Genetic association study of adiposity and melanocortin-4 receptor (MC4R) common variants: Replication and functional characterization of non-coding regions

Common genetic variants 3′ of MC4R within two large linkage disequilibrium (LD) blocks spanning 288 kb have been associated with common and rare forms of obesity. This large association region has not been refined and the relevant DNA segments within the association region have not been identified. In this study, we investigated whether common variants in the MC4R gene region were associated with adiposity-related traits in a biracial population-based study. Single nucleotide polymorphisms (SNPs) in the MC4R region were genotyped with a custom array and a genome-wide array and associations between SNPs and five adiposity-related traits were determined using race-stratified linear regression. Previously reported associations between lower BMI and the minor alleles of rs2229616/Val103Ile and rs52820871/Ile251Leu were replicated in white female participants. Among white participants, rs11152221 in a proximal 3′ LD block (closer to MC4R) was significantly associated with multiple adiposity traits, but SNPs in a distal 309 LD block (farther from MC4R ) were not. In a case-control study of severe obesity, rs11152221 was significantly associated. The association results directed our follow-up studies to the proximal LD block downstream of MC4R. By considering nucleotide conservation, the significance of association, and proximity to the MC4R gene, we identified a candidate MC4R regulatory region. This candidate region was sequenced in 20 individuals from a study of severe obesity in an attempt to identify additional variants, and the candidate region was tested for enhancer activity using in vivo enhancer assays in zebrafish and mice. Novel variants were not identified by sequencing and the candidate region did not drive reporter gene expression in zebrafish or mice. The identification of a putative insulator in this region could help to explain the challenges faced in this study and others to link SNPs associated with adiposity to altered MC4R expression. © 2014 Evans et al

From monogenic to polygenic obesity: recent advances

The heritability of obesity and body weight in general is high. A small number of confirmed monogenic forms of obesity—the respective mutations are sufficient by themselves to cause the condition in food abundant societies—have been identified by molecular genetic studies. The elucidation of these genes, mostly based on animal and family studies, has led to the identification of important pathways to the disorder and thus to a deeper understanding of the regulation of body weight. The identification of inborn deficiency of the mostly adipocyte-derived satiety hormone leptin in extremely obese children from consanguineous families paved the way to the first pharmacological therapy for obesity based on a molecular genetic finding. The genetic predisposition to obesity for most individuals, however, has a polygenic basis. A polygenic variant by itself has a small effect on the phenotype; only in combination with other predisposing variants does a sizeable phenotypic effect arise. Common variants in the first intron of the ‘fat mass and obesity associated’ gene (FTO) result in an elevated body mass index (BMI) equivalent to approximately +0.4 kg/m² per risk allele. The FTO variants were originally detected in a genome wide association study (GWAS) pertaining to type 2 diabetes mellitus. Large meta-analyses of GWAS have subsequently identified additional polygenic variants. Up to December 2009, polygenic variants have been confirmed in a total of 17 independent genomic regions. Further study of genetic effects on human body weight regulation should detect variants that will explain a larger proportion of the heritability. The development of new strategies for diagnosis, treatment and prevention of obesity can be anticipated

Eating disorders: the current status of molecular genetic research

Anorexia nervosa (AN) and bulimia nervosa (BN) are complex disorders characterized by disordered eating behavior where the patient’s attitude towards weight and shape, as well as their perception of body shape, are disturbed. Formal genetic studies on twins and families suggested a substantial genetic influence for AN and BN. Candidate gene studies have initially focused on the serotonergic and other central neurotransmitter systems and on genes involved in body weight regulation. Hardly any of the positive findings achieved in these studies were unequivocally confirmed or substantiated in meta-analyses. This might be due to too small sample sizes and thus low power and/or the genes underlying eating disorders have not yet been analyzed. However, some studies that also used subphenotypes (e.g., restricting type of AN) led to more specific results; however, confirmation is as yet mostly lacking. Systematic genome-wide linkage scans based on families with at least two individuals with an eating disorder (AN or BN) revealed initial linkage regions on chromosomes 1, 3 and 4 (AN) and 10p (BN). Analyses on candidate genes in the chromosome 1 linkage region led to the (as yet unconfirmed) identification of certain variants associated with AN. Genome-wide association studies are under way and will presumably help to identify genes and pathways involved in these eating disorders. The elucidation of the molecular mechanisms underlying eating disorders might improve therapeutic approaches

Analysis of shared heritability in common disorders of the brain

Disorders of the brain can exhibit considerable epidemiological comorbidity and often share symptoms, provoking debate about their etiologic overlap. We quantified the genetic sharing of 25 brain disorders from genome-wide association studies of 265,218 patients and 784,643 control participants and assessed their relationship to 17 phenotypes from 1,191,588 individuals. Psychiatric disorders share common variant risk, whereas neurological disorders appear more distinct from one another and from the psychiatric disorders. We also identified significant sharing between disorders and a number of brain phenotypes, including cognitive measures. Further, we conducted simulations to explore how statistical power, diagnostic misclassification, and phenotypic heterogeneity affect genetic correlations. These results highlight the importance of common genetic variation as a risk factor for brain disorders and the value of heritability-based methods in understanding their etiology