Qing Lu

Glycogen storage disease type IV (GSD IV) is an autosomal recessive disorder caused by pathogenic variants in GBE1 , resulting in deficient glycogen branching enzyme (GBE) activity and formation of abnormal glycogen (“polyglucosan”). GSD IV manifests across a spectrum of clinical dimensions—including hepatic, neurologic, muscular, and cardiac involvement—which vary in severity. The early‐onset forms, historically referred to as Andersen disease, present at different stages ranging from in utero to adolescence. The adult‐onset form, referred to as adult polyglucosan body disease (APBD), typically presents in middle to late adulthood. To date, no epidemiological study of GSD IV has been performed. Understanding the global prevalence of GSD IV is critical to increase disease awareness, improve diagnostic rates, inform therapeutic development, and engage pharmaceutical companies. In collaboration with the Rare Genomes Project at the Broad Institute of MIT and Harvard and the APBD Research Foundation, this study curated variants in GBE1 and calculated prevalence across nine genetic ancestry groups. The estimated global carrier frequency of GSD IV is 1 in 243 individuals, and the global genetic prevalence is 1 in 235 784 individuals. Based on the 2024 world population, the estimated number of affected individuals with GSD IV is approximately 34 800. These estimates highlight a significant underdiagnosis of GSD IV and underscore the urgent need for increased awareness of this metabolic disorder. This model of collaboration between researchers, patient advocacy organizations, and genetic data sharing programs provides a framework for estimating the prevalence of other rare diseases in the global population.

Kernel-based multi-marker tests for survival outcomes use primarily the Cox model to adjust for covariates. The proportional hazards assumption made by the Cox model could be unrealistic, especially in the long-term follow-up. We develop a suite of novel multi-marker survival tests for genetic association and interaction based on the accelerated failure time model, which is a popular alternative to the Cox model due to its direct physical interpretation. The tests are based on the asymptotic distributions of their test statistics and are thus computationally efficient. The association tests can account for the heterogeneity of genetic effects across subpopulations/individuals to increase the power. All the new tests can deal with competing risks and left truncation. Moreover, we develop small-sample corrections to the tests to improve their accuracy under small samples. Extensive numerical experiments show that the new tests perform very well in various scenarios. An application to a genetic dataset of Alzheimer’s disease illustrates the tests’ practical utility.

Carfilzomib is highly effective in the treatment of multiple myeloma, but it has been associated with cardiovascular adverse events that impact patient outcomes. Our prior global metabolomic analyses indicated an association between hydrophilic bile acids and carfilzomib‐cardiotoxicity risk, although a causal relationship remained to be determined. Here, our objective was to validate the previously identified bile acids in an independent cohort and investigate whether these hydrophilic bile acids play a causal role in carfilzomib‐cardiotoxicity using Mendelian randomization. Using targeted metabolomics, we validated the association between glycoursodeoxycholic acid and carfilzomib‐cardiotoxicity in an independent cohort ( n = 61). We then performed two‐sample Mendelian randomization analyses, using metabolome‐wide association study results to provide the genetic instruments for bile acid levels as exposure and genome‐wide association study summary statistics from the UK Biobank ( n = 484,598) as the outcome data for cardiovascular adverse events. Causal inference was assessed with the inverse‐variance weighted method, followed by multiple sensitivity analyses. Higher glycoursodeoxycholic acid concentration (odds ratio = 0.34, P = 0.032) was associated with lower cardiotoxicity risk after adjusting for hypertension and high levels of brain natriuretic peptides. Mendelian randomization analysis demonstrated a robust causal effect of glycoursodeoxycholic acid on cardiotoxicity risk (β = −0.00065, P = 6.2 × 10 ⁻⁵ ). Gene enrichment analysis indicated pathways involving potassium channel regulation and thromboxane signaling to be implicated. This integrative metabolomic and genetic investigation supports a potential protective role of glycoursodeoxycholic acid in cardiovascular vulnerability and motivates larger, carfilzomib‐specific studies to evaluate its utility for risk stratification.

Given the complexity of genetic risk prediction, there is a critical need for the development of novel methodologies that can effectively capture intricate genotype--phenotype relationships (e.g., nonlinear) while remaining statistically interpretable and computationally tractable. We develop a Neural Tangent Kernel (NTK) framework to integrate kernel methods into deep neural networks for genetic risk prediction analysis. We consider two approaches: NTK-LMM, which embeds the empirical NTK in a linear mixed model with variance components estimated via minimum quadratic unbiased estimator (MINQUE), and NTK-KRR, which performs kernel ridge regression with cross-validated regularization. Through simulation studies, we show that NTK-based models outperform the traditional neural network models and linear mixed models. By applying NTK to endophenotypes (e.g., hippocampal volume) and AD-related genes (e.g., APOE) from Alzheimer's Disease Neuroimaging Initiative (ADNI), we found that NTK achieved higher accuracy than existing methods for hippocampal volume and entorhinal cortex thickness. In addition to its accuracy performance, NTK has favorable optimization properties (i.e., having a closed-form or convex training) and generates interpretable results due to its connection to variance components and heritability. Overall, our results indicate that by integrating the strengths of both deep neural networks and kernel methods, NTK offers competitive performance for genetic risk prediction analysis while having the advantages of interpretability and computational efficiency.

