A common genetic variant is associated with adult and childhood obesity

A multianalytical approach to evaluate the association of 55 SNPs in 28 genes with obesity risk in North Indian adults

OBJECTIVES

The aim of the study was to investigate the association of 55 SNPs in 28 genes with obesity risk in a North Indian population using a multianalytical approach.

METHODS

Overall, 480 subjects from the North Indian population were studied using strict inclusion/exclusion criteria. SNP Genotyping was carried out by Sequenom Mass ARRAY platform (Sequenom, San Diego, CA) and validated Taqman^® allelic discrimination (Applied Biosystems^® ). Statistical analyses were performed using SPSS software version 19.0, SNPStats, GMDR software (version 6) and GENEMANIA.

RESULTS

Logistic regression analysis of 55 SNPs revealed significant associations (P < .05) of 49 SNPs with BMI linked obesity risk whereas the remaining 6 SNPs revealed no association (P > .05). The pathway-wise G-score revealed the significant role (P = .0001) of food intake-energy expenditure pathway genes. In CART analysis, the combined genotypes of FTO rs9939609 and TCF7L2 rs7903146 revealed the highest risk for BMI linked obesity. The analysis of the FTO-IRX3 locus revealed high LD and high order gene-gene interactions for BMI linked obesity. The interaction network of all of the associated genes in the present study generated by GENEMANIA revealed direct and indirect connections. In addition, the analysis with centralized obesity revealed that none of the SNPs except for FTO rs17818902 were significantly associated (P < .05).

CONCLUSIONS

In this multi-analytical approach, FTO rs9939609 and IRX3 rs3751723, along with TCF7L2 rs7903146 and TMEM18 rs6548238, emerged as the major SNPs contributing to BMI linked obesity risk in the North Indian population.

Tumour biology of obesity-related cancers: understanding the molecular concept for better diagnosis and treatment

Obesity continues to be a major global problem. Various cancers are related to obesity and proper understanding of their aetiology, especially their molecular tumour biology is important for early diagnosis and better treatment. Genes play an important role in the development of obesity. Few genes such as leptin, leptin receptor encoded by the db (diabetes), pro-opiomelanocortin, AgRP and NPY and melanocortin-4 receptors and insulin-induced gene 2 were linked to obesity. MicroRNAs control gene expression via mRNA degradation and protein translation inhibition and influence cell differentiation, cell growth and cell death. Overexpression of miR-143 inhibits tumour growth by suppressing B cell lymphoma 2, extracellular signal-regulated kinase-5 activities and KRAS oncogene. Cancers of the breast, uterus, renal, thyroid and liver are also related to obesity. Any disturbance in the production of sex hormones and insulin, leads to distortion in the balance between cell proliferation, differentiation and apoptosis. The possible mechanism linking obesity to cancer involves alteration in the level of adipokines and sex hormones. These mediators act as biomarkers for cancer progression and act as targets for cancer therapy and prevention. Interestingly, many anti-cancerous drugs are also beneficial in treating obesity and vice versa. We also reviewed the possible link in the mechanism of few drugs which act both on cancer and obesity. The present review may be important for molecular biologists, oncologists and clinicians treating cancers and also pave the way for better therapeutic options.

SETDB2 Links Glucocorticoid to Lipid Metabolism through Insig2a Regulation

Transcriptional and chromatin regulations mediate the liver response to nutrient availability. The role of chromatin factors involved in hormonal regulation in response to fasting is not fully understood. We have identified SETDB2, a glucocorticoid-induced putative epigenetic modifier, as a positive regulator of GR-mediated gene activation in liver. Insig2a increases during fasting to limit lipid synthesis, but the mechanism of induction is unknown. We show Insig2a induction is GR-SETDB2 dependent. SETDB2 facilitates GR chromatin enrichment and is key to glucocorticoid-dependent enhancer-promoter interactions. INSIG2 is a negative regulator of SREBP, and acute glucocorticoid treatment decreased active SREBP during refeeding or in livers of Ob/Ob mice, both systems of elevated SREBP-1c-driven lipogenesis. Knockdown of SETDB2 or INSIG2 reversed the inhibition of SREBP processing. Overall, these studies identify a GR-SETDB2 regulatory axis of hepatic transcriptional reprogramming and identify SETDB2 as a potential target for metabolic disorders with aberrant glucocorticoid actions.

Glucagon regulates hepatic lipid metabolism via cAMP and Insig-2 signaling: implication for the pathogenesis of hypertriglyceridemia and hepatic steatosis

Insulin induced gene-2 (Insig-2) is an ER-resident protein that inhibits the activation of sterol regulatory element-binding proteins (SREBPs). However, cellular factors that regulate Insig-2 expression have not yet been identified. Here we reported that cyclic AMP-responsive element-binding protein H (CREBH) positively regulates mRNA and protein expression of a liver specific isoform of Insig-2, Insig-2a, which in turn hinders SREBP-1c activation and inhibits hepatic de novo lipogenesis. CREBH binds to the evolutionally conserved CRE-BP binding elements located in the enhancer region of Insig-2a and upregulates its mRNA and protein expression. Metabolic hormone glucagon and nutritional fasting activated CREBH, which upregulated expression of Insig-2a in hepatocytes and inhibited SREBP-1c activation. In contrast, genetic depletion of CREBH decreased Insig-2a expression, leading to the activation of SREBP-1c and its downstream lipogenic target enzymes. Compromising CREBH-Insig-2 signaling by siRNA interference against Insig-2 also disrupted the inhibitory effect of this signaling pathway on hepatic de novo triglyceride synthesis. These actions resulted in the accumulation of lipid droplets in hepatocytes and systemic hyperlipidemia. Our study identified CREBH as the first cellular protein that regulates Insig-2a expression. Glucagon activated the CREBH-Insig-2a signaling pathway to inhibit hepatic de novo lipogenesis and prevent the onset of hepatic steatosis and hypertriglyceridemia.

Review: Genetics of the cardiometabolic syndrome: new insights and therapeutic implications

Although the definition of the phenotype is imprecise, cardiometabolic syndrome (CMS) includes a constellation of complex diseases such as type 2 diabetes, dislipidemias, central obesity and hypertension, proinflammatory and prothrombotic states, ovarian polycystosis and fatty liver. The genetics of each disease is complex in itself and varies in spectrum from monogenic and syndromic models of inheritance, usually rare, to the most common polygenic and multifactorial forms. In addition, human studies using the candidate-gene approach indicate that common genetic variants of several genes are associated with the development of CMS. Genome-wide scans have also provided several chromosomal regions associated with some of the components of CMS. In addition, through comparative genomics animal models can generate a map for candidate loci in humans and a promising approach is offered by bioinformatic tools for gene prioritization. Lastly, the involvement of genes whose products are already the targets for approved drugs, such as SLC6A4, PPARα and PPARγ , in the development of CMS suggests new avenues for CMS pharmacological treatment.

