Type 2 diabetes and its genetic susceptibility are associated with increased severity and mortality of COVID-19 in UK Biobank.

Type 2 diabetes (T2D) is known as one of the important risk factors for the severity and mortality of COVID-19. Here, we evaluate the impact of T2D and its genetic susceptibility on the severity and mortality of COVID-19, using 459,119 individuals in UK Biobank. Utilizing the polygenic risk scores (PRS) for T2D, we identified a significant association between T2D or T2D PRS, and COVID-19 severity. We further discovered the efficacy of vaccination and the pivotal role of T2D-related genetics in the pathogenesis of severe COVID-19. Moreover, we found that individuals with T2D or those in the high T2D PRS group had a significantly increased mortality rate. We also observed that the mortality rate for SARS-CoV-2-infected patients was approximately 2 to 7 times higher than for those not infected, depending on the time of infection. These findings emphasize the potential of T2D PRS in estimating the severity and mortality of COVID-19.

Bayesian mixed models for longitudinal genetic data: theory, concepts, and simulation studies.

Despite the success of recent genome-wide association studies investigating longitudinal traits, a large fraction of overall heritability remains unexplained. This suggests that some of the missing heritability may be accounted for by gene-gene and gene-time/environment interactions. In this paper, we develop a Bayesian variable selection method for longitudinal genetic data based on mixed models. The method jointly models the main effects and interactions of all candidate genetic variants and non-genetic factors and has higher statistical power than previous approaches. To account for the within-subject dependence structure, we propose a grid-based approach that models only one fixed-dimensional covariance matrix, which is thus applicable to data where subjects have different numbers of time points. We provide the theoretical basis of our Bayesian method and then illustrate its performance using data from the 1000 Genome Project with various simulation settings. Several simulation studies show that our multivariate method increases the statistical power compared to the corresponding univariate method and can detect gene-time/ environment interactions well. We further evaluate our method with different numbers of individuals, variants, and causal variants, as well as different trait-heritability, and conclude that our method performs reasonably well with various simulation settings.

Surface Enrichment of Fluorocarbon End-Capped Polymers with Different Fluorocarbon Lengths.

This study investigated the surface structure of fluorocarbon end-capped l-poly(lactide)s (F-PLLA) with different fluorocarbon lengths by electron spectroscopy for chemical analysis (ESCA). Fluorocarbon chains of three lengths (F(CF2)n(CH2)2, where n = 6, 8, and 10) have been synthesized as the terminal group to PLLA. Angular dependent ESCA revealed that the fluorocarbon groups are segregated at the surface. Further, the results show the surface coverage of fluorocarbon groups rapidly increases with increasing the length of fluorocarbon end groups (synergy effect). The surface composition of F-PLLAs can be controlled by changing the length of fluorocarbon.