Follow-up of potential novel Graves' disease susceptibility loci, identified in the UK WTCCC genome-wide nonsynonymous SNP study

Identification of novel genes associated with atherosclerosis in Bama miniature pig

BACKGROUND

Atherosclerosis is a chronic cardiovascular disease of great concern. However, it is difficult to establish a direct connection between conventional small animal models and clinical practice. The pig's genome, physiology, and anatomy reflect human biology better than other laboratory animals, which is crucial for studying the pathogenesis of atherosclerosis.

METHODS

We used whole-genome sequencing data from nine Bama minipigs to perform a genome-wide linkage analysis, and further used bioinformatic tools to filter and identify underlying candidate genes. Candidate gene function prediction was performed using the online prediction tool STRING 12.0. Immunohistochemistry and immunofluorescence were used to detect the expression of proteins encoded by candidate genes.

RESULTS

We mapped differential single nucleotide polymorphisms (SNPs) to genes and obtained a total of 102 differential genes, then we used GO and KEGG pathway enrichment analysis to identify four candidate genes, including SLA-1, SLA-2, SLA-3, and TAP2. nsSNPs cause changes in the primary and tertiary structures of SLA-I and TAP2 proteins, the primary structures of these two proteins have undergone amino acid changes, and the tertiary structures also show slight changes. In addition, immunohistochemistry and immunofluorescence results showed that the expression changes of TAP2 protein in coronary arteries showed a trend of increasing from the middle layer to the inner layer.

CONCLUSIONS

We have identified SLA-I and TAP2 as potential susceptibility genes of atherosclerosis, highlighting the importance of antigen processing and immune response in atherogenesis.

The genetics of Graves' disease

Graves' disease (GD) is the commonest cause of hyperthyroidism and has a strong female preponderance. Everyday clinical practice suggests strong aggregation within families and twin studies demonstrate that genetic factors account for 60-80% of risk of developing GD. In this review, we collate numerous genetic studies and outline the discoveries over the years, starting with historic candidate gene studies and then exploring more recent genome-wide linkage and association studies, which have involved substantial cohorts of East Asian patients as well as those of European descent. Variants in genes including HLA, CTLA4, and PTPN22 have been shown to have substantial individual effects on disease susceptibility. In addition, we examine emerging evidence concerning the possibility that genetic variants may correlate with relevant clinical phenotypes including age of onset of GD, severity of thyrotoxicosis, goitre size and relapse of hyperthyroidism following antithyroid drug therapy, as well as thyroid eye disease. This review supports the inheritance of GD as a complex genetic trait, with a growing number of more than 80 susceptibility loci identified so far. Future implementation of more targeted clinical therapies requires larger studies investigating the influence of these genetic variants on the various phenotypes and different outcomes of conventional treatments.

Identification of Differentially Expressed Genes to Establish New Biomarker for Cancer Prediction

The goal of the human genome project is to integrate genetic information into different clinical therapies. To achieve this goal, different computational algorithms are devised for identifying the biomarker genes, cause of complex diseases. However, most of the methods developed so far using DNA microarray data lack in interpreting biological findings and are less accurate in disease prediction. In the paper, we propose two parameters risk_factor and confusion_factor to identify the biologically significant genes for cancer development. First, we evaluate risk_factor of each gene and the genes with nonzero risk_factor result misclassification of data, therefore removed. Next, we calculate confusion_factor of the remaining genes which determines confusion of a gene in prediction due to closeness of the samples in the cancer and normal classes. We apply nondominated sorting genetic algorithm (NSGA-II) to select the maximally uncorrelated differentially expressed genes in the cancer class with minimum confusion_factor. The proposed Gene Selection Explore (GSE) algorithm is compared to well established feature selection algorithms using 10 microarray data with respect to sensitivity, specificity, and accuracy. The identified genes appear in KEGG pathway and have several biological importance.

The association of thyroid peroxidase antibody risk loci with susceptibility to and phenotype of Graves' disease

BACKGROUND

Despite great progress, the genetic basis of Graves' disease (GD) remains poorly understood. Recently, a population-based genomewide association study (GWAS) identified five novel loci (ATXN2/SH2B3, MAGI3, BACH2, TPO and KALRN) as significantly associated with the presence of thyroid peroxidase autoantibodies (TPOAbs), whereas several other loci showed suggestive association.

METHODS

In this study, we investigated 16 single nucleotide polymorphisms (SNPs) associated with TPOAbs for the association with susceptibility to and phenotype of GD in a cohort of 647 patients with GD and 769 controls from a Polish Caucasian population.