Discovering causal interactions using Bayesian network scoring and information gain

BACKGROUND

The problem of learning causal influences from data has recently attracted much attention. Standard statistical methods can have difficulty learning discrete causes, which interacting to affect a target, because the assumptions in these methods often do not model discrete causal relationships well. An important task then is to learn such interactions from data. Motivated by the problem of learning epistatic interactions from datasets developed in genome-wide association studies (GWAS), researchers conceived new methods for learning discrete interactions. However, many of these methods do not differentiate a model representing a true interaction from a model representing non-interacting causes with strong individual affects. The recent algorithm MBS-IGain addresses this difficulty by using Bayesian network learning and information gain to discover interactions from high-dimensional datasets. However, MBS-IGain requires marginal effects to detect interactions containing more than two causes. If the dataset is not high-dimensional, we can avoid this shortcoming by doing an exhaustive search.

RESULTS

We develop Exhaustive-IGain, which is like MBS-IGain but does an exhaustive search. We compare the performance of Exhaustive-IGain to MBS-IGain using low-dimensional simulated datasets based on interactions with marginal effects and ones based on interactions without marginal effects. Their performance is similar on the datasets based on marginal effects. However, Exhaustive-IGain compellingly outperforms MBS-IGain on the datasets based on 3 and 4-cause interactions without marginal effects. We apply Exhaustive-IGain to investigate how clinical variables interact to affect breast cancer survival, and obtain results that agree with judgements of a breast cancer oncologist.

CONCLUSIONS

We conclude that the combined use of information gain and Bayesian network scoring enables us to discover higher order interactions with no marginal effects if we perform an exhaustive search. We further conclude that Exhaustive-IGain can be effective when applied to real data.

A Statistical Approach for Testing Cross-Phenotype Effects of Rare Variants

Increasing empirical evidence suggests that many genetic variants influence multiple distinct phenotypes. When cross-phenotype effects exist, multivariate association methods that consider pleiotropy are often more powerful than univariate methods that model each phenotype separately. Although several statistical approaches exist for testing cross-phenotype effects for common variants, there is a lack of similar tests for gene-based analysis of rare variants. In order to fill this important gap, we introduce a statistical method for cross-phenotype analysis of rare variants using a nonparametric distance-covariance approach that compares similarity in multivariate phenotypes to similarity in rare-variant genotypes across a gene. The approach can accommodate both binary and continuous phenotypes and further can adjust for covariates. Our approach yields a closed-form test whose significance can be evaluated analytically, thereby improving computational efficiency and permitting application on a genome-wide scale. We use simulated data to demonstrate that our method, which we refer to as the Gene Association with Multiple Traits (GAMuT) test, provides increased power over competing approaches. We also illustrate our approach using exome-chip data from the Genetic Epidemiology Network of Arteriopathy.

A systematic review of genetic variants associated with metabolic syndrome in patients with schizophrenia

Metabolic syndrome (MetS) is a cluster of factors that increases the risk of cardiovascular disease (CVD), one of the leading causes of mortality in patients with schizophrenia. Incidence rates of MetS are significantly higher in patients with schizophrenia compared to the general population. Several factors contribute to this high comorbidity. This systematic review focuses on genetic factors and interrogates data from association studies of genes implicated in the development of MetS in patients with schizophrenia. We aimed to identify variants that potentially contribute to the high comorbidity between these disorders. PubMed, Web of Science and Scopus databases were accessed and a systematic review of published studies was conducted. Several genes showed strong evidence for an association with MetS in patients with schizophrenia, including the fat mass and obesity associated gene (FTO), leptin and leptin receptor genes (LEP, LEPR), methylenetetrahydrofolate reductase (MTHFR) gene and the serotonin receptor 2C gene (HTR2C). Genetic association studies in complex disorders are convoluted by the multifactorial nature of these disorders, further complicating investigations of comorbidity. Recommendations for future studies include assessment of larger samples, inclusion of healthy controls, longitudinal rather than cross-sectional study designs, detailed capturing of data on confounding variables for both disorders and verification of significant findings in other populations. In future, big genomic datasets may allow for the calculation of polygenic risk scores in risk prediction of MetS in patients with schizophrenia. This could ultimately facilitate early, precise, and patient-specific pharmacological and non-pharmacological interventions to minimise CVD associated morbidity and mortality.

Genetics of Obesity

Numerous classical genetic studies have proved that genes are contributory factors for obesity. Genes are directly responsible for obesity associated disorders such as Bardet-Biedl and Prader-Willi syndromes. However, both genes as well as environment are associated with obesity in the general population. Genetic epidemiological approaches, particularly genome-wide association studies, have unraveled many genes which play important roles in human obesity. Elucidation of their biological functions can be very useful for understanding pathobiology of obesity. In the near future, further exploration of obesity genetics may help to develop useful diagnostic and predictive tests for obesity treatment.

Learning Predictive Interactions Using Information Gain and Bayesian Network Scoring

BACKGROUND

The problems of correlation and classification are long-standing in the fields of statistics and machine learning, and techniques have been developed to address these problems. We are now in the era of high-dimensional data, which is data that can concern billions of variables. These data present new challenges. In particular, it is difficult to discover predictive variables, when each variable has little marginal effect. An example concerns Genome-wide Association Studies (GWAS) datasets, which involve millions of single nucleotide polymorphism (SNPs), where some of the SNPs interact epistatically to affect disease status. Towards determining these interacting SNPs, researchers developed techniques that addressed this specific problem. However, the problem is more general, and so these techniques are applicable to other problems concerning interactions. A difficulty with many of these techniques is that they do not distinguish whether a learned interaction is actually an interaction or whether it involves several variables with strong marginal effects.

METHODOLOGY/FINDINGS

We address this problem using information gain and Bayesian network scoring. First, we identify candidate interactions by determining whether together variables provide more information than they do separately. Then we use Bayesian network scoring to see if a candidate interaction really is a likely model. Our strategy is called MBS-IGain. Using 100 simulated datasets and a real GWAS Alzheimer's dataset, we investigated the performance of MBS-IGain.

CONCLUSIONS/SIGNIFICANCE

When analyzing the simulated datasets, MBS-IGain substantially out-performed nine previous methods at locating interacting predictors, and at identifying interactions exactly. When analyzing the real Alzheimer's dataset, we obtained new results and results that substantiated previous findings. We conclude that MBS-IGain is highly effective at finding interactions in high-dimensional datasets. This result is significant because we have increasingly abundant high-dimensional data in many domains, and to learn causes and perform prediction/classification using these data, we often must first identify interactions.

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.

Evaluation of a two-stage framework for prediction using big genomic data

We are in the era of abundant 'big' or 'high-dimensional' data. These data afford us the opportunity to discover predictors of an event of interest, and to estimate occurrence of the event based on values of these predictors. For example, 'genome-wide association studies' examine millions of single-nucleotide polymorphisms (SNPs), along with disease status. We can learn SNPs that affect disease status from these data sets, and use the knowledge learned to predict disease likelihood. Owing to the large number of features, it is difficult for many prediction methods to use all the features directly. The ReliefF algorithm ranks a set of features in terms of how well they predict a target. It can be used to identify good predictors, which can then be provided to a prediction method. We compared the performance of eight prediction methods when predicting binary outcomes using high-dimensional discrete data sets. We performed two-stage prediction, where ReliefF is used in the first stage to identify good predictors. Bayesian network (BN)-based methods performed best overall. Furthermore, ReliefF did not improve their performance. The BN-based methods use the Bayesian Dirichlet Equivalent Uniform score to evaluate candidate models, and use BN inference algorithms to perform prediction. This score and these algorithms were developed for discrete variables. This perhaps explains why they perform better in this domain. Many prediction methods are available, and researchers have little reason for choosing one over the other in the domain of binary prediction using high-dimensional data sets. Our results indicate that the best choices overall are BN-based methods.

