Ethnic differences in adiposity and diabetes risk – insights from genetic studies

Type 2 diabetes is more common in non-Europeans and starts at a younger age and at lower BMI cut-offs. This review discusses the insights from genetic studies about pathophysiological mechanisms which determine risk of disease with a focus on the role of adiposity and body fat distribution in ethnic disparity in risk of type 2 diabetes. During the past decade, genome-wide association studies (GWAS) have identified more than 400 genetic variants associated with the risk of type 2 diabetes. The Eurocentric nature of these genetic studies have made them less effective in identifying mechanisms that make non-Europeans more susceptible to higher risk of disease. One possible mechanism suggested by epidemiological studies is the role of ethnic difference in body fat distribution. Using genetic variants associated with an ability to store extra fat in a safe place, which is subcutaneous adipose tissue, we discuss how different ethnic groups could be genetically less susceptible to type 2 diabetes by developing a more favourable fat distribution

Replication in Genome-Wide Association Studies

Replication helps ensure that a genotype-phenotype association observed in a genome-wide association (GWA) study represents a credible association and is not a chance finding or an artifact due to uncontrolled biases. We discuss prerequisites for exact replication, issues of heterogeneity, advantages and disadvantages of different methods of data synthesis across multiple studies, frequentist vs. Bayesian inferences for replication, and challenges that arise from multi-team collaborations. While consistent replication can greatly improve the credibility of a genotype-phenotype association, it may not eliminate spurious associations due to biases shared by many studies. Conversely, lack of replication in well-powered follow-up studies usually invalidates the initially proposed association, although occasionally it may point to differences in linkage disequilibrium or effect modifiers across studies.Comment: Published in at http://dx.doi.org/10.1214/09-STS290 the Statistical Science (http://www.imstat.org/sts/) by the Institute of Mathematical Statistics (http://www.imstat.org

Two variants on T2DM susceptible gene HHEX are associated with CRC risk in a Chinese population

Increasing amounts of evidence has demonstrated that T2DM (Type 2 Diabetes Mellitus) patients have increased susceptibility to CRC (colorectal cancer). As HHEX is a recognized susceptibility gene in T2DM, this work was focused on two SNPs in HHEX, rs1111875 and rs7923837, to study their association with CRC. T2DM patients without CRC (T2DM-only, n=300), T2DM with CRC (T2DM/CRC, n=135), cancer-free controls (Control, n=570), and CRC without T2DM (CRC-only, n=642) cases were enrolled. DNA samples were extracted from the peripheral blood leukocytes of the patients and sequenced by direct sequencing. The χ(2) test was used to compare categorical data. We found that in T2DM patients, rs1111875 but not the rs7923837 in HHEX gene was associated with the occurrence of CRC (p= 0.006). for rs1111875, TC/CC patients had an increased risk of CRC (p=0.019, OR=1.592, 95%CI=1.046-2.423). Moreover, our results also indicated that the two variants of HEEX gene could be risk factors for CRC in general population, independent on T2DM (p< 0.001 for rs1111875, p=0.001 for rs7923837). For rs1111875, increased risk of CRC was observed in TC or TC/CC than CC individuals (p<0.001, OR= 1.780, 95%CI= 1.385-2.287; p<0.001, OR= 1.695, 95%CI= 1.335-2.152). For rs7923837, increased CRC risk was observed in AG, GG, and AG/GG than AA individuals (p< 0.001, OR= 1.520, 95%CI= 1.200-1.924; p=0.036, OR= 1.739, 95%CI= 0.989-3.058; p< 0.001, OR= 1.540, 95%CI= 1.225-1.936). This finding highlights the potentially functional alteration with HHEX rs1111875 and rs7923837 polymorphisms may increase CRC susceptibility. Risk effects and the functional impact of these polymorphisms need further validation

Fifteen new risk loci for coronary artery disease highlight arterial-wall-specific mechanisms

Coronary artery disease (CAD) is a leading cause of morbidity and mortality worldwide. Although 58 genomic regions have been associated with CAD thus far, most of the heritability is unexplained, indicating that additional susceptibility loci await identification. An efficient discovery strategy may be larger-scale evaluation of promising associations suggested by genome-wide association studies (GWAS). Hence, we genotyped 56,309 participants using a targeted gene array derived from earlier GWAS results and performed meta-analysis of results with 194,427 participants previously genotyped, totaling 88,192 CAD cases and 162,544 controls. We identified 25 new SNP-CAD associations (P &lt; 5 × 10(-8), in fixed-effects meta-analysis) from 15 genomic regions, including SNPs in or near genes involved in cellular adhesion, leukocyte migration and atherosclerosis (PECAM1, rs1867624), coagulation and inflammation (PROCR, rs867186 (p.Ser219Gly)) and vascular smooth muscle cell differentiation (LMOD1, rs2820315). Correlation of these regions with cell-type-specific gene expression and plasma protein levels sheds light on potential disease mechanisms

Genetics of common polygenic ischaemic stroke: current understanding and future challenges.

