Wanwen Zeng

Polygenic risk scores (PRS) are crucial in genetics for predicting individual susceptibility to complex diseases by aggregating the effects of numerous genetic variants. Whole-genome sequencing (WGS) has revolutionized our ability to detect rare and even de novo variants, creating an exciting opportunity for developing new PRS methods that can effectively leverage rare variants and capture the complex relationships among different variants. Furthermore, regulatory mechanisms play a crucial role in gene expression and disease manifestation, offering avenues to further enhance the performance and interpretation of PRS predictions. Through simulation studies, we highlighted aspects where current PRS methods face challenges when applied to WGS data, aiming to shed light on potential opportunities for further improvement. To address these challenges, we developed Epi-PRS, an approach that leverages the power of genomic large language models (LLM) to impute epigenomic signals across diverse cellular contexts, for use as intermediate variables between genotype and phenotype. A pretrained LLM is employed to transform genotypes into epigenomic signals using personal diploid sequences as inputs, and the genetic risk is then estimated based on the imputed personal epigenomic signals. Epi-PRS enhances the assessment of personal variant impacts, enabling a comprehensive and holistic consideration of genotypic and regulatory information within large genomic regions. Our simulation results demonstrated that incorporating the nuanced effects of non-linear models, rare variants, and regulatory information can provide more precise PRS prediction and better understanding of genetic risk. Applying Epi-PRS to real data from the UK Biobank, our results further showed that Epi-PRS significantly outperforms existing PRS methods in two major diseases: breast cancer and diabetes. This study suggests that PRS methods can benefit from incorporating non-linear models, rare variants, and regulatory information, highlighting the potential for significant advancements in disease risk modeling and enhancing the understanding of precision medicine.

Genome-wide association studies (GWAS) have identified numerous genetic variants associated with Alzheimer’s disease (AD) phenotypes. However, how these variants contribute to the etiology of AD remains largely elusive. Recent advances in genomic large language models (LLMs) have revolutionized regulatory genomic prediction tasks, offering new opportunities to interpret the genetic variation observed in personal genome. In this study, we propose epiBrainLLM, a novel computational framework that leverages genomic LLM to enhance our understanding of the causal pathways from genotypes to brain measures to AD-related clinical phenotypes. Our framework will first convert the personal DNA sequence into a diverse set of genomic and epigenomic features using a pretrained genomic LLM and then use these features to further predict phenotypes. Across various experimental settings, our results demonstrate that incorporating pretrained genomic LLMs significantly improves association analysis compared to using genotype information alone. We conclude that our proposed framework provides a novel perspective for understanding the regulatory mechanisms underlying the AD disease etiology, potentially offering insights into complex disease mechanisms beyond AD.

Identifying cis-regulatory elements (CREs) within non-coding genomic regions-such as enhancers, silencers, promoters, and insulators-is pivotal for elucidating the intricate gene regulatory mechanisms underlying complex biological traits. The current prevalent sequence-based methods often focus on singular CRE types, limiting insights into cell-type-specific biological implications. Here, we introduce CREATE, a multimodal deep learning model based on the Vector Quantized Variational AutoEncoder framework, designed to extract discrete CRE embeddings and classify multiple CRE classes using genomic sequences, chromatin accessibility, and chromatin interaction data. CREATE excels in accurate CRE identification and exhibits strong effectiveness and robustness. We showcase CREATE’s capability in generating comprehensive CRE-specific feature spectrum, offering quantitative and interpretable insights into CRE specificity. By enabling large-scale prediction of CREs in specific cell types, CREATE facilitates the recognition of disease- or phenotype-related biological variabilities of CREs, thereby expanding our understanding of gene regulation landscapes.

bpBERT introduces a BERT-based model to predict DNA regulatory sequences and identify variants with base-level precision.