Robust Association Tests for the Replication of Genome-Wide Association Studies

In genome-wide association study (GWAS), robust genetic association tests such as maximum of three CATTs (MAX3), each corresponding to recessive, additive, and dominant genetic models, the minimum p value of Pearson's Chi-square test with 2 degrees of freedom, and CATT based on additive genetic model (MIN2), genetic model selection (GMS), and genetic model exclusion (GME) methods have been shown to provide better power performance under wide range of underlying genetic models. In this paper, we demonstrate how these robust tests can be applied to the replication study of GWAS and how the overall statistical significance can be evaluated using the combined test formed by p values of the discovery and replication studies.

Meta-analysis in clinical trials revisited

In this paper, we revisit a 1986 article we published in this Journal, Meta-Analysis in Clinical Trials, where we introduced a random-effects model to summarize the evidence about treatment efficacy from a number of related clinical trials. Because of its simplicity and ease of implementation, our approach has been widely used (with more than 12,000 citations to date) and the "DerSimonian and Laird method" is now often referred to as the 'standard approach' or a 'popular' method for meta-analysis in medical and clinical research. The method is especially useful for providing an overall effect estimate and for characterizing the heterogeneity of effects across a series of studies. Here, we review the background that led to the original 1986 article, briefly describe the random-effects approach for meta-analysis, explore its use in various settings and trends over time and recommend a refinement to the method using a robust variance estimator for testing overall effect. We conclude with a discussion of repurposing the method for Big Data meta-analysis and Genome Wide Association Studies for studying the importance of genetic variants in complex diseases.

Understanding the Early Signs of Chronic Disease by Investigating the Overlap of Mental Health Needs and Adolescent Obesity

Objective

Childhood obesity has reached epidemic proportions with two to three-fold increases in prevalence in the past three decades. Sedentary lifestyles and nutrition have been linked to these increases though little is known about mental health illnesses in children and teens which may be precursors to negative modifiable health risk factors. The objective of this study was to investigate for a potentially more clinically practical indicator of depression over a multi-item screen in respect to reporting of overweight and obesity in adolescents. This study further investigated modifiers to this association and stability of association.

Method

This cross-sectional study aggregated 2007/2009 California Health Interview Survey data (n = 6,917 adolescents). Univariate analyses of population characteristics and modifiable behaviors with obesity/overweight and depression are presented. Multivariable weighted logistic regression was used to compare the adjusted odds of overweight and obesity for those children with reported depression.

Results

After controlling for gender, race/ethnicity, age, and modifiable behaviors, there was a statistically significant relationship between reported depression and overweight/obesity (OR = 1.24; 95% CI = 1.04, 1.49). This effect size was consistent in hierarchical models overall and stratified by gender.

Conclusions

Overweight and obesity in adolescents should be understood clinically in the context of depression and other mental health illness. This study highlights a routine primary care or parental screening assessment that could indicate tendencies in adolescent boys and girls which may be precursors to overweight or obesity. Further research should be conducted to identify ways for integrating adolescent mental health screens into primary care.

Effect of Ganoderma lucidum spores intervention on glucose and lipid metabolism gene expression profiles in type 2 diabetic rats

BACKGROUND

The fruiting body of Ganoderma lucidum has been used as a traditional herbal medicine for many years. However, to the date, there is no detailed study for describing the effect of G. lucidum spores on oxidative stress, blood glucose level and lipid compositions in animal models of type 2 diabetic rats, in particular the effect on the gene expression profiles associated with glucose and lipid metabolisms.

METHODS

G. lucidum spores powder (GLSP) with a shell-broken rate >99.9 % was used. Adult male Sprague-Dawley rats were randomly divided into three groups (n = 8/group). Group 1: Normal control, normal rats with ordinary feed; Group 2: Model control, diabetic rats with ordinary feed without intervention; Group 3: GLSP, diabetic rats with ordinary feed, an intervention group utilizing GLSP of 1 g per day by oral gavages for 4 consecutive weeks. Type 2 diabetic rats were obtained by streptozocin (STZ) injection. The changes in the levels of glucose, triglycerides, total cholesterol and HDL-cholesterol in blood samples were analyzed after GLSP intervention. Meanwhile, gene expressions associated with the possible molecular mechanism of GLSP regulation were also investigated using a quantitative RT-PCR.

RESULTS

The reduction of blood glucose level occurred within the first 2 weeks of GLSP intervention and the lipid synthesis in the diabetic rats of GLSP group was significantly decreased at 4 weeks compared to the model control group. Furthermore, it was also found that GLSP intervention greatly attenuated the level of oxidative stress in the diabetic rats. Quantitative RT-PCR analysis showed up-regulation of lipid metabolism related genes (Acox1, ACC, Insig-1 and Insig-2) and glycogen synthesis related genes (GS2 and GYG1) in GLSP group compared to model control group. Additionally, there were no significant changes in the expression of other genes, such as SREBP-1, Acly, Fas, Fads1, Gpam, Dgat1, PEPCK and G6PC1.

CONCLUSION

This study might indicate that GLSP consumption could provide a beneficial effect in terms of lowering the blood glucose levels by promoting glycogen synthesis and inhibiting gluconeogenesis. Meanwhile, GLSP treatment was also associated with the improvement of blood lipid compositions through the regulation of cholesterol homeostasis in the type 2 diabetic rats.

Genome-wide association studies of late-onset cardiovascular disease

Human genetics is a powerful tool for discovering causal mediators of human disease and physiology. Cardiovascular diseases with late onset in the lifecourse have historically not been considered genetic diseases, but in recent years the contribution of a heritable factor has been established. More importantly, over the last decade genome-wide association studies (GWASs) have identified many loci associated with late-onset cardiovascular diseases including coronary artery disease, carotid artery disease, ischemic stroke, aortic aneurysm, peripheral vascular disease, atrial fibrillation, valvular disease and correlates of vascular and myocardial function. Here we review findings from GWASs considered statistically robust with regard to multiple testing (p<5×10(-8)) for late-onset cardiovascular diseases and traits. Although for only a handful of the 92 genetic loci described here have the mechanisms underlying disease association been established, new and previously unsuspected pathways have been implicated for several conditions. Examples include a role for NO signaling in myocardial repolarization and sudden cardiac death and a role for the protein sortilin in lipid metabolism and coronary artery disease. Genetic loci with multiple trait associations have also provided novel biological insights. For example, of the 46 genetic loci associated with coronary artery disease, only 16 are also associated with conventional risk factors for cardiovascular disease whereas the remaining two thirds may reflect novel pathways. Much work remains to functionally characterize genetic loci and for clinical utility, but accruing insights into the biological basis of cardiovascular aging in human populations promise to point to novel therapeutic and preventive strategies. This article is part of a Special Issue entitled 'SI:CV Aging'.

Obesity: interactions of genome and nutrients intake

Obesity has become one of the major public health problems all over the world. Recent novel eras of research are opening for the effective management of obesity though gene and nutrient intake interactions because the causes of obesity are complex and multifactorial. Through GWASs (genome-wide association studies) and genetic variations (SNPs, single nucleotide polymorphisms), as the genetic factors are likely to determine individuals’ obesity predisposition. The understanding of genetic approaches in nutritional sciences is referred as “nutrigenomics”. Nutrigenomics explores the interaction between genetic factors and dietary nutrient intake on various disease phenotypes such as obesity. Therefore, this novel approach might suggest a solution for the effective prevention and treatment of obesity through individual genetic profiles and help improve health conditions.

