Common variants near ATM are associated with glycemic response to metformin in type 2 diabetes

Metformin is the most commonly used pharmacological therapy for type 2 diabetes. We report a genome-wide association study for glycemic response to metformin in 1,024 Scottish individuals with type 2 diabetes with replication in two cohorts including 1,783 Scottish individuals and 1,113 individuals from the UK Prospective Diabetes Study. In a combined meta-analysis, we identified a SNP, rs11212617, associated with treatment success (n = 3,920, P = 2.9 × 10(-9), odds ratio = 1.35, 95% CI 1.22-1.49) at a locus containing ATM, the ataxia telangiectasia mutated gene. In a rat hepatoma cell line, inhibition of ATM with KU-55933 attenuated the phosphorylation and activation of AMP-activated protein kinase in response to metformin. We conclude that ATM, a gene known to be involved in DNA repair and cell cycle control, plays a role in the effect of metformin upstream of AMP-activated protein kinase, and variation in this gene alters glycemic response to metformin.     

Newly identified genetic risk variants for celiac disease related to the immune response

Our genome-wide association study of celiac disease previously identified risk variants in the IL2-IL21 region. To identify additional risk variants, we genotyped 1,020 of the most strongly associated non-HLA markers in an additional 1,643 cases and 3,406 controls. Through joint analysis including the genome-wide association study data (767 cases, 1,422 controls), we identified seven previously unknown risk regions (P < 5 x 10(-7)). Six regions harbor genes controlling immune responses, including CCR3, IL12A, IL18RAP, RGS1, SH2B3 (nsSNP rs3184504) and TAGAP. Whole-blood IL18RAP mRNA expression correlated with IL18RAP genotype. Type 1 diabetes and celiac disease share HLA-DQ, IL2-IL21, CCR3 and SH2B3 risk regions. Thus, this extensive genome-wide association follow-up study has identified additional celiac disease risk variants in relevant biological pathways.     

Population genomics of human gene expression

Genetic variation influences gene expression, and this variation in gene expression can be efficiently mapped to specific genomic regions and variants. Here we have used gene expression profiling of Epstein-Barr virus-transformed lymphoblastoid cell lines of all 270 individuals genotyped in the HapMap Consortium to elucidate the detailed features of genetic variation underlying gene expression variation. We find that gene expression is heritable and that differentiation between populations is in agreement with earlier small-scale studies. A detailed association analysis of over 2.2 million common SNPs per population (5% frequency in HapMap) with gene expression identified at least 1,348 genes with association signals in cis and at least 180 in trans. Replication in at least one independent population was achieved for 37% of cis signals and 15% of trans signals, respectively. Our results strongly support an abundance of cis-regulatory variation in the human genome. Detection of trans effects is limited but suggests that regulatory variation may be the key primary effect contributing to phenotypic variation in humans. We also explore several methodologies that improve the current state of analysis of gene expression variation.     

Incremental Classification with Generalized Eigenvalues

Supervised learning techniques are widely accepted methods to analyze data for scientific and real world problems. Most of these problems require fast and continuous acquisition of data, which are to be used in training the learning system. Therefore, maintaining such systems updated may become cumbersome. Various techniques have been devised in the field of machine learning to solve this problem. In this study, we propose an algorithm to reduce the training data to a substantially small subset of the original training data to train a generalized eigenvalue classifier. The proposed method provides a constructive way to understand the influence of new training data on an existing classification function. We show through numerical experiments that this technique prevents the overfitting problem of the earlier generalized eigenvalue classifiers, while promising a comparable performance in classification with respect to the state-of-the-art classification methods.     

环氧沥青固化过程中的粘度特性

针对环氧沥青固化过程中的流变学特性，采用粘度为表征参数，重点研究固化温度、剪切速率以及稀释比对其固化速度的影响。结果表明，温度越高，环氧沥青的固化速度越快；过快的剪切速率会干扰并破坏固化结构的形成；采用基质沥青对环氧沥青进行稀释可以有效控制固化速度并降低材料成本。综合考虑施工和易性等因素，推荐稀释比(环氧沥青：基质沥青)为25∶75,材料拌合速度为20 r/min,材料在拌合、运输及压实过程中温度应保持在130℃,道路施工需在5 h内结束。