Genome-wide association analyses identify 143 risk variants and putative regulatory mechanisms for type 2 diabetes

Type 2 diabetes (T2D) is a very common disease in humans. Here we conduct a meta-analysis of genome-wide association studies (GWAS) with ~16 million genetic variants in 62,892 T2D cases and 596,424 controls of European ancestry. We identify 139 common and 4 rare variants associated with T2D, 42 of which (39 common and 3 rare variants) are independent of the known variants. Integration of the gene expression data from blood (n = 14,115 and 2765) with the GWAS results identifies 33 putative functional genes for T2D, 3 of which were targeted by approved drugs. A further integration of DNA methylation (n = 1980) and epigenomic annotation data highlight 3 genes (CAMK1D, TP53INP1, and ATP5G1) with plausible regulatory mechanisms, whereby a genetic variant exerts an effect on T2D through epigenetic regulation of gene expression. Our study uncovers additional loci, proposes putative genetic regulatory mechanisms for T2D, and provides evidence of purifying selection for T2D-associated variants

Tetramethylpyrazine attenuates spinal cord ischemic injury due to aortic cross-clamping in rabbits

BACKGROUND: Lower limb paralysis occurs in 11% of patients after surgical procedure of thoracic or thoracoabdominal aneurysms and is an unpredictable and distressful complication. The aim of this study was to investigate the effects of tetramethylpyrazine (TMP), an intravenous drug made from traditional Chinese herbs, on the neurologic outcome and hisotpathology after transient spinal cord ischemia in rabbits. METHODS: Forty-five male New Zealand white rabbits were anesthetized with isoflurane and spinal cord ischemia was induced for 20 min by infrarenal aortic occlusion. Animals were randomly allocated to one of five groups (n = 8 each). Group C received no pharmacologic intervention. Group P received intravenous infusion of 30 mg·kg(-1) TMP within 30 min before aortic occlusion. Group T(1), Group T(2) and Group T(3) received intravenous infusion of 15, 30 and 60 mg·kg(-1) TMP respectively within 30 min after reperfusion. In the sham group (n = 5), the animals underwent the same procedures as the control group except infrarental aortic unocclusion. Neurologic status was scored by using the Tarlov criteria (in which 4 is normal and 0 is paraplegia) at 4 h, 8 h, 12 h, 24 h, and 48 h after reperfusion. All animals were sacrificed at 48 h after reperfusion and the spinal cords (L(5)) were removed immediately for histopathologic study. RESULTS: All animals in the control group became paraplegic. Neurologic status and histopathology (48 h) in the Groups P, T(2) and T(3) were significantly better than those in the control group (P < 0.05). There was a strong correlation between the final neurologic scores and the number of normal neurons in the anterior spinal cord (r = 0.776, P < 0.01). CONCLUSION: Tetramethylpyrazine significantly reduces neurologic injury related to spinal cord ischemia and reperfusion after aortic occlusion within a certain range of dose

Tin Oxide Nanorod Array-Based Electrochemical Hydrogen Peroxide Biosensor

SnO2 nanorod array grown directly on alloy substrate has been employed as the working electrode of H2O2 biosensor. Single-crystalline SnO2 nanorods provide not only low isoelectric point and enough void spaces for facile horseradish peroxidase (HRP) immobilization but also numerous conductive channels for electron transport to and from current collector; thus, leading to direct electrochemistry of HRP. The nanorod array-based biosensor demonstrates high H2O2 sensing performance in terms of excellent sensitivity (379 μA mM−1 cm−2), low detection limit (0.2 μM) and high selectivity with the apparent Michaelis–Menten constant estimated to be as small as 33.9 μM. Our work further demonstrates the advantages of ordered array architecture in electrochemical device application and sheds light on the construction of other high-performance enzymatic biosensors

DeepSeek-V2: A Strong, Economical, and Efficient Mixture-of-Experts Language Model