Human genetics shape the gut microbiome

Host genetics and the gut microbiome can both influence metabolic phenotypes. However, whether host genetic variation shapes the gut microbiome and interacts with it to affect host phenotype is unclear. Here, we compared microbiotas across >1,000 fecal samples obtained from the TwinsUK population, including 416 twin pairs. We identified many microbial taxa whose abundances were influenced by host genetics. The most heritable taxon, the family Christensenellaceae, formed a co-occurrence network with other heritable Bacteria and with methanogenic Archaea. Furthermore, Christensenellaceae and its partners were enriched in individuals with low body mass index (BMI). An obese-associated microbiome was amended with Christensenella minuta, a cultured member of the Christensenellaceae, and transplanted to germ-free mice. C. minuta amendment reduced weight gain and altered the microbiome of recipient mice. Our findings indicate that host genetics influence the composition of the human gut microbiome and can do so in ways that impact host metabolism.

LEAP: biomarker inference through learning and evaluating association patterns

Single nucleotide polymorphism (SNP) high-dimensional datasets are available from Genome Wide Association Studies (GWAS). Such data provide researchers opportunities to investigate the complex genetic basis of diseases. Much of genetic risk might be due to undiscovered epistatic interactions, which are interactions in which combination of several genes affect disease. Research aimed at discovering interacting SNPs from GWAS datasets proceeded in two directions. First, tools were developed to evaluate candidate interactions. Second, algorithms were developed to search over the space of candidate interactions. Another problem when learning interacting SNPs, which has not received much attention, is evaluating how likely it is that the learned SNPs are associated with the disease. A complete system should provide this information as well. We develop such a system. Our system, called LEAP, includes a new heuristic search algorithm for learning interacting SNPs, and a Bayesian network based algorithm for computing the probability of their association. We evaluated the performance of LEAP using 100 1,000-SNP simulated datasets, each of which contains 15 SNPs involved in interactions. When learning interacting SNPs from these datasets, LEAP outperformed seven others methods. Furthermore, only SNPs involved in interactions were found to be probable. We also used LEAP to analyze real Alzheimer's disease and breast cancer GWAS datasets. We obtained interesting and new results from the Alzheimer's dataset, but limited results from the breast cancer dataset. We conclude that our results support that LEAP is a useful tool for extracting candidate interacting SNPs from high-dimensional datasets and determining their probability.

Obesity and genomics: role of technology in unraveling the complex genetic architecture of obesity

Obesity is a complex and multifactorial disease that occurs as a result of the interaction between "obesogenic" environmental factors and genetic components. Although the genetic component of obesity is clear from the heritability studies, the genetic basis remains largely elusive. Successes have been achieved in identifying the causal genes for monogenic obesity using animal models and linkage studies, but these approaches are not fruitful for polygenic obesity. The developments of genome-wide association approach have brought breakthrough discovery of genetic variants for polygenic obesity where tens of new susceptibility loci were identified. However, the common SNPs only accounted for a proportion of heritability. The arrival of NGS technologies and completion of 1000 Genomes Project have brought other new methods to dissect the genetic architecture of obesity, for example, the use of exome genotyping arrays and deep sequencing of candidate loci identified from GWAS to study rare variants. In this review, we summarize and discuss the developments of these genetic approaches in human obesity.

Resistant starch manipulated hyperglycemia/hyperlipidemia and related genes expression in diabetic rats

The effect of resistant starch (RS) administration on biological parameters including blood glucose, lipids composition and oxidative stress of type 2 diabetic rats was investigated. The results showed blood glucose level, total cholesterol and triglycerides concentrations significantly reduced, and high-density lipoprotein cholesterol concentration was doubly increased in the rats of RS administration group compared to model control group (P<0.01). The analyses of genes involved in glucose and lipid metabolism pathways demonstrated that the expression levels of lipid oxidation gene Acox1, glycogen synthesis genes, GS2 and GYG1, and insulin-induced genes, Insig-1 and Insig-2, were significantly up-regulated (P<0.01). In contrast, fatty acids and triglycerides synthesis and metabolism-related gene SREBP-1, fatty acid synthesis gene Fads1 and gluconeogenesis gene G6PC1 were greatly down-regulated. The mechanism study shows that the lowering of blood glucose level in diabetic rats by feeding RS is regulated through promoting glycogen synthesis and inhibiting gluconeogenesis, and the increased lipid metabolism is modulated through promoting lipid oxidation and cholesterol homeostasis. Our study revealed for the first time that the regulation of hepatic genes expression involved in glucose and lipids metabolisms in diabetic rats could be achieved even at a moderate level of RS consumption.

Gene polymorphisms and increased DNA damage in morbidly obese women

Obesity is characterized by increased adipose tissue mass resulting from a chronic imbalance between energy intake and expenditure. Furthermore, there is a clearly defined relationship among fat mass expansion, chronic low-grade systemic inflammation and reactive oxygen species (ROS) generation; leading to ROS-related pathological events. In the past years, genome-wide association studies have generated convincing evidence associating genetic variation at multiple regions of the genome with traits that reflect obesity. Therefore, the present study aimed to evaluate the relationships among the gene polymorphisms ghrelin (GHRL-rs26802), ghrelin receptor (GHSR-rs572169), leptin (LEP-rs7799039), leptin receptor (LEPR-rs1137101) and fat mass and obesity-associated (FTO-rs9939609) and obesity. The relationships among these gene variants and the amount of DNA damage were also investigated. Three hundred Caucasian morbidly obese and 300 eutrophic (controls) women were recruited. In summary, the results demonstrated that the frequencies of the GHRL, GHSR, LEP and LEPR polymorphisms were not different between Brazilian white morbidly obese and eutrophic women. Exceptions were the AA-FTO genotype and allele A, which were significantly more frequent in obese women than in the controls (0.23% vs. 0.10%; 0.46 vs. 0.36, respectively), and the TT-FTO genotype and the T allele, which were less frequent in morbidly obese women (p<0.01). Furthermore, significant differences in the amount of genetic lesions associated with FTO variants were observed only in obese women. In conclusion, this study demonstrated that the analyzed SNPs were not closely associated with morbid obesity, suggesting they are not the major contributors to obesity. Therefore, our data indicated that these gene variants are not good biomarkers for predicting risk susceptibility for obesity, whereas ROS generated by the inflammatory status might be one of the causes of DNA damage in obese women, favoring genetically related diseases as obesity comorbidities.

Association of INSIG2 polymorphism with overweight and LDL in children

Background

Dyslipidemia and overweight are common issues in children. Identifying genetic markers of risk could lead to targeted interventions. A polymorphism of SNP rs7566605 near insulin-induced gene 2 (INSIG2) has been identified as a strong candidate gene for obesity, through its feedback control of lipid synthesis.

Objective

To identify polymorphisms in INSIG2 which are associated with overweight (BMI ≥ 85% for age) and dyslipidemia in children. Hypothesis: The C allele of rs7566605 would be significantly associated with BMI and LDL.

