Scott T. Weiss

TorchGWAS2 improves genome-wide association testing in related samples by using a deterministic variance-correction algorithm, achieving faster and more powerful results across large-scale omics data.

The paper identifies cell types regulated by circulating miRNAs linked to asthma exacerbations using scRNA-seq and validates findings in GEO datasets.

The paper constructs a urinary microbiome catalog and identifies antimicrobial peptides from it that inhibit uropathogenic E. coli.

This study uses polygenic risk scores and proteomic analysis to identify genetic and molecular associations with asthma in two family-based cohorts, highlighting IL1R1's role in asthma pathogenesis.

This paper develops a machine learning framework to provide personalized dietary recommendations for achieving desired gut microbial compositions using population-level cohort data.

This paper introduces VIP2B, a method to extract viral features from bulk metagenomes, showing that viral data improves disease diagnosis and reveals distinct gut virome community states.

The paper quantifies variation in gut microbial metabolism of dietary compounds and identifies compounds specific to few species, proposing a method for synbiotic design.

The paper analyzes circulating metabolites using genome-wide association data from the TOPMed program, identifying 1,778 loci and highlighting sex-specific genetic contributions.

This paper develops an optimization-based dietary recommendation approach to improve diet scores using simulated annealing and evaluates it on Diet-Microbiome Association study data.

The paper uses WGCNA to identify protein modules in 1-year-old children linked to respiratory diseases by age 6, showing associations with environmental and metabolomic factors.

PHILM, a deep learning framework, predicts phage-host interactions from metagenomic data with higher accuracy than traditional methods, identifying more interactions and capturing microbial succession patterns.

The paper identifies DNA-methylation markers at birth and childhood associated with lung function and reveals sex- and genetics-related pathways linked to asthma and lung development.

This study uses single-cell RNA sequencing to analyze sputum cells from asthma patients and controls, identifying dysregulated pathways and communication networks involved in asthma pathogenesis.

This paper introduces GENN, a method for multi-omic interactome analysis that improves prediction of biomedical outcomes by learning salient associations in a low-dimensional space.

This study investigates the metabolic role of vitamin D in children's neurodevelopment using a network approach, finding associations between vitamin D deficiency and changes in tryptophan, linoleic, and fatty acid metabolism linked to ASD-related phenotypes.

The paper analyzes pathogenic variant frequencies in the All of Us cohort by ancestry, revealing disparities and potential biases in genomic data.

The paper develops OMICmAge, a multi-omics biomarker of biological age, and shows it outperforms existing markers in predicting chronic diseases and mortality.

This paper introduces a computational method to identify causal gut microbes responsible for colonization resistance against pathogens, validated using mouse infection data.

The paper identifies nutritional redundancy in dietary intake and shows its association with lower risks of cardiovascular disease and type 2 diabetes after adjusting for age.

The paper validates novel telomere length regulation genes POP5 and KBTBD6 identified through trans-ancestry GWAS using functional experiments in blood cells.

The paper presents a comprehensive catalog of structural variants across 138,134 individuals, identifying novel variants and their associations with cardio-metabolic and hematologic traits.

RITSS identifies gene-environment interactions in quantitative traits using sample splitting and robust statistics, finding significant interactions in lung function and height data.

The paper introduces MAP2B, a metagenomic profiler that reduces false positives and improves abundance estimation using type IIB restriction sites, outperforming existing methods on benchmark datasets.

This paper introduces fast algorithms for computing principal components of genomic similarity matrices, including exact methods for specific matrices and an approximate Jaccard matrix with theoretical guarantees.

The paper introduces MuXTalk, a network-based method to detect signaling crosstalk by integrating signaling and regulatory interactions, expanding crosstalk databases and improving prediction performance.

This study analyzes clonal hematopoiesis in over a million diverse individuals, identifying 72 genome-wide significant loci and linking CHIP to various phenotypes.

This paper develops a trans-ancestry polygenic risk score for type 2 diabetes and validates it across diverse populations to improve equitable risk prediction.

The paper shows vitamin D regulates two chromosomal loci linked to autoimmune and inflammatory diseases by modulating gene expression and immune pathways.