We present DeepSeek-V2, a strong Mixture-of-Experts (MoE) language model characterized by economical training and efficient inference. It comprises 236B total parameters, of which 21B are activated for each token, and supports a context length of 128K tokens. DeepSeek-V2 adopts innovative architectures including Multi-head Latent Attention (MLA) and DeepSeekMoE. MLA guarantees efficient inference through significantly compressing the Key-Value (KV) cache into a latent vector, while DeepSeekMoE enables training strong models at an economical cost through sparse computation. Compared with DeepSeek 67B, DeepSeek-V2 achieves significantly stronger performance, and meanwhile saves 42.5% of training costs, reduces the KV cache by 93.3%, and boosts the maximum generation throughput to 5.76 times. We pretrain DeepSeek-V2 on a high-quality and multi-source corpus consisting of 8.1T tokens, and further perform Supervised Fine-Tuning (SFT) and Reinforcement Learning (RL) to fully unlock its potential. Evaluation results show that, even with only 21B activated parameters, DeepSeek-V2 and its chat versions still achieve top-tier performance among open-source models

Gene discovery and polygenic prediction from a genome-wide association study of educational attainment in 1.1 million individuals

Here we conducted a large-scale genetic association analysis of educational attainment in a sample of approximately 1.1 million individuals and identify 1,271 independent genome-wide-significant SNPs. For the SNPs taken together, we found evidence of heterogeneous effects across environments. The SNPs implicate genes involved in brain-development processes and neuron-to-neuron communication. In a separate analysis of the X chromosome, we identify 10 independent genome-wide-significant SNPs and estimate a SNP heritability of around 0.3% in both men and women, consistent with partial dosage compensation. A joint (multi-phenotype) analysis of educational attainment and three related cognitive phenotypes generates polygenic scores that explain 11-13% of the variance in educational attainment and 7-10% of the variance in cognitive performance. This prediction accuracy substantially increases the utility of polygenic scores as tools in research.</p

Polygenic prediction of educational attainment within and between families from genome-wide association analyses in 3 million individuals

We conduct a genome-wide association study (GWAS) of educational attainment (EA) in a sample of ~3 million individuals and identify 3,952 approximately uncorrelated genome-wide-significant single-nucleotide polymorphisms (SNPs). A genome-wide polygenic predictor, or polygenic index (PGI), explains 12-16% of EA variance and contributes to risk prediction for ten diseases. Direct effects (i.e., controlling for parental PGIs) explain roughly half the PGI's magnitude of association with EA and other phenotypes. The correlation between mate-pair PGIs is far too large to be consistent with phenotypic assortment alone, implying additional assortment on PGI-associated factors. In an additional GWAS of dominance deviations from the additive model, we identify no genome-wide-significant SNPs, and a separate X-chromosome additive GWAS identifies 57

Genome-wide association study identifies 74 loci associated with educational attainment

Educational attainment is strongly influenced by social and other environmental factors, but genetic factors are estimated to account for at least 20% of the variation across individuals1. Here we report the results of a genome-wide association study (GWAS) for educational attainment that extends our earlier discovery sample1,2 of 101,069 individuals to 293,723 individuals, and a replication study in an independent sample of 111,349 individuals from the UK Biobank. We identify 74 genome-wide significant loci associated with the number of years of schooling completed. Single-nucleotide polymorphisms associated with educational attainment are disproportionately found in genomic regions regulating gene expression in the fetal brain. Candidate genes are preferentially expressed in neural tissue, especially during the prenatal period, and enriched for biological pathways involved in neural development. Our findings demonstrate that, even for a behavioural phenotype that is mostly environmentally determined, a well-powered GWAS identifies replicable associated genetic variants that suggest biologically relevant pathways. Because educational attainment is measured in large numbers of individuals, it will continue to be useful as a proxy phenotype in efforts to characterize the genetic influences of related phenotypes, including cognition and neuropsychiatric diseases