Design/Methods

We genotyped 15 SNPs in/near INSIG2 in 1,058 healthy children (53% non-Hispanic white (NHW), 37% overweight) participating in a school based study. Genotype was compared with BMI and lipid markers, adjusting for age, gender, and puberty.

Results

We found a significant association between the SNP rs12464355 and LDL in NHW children, p < 0.001. The G allele is protective (lower LDL). A different SNP was associated with overweight in NHW: rs17047757. SNP rs7566605 was not associated with overweight or lipid levels.

Conclusions

We identified novel genetic associations between INSIG2 and both overweight and LDL in NHW children. Polymorphisms in INSIG2 may be important in the development of obesity through its effects on lipid regulation.

Pharmacodynamic genome-wide association study identifies new responsive loci for glucocorticoid intervention in asthma

Asthma is a chronic lung disease that has a high prevalence. The therapeutic intervention of this disease can be made more effective if genetic variability in patients' response to medications is implemented. However, a clear picture of the genetic architecture of asthma intervention response remains elusive. We conducted a genome-wide association study (GWAS) to identify drug response-associated genes for asthma, in which 909 622 SNPs were genotyped for 120 randomized participants who inhaled multiple doses of glucocorticoids. By integrating pharmacodynamic properties of drug reactions, we implemented a mechanistic model to analyze the GWAS data, enhancing the scope of inference about the genetic architecture of asthma intervention. Our pharmacodynamic model observed associations of genome-wide significance between dose-dependent response to inhaled glucocorticoids (measured as %FEV1) and five loci (P=5.315 × 10(-7) to 3.924 × 10(-9)), many of which map to metabolic genes related to lung function and asthma risk. All significant SNPs detected indicate a recessive effect, at which the homozygotes for the mutant alleles drive variability in %FEV1. Significant associations were well replicated in three additional independent GWAS studies. Pooled together over these three trials, two SNPs, chr6 rs6924808 and chr11 rs1353649, display an increased significance level (P=6.661 × 10(-16) and 5.670 × 10(-11)). Our study reveals a general picture of pharmacogenomic control for asthma intervention. The results obtained help to tailor an optimal dose for individual patients to treat asthma based on their genetic makeup.

Gender-Specific Effect of -102G>A Polymorphism in Insulin Induced Gene 2 on Obesity in Chinese Children

Background. Insulin induced gene 2 (INSIG2) encodes a protein that has a biological effect on regulation of adipocyte metabolism and body weight. This study aimed to investigate the association of INSIG2 gene -102G>A polymorphism with obesity related phenotypes in Chinese children and test gender-specific effects. Methods. The 2,030 independent individuals aged from 7 to 18 years, including 705 obese cases and 1,325 nonobese controls, were recruited from local schools. We measured the obesity-related phenotypes and detected the serum lipids. We genotype -102G>A polymorphism by using the matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS). Results. In all individuals, we found that the GG/GA genotype of INSIG2 -102G>A polymorphism was associated with risk of severe obesity (OR = 1.62, 95% CI: 1.11-2.36, and P = 0.012) under the dominant model. The association with severe obesity existed only in boys (OR = 1.91, 95% CI: 1.15-3.17, P = 0.012). The GG/GA genotype of -102G>A polymorphism was also associated with higher waist circumference (β = 2.61 cm, P = 0.031) in boys. No similar association was found in girls. The polymorphism was not associated with other obesity-related phenotypes, neither in all individuals nor in gender-specific population. Conclusions. This study identified a gender-specific effect of INSIG2 -102G>A polymorphism on risk of severe obesity and waist circumference in Chinese boys.

Meta-analysis of genome-wide association studies for circulating phylloquinone concentrations

BACKGROUND

Poor vitamin K status is linked to greater risk of several chronic diseases. Age, sex, and diet are determinants of circulating vitamin K; however, there is still large unexplained interindividual variability in vitamin K status. Although a strong genetic component has been hypothesized, this has yet to be examined by a genome-wide association (GWA) study.

OBJECTIVE

The objective was to identify common genetic variants associated with concentrations of circulating phylloquinone, the primary circulating form of vitamin K.

DESIGN

We conducted a 2-stage GWA meta-analysis of circulating phylloquinone in 2 populations of European descent from the Cohorts for Heart and Aging Research in Genomic Epidemiology Consortium Nutrition Working Group. Circulating phylloquinone was measured by using reversed-phase high-performance liquid chromatography. Results from adjusted cohort-specific discovery GWA analyses were meta-analyzed with inverse variance weights (n = 2138). Associations with circulating phylloquinone at P < 1 × 10(-6) were then evaluated in a second-stage analysis consisting of one independent cohort (n = 265).

RESULTS

No significant association was observed for circulating phylloquinone at the genome-wide significance level of 5 × 10(-8). However, from the discovery GWA, there were 11 single-nucleotide polymorphism (SNP) associations with circulating phylloquinone at P < 1 × 10(-6), including a functional variant previously associated with warfarin dose and altered phylloquinone metabolism. These SNPs are on 5 independent loci on 11q23.3, 8q24.3, 5q22.3, 2p12, and 19p13.12, and they fall within or near the candidate genes APOA1/C3/A4/A5 cluster (involved in lipoprotein metabolism), COL22A1, CDO1, CTNAA2, and CYP4F2 (a phylloquinone oxidase), respectively. Second-stage analysis in an independent cohort further suggests the association of the 5q22.3 locus with circulating phylloquinone (P < 0.05).

CONCLUSIONS

Multiple candidate genes related to lipoprotein and vitamin K metabolism were identified as potential determinants of circulating phylloquinone. Further investigation with a larger sample is warranted to verify our initial findings and identify other loci contributing to circulating phylloquinone. Trials related to this study were registered at clinicaltrials.gov as NCT00005121 (Framingham Offspring Study) and NCT00005487 (Multi-Ethnic Study of Atherosclerosis).

A comparative analysis of methods for predicting clinical outcomes using high-dimensional genomic datasets

OBJECTIVE

The objective of this investigation is to evaluate binary prediction methods for predicting disease status using high-dimensional genomic data. The central hypothesis is that the Bayesian network (BN)-based method called efficient Bayesian multivariate classifier (EBMC) will do well at this task because EBMC builds on BN-based methods that have performed well at learning epistatic interactions.

METHOD

We evaluate how well eight methods perform binary prediction using high-dimensional discrete genomic datasets containing epistatic interactions. The methods are as follows: naive Bayes (NB), model averaging NB (MANB), feature selection NB (FSNB), EBMC, logistic regression (LR), support vector machines (SVM), Lasso, and extreme learning machines (ELM). We use a hundred 1000-single nucleotide polymorphism (SNP) simulated datasets, ten 10,000-SNP datasets, six semi-synthetic sets, and two real genome-wide association studies (GWAS) datasets in our evaluation.

RESULTS

In fivefold cross-validation studies, the SVM performed best on the 1000-SNP dataset, while the BN-based methods performed best on the other datasets, with EBMC exhibiting the best overall performance. In-sample testing indicates that LR, SVM, Lasso, ELM, and NB tend to overfit the data.

DISCUSSION

EBMC performed better than NB when there are several strong predictors, whereas NB performed better when there are many weak predictors. Furthermore, for all BN-based methods, prediction capability did not degrade as the dimension increased.

