Over the past ∼15 years there has been great progress in our understanding of the genetics of both type 1 diabetes and type 2 diabetes. This has been driven principally by genome-wide association ...studies (GWAS) in increasingly larger sample sizes, where many distinct loci have now been reported for both traits. One of the loci that dominates these studies is the
locus for type 2 diabetes. This genetic signal has been leveraged to explore multiple aspects of disease risk, including developments in genetic risk scores, genetic commonalities with cancer, and for gaining insights into diabetes-related molecular pathways. Furthermore, the
locus has aided in providing insights into the genetics of both latent autoimmune diabetes in adults and various presentations of type 1 diabetes. This review outlines the knowledge gained to date and highlights how work with this locus leads the way in guiding how many other genetic loci could be similarly used to gain insights into the pathogenesis of diabetes.
The genetics of human obesity Xia, Qianghua; Grant, Struan F.A.
Annals of the New York Academy of Sciences,
April 2013, Letnik:
1281, Številka:
1
Journal Article
Recenzirano
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It has long been known that there is a genetic component to obesity, and that characterizing this underlying factor would likely offer the possibility of better intervention in the future. Monogenic ...obesity has proved to be relatively straightforward, with a combination of linkage analysis and mouse models facilitating the identification of multiple genes. In contrast, genome‐wide association studies have successfully revealed a variety of genetic loci associated with the more common form of obesity, allowing for very strong consensus on the underlying genetic architecture of the phenotype for the first time. Although a number of significant findings have been made, it appears that very little of the apparent heritability of body mass index has actually been explained to date. New approaches for data analyses and advances in technology will be required to uncover the elusive missing heritability, and to aid in the identification of the key causative genetic underpinnings of obesity.
Genome-wide association studies (GWAS) have been fruitful in identifying disease susceptibility loci for common and complex diseases. A remaining question is whether we can quantify individual ...disease risk based on genotype data, in order to facilitate personalized prevention and treatment for complex diseases. Previous studies have typically failed to achieve satisfactory performance, primarily due to the use of only a limited number of confirmed susceptibility loci. Here we propose that sophisticated machine-learning approaches with a large ensemble of markers may improve the performance of disease risk assessment. We applied a Support Vector Machine (SVM) algorithm on a GWAS dataset generated on the Affymetrix genotyping platform for type 1 diabetes (T1D) and optimized a risk assessment model with hundreds of markers. We subsequently tested this model on an independent Illumina-genotyped dataset with imputed genotypes (1,008 cases and 1,000 controls), as well as a separate Affymetrix-genotyped dataset (1,529 cases and 1,458 controls), resulting in area under ROC curve (AUC) of approximately 0.84 in both datasets. In contrast, poor performance was achieved when limited to dozens of known susceptibility loci in the SVM model or logistic regression model. Our study suggests that improved disease risk assessment can be achieved by using algorithms that take into account interactions between a large ensemble of markers. We are optimistic that genotype-based disease risk assessment may be feasible for diseases where a notable proportion of the risk has already been captured by SNP arrays.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Osteoporosis is a devastating disease with an essential genetic component. GWAS have discovered genetic signals robustly associated with bone mineral density (BMD), but not the precise localization ...of effector genes. Here, we carry out physical and direct variant to gene mapping in human mesenchymal progenitor cell-derived osteoblasts employing a massively parallel, high resolution Capture C based method in order to simultaneously characterize the genome-wide interactions of all human promoters. By intersecting our Capture C and ATAC-seq data, we observe consistent contacts between candidate causal variants and putative target gene promoters in open chromatin for ~ 17% of the 273 BMD loci investigated. Knockdown of two novel implicated genes, ING3 at 'CPED1-WNT16' and EPDR1 at 'STARD3NL', inhibits osteoblastogenesis, while promoting adipogenesis. This approach therefore aids target discovery in osteoporosis, here on the example of two relevant genes involved in the fate determination of mesenchymal progenitors, and can be applied to other common genetic diseases.
Abstract
Sleep occurs universally and is a biological necessity for human functioning. The consequences of diminished sleep quality impact physical and physiological systems such as neurological, ...cardiovascular, and metabolic processes. In fact, people impacted by common complex diseases experience a wide range of sleep disturbances. It is challenging to uncover the underlying molecular mechanisms responsible for decreased sleep quality in many disease systems owing to the lack of suitable sleep biomarkers. However, the discovery of a genetic component to sleep patterns has opened a new opportunity to examine and understand the involvement of sleep in many disease states. It is now possible to use major genomic resources and technologies to uncover genetic contributions to many common diseases. Large scale prospective studies such as the genome wide association studies (GWAS) have successfully revealed many robust genetic signals associated with sleep-related traits. With the discovery of these genetic variants, a major objective of the community has been to investigate whether sleep-related traits are associated with disease pathogenesis and other health complications. Mendelian Randomization (MR) represents an analytical method that leverages genetic loci as proxy indicators to establish causal effect between sleep traits and disease outcomes. Given such variants are randomly inherited at birth, confounding bias is eliminated with MR analysis, thus demonstrating evidence of causal relationships that can be used for drug development and to prioritize clinical trials. In this review, we outline the results of MR analyses performed to date on sleep traits in relation to a multitude of common complex diseases.