Elucidating the role of 3D architecture of DNA in gene regulation is crucial for understanding cell differentiation, tissue homeostasis and disease development. Among various chromatin conformation capture methods, HiChIP has received increasing attention for its significant improvement over other methods in profiling of regulatory (e.g. H3K27ac) and structural (e.g. cohesin) interactions. To facilitate the studies of 3D regulatory interactions, we developed a HiChIP interactions database, HiChIPdb (http://health.tsinghua.edu.cn/hichipdb/). The current version of HiChIPdb contains ∼262M annotated HiChIP interactions from 200 high-throughput HiChIP samples across 108 cell types. The functionalities of HiChIPdb include: (i) standardized categorization of HiChIP interactions in a hierarchical structure based on organ, tissue and cell line and (ii) comprehensive annotations of HiChIP interactions with regulatory genes and GWAS Catalog SNPs. To the best of our knowledge, HiChIPdb is the first comprehensive database that utilizes a unified pipeline to map the functional interactions across diverse cell types and tissues in different resolutions. We believe this database has the potential to advance cutting-edge research in regulatory mechanisms in development and disease by removing the barrier in data aggregation, preprocessing, and analysis.

Overall, we have demonstrated the robustness and re-usability of the GEEK embedding framework and shown its convenience for follow-up studies by representing the network as a vector. After systematic evaluation of GEEK and other applications, we expect that the incorporation of sequence-based features and 3D chromatin interaction-based features into a unified framework may provide a holistic perspective to understand gene transcriptional control and potentially provide insights to identify genome-wide association study risk variants. In addition, multidimensional summaries of omics data can be further integrated into sophisticated statistical models. For example, the STAAR model introduces ‘annotation principal components’ to effectively summarize multiple qualitative and quantitative variant functional annotations to boost the power of variant set tests for continuous and binary traits in whole-genome sequencing rare variants association studies.

Gene regulatory elements, including promoters, enhancers, silencers, etc., control transcriptional programs in a spatiotemporal manner. Though these elements are known to be able to induce either positive or negative transcriptional control, the community has been mostly studying enhancers which amplify transcription initiation, with less emphasis given to silencers which repress gene expression. To facilitate the study of silencers and the investigation of their potential roles in transcriptional control, we developed SilencerDB (http://health.tsinghua.edu.cn/silencerdb/), a comprehensive database of silencers by manually curating silencers from 2300 published articles. The current version, SilencerDB 1.0, contains (1) 33 060 validated silencers from experimental methods, and (ii) 5 045 547 predicted silencers from state-of-the-art machine learning methods. The functionality of SilencerDB includes (a) standardized categorization of silencers in a tree-structured class hierarchy based on species, organ, tissue and cell line and (b) comprehensive annotations of silencers with the nearest gene and potential regulatory genes. SilencerDB, to the best of our knowledge, is the first comprehensive database at this scale dedicated to silencers, with reliable annotations and user-friendly interactive database features. We believe this database has the potential to enable advanced understanding of silencers in regulatory mechanisms and to empower researchers to devise diverse applications of silencers in disease development.

We propose DeepExpression, a densely connected convolutional neural network, to predict gene expression using both promoter sequences and enhancer–promoter interactions. We demonstrate that our model consistently outperforms baseline methods, not only in the classification of binary gene expression status but also in regression of continuous gene expression levels, in both cross-validation experiments and cross-cell line predictions. We show that the sequential promoter information is more informative than the experimental enhancer information; meanwhile, the enhancer–promoter interactions within ±100 kbp around the TSS of a gene are most beneficial. We finally visualize motifs in both promoter and enhancer regions and show the match of identified sequence signatures with known motifs. We expect to see a wide spectrum of applications using HiChIP data in deciphering the mechanism of gene regulation.

Characterizing and interpreting heterogeneous mixtures at the cellular level is a critical problem in genomics. Single-cell assays offer an opportunity to resolve cellular level heterogeneity, e.g., scRNA-seq enables single-cell expression profiling, and scATAC-seq identifies active regulatory elements. Furthermore, while scHi-C can measure the chromatin contacts (i.e., loops) between active regulatory elements to target genes in single cells, bulk HiChIP can measure such contacts in a higher resolution. In this work, we introduce DC3 (De-Convolution and Coupled-Clustering) as a method for the joint analysis of various bulk and single-cell data such as HiChIP, RNA-seq and ATAC-seq from the same heterogeneous cell population. DC3 can simultaneously identify distinct subpopulations, assign single cells to the subpopulations (i.e., clustering) and de-convolve the bulk data into subpopulation-specific data. The subpopulation-specific profiles of gene expression, chromatin accessibility and enhancer-promoter contact obtained by DC3 provide a comprehensive characterization of the gene regulatory system in each subpopulation.