Understanding genetic regulation of metabolism is critical for gaining insights into the causes of metabolic diseases. Traditional metabolome-based genome-wide association studies (mGWAS) focus on static associations between single nucleotide polymorphisms (SNPs) and metabolite levels, overlooking the changing relationships caused by genotypes within the metabolic network. Notably, some metabolites exhibit changes in correlation patterns with other metabolites under certain physiological conditions while maintaining their overall abundance level. In this manuscript, we develop Metabolic Differential-coordination GWAS (mdGWAS), an innovative framework that detects SNPs associated with the changing correlation patterns between metabolites and metabolic pathways. This approach transcends and complements conventional mean-based analyses by identifying latent regulatory factors that govern the system-level metabolic coordination. Through comprehensive simulation studies, mdGWAS demonstrated robust performance in detecting SNP-metabolite-metabolite associations. Applying mdGWAS to genotyping and mass spectrometry (MS)-based metabolomics data of the METabolic Syndrome In Men (METSIM) Study revealed novel SNPs and genes potentially involved in the regulation of the coordination between metabolic pathways.

MOTIVATION: Fine-mapping aims to prioritize causal variants underlying complex traits by accounting for the linkage disequilibrium of genome-wide association study risk locus. The expanding resources of functional annotations serve as auxiliary evidence to improve the power of fine-mapping. However, existing fine-mapping methods tend to generate many false positive results when integrating a large number of annotations. RESULTS: In this study, we propose a unified method to integrate high-dimensional functional annotations with fine-mapping (Funmap). Funmap can effectively improve the power of fine-mapping by borrowing information from hundreds of functional annotations. Meanwhile, it relates the annotation to the causal probability with a random effects model that avoids the over-fitting issue, thereby producing a well-controlled false positive rate. Paired with a fast algorithm, Funmap enables scalable integration of a large number of annotations to facilitate prioritizing multiple causal single nucleotide polymorphisms. Our comprehensive simulations across a wide range of annotation relevance settings demonstrate that Funmap is the only method that produces well-calibrated false discovery rate under the setting of high-dimensional annotations while achieving better or comparable power gains as compared to existing methods. By integrating genome-wide association studies of 4 lipid traits with 187 functional annotations, Funmap consistently identified more variants that can be replicated in an independent cohort, achieving 15.5%-26.2% improvement over the runner-up in terms of replication rate. AVAILABILITY AND IMPLEMENTATION: The Funmap software and all analysis code are available at https://github.com/LeeHITsz/Funmap.

Spatial transcriptomics offers deep insights into cellular functional localization and communication by mapping gene expression to spatial locations. Traditional approaches that focus on selecting spatially variable genes often overlook the complexity of biological pathways and the interactions among genes. Here, we introduce a novel framework that shifts the focus towards directly identifying functional pathways associated with spatial variability by adapting the Brownian distance covariance test in an innovative manner to explore the heterogeneity of biological functions over space. Unlike most other methods, this statistical testing approach is free of gene selection and parameter selection and allows nonlinear and complex dependencies. It allows for a deeper understanding of how cells coordinate their activities across different spatial domains through biological pathways. By analyzing real human and mouse datasets, the method found significant pathways that were associated with spatial variation, as well as different pathway patterns among inner- and edge-cancer regions. This innovative framework offers a new perspective on analyzing spatial transcriptomic data, contributing to our understanding of tissue architecture and disease pathology. The implementation is publicly available at https://github.com/tianlq-prog/STpathway.

MOTIVATION: The findings from genome-wide association studies (GWASs) have greatly helped us to understand the genetic basis of human complex traits and diseases. Despite the tremendous progress, much effects are still needed to address several major challenges arising in GWAS. First, most GWAS hits are located in the non-coding region of human genome, and thus their biological functions largely remain unknown. Second, due to the polygenicity of human complex traits and diseases, many genetic risk variants with weak or moderate effects have not been identified yet. RESULTS: To address the above challenges, we propose a powerful and adaptive latent model (PALM) to integrate cell-type/tissue-specific functional annotations with GWAS summary statistics. Unlike existing methods, which are mainly based on linear models, PALM leverages a tree ensemble to adaptively characterize non-linear relationship between functional annotations and the association status of genetic variants. To make PALM scalable to millions of variants and hundreds of functional annotations, we develop a functional gradient-based expectation-maximization algorithm, to fit the tree-based non-linear model in a stable manner. Through comprehensive simulation studies, we show that PALM not only controls false discovery rate well, but also improves statistical power of identifying risk variants. We also apply PALM to integrate summary statistics of 30 GWASs with 127 cell type/tissue-specific functional annotations. The results indicate that PALM can identify more risk variants as well as rank the importance of functional annotations, yielding better interpretation of GWAS results. AVAILABILITY AND IMPLEMENTATION: The source code is available at https://github.com/YangLabHKUST/PALM. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

MOTIVATION: As increasing sample sizes from genome-wide association studies (GWASs), polygenic risk scores (PRSs) have shown great potential in personalized medicine with disease risk prediction, prevention and treatment. However, the PRS constructed using European samples becomes less accurate when it is applied to individuals from non-European populations. It is an urgent task to improve the accuracy of PRSs in under-represented populations, such as African populations and East Asian populations. RESULTS: In this article, we propose a cross-population and cross-phenotype (XPXP) method for construction of PRSs in under-represented populations. XPXP can construct accurate PRSs by leveraging biobank-scale datasets in European populations and multiple GWASs of genetically correlated phenotypes. XPXP also allows to incorporate population-specific and phenotype-specific effects, and thus further improves the accuracy of PRS. Through comprehensive simulation studies and real data analysis, we demonstrated that our XPXP outperformed existing PRS approaches. We showed that the height PRSs constructed by XPXP achieved 9% and 18% improvement over the runner-up method in terms of predicted R2 in East Asian and African populations, respectively. We also showed that XPXP substantially improved the stratification ability in identifying individuals at high genetic risk of type 2 diabetes. AVAILABILITY AND IMPLEMENTATION: The XPXP software and all analysis code are available at github.com/YangLabHKUST/XPXP. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.