Identification of genomic factors using family-based association studies

Genome-wide association studies become increasingly popular and important for detecting genetic associations of complex traits. However, it is well known that spurious associations could arise from statistical analysis without proper consideration of genetic relatedness of samples. Many methods have been proposed to guard against these spurious associations. Here we focus on multi-locus association studies of quantitative traits and the case-control status, and propose algorithms that take into consideration of genetic related samples to address possible confounding issues. As supervised dimension reduction methods, these algorithms performs well to conduct association studies with a large number of biomarkers but a relative small number of samples.^ Recently, Linear mixed models have demonstrated its efficiency in GWAS of quantitative traits with multiple levels of sample structures. Most of the current mixed model based methods such as EMMA, EMMAX, and GEMMA, can be viewed as single-locus methods by testing each SNP separately. Complex traits, however, are known to be controlled by multiple loci, thus including multiple loci in the statistical model seems more appropriate. In the first part of my dissertation, we propose an algorithm that extends penalized orthogonal component regression to family-based association studies (fPOCRE) of continuous traits. While multiple loci can be investigated at the same time, the sample relatedness is modeled through the kinship matrix and the shared confounding effects are included as random effects in the linear mixed model. Our proposed algorithm simultaneously selects biomarkers and constructs their linear combinations as components which optimally account for variation in traits. We compare fPOCRE with EMMAX, which is one of the most frequently used single-locus approach, and also compare it with MLMM, a recently developed multi-locus approach. Our simulation study demonstrates fPOCRE has promising performance over both EMMAX and MLMM in terms of higher power and fewer false positives when causal effects are from clusters of correlated SNPs. Real data are analyzed to illustrate the proposed approach and provide further comparisons.^ Case-control association study is a widely used study design in genetic epidemiology and pharmacology and this study design is also susceptible to the potential confounding by sample structure. In the second part of my dissertation, we employ a multi-locus generalized estimation equation (GEE) model to study genetic associations of binary traits, capturing multiple levels of the sample structure with working correlation matrix. The kinship matrix is used to model the working correlation matrix, and the penalized orthogonal-components regression method is developed to build such a multi-locus GEE model (aka GEE-POCRE). GEE-POCRE is compared with gPOCRE, a multi-locus method that does not consider pedigree information, also compared with TDT, FBAT, and ROADTRIPS that are single-locus methods considering sample structure. In our simulation studies, GEE-POCRE demonstrates good performance in terms of protecting against spurious associations caused by the sample structure as well as having increased power

Existence Results for a p

We consider the existence and multiplicity of solutions for the px-Kirchhoff-type equations without Ambrosetti-Rabinowitz condition. Using the Mountain Pass Lemma, the Fountain Theorem, and its dual, the existence of solutions and infinitely many solutions were obtained, respectively

Investigating the impact of social support embedded in online consultation on physicians’ online reputation: The moderating role of media capabilities

The importance of physicians’ online reputation (POR) has been recognized in the healthcare consultation process. However, few studies provide physicians with practical advice that help them improve their online reputation. Drawing on the taxonomy of social support and media synchronicity theory, this study proposes a theoretical model to study the relationship between physicians’ computer-mediated social support (CMSS) and POR, and the moderating effect of media capabilities on above relationships. This study collects online consultation records from a leading Chinese online consultation platform and employs the long short-term memory (LSTM) model to extract measurements of two types of CMSS. Our finding suggests that physicians’ action-facilitating support and nurturant support have significant positive impacts on POR. Furthermore, physicians’ communication frequency and communication depth strengthen the relationship between physicians’ action-facilitating support and POR. Readability strengthens the relationship between social support and POR. This study provides implications on how physicians can improve their online reputation

Chemically Ordered Pt–Co–Cu/C as Excellent Electrochemical Catalyst for Oxygen Reduction Reaction

This paper reveals the ordered structure and composition effect to electrochemical catalytic activity towards oxygen reduction reaction (ORR) of ternary metallic Pt–Co–Cu/C catalysts. Bimetallic Pt-Co alloy nanoparticles (NPs) represent an emerging class of electrocatalysts for ORR, but practical applications, e.g. in fuel cells, have been hindered by low catalytic performances owning to crystal phase and atomic composition. Cu is introduced into Pt-Co/C lattices to form PtCoxCu1−x/C (x = 0.25, 0.5 and 0.75) ternary-face-centered tetragonal (fct) ordered ternary metallic NPs. The chemically ordered Pt–Co–Cu/C catalysts exhibit excellent performance of 1.31 A mg−1 Pt in mass activity and 0.59 A cm−2 Pt in specific activity which are significantly higher than Pt-Co/C and commercial Johnson Matthey (JM) Pt/C catalysts, because of the ordered crystal phase and composition control modified the Pt-Pt atoms distance and the surface electronic properties. The presence of Cu improves the surface electronic structure, as well as enhances the stability of catalysts