Stroke is the third commonest cause of death and the major cause of adult neurological disability worldwide. While much is known about conventional risk factors such as hypertension, diabetes and incidence of smoking, these environmental factors only account for a proportion of stroke risk. Up to 50% of stroke risk can be attributed to genetic risk factors, although to date no single risk allele has been convincingly identified as contributing to this risk. Advances in the field of genetics, most notably genome wide association studies (GWAS), have revealed genetic risks in other cardiovascular disease and these techniques are now being applied to ischaemic stroke. This paper covers previous genetic studies in stroke including candidate gene studies, discusses the genome wide association approach, and future techniques such as next generation sequencing and the post-GWAS era. The review also considers the overlap from other cardiovascular diseases and whether findings from these may also be informative in ischaemic stroke

Role of estrogen related receptor beta (ESRRB) in DFN35B hearing impairment and dental decay

BACKGROUND: Congenital forms of hearing impairment can be caused by mutations in the estrogen related receptor beta (ESRRB) gene. Our initial linkage studies suggested the ESRRB locus is linked to high caries experience in humans. METHODS: We tested for association between the ESRRB locus and dental caries in 1,731 subjects, if ESRRB was expressed in whole saliva, if ESRRB was associated with the microhardness of the dental enamel, and if ESRRB was expressed during enamel development of mice. RESULTS: Two families with recessive ESRRB mutations and DFNB35 hearing impairment showed more extensive dental destruction by caries. Expression levels of ESRRB in whole saliva samples showed differences depending on sex and dental caries experience. CONCLUSIONS: The common etiology of dental caries and hearing impairment provides a venue to assist in the identification of individuals at risk to either condition and provides options for the development of new caries prevention strategies, if the associated ESRRB genetic variants are correlated with efficacy.Fil: Weber, Megan L.. University of Pittsburgh; Estados UnidosFil: Hsin, Hong Yuan. University of Pittsburgh; Estados UnidosFil: Kalay, Ersan. Karadeniz Technical University; TurquíaFil: Brožková, Dana Š. Charles University; República Checa. University Hospital Motol; República ChecaFil: Shimizu, Takehiko. Nihon University. School of Dentistry; JapónFil: Bayram, Merve. Medipol Istanbul University; TurquíaFil: Deeley, Kathleen. University of Pittsburgh; Estados UnidosFil: Küchler, Erika C.. University of Pittsburgh; Estados UnidosFil: Forella, Jessalyn. University of Pittsburgh; Estados UnidosFil: Ruff, Timothy D.. University of Pittsburgh; Estados UnidosFil: Trombetta, Vanessa M.. University of Pittsburgh; Estados UnidosFil: Sencak, Regina C.. University of Pittsburgh; Estados UnidosFil: Hummel, Michael. University of Pittsburgh; Estados UnidosFil: Briseño Ruiz, Jessica. University of Pittsburgh; Estados UnidosFil: Revu, Shankar K.. University of Pittsburgh; Estados UnidosFil: Granjeiro, José M.. Universidade Federal Fluminense; BrasilFil: Antunes, Leonardo S.. Universidade Federal Fluminense; BrasilFil: Antunes, Livia A.. Universidade Federal Fluminense; BrasilFil: Abreu, Fernanda V.. Universidade Federal Fluminense; BrasilFil: Costabel, Marcelo C.. Universidade Federal do Rio de Janeiro; BrasilFil: Tannure, Patricia N.. Veiga de Almeida University; Brasil. Salgado de Oliveira University; BrasilFil: Koruyucu, Mine. Istanbul University; TurquíaFil: Patir, Asli. Medipol Istanbul University; TurquíaFil: Poletta, Fernando Adrián. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. CEMIC-CONICET. Centro de Educaciones Médicas e Investigaciones Clínicas ; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Mereb, Juan C.. Estudio Colaborativo Latino Americano de Malformaciones Congénitas; ArgentinaFil: Castilla, Eduardo Enrique. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. CEMIC-CONICET. Centro de Educaciones Médicas e Investigaciones Clínicas ; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Orioli, Iêda M.. Universidade Federal do Rio de Janeiro; BrasilFil: Marazita, Mary L.. University of Pittsburgh; Estados UnidosFil: Ouyang, Hongjiao. University of Pittsburgh; Estados UnidosFil: Jayaraman, Thottala. University of Pittsburgh; Estados UnidosFil: Seymen, Figen. Istanbul University; TurquíaFil: Vieira, Alexandre R.. University of Pittsburgh; Estados Unido