Comprehensive identification and cataloging of copy number variations (CNVs) is required to provide a complete view of human genetic variation. The resolution of CNV detection in previous ...experimental designs has been limited to tens or hundreds of kilobases. Here we present PennCNV, a hidden Markov model (HMM) based approach, for kilobase-resolution detection of CNVs from Illumina high-density SNP genotyping data. This algorithm incorporates multiple sources of information, including total signal intensity and allelic intensity ratio at each SNP marker, the distance between neighboring SNPs, the allele frequency of SNPs, and the pedigree information where available. We applied PennCNV to genotyping data generated for 112 HapMap individuals; on average, we detected approximately 27 CNVs for each individual with a median size of approximately 12 kb. Excluding common rearrangements in lymphoblastoid cell lines, the fraction of CNVs in offspring not detected in parents (CNV-NDPs) was 3.3%. Our results demonstrate the feasibility of whole-genome fine-mapping of CNVs via high-density SNP genotyping.
Common copy number variations (CNVs) represent a significant source of genetic diversity, yet their influence on phenotypic variability, including disease susceptibility, remains poorly understood. ...To address this problem in human cancer, we performed a genome-wide association study of CNVs in the childhood cancer neuroblastoma, a disease in which single nucleotide polymorphism variations are known to influence susceptibility. We first genotyped 846 Caucasian neuroblastoma patients and 803 healthy Caucasian controls at approximately 550,000 single nucleotide polymorphisms, and performed a CNV-based test for association. We then replicated significant observations in two independent sample sets comprised of a total of 595 cases and 3,357 controls. Here we describe the identification of a common CNV at chromosome 1q21.1 associated with neuroblastoma in the discovery set, which was confirmed in both replication sets. This CNV was validated by quantitative polymerase chain reaction, fluorescent in situ hybridization and analysis of matched tumour specimens, and was shown to be heritable in an independent set of 713 cancer-free parent-offspring trios. We identified a previously unknown transcript within the CNV that showed high sequence similarity to several neuroblastoma breakpoint family (NBPF) genes and represents a new member of this gene family (NBPF23). This transcript was preferentially expressed in fetal brain and fetal sympathetic nervous tissues, and the expression level was strictly correlated with CNV state in neuroblastoma cells. These data demonstrate that inherited copy number variation at 1q21.1 is associated with neuroblastoma and implicate a previously unknown neuroblastoma breakpoint family gene in early tumorigenesis of this childhood cancer.
Celotno besedilo
Dostopno za:
DOBA, IJS, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
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.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Ikaros is a transcriptional factor required for conventional T cell development, differentiation, and anergy. While the related factors Helios and Eos have defined roles in regulatory T cells (Treg), ...a role for Ikaros has not been established. To determine the function of Ikaros in the Treg lineage, we generated mice with Treg-specific deletion of the Ikaros gene (
). We find that Ikaros cooperates with Foxp3 to establish a major portion of the Treg epigenome and transcriptome. Ikaros-deficient Treg exhibit Th1-like gene expression with abnormal production of IL-2, IFNg, TNFa, and factors involved in Wnt and Notch signaling. While
-Treg-cko mice do not develop spontaneous autoimmunity, Ikaros-deficient Treg are unable to control conventional T cell-mediated immune pathology in response to TCR and inflammatory stimuli in models of IBD and organ transplantation. These studies establish Ikaros as a core factor required in Treg for tolerance and the control of inflammatory immune responses.
Obesity is a common complex trait that elevates the risk for various diseases, including type 2 diabetes and cardiovascular disease. A combination of environmental and genetic factors influences the ...pathogenesis of obesity. Advances in genomic technologies have driven the identification of multiple genetic loci associated with this disease, ranging from studying severe onset cases to investigating common multifactorial polygenic forms. Additionally, findings from epigenetic analyses of modifications to the genome that do not involve changes to the underlying DNA sequence have emerged as key signatures in the development of obesity. Such modifications can mediate the effects of environmental factors, including diet and lifestyle, on gene expression and clinical presentation. This review outlines what is known about the genetic and epigenetic contributors to obesity susceptibility, along with the albeit limited therapeutic options currently available. Furthermore, we delineate the potential mechanisms of actions through which epigenetic changes can mediate environmental influences and the related opportunities they present for future interventions in the management of obesity.
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