Publications & Preprints

2026

1. AJHG

   A flexible and unified framework for single-and multi-outcome Mendelian randomization using summary statistics

   Bowei Kang, David Li, Ke Xu, and 8 more authors

   The American Journal of Human Genetics, 2026

   Abs DOI

   Mendelian randomization (MR) is widely used to evaluate causal effects of complex trait exposures on disease outcomes. Recently, MR has been increasingly applied to molecular traits, such as gene expression, to map risk genes. However, transcriptome-wide MR (TWMR) faces unique challenges. The number of available cis-QTLs as instrumental variables (IVs) is often limited, and horizontal pleiotropy is pervasive, violating core MR assumptions and compromising inference validity. We introduce FusioMR, a robust MR framework tailored for molecular trait exposures while also applicable to complex trait exposures. Our single-outcome model, FusioMRs, incorporates gene-region-specific empirical priors informed by the number and strength of QTLs, linkage disequilibrium, and effect size consistency. It uses sampling-based inference to improve robustness when instruments are limited. Our multi-outcome model, FusioMRm, is motivated by the observation that many complex diseases have correlated diseases, subtypes, or comorbidities, which could be affected by shared or correlated exposures. FusioMRm jointly analyzes two correlated outcomes, leveraging shared IVs and pleiotropic effects of shared/correlated exposures to improve estimation precision and power, particularly for underpowered outcomes. We applied FusioMRs to identify cell-type-specific gene expression traits associated with Alzheimer disease using single-cell eQTL and GWAS summary data. We applied FusioMRm to detect alternative polyadenylation events affecting atrial fibrillation and ischemic stroke, and to estimate the causal effect of low-density lipoprotein on ischemic stroke in South Asian populations by borrowing information from European ancestry data. These applications highlight the generalizability of FusioMR for both molecular and complex trait exposures.

2025

1. Preprint

   Modewise Additive Factor Model for Matrix Time Series

   Elynn Chen, Yuefeng Han, Jiayu Li, and 1 more author

   arXiv preprint arXiv:2512.25025, 2025

   Abs DOI

   We introduce a Modewise Additive Factor Model (MAFM) for matrix-valued time series that captures row-specific and column-specific latent effects through an additive structure, offering greater flexibility than multiplicative frameworks such as Tucker and CP factor models. In MAFM, each observation decomposes into a row-factor component, a column-factor component, and noise, allowing distinct sources of variation along different modes to be modeled separately. We develop a computationally efficient two-stage estimation procedure: Modewise Inner-product Eigendecomposition (MINE) for initialization, followed by Complement-Projected Alternating Subspace Estimation (COMPAS) for iterative refinement. The key methodological innovation is that orthogonal complement projections completely eliminate cross-modal interference when estimating each loading space. We establish convergence rates for the estimated factor loading matrices under proper conditions. We further derive asymptotic distributions for the loading matrix estimators and develop consistent covariance estimators, yielding a data-driven inference framework that enables confidence interval construction and hypothesis testing. As a technical contribution of independent interest, we establish matrix Bernstein inequalities for quadratic forms of dependent matrix time series. Numerical experiments on synthetic and real data demonstrate the advantages of the proposed method over existing approaches.

2. Preprint

   Guaranteed Noisy CP Tensor Recovery via Riemannian Optimization on the Segre Manifold

   Ke Xu and Yuefeng Han

   arXiv preprint arXiv:2510.00569, 2025

   Abs DOI

   Recovering a low-CP-rank tensor from noisy linear measurements is a central challenge in high-dimensional data analysis, with applications spanning tensor PCA, tensor regression, and beyond. We exploit the intrinsic geometry of rank-one tensors by casting the recovery task as an optimization problem over the Segre manifold, the smooth Riemannian manifold of rank-one tensors. This geometric viewpoint yields two powerful algorithms: Riemannian Gradient Descent (RGD) and Riemannian Gauss-Newton (RGN), each of which preserves feasibility at every iteration. Under mild noise assumptions, we prove that RGD converges at a local linear rate, while RGN exhibits an initial local quadratic convergence phase that transitions to a linear rate as the iterates approach the statistical noise floor. Extensive synthetic experiments validate these convergence guarantees and demonstrate the practical effectiveness of our methods.

3. PLoS Genetics

   Integrative Mendelian randomization for detecting exposure-by-group interactions using group-specific and combined summary statistics

   Ke Xu, Nathaniel Maydanchik, Bowei Kang, and 6 more authors

   PLoS genetics, 2025

   Abs DOI

   Interactions between risk factors and covariate-defined groups are commonly observed in complex diseases. Existing methods for detecting interactions typically require individual-level data. The data availability and the measurements of risk exposures and covariates often limit the power and applicability in assessing interactions. To address these limitations, we propose int2MR, an integrative Mendelian randomization (MR) method that leverages GWAS summary statistics on exposure traits and group-separated and/or combined GWAS statistics on outcome traits. int2MR can assess a broad range of risk exposure effects on diseases and traits, revealing interactions unattainable with incomplete or limited individual-level data. Simulation studies demonstrated that int2MR effectively controls type I error rates under various settings while achieving considerable power gains with the integration of additional group-combined GWAS data. We applied int2MR to two data analyses. First, we identified risk exposures with sex-interaction effects on ADHD, and our results suggested potentially elevated inflammation in males. Second, we detected age-group-specific risk factors for Alzheimer’s disease pathologies in the oldest-old (age 95+), many of which were related to immune and inflammatory processes. Our findings suggest that reduced chronic inflammation may underlie the distinct pathological mechanisms observed in this age group. int2MR is a robust and flexible tool for assessing group-specific or interaction effects, providing insights into disease mechanisms.