RESULTS

SNPs within/near HCP5 (rs3094228, P = 1·6 × 10(-12) , OR = 1·88), MAGI3 (rs1230666, P = 1·9 × 10(-5) , OR = 1·51) and ATXN2/SH2B3 (rs653178, P = 0·0015, OR = 1·28) loci were significantly associated with susceptibility to GD. Allele frequencies differed significantly in subgroups of patients with GD stratified by age of GD onset for HCP5 (P = 0·0014, OR = 1·50) and showed a suggestive difference for MAGI3 (P = 0·0035, OR = 1·50) SNPs. Although rs11675434 located near TPO showed no association with GD susceptibility, it was significantly associated with the presence of clinically evident Graves' ophthalmopathy (GO, P = 5·2 × 10(-5) , OR = 1·64), and this effect was independent from smoking status, age of GD onset and gender.

CONCLUSIONS

This is the first study showing an association of the ATXN2/SH2B3 locus with susceptibility to GD. Furthermore, we observed a novel significant association within the HLA region at a SNP located near HCP5 and confirmed the association of the MAGI3 locus with GD susceptibility. HCP5 and MAGI3 SNPs were further correlated with age of GD onset. Finally, we identified TPO as a new susceptibility locus for GO.

Epigenetic pathway targets for the treatment of disease: accelerating progress in the development of pharmacological tools: IUPHAR Review 11

The properties of a cell are determined both genetically by the DNA sequence of its genes and epigenetically through processes that regulate the pattern, timing and magnitude of expression of its genes. While the genetic basis of disease has been a topic of intense study for decades, recent years have seen a dramatic increase in the understanding of epigenetic regulatory mechanisms and a growing appreciation that epigenetic misregulation makes a significant contribution to human disease. Several large protein families have been identified that act in different ways to control the expression of genes through epigenetic mechanisms. Many of these protein families are finally proving tractable for the development of small molecules that modulate their function and represent new target classes for drug discovery. Here, we provide an overview of some of the key epigenetic regulatory proteins and discuss progress towards the development of pharmacological tools for use in research and therapy.

Large scale analysis of phenotype-pathway relationships based on GWAS results

The widely used pathway-based approach for interpreting Genome Wide Association Studies (GWAS), assumes that since function is executed through the interactions of multiple genes, different perturbations of the same pathway would result in a similar phenotype. This assumption, however, was not systemically assessed on a large scale. To determine whether SNPs associated with a given complex phenotype affect the same pathways more than expected by chance, we analyzed 368 phenotypes that were studied in >5000 GWAS. We found 216 significant phenotype-pathway associations between 70 of the phenotypes we analyzed and known pathways. We also report 391 strong phenotype-phenotype associations between phenotypes that are affected by the same pathways. While some of these associations confirm previously reported connections, others are new and could shed light on the molecular basis of these diseases. Our findings confirm that phenotype-associated SNPs cluster into pathways much more than expected by chance. However, this is true for <20% (70/368) of the phenotypes. Different types of phenotypes show markedly different tendencies: Virtually all autoimmune phenotypes show strong clustering of SNPs into pathways, while most cancers and metabolic conditions, and all electrophysiological phenotypes, could not be significantly associated with any pathway despite being significantly associated with a large number of SNPs. While this may be due to missing data, it may also suggest that these phenotypes could result only from perturbations of specific genes and not from other perturbations of the same pathway. Further analysis of pathway-associated versus gene-associated phenotypes is, therefore, needed in order to understand disease etiology and in order to promote better drug target selection.

Dual effect of a polymorphism in the macrophage migration inhibitory factor gene is associated with new-onset Graves disease in a Taiwanese Chinese population

Graves disease (GD) is an autoimmune disease. Macrophage migration inhibitory factor (MIF) is a potent cytokine that plays an important role in the regulation of immune responses. Two polymorphisms in the promoter region of MIF, rs5844572 and rs755622, are known to affect MIF expression. The purpose of this study was to investigate the relationship between polymorphisms in the MIF gene promoter and the severity of GD. A total of 677 individuals, including 481 GD patients and 196 ethnically matched healthy controls, were genotyped to identify differences in the distribution of the MIF polymorphisms rs5844572 and rs755622. Although there were no significant differences in the allele or genotype distributions among patients with different grades of goiter in GD and healthy controls, the distribution of the C allele, especially C/C genotype, of the rs755622 single nucleotide polymorphism (SNP) in MIF, may be as a risk factor for goiter initiation whereas a protector against development of severe goiter in patients with untreated GD (p<0.05). A goiter-developmental model incorporating genetic (MIF SNP rs755622) and environmental risk factors (gender, radioiodine treatment, thyroid gland surgery and vitiligo) significantly increased the prediction accuracy. Further studies are required to address the role of MIF polymorphisms, as well as their association with other candidate genes, in GD.

