STATISTICAL METHOD of GENETIC ASSOCIATION STUDIES

In genome-wide association studies (GWAS) for thousands of phenotypes in biobanks, most binary phenotypes have substantially fewer cases than controls. Many widely used approaches for joint analysis of multiple phenotypes in association studies produce inflated type I error rates for such extremely unbalanced case-control phenotypes. In our research, we develop two novel methods to jointly analyze multiple unbalanced case-control phenotypes to circumvent this issue. In the first method, we cluster multiple phenotypes into different clusters based on a hierarchical clustering method, then we merge phenotypes in each cluster into a single phenotype. In each cluster, we use the saddlepoint approximation to estimate the p-value of an association test between the merged phenotype and a SNP which eliminates the issue of inflated type I error rate of the test for extremely unbalanced case-control phenotypes. Finally, we use the Cauchy combination method to obtain an integrated p-value for all clusters to test the association between multiple phenotypes and a SNP. In the second method, we first construct a Multi-Layer Network (MLN) using all individuals with at least one case status among all phenotypes. Then, we introduce a computational efficient community detection method to group phenotypes into different disjoint clusters based on the MLN. The phenotypes in the same cluster are merged to a single phenotype which mainly eliminates the issue of inflated type I error rate of test for extremely unbalanced binary phenotypes. Finally, to test the association between all phenotypes and a SNP, we use the score test statistic to test the association between each merged phenotype and a SNP and then use the Omnibus test to obtain an overall p-value (MLN-O). Extensive simulation studies reveal that the newly proposed approaches can control type I error rates and are more powerful than other methods we compared with. The real data analyses also show that our methods outperform other methods we compared with

Intrinsic and extrinsic influences on self-recognition of actions

Despite minimal visual experience and unfamiliar third-person viewpoints, humans are able to recognize their own body movements even when actions are reduced to point-light displays. What factors influence visual self-recognition of own actions? To address this question, we recorded whole-body movements of a large sample of participants (N = 101) performing a range of actions. After a delay period, participants were tested in a self-recognition task: identifying own actions depicted in point-light displays amongst three other point-light actors performing identical actions. While participants showed above-chance accuracy on average for self-recognition, we found substantial differences in performance across actions and individuals. Self-recognition performance was modulated by interactions between extrinsic factors (associated with the degree of motor planning in performed actions) and intrinsic traits linked to individuals’ motor imagery ability and sensorimotor self-processing ability (autism and schizotypal traits). These interactions shed light on mechanistic possibilities for how the motor system may augment vision to construct the core of self-awareness

Determining Equivalent Administrative Charges for Defined Contribution Pension Plans under CEV Model

In defined contribution pension plan, the determination of the equivalent administrative charges on balance and on flow is investigated if the risk asset follows a constant elasticity of variance (CEV) model. The maximum principle and the stochastic control theory are applied to derive the explicit solutions of the equivalent equation about the charges. Using the power utility function, our conclusion shows that the equivalent charge on balance is related to the charge on flow, risk-free interest rate, and the length of accumulation phase. Moreover, numerical analysis is presented to show our results

Joint analysis of multiple phenotypes for extremely unbalanced case-control association studies

In genome-wide association studies (GWAS) for thousands of phenotypes in biobanks, most binary phenotypes have substantially fewer cases than controls. Many widely used approaches for joint analysis of multiple phenotypes produce inflated type I error rates for such extremely unbalanced case-control phenotypes. In this research, we develop a method to jointly analyze multiple unbalanced case-control phenotypes to circumvent this issue. We first group multiple phenotypes into different clusters based on a hierarchical clustering method, then we merge phenotypes in each cluster into a single phenotype. In each cluster, we use the saddlepoint approximation to estimate the p value of an association test between the merged phenotype and a single nucleotide polymorphism (SNP) which eliminates the issue of inflated type I error rate of the test for extremely unbalanced case-control phenotypes. Finally, we use the Cauchy combination method to obtain an integrated p value for all clusters to test the association between multiple phenotypes and a SNP. We use extensive simulation studies to evaluate the performance of the proposed approach. The results show that the proposed approach can control type I error rate very well and is more powerful than other available methods. We also apply the proposed approach to phenotypes in category IX (diseases of the circulatory system) in the UK Biobank. We find that the proposed approach can identify more significant SNPs than the other viable methods we compared with

Exploration of Exosomal miRNAs from Serum and Synovial Fluid in Arthritis Patients

Arthritis is caused by inflammation, infection, degeneration, trauma, or other factors that affect approximately 250 million people all over the world. Early diagnosis and prediction are essential for treatment. Exosomes are nanoscale vesicles that participate in the process of joint disease. Serum is the mainly used sources in the study of arthritis-related exosomes, while whether serum exosomes can reflect the contents of synovial fluid exosomes is still unknown. In this work, we separated exosomes from serum and the synovial fluid of osteoarthritis patients and compared their miRNA expression utilizing miRNA sequencing. The results revealed that 31 upregulated and 33 downregulated miRNAs were found in synovial fluid compared to serum. Transcriptome analysis showed that these differentially expressed miRNAs were mainly associated with intercellular processes and metabolic pathways. Our results show that serum-derived exosomes cannot fully represent the exosomes of synovial fluid, which may be helpful for the study of joint diseases and the discovery of early diagnostic biomarkers of arthritis

Improved thermoelectric performance in n-type flexible Bi2Se3+x/PVDF composite films

Bismuth selenide materials (Bi2Se3) have high performance around room temperature, demonstrating potential in thermoelectric applications. Presently, most vacuum preparation techniques used to fabricate the film materials, such as magnetron sputtering and molecular beam epitaxy, usually require complex and expensive equipment. This limits the practical applications of flexible thermoelectric films. Here, we prepared Bi2Se3+x nanoplate/polyvinylidene fluoride composite films with good flexibility using a facile chemical reaction method. Their thermoelectric performance and microstructures were systematically studied. The composite films exhibit a highly preferred orientation along (015). The carrier concentration and mobility were optimized by adding excessive element Se, eventually leading to an improvement in thermoelectric performance. The optimized power factor is 5.2 μW/K2m at 300 K. Furthermore, the performance remains stable after 2500 bending cycles at a radius of 1 cm, suggesting promising applications in wearable/portable electronics