Mendelian randomization (MR) harnesses genetic variants as instrumental variables (IVs) to study the causal effect of exposure on outcome using summary statistics from genome-wide association studies. Classic MR assumptions are violated when IVs are associated with unmeasured confounders, i.e., when correlated horizontal pleiotropy (CHP) arises. Such confounders could be a shared gene or inter-connected pathways underlying exposure and outcome. We propose MR-CUE (MR with Correlated horizontal pleiotropy Unraveling shared Etiology and confounding), for estimating causal effect while identifying IVs with CHP and accounting for estimation uncertainty. For those IVs, we map their cis-associated genes and enriched pathways to inform shared genetic etiology underlying exposure and outcome. We apply MR-CUE to study the effects of interleukin 6 on multiple traits/diseases and identify several S100 genes involved in shared genetic etiology. We assess the effects of multiple exposures on type 2 diabetes across European and East Asian populations.

Biological techniques for spatially resolved transcriptomics (SRT) have advanced rapidly in both throughput and spatial resolution for a single spatial location. This progress necessitates the development of efficient and scalable spatial dimension reduction methods that can handle large-scale SRT data from multiple sections. Here, we developed ProFAST as a fast and efficient probabilistic factor analysis for spatially aware dimension reduction, which simultaneously extracts a low-dimensional representation of SRT data across multiple sections, while preserving biological effects with consideration of spatial smoothness among nearby locations. Compared with existing methods, ProFAST is applicable to SRT data across multiple sections while using a local spatial dependence with scalable computational complexity. Using both simulated and real datasets, we demonstrated the improved correlation between ProFAST estimated embeddings and annotated cell/domain types. Furthermore, ProFAST exhibits remarkable speed, being >700 times faster, and requiring only 3% of the memory compared to the well-established tool, SpatialPCA, when applied to a Xenium dataset. Indeed, ProFAST was used to analyze a mouse embryo Stereo-seq dataset with >2.3 million locations in only 2 hours. More importantly, ProFAST identified differential activities of immune-related transcription factors between tumor and non-tumor clusters and also predicted a carcinogenesis factor CCNH as the upstream regulator of differentially expressed genes in a breast cancer Xenium dataset.