GWAS in autoimmune thyroid disease: redefining our understanding of pathogenesis

The ability of the immune system to protect the body from attack by foreign antigens is essential for human survival. The immune system can, however, start to attack the body's own organs. An autoimmune response against components of the thyroid gland affects 2-5% of the general population. Considerable familial clustering is also observed in autoimmune thyroid disease (AITD). Teasing out the genetic contribution to AITD over the past 40 years has helped unravel how immune disruption leads to disease onset. Breakthroughs in genome-wide association studies (GWAS) in the past decade have facilitated screening of a greater proportion of the genome, leading to the identification of a before unimaginable number of AITD susceptibility loci. This Review will focus on the new susceptibility loci identified by GWAS, what insights these loci provide about the pathogenesis of AITD and how genetic susceptibility loci shared between different autoimmune diseases could help explain disease co-clustering within individuals and families. This Review also discusses where future efforts should be focused to translate this step forward in our understanding of the genetic contribution to AITD into a better understanding of disease presentation and progression, and improved therapeutic options.

Immunogenetic mechanisms leading to thyroid autoimmunity: recent advances in identifying susceptibility genes and regions

The autoimmune thyroid diseases (AITD) include Graves’ disease (GD) and Hashimoto’s thyroiditis (HT), which are characterised by a breakdown in immune tolerance to thyroid antigens. Unravelling the genetic architecture of AITD is vital to better understanding of AITD pathogenesis, required to advance therapeutic options in both disease management and prevention. The early whole-genome linkage and candidate gene association studies provided the first evidence that the HLA region and CTLA-4 represented AITD risk loci. Recent improvements in; high throughput genotyping technologies, collection of larger disease cohorts and cataloguing of genome-scale variation have facilitated genome-wide association studies and more thorough screening of candidate gene regions. This has allowed identification of many novel AITD risk genes and more detailed association mapping. The growing number of confirmed AITD susceptibility loci, implicates a number of putative disease mechanisms most of which are tightly linked with aspects of immune system function. The unprecedented advances in genetic study will allow future studies to identify further novel disease risk genes and to identify aetiological variants within specific gene regions, which will undoubtedly lead to a better understanding of AITD patho-physiology.

Novel associations for hypothyroidism include known autoimmune risk loci

Hypothyroidism is the most common thyroid disorder, affecting about 5% of the general population. Here we present the current largest genome-wide association study of hypothyroidism, in 3,736 cases and 35,546 controls. Hypothyroidism was assessed via web-based questionnaires. We identify five genome-wide significant associations, three of which are well known to be involved in a large spectrum of autoimmune diseases: rs6679677 near PTPN22, rs3184504 in SH2B3, and rs2517532 in the HLA class I region (-values , , and , respectively). We also report associations with rs4915077 near VAV3 (-value ) and rs925489 near FOXE1 (-value ). VAV3 is involved in immune function, and FOXE1 and PTPN22 have previously been associated with hypothyroidism. Although the HLA class I region and SH2B3 have previously been linked with a number of autoimmune diseases, this is the first report of their association with thyroid disease. The VAV3 association is also novel. We also show suggestive evidence of association for hypothyroidism with a SNP in the HLA class II region (independent of the other HLA association) as well as SNPs in CAPZB, PDE8B, and CTLA4. CAPZB and PDE8B have been linked to TSH levels and CTLA4 to a variety of autoimmune diseases. These results suggest heterogeneity in the genetic etiology of hypothyroidism, implicating genes involved in both autoimmune disorders and thyroid function. Using a genetic risk profile score based on the top association from each of the five genome-wide significant regions in our study, the relative risk between the highest and lowest deciles of genetic risk is 2.0.

New genetic insights from autoimmune thyroid disease

The autoimmune thyroid diseases (AITDs) (Graves' disease and Hashimoto's thyroiditis) are complex genetic diseases which most likely have more than 20 genes contributing to the clinical phenotypes. To date, the genes known to be contributing fall into two categories: immune regulatory genes (including HLA, CTLA4, PTPN22, CD40, CD25, and FCRL3) and thyroid-specific genes (TG and TSHR). However, none of these genes contribute more than a 4-fold increase in risk of developing one of these diseases, and none of the polymorphisms discovered is essential for disease development. Hence, it appears that a variety of different gene interactions can combine to cause the same clinical disease pattern, but the contributing genes may differ from patient to patient and from population to population. Furthermore, this possible mechanism leaves open the powerful influence of the environment and epigenetic modifications of gene expression. For the clinician, this means that genetic profiling of such patients is unlikely to be fruitful in the near future.