Molecular genetics of human obesity: A comprehensive review

Obesity and its related health complications is a major problem worldwide. Hypothalamus and their signalling molecules play a critical role in the intervening and coordination with energy balance and homeostasis. Genetic factors play a crucial role in determining an individual's predisposition to the weight gain and being obese. In the past few years, several genetic variants were identified as monogenic forms of human obesity having success over common polygenic forms. In the context of molecular genetics, genome-wide association studies (GWAS) approach and their findings signified a number of genetic variants predisposing to obesity. However, the last couple of years, it has also been noticed that alterations in the environmental and epigenetic factors are one of the key causes of obesity. Hence, this review might be helpful in the current scenario of molecular genetics of human obesity, obesity-related health complications (ORHC), and energy homeostasis. Future work based on the clinical discoveries may play a role in the molecular dissection of genetic approaches to find more obesity-susceptible gene loci.     

Evolution, revolution and heresy in the genetics of infectious disease susceptibility

Infectious pathogens have long been recognized as potentially powerful agents impacting on the evolution of human genetic diversity. Analysis of large-scale case-control studies provides one of the most direct means of identifying human genetic variants that currently impact on susceptibility to particular infectious diseases. For over 50 years candidate gene studies have been used to identify loci for many major causes of human infectious mortality, including malaria, tuberculosis, human immunodeficiency virus/acquired immunodeficiency syndrome, bacterial pneumonia and hepatitis. But with the advent of genome-wide approaches, many new loci have been identified in diverse populations. Genome-wide linkage studies identified a few loci, but genome-wide association studies are proving more successful, and both exome and whole-genome sequencing now offer a revolutionary increase in power. Opinions differ on the extent to which the genetic component to common disease susceptibility is encoded by multiple high frequency or rare variants, and the heretical view that most infectious diseases might even be monogenic has been advocated recently. Review of findings to date suggests that the genetic architecture of infectious disease susceptibility may be importantly different from that of non-infectious diseases, and it is suggested that natural selection may be the driving force underlying this difference.     

Evaluation and integration of 49 genome-wide experiments and the prediction of previously unknown obesity-related genes

MOTIVATION: Genome-wide experiments only rarely show resounding success in yielding genes associated with complex polygenic disorders. We evaluate 49 obesity-related genome-wide experiments with publicly available findings including microarray, genetics, proteomics and gene knock-down from human, mouse, rat and worm, in terms of their ability to rediscover a comprehensive set of genes previously found to be causally associated or having variants associated with obesity. RESULTS: Individual experiments show poor predictive ability for rediscovering known obesity-associated genes. We show that intersecting the results of experiments significantly improves the sensitivity, specificity and precision of the prediction of obesity-associated genes. We create an integrative model that statistically significantly outperforms all 49 individual genome-wide experiments. We find that genes known to be associated with obesity are significantly implicated in more obesity-related experiments and use this to provide a list of genes that we predict to have the highest likelihood of association for obesity. The approach described here can include any number and type of genome-wide experiments and might be useful for other complex polygenic disorders as well.     

Genome-wide association study to identify common variants associated with brachial circumference: a meta-analysis of 14 cohorts

Brachial circumference (BC), also known as upper arm or mid arm circumference, can be used as an indicator of muscle mass and fat tissue, which are distributed differently in men and women. Analysis of anthropometric measures of peripheral fat distribution such as BC could help in understanding the complex pathophysiology behind overweight and obesity. The purpose of this study is to identify genetic variants associated with BC through a large-scale genome-wide association scan (GWAS) meta-analysis. We used fixed-effects meta-analysis to synthesise summary results across 14 GWAS discovery and 4 replication cohorts comprising overall 22,376 individuals (12,031 women and 10,345 men) of European ancestry. Individual analyses were carried out for men, women, and combined across sexes using linear regression and an additive genetic model: adjusted for age and adjusted for age and BMI. We prioritised signals for follow-up in two-stages. We did not detect any signals reaching genome-wide significance. The FTO rs9939609 SNP showed nominal evidence for association (p<0.05) in the age-adjusted strata for men and across both sexes. In this first GWAS meta-analysis for BC to date, we have not identified any genome-wide significant signals and do not observe robust association of previously established obesity loci with BC. Large-scale collaborations will be necessary to achieve higher power to detect loci underlying BC.     