4. Preprint

   Statistical Inference for Low-Rank Tensor Models

   Ke Xu, Elynn Chen, and Yuefeng Han

   arXiv preprint arXiv:2501.16223, 2025

   Abs DOI

   Statistical inference for tensors has emerged as a critical challenge in analyzing high-dimensional data in modern data science. This paper introduces a unified framework for inferring general and low-Tucker-rank linear functionals of low-Tucker-rank signal tensors for several low-rank tensor models. Our methodology tackles two primary goals: achieving asymptotic normality and constructing minimax-optimal confidence intervals. By leveraging a debiasing strategy and projecting onto the tangent space of the low-Tucker-rank manifold, we enable inference for general and structured linear functionals, extending far beyond the scope of traditional entrywise inference. Specifically, in the low-Tucker-rank tensor regression or PCA model, we establish the computational and statistical efficiency of our approach, achieving near-optimal sample size requirements (in regression model) and signal-to-noise ratio (SNR) conditions (in PCA model) for general linear functionals without requiring sparsity in the loading tensor. Our framework also attains both computationally and statistically optimal sample size and SNR thresholds for low-Tucker-rank linear functionals. Numerical experiments validate our theoretical results, showcasing the framework’s utility in diverse applications. This work addresses significant methodological gaps in statistical inference, advancing tensor analysis for complex and high-dimensional data environments.

2024

1. AJHG

   An integrative multi-context Mendelian randomization method for identifying risk genes across human tissues

   Yihao Lu, Ke Xu, Nathaniel Maydanchik, and 4 more authors

   The American Journal of Human Genetics, 2024

   Abs DOI

   Mendelian randomization (MR) provides valuable assessments of the causal effect of exposure on outcome, yet the application of conventional MR methods for mapping risk genes encounters new challenges. One of the issues is the limited availability of expression quantitative trait loci (eQTLs) as instrumental variables (IVs), hampering the estimation of sparse causal effects. Additionally, the often context/tissue-specific eQTL effects challenge the MR assumption of consistent IV effects across eQTL and GWAS data. To address these challenges, we propose a multi-context multivariable integrative MR framework, mintMR, for mapping expression and molecular traits as joint exposures. It models the effects of molecular exposures across multiple tissues in each gene region, while simultaneously estimating across multiple gene regions. It uses eQTLs with consistent effects across more than one tissue type as IVs, improving IV consistency. A major innovation of mintMR involves employing multi-view learning methods to collectively model latent indicators of disease relevance across multiple tissues, molecular traits, and gene regions. The multi-view learning captures the major patterns of disease-relevance and uses these patterns to update the estimated tissue relevance probabilities. The proposed mintMR iterates between performing a multi-tissue MR for each gene region and joint learning the disease-relevant tissue probabilities across gene regions, improving the estimation of sparse effects across genes. We apply mintMR to evaluate the causal effects of gene expression and DNA methylation for 35 complex traits using multi-tissue QTLs as IVs. The proposed mintMR controls genome-wide inflation and offers new insights into disease mechanisms.

2. iMeta

   Infant age inversely correlates with gut carriage of resistance genes, reflecting modifications in microbial carbohydrate metabolism during early life

   Xinming Xu, Qingying Feng, Tao Zhang, and 8 more authors

   Imeta, 2024

   Abs DOI

   The infant gut microbiome is increasingly recognized as a reservoir of antibiotic resistance genes, yet the assembly of gut resistome in infants and its influencing factors remain largely unknown. We characterized resistome in 4132 metagenomes from 963 infants in six countries and 4285 resistance genes were observed. The inherent resistome pattern of healthy infants (N = 272) could be distinguished by two stages: a multicompound resistance phase (Months 0–7) and a tetracycline-mupirocin-β-lactam-dominant phase (Months 8–14). Microbial taxonomy explained 40.7% of the gut resistome of healthy infants, with Escherichia (25.5%) harboring the most resistance genes. In a further analysis with all available infants (N = 963), we found age was the strongest influencer on the resistome and was negatively correlated with the overall resistance during the first 3 years (p < 0.001). Using a random-forest approach, a set of 34 resistance genes could be used to predict age (R2 = 68.0%). Leveraging microbial host inference analyses, we inferred the age-dependent assembly of infant resistome was a result of shifts in the gut microbiome, primarily driven by changes in taxa that disproportionately harbor resistance genes across taxa (e.g., Escherichia coli more frequently harbored resistance genes than other taxa). We performed metagenomic functional profiling and metagenomic assembled genome analyses whose results indicate that the development of gut resistome was driven by changes in microbial carbohydrate metabolism, with an increasing need for carbohydrate-active enzymes from Bacteroidota and a decreasing need for Pseudomonadota during infancy. Importantly, we observed increased acquired resistance genes over time, which was related to increased horizontal gene transfer in the developing infant gut microbiome. In summary, infant age was negatively correlated with antimicrobial resistance gene levels, reflecting a composition shift in the gut microbiome, likely driven by the changing need for microbial carbohydrate metabolism during early life.