To characterise type 2 diabetes (T2D) associated variation across the allele frequency spectrum, we conducted a meta-analysis of genome-wide association data from 26,676 T2D cases and 132,532 controls of European ancestry after imputation using the 1000 Genomes multi-ethnic reference panel. Promising association signals were followed-up in additional data sets (of 14,545 or 7,397 T2D cases and 38,994 or 71,604 controls). We identified 13 novel T2D-associated loci (p<5×10-8), including variants near the GLP2R, GIP, and HLA-DQA1 genes. Our analysis brought the total number of independent T2D associations to 128 distinct signals at 113 loci. Despite substantially increased sample size and more complete coverage of low-frequency variation, all novel associations were driven by common SNVs. Credible sets of potentially causal variants were generally larger than those based on imputation with earlier reference panels, consistent with resolution of causal signals to common risk haplotypes. Stratification of T2D-associated loci based on T2D-related quantitative trait associations revealed tissue-specific enrichment of regulatory annotations in pancreatic islet enhancers for loci influencing insulin secretion, and in adipocytes, monocytes and hepatocytes for insulin action-associated loci. These findings highlight the predominant role played by common variants of modest effect and the diversity of biological mechanisms influencing T2D pathophysiology.

We performed fine-mapping of 39 established type 2 diabetes (T2D) loci in 27,206 cases and 57,574 controls of European ancestry. We identified 49 distinct association signals at these loci, including five mapping in/near KCNQ1. “Credible sets” of variants most likely to drive each distinct signal mapped predominantly to non-coding sequence, implying that T2D association is mediated through gene regulation. Credible set variants were enriched for overlap with FOXA2 chromatin immunoprecipitation binding sites in human islet and liver cells, including at MTNR1B, where fine-mapping implicated rs10830963 as driving T2D association. We confirmed that this T2D-risk allele increases FOXA2-bound enhancer activity in islet- and liver-derived cells. We observed allele-specific differences in NEUROD1 binding in islet-derived cells, consistent with evidence that the T2D-risk allele increases islet MTNR1B expression. Our study demonstrates how integration of genetic and genomic information can define molecular mechanisms through which variants underlying association signals exert their effects on disease.

With the increasing availability of functional genomic data, incorporating genomic annotations into genetic association analysis has become a standard procedure. However, the existing methods often lack rigor and/or computational efficiency and consequently do not maximize the utility of functional annotations. In this paper, we propose a rigorous inference procedure to perform integrative association analysis incorporating genomic annotations for both traditional GWASs and emerging molecular QTL mapping studies. In particular, we propose an algorithm, named deterministic approximation of posteriors (DAP), which enables highly efficient and accurate joint enrichment analysis and identification of multiple causal variants. We use a series of simulation studies to highlight the power and computational efficiency of our proposed approach and further demonstrate it by analyzing the cross-population eQTL data from the GEUVADIS project and the multi-tissue eQTL data from the GTEx project. In particular, we find that genetic variants predicted to disrupt transcription factor binding sites are enriched in cis-eQTLs across all tissues. Moreover, the enrichment estimates obtained across the tissues are correlated with the cell types for which the annotations are derived.

We investigated the infection rate for severe fever with thrombocytopenia syndrome virus (SFTSV) among ticks collected from humans during May–October 2013 in South Korea. Haemaphysalis longicornis ticks have been considered the SFTSV vector. However, we detected the virus in H. longicornis, Amblyomma testudinarium, and Ixodes nipponensis ticks, indicating additional potential SFTSV vectors.

Coronavirus Disease-19 (COVID-19) is a respiratory infection characterized by the main symptoms of pneumonia and fever. It is caused by the novel coronavirus severe acute respiratory syndrome Coronavirus-2 (SARS-CoV-2), which is known to spread via respiratory droplets. We aimed to determine the rate and likelihood of SARS-CoV-2 transmission from COVID-19 patients through nonrespiratory routes. Methods: Serum, urine, and stool samples were collected from 74 hospitalized patients diagnosed with COVID-19 based on the detection of SARS-CoV-2 in respiratory samples. The SARS-CoV-2 RNA genome was extracted from each specimen and real-time reverse transcription polymerase chain reaction performed. CaCo-2 cells were inoculated with the specimens containing the SARS-COV-2 genome, and subcultured for virus isolation. After culturing, viral replication in the cell supernatant was assessed. Results: Of the samples collected from 74 COVID-19 patients, SARS-CoV-2 was detected in 15 serum, urine, or stool samples. The virus detection rate in the serum, urine, and stool samples were 2.8% (9/323), 0.8% (2/247), and 10.1% (13/129), and the mean viral load was 1,210 ± 1,861, 79 ± 30, and 3,176 ± 7,208 copy/µL, respectively. However, the SARS-CoV-2 was not isolated by the culture method from the samples that tested positive for the SARS-CoV-2 gene. Conclusion: While the virus remained detectable in the respiratory samples of COVID-19 patients for several days after hospitalization, its detection in the serum, urine, and stool samples was intermittent. Since the virus could not be isolated from the SARS-COV-2-positive samples, the risk of viral transmission via stool and urine is expected to be low.