Identification of molecular genetic factors that influence migraine

Migraine is a common neurological disorder with a strong genetic basis. However, the complex nature of the disorder has meant that few genes or susceptibility loci have been identified and replicated consistently to confirm their involvement in migraine. Approaches to genetic studies of the disorder have included analysis of the rare migraine subtype, familial hemiplegic migraine with several causal genes identified for this severe subtype. However, the exact genetic contributors to the more common migraine subtypes are still to be deciphered. Genome-wide studies such as genome-wide association studies and linkage analysis as well as candidate genes studies have been employed to investigate genes involved in common migraine. Neurological, hormonal and vascular genes are all considered key factors in the pathophysiology of migraine and are a focus of many of these studies. It is clear that the influence of individual genes on the expression of this disorder will vary. Furthermore, the disorder may be dependent on gene–gene and gene–environment interactions that have not yet been considered. In addition, identifying susceptibility genes may require phenotyping methods outside of the International Classification of Headache Disorders II criteria, such as trait component analysis and latent class analysis to better define the ambit of migraine expression.     

Mouse models and the interpretation of human GWAS in type 2 diabetes and obesity

Within the last 3 years, genome-wide association studies (GWAS) have had unprecedented success in identifying loci that are involved in common diseases. For example, more than 35 susceptibility loci have been identified for type 2 diabetes and 32 for obesity thus far. However, the causal gene and variant at a specific linkage disequilibrium block is often unclear. Using a combination of different mouse alleles, we can greatly facilitate the understanding of which candidate gene at a particular disease locus is associated with the disease in humans, and also provide functional analysis of variants through an allelic series, including analysis of hypomorph and hypermorph point mutations, and knockout and overexpression alleles. The phenotyping of these alleles for specific traits of interest, in combination with the functional analysis of the genetic variants, may reveal the molecular and cellular mechanism of action of these disease variants, and ultimately lead to the identification of novel therapeutic strategies for common human diseases. In this Commentary, we discuss the progress of GWAS in identifying common disease loci for metabolic disease, and the use of the mouse as a model to confirm candidate genes and provide mechanistic insights.     

Gene-diet interactions in childhood obesity

Childhood overweight and obesity have reached epidemic proportions worldwide, and the increase in weight-associated co-morbidities including premature type 2 diabetes mellitus (T2DM) and atherosclerotic cardiovascular disease will soon become major healthcare and economic problems. A number of studies now indicate that the childhood obesity epidemic which has emerged during the past 30 years is a complex multi-factorial disease resulting from interaction of susceptibility genes with an obesogenic environment. This review will focus on gene-diet interactions suspected of having a prominent role in promoting childhood obesity. In particular, the specific genes that will be presented (FTO, MC4R, and NPC1) have recently been associated with childhood obesity through a genome-wide association study (GWAS) and were shown to interact with nutritional components to increase weight gain. Although a fourth gene (APOA2) has not yet been associated with childhood obesity, this review will also present information on what now represents the best characterized gene-diet interaction in promoting weight gain.     

Genetic insights into age-related macular degeneration: controversies addressing risk, causality, and therapeutics

Age-related macular degeneration (AMD) is a common condition among the elderly population that leads to the progressive central vision loss and serious compromise of quality of life for its sufferers. It is also one of the few disorders for whom the investigation of its genetics has yielded rich insights into its diversity and causality and holds the promise of enabling clinicians to provide better risk assessments for individuals as well as to develop and selectively deploy new therapeutics to either prevent or slow the development of disease and lessen the threat of vision loss. The genetics of AMD began initially with the appreciation of familial aggregation and increase risk and expanded with the initial association of APOE variants with the disease. The first major breakthroughs came with family-based linkage studies of affected (and discordant) sibs, which identified a number of genetic loci and led to the targeted search of the 1q31 and 10q26 loci for associated variants. Three of the initial four reports for the CFH variant, Y402H, were based on regional candidate searches, as were the two initial reports of the ARMS2/HTRA1 locus variants. Case-control association studies initially also played a role in discovering the major genetic variants for AMD, and the success of those early studies have been used to fuel enthusiasm for the methodology for a number of diseases. Until 2010, all of the subsequent genetic variants associated with AMD came from candidate gene testing based on the complement factor pathway. In 2010, several large-scale genome-wide association studies (GWAS) identified genes that had not been previously identified. Much of this historical information is available in a number of recent reviews (Chen et al., 2010b; Deangelis et al., 2011; Fafowora and Gorin, 2012b; Francis and Klein, 2011; Kokotas et al., 2011). Large meta analysis of AMD GWAS has added new loci and variants to this collection (Chen et al., 2010a; Kopplin et al., 2010; Yu et al., 2011). This paper will focus on the ongoing controversies that are confronting AMD genetics at this time, rather than attempting to summarize this field, which has exploded in the past 5 years.     

