Bayesian Model Comparison in Genetic Association Analysis: Linear Mixed Modeling and SNP Set Testing

We consider the problems of hypothesis testing and model comparison under a flexible Bayesian linear regression model whose formulation is closely connected with the linear mixed effect model and the parametric models for SNP set analysis in genetic association studies. We derive a class of analytic approximate Bayes factors and illustrate their connections with a variety of frequentist test statistics, including the Wald statistic and the variance component score statistic. Taking advantage of Bayesian model averaging and hierarchical modeling, we demonstrate some distinct advantages and flexibilities in the approaches utilizing the derived Bayes factors in the context of genetic association studies. We demonstrate our proposed methods using real or simulated numerical examples in applications of single SNP association testing, multi-locus fine-mapping and SNP set association testing

A statistical framework for joint eQTL analysis in multiple tissues

Mapping expression Quantitative Trait Loci (eQTLs) represents a powerful and widely-adopted approach to identifying putative regulatory variants and linking them to specific genes. Up to now eQTL studies have been conducted in a relatively narrow range of tissues or cell types. However, understanding the biology of organismal phenotypes will involve understanding regulation in multiple tissues, and ongoing studies are collecting eQTL data in dozens of cell types. Here we present a statistical framework for powerfully detecting eQTLs in multiple tissues or cell types (or, more generally, multiple subgroups). The framework explicitly models the potential for each eQTL to be active in some tissues and inactive in others. By modeling the sharing of active eQTLs among tissues this framework increases power to detect eQTLs that are present in more than one tissue compared with "tissue-by-tissue" analyses that examine each tissue separately. Conversely, by modeling the inactivity of eQTLs in some tissues, the framework allows the proportion of eQTLs shared across different tissues to be formally estimated as parameters of a model, addressing the difficulties of accounting for incomplete power when comparing overlaps of eQTLs identified by tissue-by-tissue analyses. Applying our framework to re-analyze data from transformed B cells, T cells and fibroblasts we find that it substantially increases power compared with tissue-by-tissue analysis, identifying 63% more genes with eQTLs (at FDR=0.05). Further the results suggest that, in contrast to previous analyses of the same data, the majority of eQTLs detectable in these data are shared among all three tissues.Comment: Summitted to PLoS Genetic

Non-parametric Bayesian mixture model to study adverse events of COVID-19 vaccines

The vaccine adverse event reporting system (VAERS) is a vital resource for post-licensure vaccine safety monitoring and has played a key role in assessing the safety of COVID-19 vaccines. However it is difficult to properly identify rare adverse events (AEs) associated with vaccines due to small or zero counts. We propose a Bayesian model with a Dirichlet Process Mixture prior to improve accuracy of the AE estimates with small counts by allowing data-guided information sharing between AE estimates. We also propose a negative control procedure embedded in our Bayesian model to mitigate the reporting bias due to the heightened awareness of COVID-19 vaccines, and use it to identify associated AEs as well as associated AE groups defined by the organ system in the Medical Dictionary for Regulatory Activities (MedDRA) ontology. The proposed model is evaluated using simulation studies, in which it outperforms baseline models without information sharing and is applied to study the safety of COVID-19 vaccines using VAERS data

ChatHome: Development and Evaluation of a Domain-Specific Language Model for Home Renovation

This paper presents the development and evaluation of ChatHome, a domain-specific language model (DSLM) designed for the intricate field of home renovation. Considering the proven competencies of large language models (LLMs) like GPT-4 and the escalating fascination with home renovation, this study endeavors to reconcile these aspects by generating a dedicated model that can yield high-fidelity, precise outputs relevant to the home renovation arena. ChatHome's novelty rests on its methodology, fusing domain-adaptive pretraining and instruction-tuning over an extensive dataset. This dataset includes professional articles, standard documents, and web content pertinent to home renovation. This dual-pronged strategy is designed to ensure that our model can assimilate comprehensive domain knowledge and effectively address user inquiries. Via thorough experimentation on diverse datasets, both universal and domain-specific, including the freshly introduced "EvalHome" domain dataset, we substantiate that ChatHome not only amplifies domain-specific functionalities but also preserves its versatility.Comment: ChatHome,DSLM for home renovatio

On weakly bounded well-filtered spaces

In [16], using Rudin sets, Miao, Li and Zhao introduced a new concept of weakly well-filtered spaces—$k$-bounded well-filtered spaces. Now, also using Rudin sets, we introduce another type of $T_0$ spaces—weakly bounded well-filtered spaces, which are strictly stronger than $k$-bounded well-filtered spaces. Some basic properties of $k$-bounded well-filtered spaces and weakly bounded well-filtered spaces are investigated and the relationships among some kinds of weakly sober spaces and weakly well-filtered spaces are posed. It is proved that the category ${\bf KBWF}$ is not reflective in the category ${\bf Top}_{0}$