CONCLUSIONS

Our results support the hypothesis that EBMC performs well at binary outcome prediction using high-dimensional discrete datasets containing epistatic-like interactions. Future research using more GWAS datasets is needed to further investigate the potential of EBMC.

Association study of childhood obesity with eight genetic variants recently identified by genome-wide association studies

BACKGROUND

Being overweight or obese is becoming increasingly common in low- and middle-income countries. The present study aimed to examine association of eight genetic variants with obesity and to estimate the cumulative effects of these variants in Chinese children.

METHODS

We conducted the case-control study in a total of 2,030 subjects. Genotyping of seven novel variants was performed with matrix-assisted laser desorption ionization time of flight mass spectrometry, while rs9939609 was genotyped with tetra-primer amplification refractory mutation system analysis.

RESULTS

The association of two fat mass and obesity-associated gene (FTO) single-nucleotide polymorphisms (SNPs; rs9939609 and rs62048402) with body mass index (BMI) or obesity reached nominal significance at P < 0.05. We found a cumulative effect of five SNPs on the risk of overweight and obesity (odds ratio (OR) = 1.197, 95% confidence interval (CI) = 1.068-1.342, P = 0.002). Subjects carrying 9 or more effect alleles had a 127% increased risk of overweight and obesity (OR = 2.270, 95% CI = 1.403-3.671, P = 0.001) compared with subjects who carry 6 or fewer effect alleles.

CONCLUSION

We confirmed two FTO SNPs (rs62048402 and rs9939609) had nominal significant effects on BMI or obesity. We identified the cumulative effect of five SNPs on risk of overweight and obesity. The results provided evidence for identifying genetic factors related to childhood obesity.

Is the gene-environment interaction paradigm relevant to genome-wide studies? The case of education and body mass index

This study uses data from the Framingham Heart Study to examine the relevance of the gene-environment interaction paradigm for genome-wide association studies (GWAS). We use completed college education as our environmental measure and estimate the interactive effect of genotype and education on body mass index (BMI) using 260,402 single-nucleotide polymorphisms (SNPs). Our results highlight the sensitivity of parameter estimates obtained from GWAS models and the difficulty of framing genome-wide results using the existing gene-environment interaction typology. We argue that SNP-environment interactions across the human genome are not likely to provide consistent evidence regarding genetic influences on health that differ by environment. Nevertheless, genome-wide data contain rich information about individual respondents, and we demonstrate the utility of this type of data. We highlight the fact that GWAS is just one use of genome-wide data, and we encourage demographers to develop methods that incorporate this vast amount of information from respondents into their analyses.

Genetics of antipsychotic-induced weight gain: update and current perspectives

Antipsychotic medications are used to effectively treat various symptoms for different psychiatric conditions. Unfortunately, antipsychotic-induced weight gain (AIWG) is a common side effect that frequently results in obesity and secondary medical conditions. Twin and sibling studies have indicated that genetic factors are likely to be highly involved in AIWG. Over recent years, there has been considerable progress in this area, with several consistently replicated findings, as well as the identification of new genes and implicated pathways. Here, we will review the most recent genetic studies related to AIWG using the Medline database (PubMed) and Google Scholar. Among the steadiest findings associated with AIWG are serotonin 2C receptors (HTR2C) and leptin promoter gene variants, with more recent studies implicating MTHFR and, in particular, MC4R genes. Additional support was reported for the HRH1, BDNF, NPY, CNR1, GHRL, FTO and AMPK genes. Notably, some of the reported variants appear to have relatively large effect sizes. These findings have provided insights into the mechanisms involved in AIWG and will help to develop predictive genetic tests in the near future.

Genetic association study with metabolic syndrome and metabolic-related traits in a cross-sectional sample and a 10-year longitudinal sample of chinese elderly population

Background

The metabolic syndrome (MetS) has been known as partly heritable, while the number of genetic studies on MetS and metabolic-related traits among Chinese elderly was limited.

Methods

A cross-sectional analysis was performed among 2 014 aged participants from September 2009 to June 2010 in Beijing, China. An additional longitudinal study was carried out among the same study population from 2001 to 2010. Biochemical profile and anthropometric parameters of all the participants were measured. The associations of 23 SNPs located within 17 candidate genes (MTHFR, PPARγ, LPL, INSIG, TCF7L2, FTO, KCNJ11, JAZF1, CDKN2A/B, ADIPOQ, WFS1, CDKAL1, IGF2BP2, KCNQ1, MTNR1B, IRS1, ACE) with overweight and obesity, diabetes, metabolic phenotypes, and MetS were examined in both studies.

Results

In this Chinese elderly population, prevalence of overweight, central obesity, diabetes, dyslipidemia, hypertension, and MetS were 48.3%, 71.0%, 32.4%, 75.7%, 68.3% and 54.5%, respectively. In the cross-sectional analyses, no SNP was found to be associated with MetS. Genotype TT of SNP rs4402960 within the gene IGF2BP2 was associated with overweight (odds ratio (OR)  = 0.479, 95% confidence interval (CI): 0.316-0.724, p = 0.001) and genotype CA of SNP rs1801131 within the gene MTHFR was associated with hypertension (OR = 1.560, 95% CI: 1.194–2.240, p = 0.001). However, these associations were not observed in the longitudinal analyses.

Conclusions

The associations of SNP rs4402960 with overweight as well as the association of SNP rs1801131 with hypertension were found to be statistically significant. No SNP was identified to be associated with MetS in our study with statistical significance.

The gene-gene interaction of INSIG-SCAP-SREBP pathway on the risk of obesity in Chinese children

Background. Childhood obesity has become a global public health problem in recent years. This study aimed to explore the association of genetic variants in INSIG-SCAP-SREBP pathway with obesity in Chinese children. Methods. A case-control study was conducted, including 705 obese cases and 1,325 nonobese controls. We genotyped 15 single nucleotide polymorphisms (SNPs) of five genes in INSIG-SCAP-SREBP pathway, including insulin induced gene 1 (INSIG1), insulin induced gene 2 (INSIG2), SREBP cleavage-activating protein gene (SCAP), sterol regulatory element binding protein gene 1 (SREBP1), and sterol regulatory element binding protein gene 2 (SREBP2). We used generalized multifactor dimensionality reduction (GMDR) and logistic regression to investigate gene-gene interactions. Results. Single polymorphism analyses showed that SCAP rs12487736 and rs12490383 were nominally associated with obesity. We identified a 3-locus interaction on obesity in GMDR analyses (P = 0.001), involving 3 genetic variants of INSIG2, SCAP, and SREBP2. The individuals in high-risk group of the 3-locus combinations had a 79.9% increased risk of obesity compared with those in low-risk group (OR = 1.799, 95% CI: 1.475–2.193, P = 6.61 × 10⁻⁹). Conclusion. We identified interaction of three genes in INSIG-SCAP-SREBP pathway on risk of obesity, revealing that these genes affect obesity more likely through a complex interaction pattern than single gene effect.

On family-based genome-wide association studies with large pedigrees: observations and recommendations

Family based association studies are employed less often than case-control designs in the search for disease-predisposing genes. The optimal statistical genetic approach for complex pedigrees is unclear when evaluating both common and rare variants. We examined the empirical power and type I error rates of 2 common approaches, the measured genotype approach and family-based association testing, through simulations from a set of multigenerational pedigrees. Overall, these results suggest that much larger sample sizes will be required for family-based studies and that power was better using MGA compared to FBAT. Taking into account computational time and potential bias, a 2-step strategy is recommended with FBAT followed by MGA.

