The genetic architecture of type 2 diabetes

The genetic architecture of common traits, including the number, frequency, and effect sizes of inherited variants that contribute to individual risk, has been long debated. Genome-wide association studies have identified scores of common variants associated with type 2 diabetes, but in aggregate, these explain only a fraction of heritability. To test the hypothesis that lower-frequency variants explain much of the remainder, the GoT2D and T2D-GENES consortia performed whole genome sequencing in 2,657 Europeans with and without diabetes, and exome sequencing in a total of 12,940 subjects from five ancestral groups. To increase statistical power, we expanded sample size via genotyping and imputation in a further 111,548 subjects. Variants associated with type 2 diabetes after sequencing were overwhelmingly common and most fell within regions previously identified by genome-wide association studies. Comprehensive enumeration of sequence variation is necessary to identify functional alleles that provide important clues to disease pathophysiology, but large-scale sequencing does not support a major role for lower-frequency variants in predisposition to type 2 diabetes

Genetic Drivers of Heterogeneity in Type 2 Diabetes Pathophysiology

Type 2 diabetes (T2D) is a heterogeneous disease that develops through diverse pathophysiological processes1,2 and molecular mechanisms that are often specific to cell type3,4. Here, to characterize the genetic contribution to these processes across ancestry groups, we aggregate genome-wide association study data from 2,535,601 individuals (39.7% not of European ancestry), including 428,452 cases of T2D. We identify 1,289 independent association signals at genome-wide significance (P \u3c 5 × 10-8) that map to 611 loci, of which 145 loci are, to our knowledge, previously unreported. We define eight non-overlapping clusters of T2D signals that are characterized by distinct profiles of cardiometabolic trait associations. These clusters are differentially enriched for cell-type-specific regions of open chromatin, including pancreatic islets, adipocytes, endothelial cells and enteroendocrine cells. We build cluster-specific partitioned polygenic scores5 in a further 279,552 individuals of diverse ancestry, including 30,288 cases of T2D, and test their association with T2D-related vascular outcomes. Cluster-specific partitioned polygenic scores are associated with coronary artery disease, peripheral artery disease and end-stage diabetic nephropathy across ancestry groups, highlighting the importance of obesity-related processes in the development of vascular outcomes. Our findings show the value of integrating multi-ancestry genome-wide association study data with single-cell epigenomics to disentangle the aetiological heterogeneity that drives the development and progression of T2D. This might offer a route to optimize global access to genetically informed diabetes care

Genetic drivers of heterogeneity in type 2 diabetes pathophysiology

Type 2 diabetes (T2D) is a heterogeneous disease that develops through diverse pathophysiological processes1,2 and molecular mechanisms that are often specific to cell type3,4. Here, to characterize the genetic contribution to these processes across ancestry groups, we aggregate genome-wide association study data from 2,535,601 individuals (39.7% not of European ancestry), including 428,452 cases of T2D. We identify 1,289 independent association signals at genome-wide significance (P &lt; 5 × 10-8) that map to 611 loci, of which 145 loci are, to our knowledge, previously unreported. We define eight non-overlapping clusters of T2D signals that are characterized by distinct profiles of cardiometabolic trait associations. These clusters are differentially enriched for cell-type-specific regions of open chromatin, including pancreatic islets, adipocytes, endothelial cells and enteroendocrine cells. We build cluster-specific partitioned polygenic scores5 in a further 279,552 individuals of diverse ancestry, including 30,288 cases of T2D, and test their association with T2D-related vascular outcomes. Cluster-specific partitioned polygenic scores are associated with coronary artery disease, peripheral artery disease and end-stage diabetic nephropathy across ancestry groups, highlighting the importance of obesity-related processes in the development of vascular outcomes. Our findings show the value of integrating multi-ancestry genome-wide association study data with single-cell epigenomics to disentangle the aetiological heterogeneity that drives the development and progression of T2D. This might offer a route to optimize global access to genetically informed diabetes care.</p

CFD simulations of the effects of fouling and antifouling

Biofouling is a global problem in the marine industry though its effects on lift and drag are rarely discussed. This paper seeks to employ Computational Fluid Dynamics (CFD) method to quantify the effects of this problem based on the obtained flow field information. The simulation is carried out on a NACA 4424 airfoil and Defence Research Establishment Atlantic (DREA) Submarine Hull. Different levels of fouling are studied with quantified fouling height and density. As an extension, the effects of two common antifouling methods, i.e. tin-free Self-Polishing Copolymer (SPC) and Foul Release on the drag of NACA 4424 foil and the submarine hull is investigated. For NACA 4424 airfoil, fouling reduces lift-drag ratio (CL/CD) by up to 80% in maximum and therefore result in the significant increase in fuel consumption. Predicted flow data shows this is related to the increased flow separation region caused by the fouling. It is found that pressure gradient gradually increases from the smallest fouling height to the largest but does not vary that much for fouling of varying densities. The general trend of CL/CD varies with angle of attack agrees well with others experimental data. Computed results also show good agreement with experimental data for the DREA bare hull. As to antifouling, Foul Release, despite being 30% more expensive than SPC, exhibits 10―40% higher CL/CD as compared to SPC for NACA foil and submarine hull

