dissecting and validating gene–gene interactions in current and future GWAS.

Objective

To compare the sensitivities of BMI, waist circumference and waist hip ratio (WHR) in identifying subjects who should be screened for diabetes and/or for obesity-associated dyslipidaemia.

Design

Cross-sectional study.

Setting

Central-western France.

Participants

More than 3000 men and women, aged 40 to 64 years, from the French study: Data from an Epidemiological Study on the Insulin Resistance syndrome (D.E.S.I.R.).

Main outcome measures

Sensitivity and specificity for screened diabetes (fasting plasma glucose ≥ 7.0 mmol/l) and screened dyslipidaemia (triglycerides ≥ 2.3 mmol/l and/or HDL-cholesterol < 0.9/1.1 mmol/l (men/women)) according to BMI, waist circumference and WHR..

Results

Sensitivities increased as more corpulent subjects were screened, but they increased slowly after screening the top 30%: BMI ≥ 27/26 kg/m2 (men/women) or waist ≥ 96/83 cm or WHR ≥ 0.96/0.83, These values were chosen as thresholds. In men, BMI had a non-significantly higher sensitivity than waist or WHR for both diabetes and dyslipidaemia (77% vs 74% and 66% P < 0.3, 0.09; 56% vs 54% and 49% P < 0.5, 0.16). For women, waist had a slightly higher sensitivity than BMI or WHR (82% vs 77% and 77% P < 0.8, 0.7) for diabetes; for dyslipidaemia, waist and WHR had similar sensitivities, higher than for BMI (65% and 67% vs 54% P < 0.16, 0.13).

Conclusions

We propose that for screening in a French population 40 to 64 years of age, the more obese 30% of the population, identified either by BMI, waist or WHR be screened for diabetes and obesity-associated dyslipidaemia.

Background

Adiposity predicts health outcomes, but this relationship could depend on population characteristics and adiposity indicator employed. In a representative sample of 11,437 US adults (National Health and Nutrition Examination Survey, 1988–1994, ages 18–64) we estimated associations with all-cause mortality for body mass index (BMI) and four abdominal adiposity indicators (waist circumference [WC], waist-to-height ratio [WHtR], waist-to-hip ratio [WHR], and waist-to-thigh ratio [WTR]). In a fasting subsample we considered the lipid accumulation product (LAP; [WC enlargement*triglycerides]).

Methods and Findings

For each adiposity indicator we estimated linear and categorical mortality risks using sex-specific, proportional-hazards models adjusted for age, black ancestry, tobacco exposure, and socioeconomic position. There were 1,081 deaths through 2006. Using linear models we found little difference among indicators (adjusted hazard ratios [aHRs] per SD increase 1.2–1.4 for men, 1.3–1.5 for women). Using categorical models, men in adiposity midrange (quartiles 2+3; compared to quartile 1) were not at significantly increased risk (aHRs<1.1) unless assessed by WTR (aHR 1.4 [95%CI 1.0–1.9]). Women in adiposity midrange, however, tended toward elevated risk (aHRs 1.2–1.5), except for black women assessed by BMI, WC or WHtR (aHRs 0.7–0.8). Men or women in adiposity quartile 4 (compared to midrange) were generally at risk (aHRs>1.1), especially black men assessed by WTR (aHR 1.9 [1.4–2.6]) and black women by LAP (aHR 2.2 [1.4–3.5]). Quartile 4 of WC or WHtR carried no significant risk for diabetic persons (aHRs 0.7–1.1), but elevated risks for those without diabetes (aHRs>1.5). For both sexes, quartile 4 of LAP carried increased risks for tobacco-exposed persons (aHRs>1.6) but not for non-exposed (aHRs<1.0).

Conclusions

Predictions of mortality risk associated with top-quartile adiposity vary with the indicator used, sex, ancestry, and other characteristics. Interpretations of adiposity should consider how variation in the physiology and expandability of regional adipose-tissue depots impacts health.

Recent genome-wide association (GWA) studies of lipids have been conducted in samples ascertained for other phenotypes, particularly diabetes. Here we report the first GWA analysis of loci affecting total cholesterol (TC), low-density lipoprotein (LDL) cholesterol, high-density lipoprotein (HDL) cholesterol and triglycerides sampled randomly from 16 population-based cohorts and genotyped using mainly the Illumina HumanHap300-Duo platform. Our study included a total of 17,797-22,562 persons, aged 18-104 years and from geographic regions spanning from the Nordic countries to Southern Europe. We established 22 loci associated with serum lipid levels at a genome-wide significance level (P < 5 × 10-8), including 16 loci that were identified by previous GWA studies. The six newly identified loci in our cohort samples are ABCG5 (TC, P = 1.5 × 10-11; LDL, P = 2.6 × 10-10), TMEM57 (TC, P = 5.4 × 10-10), CTCF-PRMT8 region (HDL, P = 8.3 × 10-16), DNAH11 (LDL, P = 6.1 × 10-9), FADS3-FADS2 (TC, P = 1.5 × 10-10; LDL, P = 4.4 × 10-13) and MADD-FOLH1 region (HDL, P = 6 × 10-11). For three loci, effect sizes differed significantly by sex. Genetic risk scores based on lipid loci explain up to 4.8% of variation in lipids and were also associated with increased intima media thickness (P = 0.001) and coronary heart disease incidence (P = 0.04). The genetic risk score improves the screening of high-risk groups of dyslipidemia over classical risk factors.