Quantitative genomics of voluntary exercise in mice: transcriptional analysis and mapping of expression QTL in muscle

Motivation and ability both underlie voluntary exercise, each with a potentially unique genetic architecture. Muscle structure and function are one of many morphological and physiological systems acting to simultaneously determine exercise ability. We generated a large (n = 815) advanced intercross line of mice (G4) derived from a line selectively bred for increased wheel running (high runner) and the C57BL/6J inbred strain. We previously mapped quantitative trait loci (QTL) contributing to voluntary exercise, body composition, and changes in body composition as a result of exercise. Using brain tissue in a subset of the G4 (n = 244), we have also previously reported expression QTL (eQTL) colocalizing with the QTL for the higher-level phenotypes. Here, we examined the transcriptional landscape of hind limb muscle tissue via global mRNA expression profiles. Correlations revealed an ∼1,168% increase in significant relationships between muscle transcript expression levels and the same exercise and body composition phenotypes examined previously in the brain. The exercise trait most often significantly correlated with gene expression in the brain was running duration while in the muscle it was maximum running speed. This difference may indicate that time spent engaging in exercise behavior may be more influenced by central (neurobiological) mechanisms, while intensity of exercise may be largely controlled by peripheral mechanisms. Additionally, we used subsets of cis-acting eQTL, colocalizing with QTL, to identify candidate genes based on both positional and functional evidence. We discuss three plausible candidate genes (Insig2, Prcp, Sparc) and their potential regulatory role.

Genetic variation at the CELF1 (CUGBP, elav-like family member 1 gene) locus is genome-wide associated with Alzheimer's disease and obesity

Deviations from normal body weight are observed prior to and after the onset of Alzheimer's disease (AD). Midlife obesity confers increased AD risk in later life, whereas late-life obesity is associated with decreased AD risk. The role of underweight and weight loss for AD risk is controversial. Based on the hypothesis of shared genetic variants for both obesity and AD, we analyzed the variants identified for AD or obesity from genome-wide association meta-analyses of the GERAD (AD, cases = 6,688, controls = 13,685) and GIANT (body mass index [BMI] as measure of obesity, n = 123,865) consortia. Our cross-disorder analysis of genome-wide significant 39 obesity SNPs and 23 AD SNPs in these two large data sets revealed that: (1) The AD SNP rs10838725 (pAD  = 1.1 × 10(-08)) at the locus CELF1 is also genome-wide significant for obesity (pBMI  = 7.35 × 10(-09) ). (2) Four additional AD risk SNPs were nominally associated with obesity (rs17125944 at FERMT2, pBMI  = 4.03 × 10(-05), pBMI corr  = 2.50 × 10(-03) ; rs3851179 at PICALM; pBMI  = 0.002, rs2075650 at TOMM40/APOE, pBMI  = 0.024, rs3865444 at CD33, pBMI  = 0.024). (3) SNPs at two of the obesity risk loci (rs4836133 downstream of ZNF608; pAD  = 0.002 and at rs713586 downstream of RBJ/DNAJC27; pAD  = 0.018) were nominally associated with AD risk. Additionally, among the SNPs used for confirmation in both studies the AD risk allele of rs1858973, with an AD association just below genome-wide significance (pAD  = 7.20 × 10(-07)), was also associated with obesity (SNP at IQCK/GPRC5B; pBMI  = 5.21 × 10(-06) ; pcorr  = 3.24 × 10(-04)). Our first GWAS based cross-disorder analysis for AD and obesity suggests that rs10838725 at the locus CELF1 might be relevant for both disorders.

Ovine insulin induced-gene-2: molecular characterization, polymorphisms and association with milk traits

The aim was to characterize the INSIG-2 gene in Sarda sheep and to highlight associations between polymorphisms and milk traits. Two-hundred ewes, in their third or fourth lactation who lambed a single lamb between 20th and 30th of November, were chosen. Monthly individual milk yield was recorded and from each ewe a sample of milk was taken to analyze fat and protein content. PCR-RFLP and DNA sequencing were carried out to detect polymorphisms. Five exons have been characterized and five mutations have been found G88A, 436TCAGdel, A471G, C1071T and T1737G all in the intronic regions. The ovine sequence and related variations were deposited in GenBank with accession number JX843812.1. The animals carrying AA genotype at position 88 showed a lower milk fat concentration than those with the AG or GG genotype (P < 0.05). A lower milk fat concentration was registered also in the animals with the TCAG deletion in position 436 (P < 0.05) and in the animals carrying AA genotype at position 471 compared to those with the AG or GG genotype (P < 0.05). Moreover, the animals carrying CC genotype at position 1071 had a greater milk yield than those with CT or TT genotype (P < 0.05) while ewes with TT genotype showed a higher milk protein concentration compared to the others (P < 0.05). A total of 11 haplotypes were detected but no significant associations with milk traits were found. In conclusion for the first time the complete coding sequence of INSIG-2 gene and its association with milk trait has been reported in this study.

Controversial association results for INSIG2 on body mass index may be explained by interactions with age and with MC4R

Among the single-nucleotide polymorphisms (SNPs) previously reported to be associated with body mass index (BMI) and obesity, we focus on a common risk variant rs7566605 upstream of the insulin-induced gene 2 (INSIG2) gene and a rare protective variant rs2229616 on the melanocortin-4 receptor (MC4R) gene. INSIG2 is involved in adipogenesis and MC4R effects hormonal appetite control in response to the amount of adipose tissue. The influence of rs2229616 (MC4R) on BMI and obesity has been confirmed repeatedly and insight into the underlying mechanism provided. However, a main effect of rs7566605 (INSIG2) is under debate because of inconsistent replications of association. Interaction of rs7566605 with age may offer an explanation. SNP-age and SNP-SNP interaction models were tested on independent individuals from three population-based longitudinal cohorts, restricting the analysis to an observed age of 25-74 years. KORA S3/F3, KORA S4/F4 (Augsburg, Germany, 1994-2005, 1999-2008), and Framingham-Offspring data (Framingham, USA, 1971-2001) were analysed, with a total sample size of N=6926 in the joint analysis. The effect of interaction between rs7566605 and age on BMI and obesity status is significant and consistent across studies. This new evidence for rs7566605 (INSIG2) complements previous research. In addition, the interaction effect of rs7566605 with the MC4R variant rs2229616 on BMI was observed. This effect size was three times larger than that in a previously reported single-locus main effect of rs2229616. This leads to the conclusion that SNP-age or SNP-SNP interactions can mask genetic effects for complex diseases if left unaccounted for.

