Tiffany Amariuta

This study improves gene expression prediction models by incorporating tandem repeats, showing significant performance gains and identifying nonlinear effects on gene expression.

The paper evaluates how different model architectures, LD filtering thresholds, and variant prioritization methods affect gene-level eQTL score prediction quality using over 3 million models.

This paper develops a multimodal framework to prioritize causal genes in childhood-onset asthma by integrating GWAS, bulk eQTL, and single-cell eQTL data, revealing key immune and metabolic genes and improving risk prediction across cohorts.

This paper introduces EGRET, a framework that models both cis and trans-eQTLs to improve gene expression prediction and disease gene discovery, finding it outperforms existing methods in identifying disease-critical genes.

scLong is a billion-parameter foundation model that captures long-range gene context in scRNA-seq by modeling all genes and integrating gene knowledge, outperforming existing models in various tasks.

The paper introduces PIPSORT, a method to detect shared and ancestry-specific genetic signals in complex traits, and applies it to GWAS data from diverse populations.

The paper introduces scGeneHE, a method to estimate cis-heritability from scRNA-seq data, reducing false positives in eGene detection.

This paper introduces TGFM, a method for fine-mapping causal tissues and genes at disease-associated loci using GWAS and eQTL data, identifying 147 causal elements per disease with 11% as gene-tissue pairs.

This paper introduces MAGEPRO, a method to map cis-genetic effects on gene expression across diverse populations, revealing novel gene-trait associations and improving gene expression prediction.

This study identifies genetic variants associated with cellular morphology using high-content imaging and genomic data from iPSCs.

This paper introduces TCSC, a method to identify tissue-specific disease contributions by distinguishing causal tissues from tagging tissues using GWAS and gene expression data.

This study identifies 565 genetic loci associated with sitting height ratio (SHR) in two populations, finding distinct genetic signals from height and highlighting ancestry-specific effects.

This study conducts a trans-ancestry GWAS of rheumatoid arthritis, identifying 124 loci, 34 novel, and shows improved polygenic risk score performance across populations.

The paper identifies state-dependent eQTLs in memory T cells by analyzing single-cell RNA and protein data from Peruvians, showing that gene regulation varies with cell states and disease-associated variants are enriched in these contexts.

The paper develops a method to identify population-specific causal effect sizes in functionally important regions using trans-ethnic genetic correlations.

A large-scale GWAS in 212,453 Japanese individuals identified 45 novel susceptibility loci for 22 diseases, highlighting the importance of non-European population studies.

The paper integrates thousands of epigenetic datasets into 707 cell-type regulatory annotations to improve the trans-ethnic portability of polygenic risk scores by prioritizing variants in IMPACT regulatory elements.

The paper identifies dynamic allele-specific expression changes in T cell activation, particularly in HLA genes, showing regulatory effects linked to autoimmune risk.

The paper constructs cell-type-specific TF binding annotations by combining sequence-based predictions with chromatin data, showing increased heritability enrichment for 49 diseases.

The paper identifies a genetic variant on 3q23 associated with early progression from Mycobacterium tuberculosis exposure to active tuberculosis in Peruvians, showing higher heritability than general TB susceptibility.

Benchmarker evaluates gene prioritization algorithms using GWAS data through cross-validation, showing that combining strategies improves gene prioritization quality.

The paper assesses gene network connectivity's role in disease heritability using LD score regression and finds that network annotations are enriched but largely explained by regulatory annotations.

IMPACT improves regulatory element annotation by integrating TF binding profiles with epigenomic data, showing better performance in predicting eQTLs and capturing RA heritability.

The paper identifies in vivo eQTL interactions with cytokine levels in a lupus trial, finding novel regulatory mechanisms and potential therapeutic targets.