Osteoporosis is a complex disorder and commonly leads to fractures in elderly persons. Genome-wide association studies (GWAS) have become an unbiased approach to identify variations in the genome that potentially affect health. However, the genetic variants identified so far only explain a small proportion of the heritability for complex traits. Due to the modest genetic effect size and inadequate power, true association signals may not be revealed based on a stringent genome-wide significance threshold. Here, we take advantage of SNP and transcript arrays and integrate GWAS and expression signature profiling relevant to the skeletal system in cellular and animal models to prioritize the discovery of novel candidate genes for osteoporosis-related traits, including bone mineral density (BMD) at the lumbar spine (LS) and femoral neck (FN), as well as geometric indices of the hip (femoral neck-shaft angle, NSA; femoral neck length, NL; and narrow-neck width, NW). A two-stage meta-analysis of GWAS from 7,633 Caucasian women and 3,657 men, revealed three novel loci associated with osteoporosis-related traits, including chromosome 1p13.2 (RAP1A, p = 3.6×10−8), 2q11.2 (TBC1D8), and 18q11.2 (OSBPL1A), and confirmed a previously reported region near TNFRSF11B/OPG gene. We also prioritized 16 suggestive genome-wide significant candidate genes based on their potential involvement in skeletal metabolism. Among them, 3 candidate genes were associated with BMD in women. Notably, 2 out of these 3 genes (GPR177, p = 2.6×10−13; SOX6, p = 6.4×10−10) associated with BMD in women have been successfully replicated in a large-scale meta-analysis of BMD, but none of the non-prioritized candidates (associated with BMD) did. Our results support the concept of our prioritization strategy. In the absence of direct biological support for identified genes, we highlighted the efficiency of subsequent functional characterization using publicly available expression profiling relevant to the skeletal system in cellular or whole animal models to prioritize candidate genes for further functional validation.

Author Summary

BMD and hip geometry are two major predictors of osteoporotic fractures, the most severe consequence of osteoporosis in elderly persons. We performed sex-specific genome-wide association studies (GWAS) for BMD at the lumbar spine and femor neck skeletal sites as well as hip geometric indices (NSA, NL, and NW) in the Framingham Osteoporosis Study and then replicated the top findings in two independent studies. Three novel loci were significant: in women, including chromosome 1p13.2 (RAP1A) for NW; in men, 2q11.2 (TBC1D8) for NSA and 18q11.2 (OSBPL1A) for NW. We confirmed a previously reported region on 8q24.12 (TNFRSF11B/OPG) for lumbar spine BMD in women. In addition, we integrated GWAS signals with eQTL in several tissues and publicly available expression signature profiling in cellular and whole-animal models, and prioritized 16 candidate genes/loci based on their potential involvement in skeletal metabolism. Among three prioritized loci (GPR177, SOX6, and CASR genes) associated with BMD in women, GPR177 and SOX6 have been successfully replicated later in a large-scale meta-analysis, but none of the non-prioritized candidates (associated with BMD) did. Our results support the concept of using expression profiling to support the candidacy of suggestive GWAS signals that may contain important genes of interest.

Serum concentrations of total cholesterol, low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C), and triglycerides (TG) are among the most important risk factors for coronary artery disease (CAD) and are targets for therapeutic intervention. We screened the genome for common variants associated with serum lipids in >100,000 individuals of European ancestry. Here we report 95 significantly associated loci (P < 5 × 10-8), with 59 showing genome-wide significant association with lipid traits for the first time. The newly reported associations include single nucleotide polymorphisms (SNPs) near known lipid regulators (e.g., CYP7A1, NPC1L1, and SCARB1) as well as in scores of loci not previously implicated in lipoprotein metabolism. The 95 loci contribute not only to normal variation in lipid traits but also to extreme lipid phenotypes and impact lipid traits in three non-European populations (East Asians, South Asians, and African Americans). Our results identify several novel loci associated with serum lipids that are also associated with CAD. Finally, we validated three of the novel genes—GALNT2, PPP1R3B, and TTC39B—with experiments in mouse models. Taken together, our findings provide the foundation to develop a broader biological understanding of lipoprotein metabolism and to identify new therapeutic opportunities for the prevention of CAD.

BACKGROUND

Central obesity is an important risk factor for cardiovascular diseases (CVD). Preventive interventions from childhood are necessary due to the increasing prevalence of childhood obesity. Body mass index (BMI), waist circumference (WC), waist to hip ratio (WHR) and waist to height ratio (WSR) are anthropometric indices for measurement of obesity. This study aimed to assess the association between these anthropometric indices and dyslipidemia in obese children and adolescents.

METHODS

This retrospective study was done on the records of 2064 obese children and adolescents aged 6-18 years at the obesity clinic, in Isfahan Cardiovascular Research center. Age, gender, weight, height, WC, hip circumference (HC), triglyceride (TG), total cholesterol (TC), LDL-cholesterol (LDL-C), HDL-cholesterol (HDL-C), Fasting blood sugar (FBS), diastolic blood pressure (DBP) and systolic blood pressure (SBP) were taken from patients’ record. Receiver operating characteristics (ROC) curve and Pearson correlation were used to analyze the data.

RESULTS

2064 girls and boys aged 6-18 years were divided into 3 age groups of 6-9.9 years, 10-13.9 years and 14-18 years. Prevalence of high LDL-C, TC, TG, FBS, SBP, DBP and low HDL-C was higher among the boys compared to the girls. There was a significant association between TC, LDL-C, TG and FBS with BMI, WC, WHR and WSR. However, no significant correlation was seen between HDL-C and the four anthropometric indices.

CONCLUSION

Our study showed a significant correlation between BMI, WC and WSR with high levels of TC, TG and LDL-C in children and adolescents. Correlation between WHR and dyslipidemia in this study was significant but its predictive value was weaker than other three indices.