Biomedical subjects

Yixuan He

Publications and source records attributed to Yixuan He.

3 recordsLinked to original sources

PubMed2026

Multi-trait polygenic scores for COPD and COPD exacerbations implicate druggable proteins.

BACKGROUNDWe constructed multi-trait polygenic risk scores (PRSs) predicting chronic obstructive pulmonary disease (COPD) and exacerbations, validated their performance in diverse cohorts, and identified PRS-related proteins for potential therapeutic targeting.METHODSPRSmix+, a multi-trait PRS framework, is used to train a composite PRS (PRSmulti) in COPDGene non-Hispanic White participants (n = 6,647). Associations of PRSmulti with COPD status (GOLD 2-4 vs. GOLD 0 or ICD) and exacerbation frequency were tested in COPDGene African American (n = 2,466), ECLIPSE (n = 1,858), Mass General Brigham Biobank (n = 15,152), and All of Us (n = 118,566). Protein prediction models were applied to GWAS summary statistics from traits contributing to PRSmulti and were validated with proteomic data in COPDGene (n = 5,173) and UK Biobank (n = 5,012).RESULTSPRSmix+ selected 7 traits for PRSmulti. In multivariable models, PRSmulti was associated with COPD status (meta-analysis random effects [RE] OR 1.58 [95% CI: 1.28-1.94]) and exacerbation frequency (meta-analysis RE β 0.21 [95% CI: 0.11-0.31]), with higher effect sizes observed in smoking-enriched cohorts. PRSmulti outperformed traditional single-trait PRS in all tested cohorts. Using protein prediction models, we identified 73 proteins associated with the PRSs that were also validated with measured protein levels in COPDGene and UK Biobank. Of these proteins, 25 were linked to approved or investigational drugs. Notable targets include RAGE/sRAGE, IL1RL1, and SCARF2, all implicated in COPD pathogenesis and exacerbations.CONCLUSIONSMulti-trait PRS improves prediction of COPD and exacerbation risk. Integration with proteomic data identifies druggable protein targets, offering a promising avenue for precision medicine in COPD management.TRIAL REGISTRATIONCOPDGene: ClinicalTrials.gov NCT00608764; ECLIPSE: ClinicalTrials.gov NCT00292552.

Humans↗

PubMed2025

GWAS for Periodontitis Phenotypes Using Multi-Ancestry All of Us Research Platform.

Periodontitis is a multifactorial inflammatory disease whose pathogenesis is associated with intricate interactions between genetic and environmental factors. Leveraging electronic health records data from the All of Us Research Program, we stratified periodontitis by clinically relevant dimensions: stage, grade, and extent. Based on these phenotypes, we performed a multi-ancestry genome-wide association study, focusing on predominant ancestry populations of African, European, and Admixed American. Our study cohort comprised 3,881 periodontitis patients and a control group of 10,760 patients with dental caries and without periodontitis. Ancestry-specific GWAS revealed significant genetic associations (P<5×10-8) in periodontitis grade phenotypes at the LINC00294 and CLMN loci in the African ancestry population and also confirmed via the multi-ancestry meta-analysis. In addition, the XYLT1 locus emerged as a significant signal associated with periodontitis grade phenotype in the admixed American GWAS. Our GWAS comparing periodontitis to dental caries in the admixed American population identified several significant loci, including RABGAP1L, previously linked to immune regulation, DCHS2, a cadherin-related gene involved in bone mineralization and tissue morphogenesis, and OSTM1, known to be crucial for bone remodeling. The findings of our study highlight the potential of integrating EHR and genomic data from large-scale biobanks to achieve informative dental phenotyping, uncover novel molecular insights into periodontal disease, and personalize treatment approaches.

Journal Article↗

PubMed2023

Inferring Metabolic States from Single Cell Transcriptomic Data via Geometric Deep Learning.

The ability to measure gene expression at single-cell resolution has elevated our understanding of how biological features emerge from complex and interdependent networks at molecular, cellular, and tissue scales. As technologies have evolved that complement scRNAseq measurements with things like single-cell proteomic, epigenomic, and genomic information, it becomes increasingly apparent how much biology exists as a product of multimodal regulation. Biological processes such as transcription, translation, and post-translational or epigenetic modification impose both energetic and specific molecular demands on a cell and are therefore implicitly constrained by the metabolic state of the cell. While metabolomics is crucial for defining a holistic model of any biological process, the chemical heterogeneity of the metabolome makes it particularly difficult to measure, and technologies capable of doing this at single-cell resolution are far behind other multiomics modalities. To address these challenges, we present GEFMAP (Gene Expression-based Flux Mapping and Metabolic Pathway Prediction), a method based on geometric deep learning for predicting flux through reactions in a global metabolic network using transcriptomics data, which we ultimately apply to scRNAseq. GEFMAP leverages the natural graph structure of metabolic networks to learn both a biological objective for each cell and estimate a mass-balanced relative flux rate for each reaction in each cell using novel deep learning models.

Preprint↗