We introduce DiffKnock, a diffusion-based knockoff framework for high-dimensional feature selection with finite-sample false discovery rate (FDR) control. DiffKnock addresses two key limitations of existing knockoff methods: preserving complex feature dependencies and detecting non-linear associations. Our approach trains diffusion models to generate valid knockoffs and uses neural network--based gradient and filter statistics to construct antisymmetric feature importance measures. Through simulations, we showed that DiffKnock achieved higher power than autoencoder-based knockoffs while maintaining target FDR, indicating its superior performance in scenarios involving complex non-linear architectures. Applied to murine single-cell RNA-seq data of LPS-stimulated macrophages, DiffKnock identifies canonical NF-$κ$B target genes (Ccl3, Hmox1) and regulators (Fosb, Pdgfb). These results highlight that, by combining the flexibility of deep generative models with rigorous statistical guarantees, DiffKnock is a powerful and reliable tool for analyzing single-cell RNA-seq data, as well as high-dimensional and structured data in other domains.

Variations in complex traits are influenced by multiple genetic variants, environmental risk factors, and their interactions. Though substantial progress has been made in identifying single genetic variants associated with complex traits, detecting the gene-gene and gene-environment interactions remains a great challenge. When a large number of genetic variants and environmental risk factors are involved, searching for interactions is limited to pair-wise interactions due to the exponentially increased feature space and computational intensity. Alternatively, recursive partitioning approaches, such as random forests, have gained popularity in high-dimensional genetic association studies. In this article, we propose a U-Statistic-based random forest approach, referred to as Forest U-Test, for genetic association studies with quantitative traits. Through simulation studies, we showed that the Forest U-Test outperformed existing methods. The proposed method was also applied to study Cannabis Dependence CD, using three independent datasets from the Study of Addiction: Genetics and Environment. A significant joint association was detected with an empirical p-value less than 0.001. The finding was also replicated in two independent datasets with p-values of 5.93e-19 and 4.70e-17, respectively.

Risk prediction that capitalizes on emerging genetic findings holds great promise for improving public health and clinical care. However, recent risk prediction research has shown that predictive tests formed on existing common genetic loci, including those from genome-wide association studies, have lacked sufficient accuracy for clinical use. Because most rare variants on the genome have not yet been studied for their role in risk prediction, future disease prediction discoveries should shift toward a more comprehensive risk prediction strategy that takes into account both common and rare variants. We are proposing a collapsing receiver operating characteristic CROC approach for risk prediction research on both common and rare variants. The new approach is an extension of a previously developed forward ROC FROC approach, with additional procedures for handling rare variants. The approach was evaluated through the use of 533 single-nucleotide polymorphisms SNPs in 37 candidate genes from the Genetic Analysis Workshop 17 mini-exome data set. We found that a prediction model built on all SNPs gained more accuracy AUC = 0.605 than one built on common variants alone AUC = 0.585. We further evaluated the performance of two approaches by gradually reducing the number of common variants in the analysis. We found that the CROC method attained more accuracy than the FROC method when the number of common variants in the data decreased. In an extreme scenario, when there are only rare variants in the data, the CROC reached an AUC value of 0.603, whereas the FROC had an AUC value of 0.524.

Restricted maximum likelihood (REML) estimators are commonly used to obtain unbiased estimators for the variance components in linear mixed models. In modern applications, particularly in genomic studies, the dimension of the design matrix with respect to the random effects can be high. Motivated by this, we first introduce high-dimensional kernel linear mixed models, derive the REML equations, and establish theoretical results on the consistency of REML estimators for several commonly used kernel matrices. The validity of the theories is demonstrated via simulation studies. Our results provide rigorous justification for the consistency of REML estimators in high-dimensional kernel linear mixed models and offer insights into the application of estimating genetic heritability. Finally, we apply the kernel linear mixed models to estimate genetic heritability in a real-world data application.

Correlated outcomes are common in many practical problems. In some settings, one outcome is of particular interest, and others are auxiliary. To leverage information shared by all the outcomes, traditional multi-task learning (MTL) minimizes an averaged loss function over all the outcomes, which may lead to biased estimation for the target outcome, especially when the MTL model is misspecified. In this work, based on a decomposition of estimation bias into two types, within-subspace and against-subspace, we develop a robust transfer learning approach to estimating a high-dimensional linear decision rule for the outcome of interest with the presence of auxiliary outcomes. The proposed method includes an MTL step using all outcomes to gain efficiency and a subsequent calibration step using only the outcome of interest to correct both types of biases. We show that the final estimator can achieve a lower estimation error than the one using only the single outcome of interest. Simulations and real data analysis are conducted to justify the superiority of the proposed method.