Optimum speed analysis for large containerships

During these times of fluctuating freight rates and oversupply, selection of optimum speed will give an operator a crucial advantage. Until the recession, the emphasis has always been on larger capacity and higher speed. Now, design innovation and slow steaming are becoming important and this article details new software, which is set up to assist the speed optimization process for large container ships. The software uses both propulsion and revenue-cost analysis with an ultralarge container ship model of 20,000 20-ft.unit (TEU) as a case study to derive the most profitable speed. Shippers' costs are also estimated to assess the impact on shippers. This software can be used for new construction or existing ships. The results show that 19.5 knots is the optimum speed, which is a departure from the current trend of 25 knots but supporting the concept of slow steaming. The results are validated by Det Norske Veritas container cost models. Besides that, the multivariable nature of the optimum speed problem is discussed and further analysis has been carried out to determine the range of fuel cost, freight rate, and load factor in which this optimum speed is applicable (in this case, $1100/TEU < freight rate <$1300/TEU, $650/MT < fuel cost <$750/MT and load factor = 40%). This will be useful to ship operators for forecasting purposes

Identification of type 2 diabetes loci in 433,540 East Asian individuals.

Meta-analyses of genome-wide association studies (GWAS) have identified more than 240 loci that are associated with type 2 diabetes (T2D)1,2; however, most of these loci have been identified in analyses of individuals with European ancestry. Here, to examine T2D risk in East Asian individuals, we carried out a meta-analysis of GWAS data from 77,418 individuals with T2D and 356,122 healthy control individuals. In the main analysis, we identified 301 distinct association signals at 183 loci, and across T2D association models with and without consideration of body mass index and sex, we identified 61 loci that are newly implicated in predisposition to T2D. Common variants associated with T2D in both East Asian and European populations exhibited strongly correlated effect sizes. Previously undescribed associations include signals in or near GDAP1, PTF1A, SIX3, ALDH2, a microRNA cluster, and genes that affect the differentiation of muscle and adipose cells3. At another locus, expression quantitative trait loci at two overlapping T2D signals affect two genes-NKX6-3 and ANK1-in different tissues4-6. Association studies in diverse populations identify additional loci and elucidate disease-associated genes, biology, and pathways

Identification of type 2 diabetes loci in 433,540 East Asian individuals

Meta-analyses of genome-wide association studies (GWAS) have identified more than 240 loci that are associated with type 2 diabetes (T2D)1,2; however, most of these loci have been identified in analyses of individuals with European ancestry. Here, to examine T2D risk in East Asian individuals, we carried out a meta-analysis of GWAS data from 77,418 individuals with T2D and 356,122 healthy control individuals. In the main analysis, we identified 301 distinct association signals at 183 loci, and across T2D association models with and without consideration of body mass index and sex, we identified 61 loci that are newly implicated in predisposition to T2D. Common variants associated with T2D in both East Asian and European populations exhibited strongly correlated effect sizes. Previously undescribed associations include signals in or near GDAP1, PTF1A, SIX3, ALDH2, a microRNA cluster, and genes that affect the differentiation of muscle and adipose cells3. At another locus, expression quantitative trait loci at two overlapping T2D signals affect two genes-NKX6-3 and ANK1-in different tissues4-6. Association studies in diverse populations identify additional loci and elucidate disease-associated genes, biology, and pathways

A common variant mapping to <i>CACNA1A </i>is associated with susceptibility to exfoliation syndrome

Author manuscript available from PMC http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4605818/Exfoliation syndrome (XFS) is the most common recognizable cause of open-angle glaucoma worldwide. To better understand the etiology of XFS, we conducted a genome-wide association study (GWAS) of 1,484 cases and 1,188 controls from Japan and followed up the most significant findings in a further 6,901 cases and 20,727 controls from 17 countries across 6 continents. We discovered a genome-wide significant association between a new locus (CACNA1A rs4926244) and increased susceptibility to XFS (odds ratio (OR) = 1.16, P = 3.36 × 10−11). Although we also confirmed overwhelming association at the LOXL1 locus, the key SNP marker (LOXL1 rs4886776) demonstrated allelic reversal depending on the ancestry group (Japanese: ORA allele = 9.87, P = 2.13 × 10−217; non-Japanese: ORA allele = 0.49, P = 2.35 × 10−31). Our findings represent the first genetic locus outside of LOXL1 surpassing genome-wide significance for XFS and provide insight into the biology and pathogenesis of the disease