Gao T. Wang

Torino, a reference-free framework for isoform discovery, identifies RNA processing patterns linked to disease risk by analyzing read coverage variation across species and disease states.

The paper shows that LDSC regression can underestimate heritability when using meta-analysis or imputed variant summary statistics instead of genotyped data.

This paper introduces MMTKPred, a machine learning model that predicts transporter kinetic parameters Vmax and Km, showing effectiveness across biosystem scales.

The paper presents an xQTL atlas of regulatory variation in the aging human brain, integrating multiple modalities and cell types across four studies, identifying more variant-molecular-trait associations by including TAD boundaries.

The paper introduces TransCisPredict to predict protein expression using cis- and trans-variants, improving proteome-wide association study power for type 2 diabetes.

The paper introduces mvSuSiE, a multi-trait fine-mapping method that improves causal SNP identification by jointly analyzing multiple traits, discovering more causal SNPs and narrower credible sets compared to single-trait approaches.

The paper identifies EGFR as a potential therapeutic target in Alzheimer's disease by linking a risk variant to increased EGFR expression in brain cell types through genomic analysis and CRISPRi validation.

The paper develops scEEMS models to predict cell-type-specific eQTLs, enhancing discovery of AD risk variants by linking them to cell-type-specific genes.

mfSuSiE improves fine-mapping of chromatin accessibility QTLs across multiple cell types and integrates multi-omic data in aging human brain samples.

This paper links molecular mechanisms to Alzheimer's disease and aging by integrating neuroimaging endophenotypes with molecular quantitative trait loci.

SuSiE 2.0 improves genetic fine-mapping and phenotype prediction with faster performance, better calibration, and new methods like SuSiE-ash.

Spacelink integrates spatial transcriptomics with genetics to identify spatially variable genes linked to disease, showing improved detection power and disease informativeness.

fSuSiE improves fine-mapping of QTLs by modeling spatial genetic effects on molecular traits, achieving higher resolution in identifying causal variants and their molecular targets.

The paper evaluates the power of rare variant aggregate association analysis using imputed data and finds that imputation can detect significant associations even for ultra-rare variants, though with reduced power compared to sequence data.

LeafCutter2 identifies and quantifies unproductive splicing events from RNA-seq data, revealing their role in gene regulation and disease, including Alzheimer's.

This study identifies 236 MS susceptibility variants and highlights neuronal and glial dysfunction as key factors in MS susceptibility.

xQTLbiolinks is a tool for integrative analysis of molecular QTLs, enabling retrieval, preprocessing, and colocalization with GWAS data to identify disease-risk genes.

The paper introduces a flexible empirical Bayes method for multivariate multiple regression to improve prediction of multi-tissue gene expression from genotypes.

The paper identifies cell-type and cell-subtype specific eQTLs in aging human cortex, revealing variant effects on gene expression and disease associations.

ERStruct estimates the number of top principal components from whole-genome sequencing data using parallel computing and GPU acceleration.

This paper extends the SuSiE model to work with summary data, showing it is competitive with existing methods in speed and accuracy for fine-mapping genetic variants.

The paper identifies an atlas of 3QTLs associated with alternative polyadenylation across 46 tissues, showing they explain 16.1% of trait-associated variants and contribute to disease independently of gene expression.

The paper uses GTEx data to analyze GWAS loci, showing dose-dependent RNA effects and proposing target genes for 47% of loci using combined methods.

The paper presents a large ASE resource from GTEx v8 with 431 million SNP-level and 153 million haplotype-level measurements, and extends phASER to estimate cis regulatory variant effect sizes.

The paper uses bulk RNA-seq data to estimate cell type abundances and identifies cell type interaction QTLs that provide finer resolution of tissue specificity than bulk cis-eQTLs.

The paper examines telomere length variation across 6,391 tissue samples from 952 individuals, showing tissue-specific differences, positive correlations between tissues, and associations with age and ancestry.

The paper quantifies protein levels across 32 human tissues and compares them to transcriptome data, revealing insights into post-transcriptional regulation and disease mechanisms.

SuSiE writes sparse regression coefficients as a sum of single-effect vectors; its IBSS algorithm returns a credible set per effect and outperformed existing fine-mapping methods in numerical experiments.

The paper improves causal gene identification and transcriptome prediction by integrating fine-mapping and QTL tissue-sharing information into transcriptomic imputation models.