复杂疾病全基因组关联研究进展——遗传统计分析

2005年, Science杂志首次报道了有关人类年龄相关性黄斑变性的全基因组关联研究, 此后有关肥胖、2型糖尿病、冠心病、阿尔茨海默病等一系列复杂疾病的全基因组关联研究被陆续报道, 这一阶段被称为人类全基因组关联研究的第一次浪潮。文章分别介绍了全基因组关联研究统计分析的方法、软件和应用实例; 比较了关联分析中多重检验的P值调整方法, 包括Bonferroni、递减的Bonferroni校正法、模拟运算法和控制错误发现率的方法; 还讨论了人群混杂对关联分析结果可能产生的影响及原理, 以及全基因组关联研究中控制人群混杂的方法的研究进展和应用实例。在全基因组关联研究的第一次浪潮中, 应用经典的遗传统计方法发现了许多基因-表型之间的关联并且能够对这些关联做出解释, 其中包括许多基因组中的未知基因和染色体区域。然而, 全基因组关联研究的继续发展需要进一步阐述基因组内基因之间相互作用、基因-基因之间的复杂作用网络与环境因素的相互作用在复杂疾病发生中的作用, 现有的统计分析方法肯定不能满足需要, 开发更为高级的统计分析方法势在必行。最后, 文章还给出了全基因组关联研究统计分析软件的相关网站信息。     

Gene-nutrient interactions and susceptibility to human obesity

A large number of genome-wide association studies, transferability studies, and candidate gene studies performed in diverse populations around the world have identified gene variants that are associated with common human obesity. The mounting evidence suggests that these obesity gene variants interact with multiple environmental factors and increase susceptibility to this complex metabolic disease. The objective of this review article is to provide concise and updated information on energy balance, heritability of body weight, origins of gene variants, and gene-nutrient interactions in relation to human obesity. It is proposed that knowledge of these related topics will provide valuable insight for future preventative lifestyle intervention using targeted nutritional and medicinal therapies.     

The genome revolution and its role in understanding complex diseases

The completion of the human genome sequence in 2003 clearly marked the beginning of a new era for biomedical research. It spurred technological progress that was unprecedented in the life sciences, including the development of high-throughput technologies to detect genetic variation and gene expression. The study of genetics has become “big data science”. One of the current goals of genetic research is to use genomic information to further our understanding of common complex diseases. An essential first step made towards this goal was by the identification of thousands of single nucleotide polymorphisms showing robust association with hundreds of different traits and diseases. As insight into common genetic variation has expanded enormously and the technology to identify more rare variation has become available, we can utilize these advances to gain a better understanding of disease etiology. This will lead to developments in personalized medicine and P4 healthcare. Here, we review some of the historical events and perspectives before and after the completion of the human genome sequence. We also describe the success of large-scale genetic association studies and how these are expected to yield more insight into complex disorders. We show how we can now combine gene-oriented research and systems-based approaches to develop more complex models to help explain the etiology of common diseases. This article is part of a Special Issue entitled: From Genome to Function.     

Genome-wide association studies and type 2 diabetes

In recent years, the search for genetic determinants of type 2 diabetes (T2D) has changed dramatically. Although linkage and small-scale candidate gene studies were highly successful in the identification of genes, which, when mutated, caused monogenic forms of T2D, they were largely unsuccessful when applied to the more common forms of the disease. To date, these approaches have only identified two loci (PPARG, KCNJ11) robustly implicated in T2D susceptibility. The ability to perform large-scale association analysis, including genome-wide association studies (GWAS) in many thousands of samples from different populations, and subsequently, the shift to form large international collaborations to perform meta-analyses across many studies has taken the number of independent loci showing genome-wide significant associations with T2D to 44. This number includes six loci identified initially through the analysis of quantitative glycaemic phenotypes, illustrating the usefulness of this approach both to identify new disease genes and gain insight into the mechanisms leading to disease. Combined, these loci still only account for ～10% of the observed familial clustering in Europeans, leaving much of the variance unexplained. In this review, we will describe what GWAS have taught us about the genetic basis of T2D and discuss possible next steps to uncover the remaining heritability.     

