A Copy Number Variant on Chromosome 20q13.3 Implicated in Thinness and Severe Obesity

[Genetic Analyses of Complex Phenotypes Through the Example of Anorexia Nervosa and Bodyweight Regulation]

Genetic Analyses of Complex Phenotypes Through the Example of Anorexia Nervosa and Bodyweight Regulation Abstract. Genetics variants are important for the regulation of bodyweight and also contribute to the genetic architecture of eating disorders. For many decades, family studies, a subentity of so-called formal genetic studies, were employed to determine the genetic share of bodyweight and eating disorders and found heritability rates exceeding 50 % with both phenotypes. Because of this significant contribution of genetics, the search for those genes and their variants related to the variance in bodyweight and the etiology of eating disorders - or both - was commenced by the early 1990s. Initially, candidate genes studies were conducted targeting those genes most plausibly related to either phenotype, especially based on pathophysiological considerations. This approach, however, implicated only a few genes in the regulation of bodyweight and did not provide significant insights into the genetics of eating disorders. Driven by considerable methodological advances in genetic research, especially related to the introduction of so-called genome-wide association studies by the beginning of the 21st century, today more than 1,000 variants/loci have been detected that affect the regulation of bodyweight. Eight such loci have been identified regarding anorexia nervosa (AN). These results as well as those from cross-disorder analyses provide insights into the complex regulation of bodyweight and demonstrated unforeseen pathomechanisms for AN.

Ring Chromosome 20 Syndrome: Genetics, Clinical Characteristics, and Overlapping Phenotypes

Ring chromosome 20 [r(20)] syndrome is a rare condition characterized by a non-supernumerary ring chromosome 20 replacing a normal chromosome 20. It is commonly seen in a mosaic state and is diagnosed by means of karyotyping. r(20) syndrome is characterized by a recognizable epileptic phenotype with typical EEG pattern, intellectual disability manifesting after seizure onset in otherwise normally developing children, and behavioral changes. Despite the distinctive phenotype, many patients still lack a diagnosis-especially in the genomic era-and the pathomechanisms of ring formation are poorly understood. In this review we address the genetic and clinical aspects of r(20) syndrome, and discuss differential diagnoses and overlapping phenotypes, providing the reader with useful tools for clinical and laboratory practice. We also discuss the current issues in understanding the mechanisms through which ring 20 chromosome causes the typical manifestations, and present unpublished data about methylation studies. Ultimately, we explore future perspectives of r(20) research. Our intended audience is clinical and laboratory geneticists, child and adult neurologists, and genetic counselors.

Genetics of non-syndromic childhood obesity and the use of high-throughput DNA sequencing technologies

BACKGROUND

Childhood obesity is a serious public health problem associated with the development of several chronic diseases, such as type 2 diabetes mellitus, dyslipidemia, and hypertension. The elevated prevalence of obesity is mostly due to inadequate diet and lifestyle, but it is also influenced by genetic factors.

OBJECTIVES

To review recent advances in the field of the genetics of obesity. We summarize the list of genes associated with the rare non-syndromic forms of obesity, and explain their function. Furthermore, we discuss the technologies that are available for the genetic diagnosis of obesity.

RESULTS

Several studies reported that single gene variants cause Mendelian forms of obesity, determined by mutations of major effect in single genes. Rare, non-syndromic forms of obesity are a result of loss-of-function mutations in genes that act on the development and function of the hypothalamus or the leptin-melanocortin pathway. These variants disrupt enzymes and receptors that play a role in energy homeostasis, resulting in severe early-onset obesity and endocrine dysfunctions. Different approaches and technologies have been used to understand the genetic background of obesity. Currently, whole genome and whole exome sequencing are important diagnostic tools to identify new genes and variants associated with severe obesity, but other approaches are also useful at individual or population levels, such as linkage analysis, candidate gene sequencing, chromosomal microarray analysis, and genome-wide association studies.

CONCLUSIONS

The understanding of the genetic causes of obesity and the usefulness and limitations of the genetic diagnostic approaches can contribute to the development of new personalized therapeutic targets against obesity.

Genome-Wide Association of Copy Number Polymorphisms and Kidney Function

Genome-wide association studies (GWAS) using single nucleotide polymorphisms (SNPs) have identified more than 50 loci associated with estimated glomerular filtration rate (eGFR), a measure of kidney function. However, significant SNPs account for a small proportion of eGFR variability. Other forms of genetic variation have not been comprehensively evaluated for association with eGFR. In this study, we assess whether changes in germline DNA copy number are associated with GFR estimated from serum creatinine, eGFRcrea. We used hidden Markov models (HMMs) to identify copy number polymorphic regions (CNPs) from high-throughput SNP arrays for 2,514 African (AA) and 8,645 European ancestry (EA) participants in the Atherosclerosis Risk in Communities (ARIC) study. Separately for the EA and AA cohorts, we used Bayesian Gaussian mixture models to estimate copy number at regions identified by the HMM or previously reported in the HapMap Project. We identified 312 and 464 autosomal CNPs among individuals of EA and AA, respectively. Multivariate models adjusted for SNP-derived covariates of population structure identified one CNP in the EA cohort near genome-wide statistical significance (Bonferroni-adjusted p = 0.067) located on chromosome 5 (876-880kb). Overall, our findings suggest a limited role of CNPs in explaining eGFR variability.