Hundreds of variants clustered in genomic loci and biological pathways affect human height

Most common human traits and diseases have a polygenic pattern of inheritance: DNA sequence variants at many genetic loci influence the phenotype. Genome-wide association (GWA) studies have identified more than 600 variants associated with human traits, but these typically explain small fractions of phenotypic variation, raising questions about the use of further studies. Here, using 183,727 individuals, we show that hundreds of genetic variants, in at least 180 loci, influence adult height, a highly heritable and classic polygenic trait. The large number of loci reveals patterns with important implications for genetic studies of common human diseases and traits. First, the 180 loci are not random, but instead are enriched for genes that are connected in biological pathways (P = 0.016) and that underlie skeletal growth defects (P < 0.001). Second, the likely causal gene is often located near the most strongly associated variant: in 13 of 21 loci containing a known skeletal growth gene, that gene was closest to the associated variant. Third, at least 19 loci have multiple independently associated variants, suggesting that allelic heterogeneity is a frequent feature of polygenic traits, that comprehensive explorations of already-discovered loci should discover additional variants and that an appreciable fraction of associated loci may have been identified. Fourth, associated variants are enriched for likely functional effects on genes, being over-represented among variants that alter amino-acid structure of proteins and expression levels of nearby genes. Our data explain approximately 10% of the phenotypic variation in height, and we estimate that unidentified common variants of similar effect sizes would increase this figure to approximately 16% of phenotypic variation (approximately 20% of heritable variation). Although additional approaches are needed to dissect the genetic architecture of polygenic human traits fully, our findings indicate that GWA studies can identify large numbers of loci that implicate biologically relevant genes and pathways.

Creation of a Computational Pipeline to Extract Genes from Quantitative Trait Loci for Diabetes and Obesity

Type 2 Diabetes is a disease of relative insulin deficiency resulting from a combination of insulin resistance and decreased beta-cell function. Over the past several years, over 60 genes have been identified for Type 2 Diabetes in human genome-wide association studies (GWAS). It is important to understand the genetics involved with Type 2 diabetes in order to improve treatment and understand underlying molecular mechanisms. Heterogeneous stock (HS) rats are derived from 8 inbred founder strains and are powerful tools for genetic studies because they provide a basis for high resolution mapping of quantitative trait loci (QTL) in a relatively short time period. By measuring diabetic traits in 1090 HS male rats and genotyping 10K single nucleotide polymorphisms (SNPs) within these rats, Dr. Solberg Woods\u27 lab conducted genetic analysis to identify 85 QTL for diabetes and adiposity traits. To identify candidate genes within these QTL, we propose creation of a bioinformatics pipeline that combines general gene information, information from the rat genome database including disease portals and Variant Visualizer as well as the Attie Diabetes Expression Database. My project has involved writing code to pull data from these databases to determine which genes within each QTL are potential candidate genes. I have scripted the code to analyze genes within a single QTL or multiple QTL simultaneously. The resulting output is a single excel file for each QTL, listing all genes that are found in the disease portals, all genes that have a highly conserved non-synonymous variant change and all genes that are differentially expressed in the Attie database. The program also highlights genes that are found in all three categories. After creating the pipeline, I ran the program for 85 QTL identified in my laboratory. The program identified 63 high priority candidate genes for future follow-up. This work has helped my laboratory rapidly identify candidate genes for type 2 diabetes and obesity. In the future, the code can be modified to identify candidate genes within QTL for any complex trait

The Protein Tyrosine Phosphatase Non-Receptor Type 22 (PTPN22) Gene Polymorphism and Susceptibility to Autoimmune Diseases

The protein tyrosine phosphatase non-receptor type 22 (PTPN22) gene located on chromosomes 1p 13.3–13 encodes a lymphoid-specific tyrosine phosphatase (Lyp) which is involved in autoimmunity by preventing spontaneous T-cell activation and T-cell development and inactivating T-cell receptor-associated kinases and their substrates. Several single nucleotide polymorphisms (SNPs) have been identified in PTPN22, but only one PTPN22 C1858T has been intensively studied in relation to autoimmune diseases. The PTPN22 C1858T functional polymorphism is a strong non-HLA risk factor for several autoimmune diseases and considered to play an important role in etiology of diseases due to significant production of autoantibodies. However, available literature on PTPN22 C1858T polymorphism and autoimmune diseases shows inconsistencies and ethnic variations. Therefore, further genetic studies on patients suffering from various autoimmune diseases from different ethnicities and PTPN22 gene polymorphisms are expected to help better understand the pathogenesis and will contribute to the development of more targeted therapies and biomarkers