A genome-wide association study on obesity and obesity-related traits

Large-scale genome-wide association studies (GWAS) have identified many loci associated with body mass index (BMI), but few studies focused on obesity as a binary trait. Here we report the results of a GWAS and candidate SNP genotyping study of obesity, including extremely obese cases and never overweight controls as well as families segregating extreme obesity and thinness. We first performed a GWAS on 520 cases (BMI>35 kg/m(2)) and 540 control subjects (BMI<25 kg/m(2)), on measures of obesity and obesity-related traits. We subsequently followed up obesity-associated signals by genotyping the top ～500 SNPs from GWAS in the combined sample of cases, controls and family members totaling 2,256 individuals. For the binary trait of obesity, we found 16 genome-wide significant signals within the FTO gene (strongest signal at rs17817449, P = 2.5 × 10(-12)). We next examined obesity-related quantitative traits (such as total body weight, waist circumference and waist to hip ratio), and detected genome-wide significant signals between waist to hip ratio and NRXN3 (rs11624704, P = 2.67 × 10(-9)), previously associated with body weight and fat distribution. Our study demonstrated how a relatively small sample ascertained through extreme phenotypes can detect genuine associations in a GWAS.     

Impact of variation in the FTO gene on whole body fat distribution, ectopic fat, and weight loss

Polymorphisms in the fat mass- and obesity-associated (FTO) gene have been identified to be associated with obesity and diabetes in large genome-wide association studies. We hypothesized that variation in the FTO gene has an impact on whole body fat distribution and insulin sensitivity, and influences weight change during lifestyle intervention. To test this hypothesis, we genotyped 1,466 German subjects, with increased risk for type 2 diabetes, for single-nucleotide polymorphism rs8050136 in the FTO gene and estimated glucose tolerance and insulin sensitivity from an oral glucose tolerance test (OGTT). Distribution of fat depots was quantified using whole body magnetic resonance (MR) imaging and spectroscopy in 298 subjects. Two-hundred and four subjects participated in a lifestyle intervention program and were examined after a follow-up of 9 months. In the cross-sectional analysis, the A allele of rs8050136 in FTO was associated with a higher BMI, body fat, and lean body mass (all P < 0.001). There was a significant effect of variation in the FTO gene on subcutaneous fat (P < or = 0.05) and a trend for liver fat content, nonvisceral adipose tissue, and visceral fat (all P < or = 0.1). However, the single-nucleotide polymorphism was not associated with insulin sensitivity or secretion independent of BMI (all P > 0.05). During lifestyle intervention, there was also no influence of the FTO polymorphism on changes in body weight or fat distribution. In conclusion, despite an association with BMI and whole body fat distribution, variation in the FTO locus has no effect on the success of a lifestyle intervention program.     

The <Emphasis Type="Italic">FTO</Emphasis> Gene,Implicated in Human Obesity,Is Found Only in Vertebrates and Marine Algae

Human obesity is a main cause of morbidity and mortality. Recently, several studies have demonstrated an association between the FTO gene locus and early onset and severe obesity. To date, the FTO gene has only been discovered in vertebrates. We identified FTO homologs in the complete genome sequences of various evolutionary diverse marine eukaryotic algae, ranging from unicellular photosynthetic picoplankton to a multicellular seaweed. However, FTO homologs appear to be absent from all other completely sequenced genomes of plants, fungi, and invertebrate animals. Although the biological roles of these marine algal FTO homologs are still unknown, these genes will be useful for exploring basic protein features and could hence help unravel the function of the FTO gene in vertebrates and its inferred link with obesity in humans.     

Searching for the regulators of human gene expression

Many common human traits are believed to be a composite reflection of multiple genetic and non-genetic factors and the genetic contribution is consequently often difficult to characterise. Recent advances suggest that subtle variation in the regulation of gene expression may contribute to complex human traits. In two reports, Cheung and colleagues scale up human genetics analysis to an impressive level in a genome-wide search for the regulators of gene expression. They perform linkage analysis on expression profiles for over 3,500 genes and then employ the HapMap resource to take positive findings through to association studies at the genome-wide level. This work gives new insights into the complexities of gene regulation and the plausibility of genome-wide study design.     

The emerging genetic architecture of type 2 diabetes

Type 2 diabetes is a genetically heterogeneous disease, with several relatively rare monogenic forms and a number of more common forms resulting from a complex interaction of genetic and environmental factors. Previous studies using a candidate gene approach, family linkage studies, and gene expression profiling uncovered a number of type 2 genes, but the genetic basis of common type 2 diabetes remained unknown. Recently, a new window has opened on defining potential type 2 diabetes genes through genome-wide SNP association studies of very large populations of individuals with diabetes. This review explores the pathway leading to discovery of these genetic effects, the impact of these genetic loci on diabetes risk, the potential mechanisms of action of the genes to alter glucose homeostasis, and the limitations of these studies in defining the role of genetics in this important disease.