Variation in the MC4R gene is associated with bone phenotypes in elderly Swedish women

Osteoporosis is characterized by reduced bone mineral density (BMD) and increased fracture risk. Fat mass is a determinant of bone strength and both phenotypes have a strong genetic component. In this study, we examined the association between obesity associated polymorphisms (SNPs) with body composition, BMD, Ultrasound (QUS), fracture and biomarkers (Homocysteine (Hcy), folate, Vitamin D and Vitamin B12) for obesity and osteoporosis. Five common variants: rs17782313 and rs1770633 (melanocortin 4 receptor (MC4R); rs7566605 (insulin induced gene 2 (INSIG2); rs9939609 and rs1121980 (fat mass and obesity associated (FTO) were genotyped in 2 cohorts of Swedish women: PEAK-25 (age 25, n = 1061) and OPRA (age 75, n = 1044). Body mass index (BMI), total body fat and lean mass were strongly positively correlated with QUS and BMD in both cohorts (r² = 0.2–0.6). MC4R rs17782313 was associated with QUS in the OPRA cohort and individuals with the minor C-allele had higher values compared to T-allele homozygotes (TT vs. CT vs. CC: BUA: 100 vs. 103 vs. 103; p = 0.002); (SOS: 1521 vs. 1526 vs. 1524; p = 0.008); (Stiffness index: 69 vs. 73 vs. 74; p = 0.0006) after adjustment for confounders. They also had low folate (18 vs. 17 vs. 16; p = 0.03) and vitamin D (93 vs. 91 vs. 90; p = 0.03) and high Hcy levels (13.7 vs 14.4 vs. 14.5; p = 0.06). Fracture incidence was lower among women with the C-allele, (52% vs. 58%; p = 0.067). Variation in MC4R was not associated with BMD or body composition in either OPRA or PEAK-25. SNPs close to FTO and INSIG2 were not associated with any bone phenotypes in either cohort and FTO SNPs were only associated with body composition in PEAK-25 (p≤0.001). Our results suggest that genetic variation close to MC4R is associated with quantitative ultrasound and risk of fracture.

Association of INSIG2 rs9308762 with ALT level independent of BMI

OBJECTIVES

An increasing number of people are at risk for developing nonalcoholic fatty liver disease (NAFLD). Because obesity is a risk factor for NAFLD, the common variants of obesity-susceptible genes may be associated with NAFLD. Our aim was to identify whether the obesity-susceptible gene variants (rs9939609, rs9930506, and rs4783819 in fat mass and obesity-associated gene (FTO); rs12970134 and rs17782313 in melanocortin-4 receptor gene (MC4R); and rs7566605, rs13428113, and rs9308762 in insulin-induced gene 2 [INSIG2]) were associated with NAFLD.

METHODS

The case-control study recruited 1027 Chinese children ages 7 to 18 years, including 162 children with NAFLD and 865 children without NAFLD. Anthropometric measurements, alanine transaminase (ALT) detection, liver ultrasound examination, and genotyping of 8 gene variants were performed.

RESULTS

The A-allele of FTO rs9939609 was associated with increased NAFLD risk (P = 0.029, odds ratio  1.43), but was not significant after being adjusted for body mass index (BMI) (P = 0.268). We also found an association between the 2 variants (rs12970134 in MC4R and rs9308762 in INSIG2) and ALT level. For rs12970134, each additional A-allele increased ALT level by 1.87 IU/L (P = 0.032). For rs9308762, the homozygotes of the C-allele had a higher ALT level than the T-allele carriers (β = 3.19, P = 0.007). After adjustment for BMI, the former association did not exist, whereas the latter reminded significant (P = 0.003).

CONCLUSIONS

The FTO rs9939609 A-allele increased risk of NAFLD and MC4R rs12970134 was associated with ALT level through an effect on BMI. The association between INSIG2 rs9308762 and ALT level was independent of BMI. The results provided evidence for identifying genetic factors of NAFLD and may be useful for risk assessment and personalized medicine of NAFLD.

Genome-wide association studies of obesity and metabolic syndrome

Until just a few years ago, the genetic determinants of obesity and metabolic syndrome were largely unknown, with the exception of a few forms of monogenic extreme obesity. Since genome-wide association studies (GWAS) became available, large advances have been made. The first single nucleotide polymorphism robustly associated with increased body mass index (BMI) was in 2007 mapped to a gene with for the time unknown function. This gene, now known as fat mass and obesity associated (FTO) has been repeatedly replicated in several ethnicities and is affecting obesity by regulating appetite. Since the first report from a GWAS of obesity, an increasing number of markers have been shown to be associated with BMI, other measures of obesity or fat distribution and metabolic syndrome. This systematic review of obesity GWAS will summarize genome-wide significant findings for obesity and metabolic syndrome and briefly give a few suggestions of what is to be expected in the next few years.

Obesity--a neuropsychological disease? Systematic review and neuropsychological model

Obesity is a global epidemic associated with a series of secondary complications and comorbid diseases such as diabetes mellitus, cardiovascular disease, sleep-breathing disorders, and certain forms of cancer. On the surface, it seems that obesity is simply the phenotypic manifestation of deliberately flawed food intake behavior with the consequence of dysbalanced energy uptake and expenditure and can easily be reversed by caloric restriction and exercise. Notwithstanding this assumption, the disappointing outcomes of long-term clinical studies based on this assumption show that the problem is much more complex. Obviously, recent studies render that specific neurocircuits involved in appetite regulation are etiologically integrated in the pathomechanism, suggesting obesity should be regarded as a neurobiological disease rather than the consequence of detrimental food intake habits. Moreover, apart from the physical manifestation of overeating, a growing body of evidence suggests a close relationship with psychological components comprising mood disturbances, altered reward perception and motivation, or addictive behavior. Given that current dietary and pharmacological strategies to overcome the burgeoning threat of the obesity problem are of limited efficacy, bear the risk of adverse side-effects, and in most cases are not curative, new concepts integratively focusing on the fundamental neurobiological and psychological mechanisms underlying overeating are urgently required. This new approach to develop preventive and therapeutic strategies would justify assigning obesity to the spectrum of neuropsychological diseases. Our objective is to give an overview on the current literature that argues for this view and, on the basis of this knowledge, to deduce an integrative model for the development of obesity originating from disturbed neuropsychological functioning.

Integrating genetics and social science: genetic risk scores

The sequencing of the human genome and the advent of low-cost genome-wide assays that generate millions of observations of individual genomes in a matter of hours constitute a disruptive innovation for social science. Many public use social science datasets have or will soon add genome-wide genetic data. With these new data come technical challenges, but also new possibilities. Among these, the lowest-hanging fruit and the most potentially disruptive to existing research programs is the ability to measure previously invisible contours of health and disease risk within populations. In this article, we outline why now is the time for social scientists to bring genetics into their research programs. We discuss how to select genetic variants to study. We explain how the polygenic architecture of complex traits and the low penetrance of individual genetic loci pose challenges to research integrating genetics and social science. We introduce genetic risk scores as a method of addressing these challenges and provide guidance on how genetic risk scores can be constructed. We conclude by outlining research questions that are ripe for social science inquiry.

Testing for direct genetic effects using a screening step in family-based association studies

In genome wide association studies (GWAS), family-based studies tend to have less power to detect genetic associations than population-based studies, such as case-control studies. This can be an issue when testing if genes in a family-based GWAS have a direct effect on the phenotype of interest over and above their possible indirect effect through a secondary phenotype. When multiple SNPs are tested for a direct effect in the family-based study, a screening step can be used to minimize the burden of multiple comparisons in the causal analysis. We propose a 2-stage screening step that can be incorporated into the family-based association test (FBAT) approach similar to the conditional mean model approach in the Van Steen-algorithm (Van Steen et al., 2005). Simulations demonstrate that the type 1 error is preserved and this method is advantageous when multiple markers are tested. This method is illustrated by an application to the Framingham Heart Study.