Hae Kyung Im

MetaboXcan predicts plasma metabolites from genetic data and associates them with complex traits, identifying disease risk genes and metabolites in chronic kidney disease.

This study improves proteome-wide association study results by using multi-ancestry data, finding more protein-phenotype associations and better prediction models.

The paper investigates the role of the EPAS1 enhancer ENH5 in adipocytes, showing its conditional effects on thermogenesis and adipocyte differentiation under hypoxia and thermogenic stimulation.

OmicsPred collects 3,339,469 genetic prediction models for transcript, protein and metabolite traits with harmonised metadata and MetaXcan/PGSC-Calculator files, plus multi-ancestry Million Veterans Program phenome-wide association results.

This paper uses network pharmacology to identify and validate drug repurposing candidates for Alzheimer's Disease, finding arundine and TUDCA as promising candidates.

The paper integrates genomics and transcriptomics data from human heart samples to identify regulatory mechanisms linking genetic variation to dilated cardiomyopathy.

RatXcan extends transcriptome-wide association studies to rats, enabling cross-species integration of GWAS and gene expression data to identify shared genetic mechanisms of complex traits.

BrainXcan uses genetic and imaging data to identify brain features linked to psychiatric traits, finding reduced axonal density and hippocampal features associated with schizophrenia risk.

scPrediXcan improves transcriptome-wide association studies by integrating deep learning for cell-type-specific gene expression prediction, identifying more causal genes and explaining more GWAS loci in type 2 diabetes and systemic lupus erythematosus.

This paper introduces INTERFACE, a probabilistic fine-mapping method that improves the identification of causal genes by integrating colocalization and transcriptome-wide association evidence.

This study performs a multi-tissue splicing-TWAS to identify breast cancer susceptibility genes, finding 240 significant genes and highlighting the importance of splicing over expression.

The paper introduces Multi-INTACT, a method that integrates transcriptome-wide association studies with protein QTL data to identify causal genes for metabolites, finding that both gene products are relevant for most gene nominations.

The paper shows that polygenicity inflates false positives in transcriptome-wide association studies and proposes a variance control method to correct for this.

This study shows that MASHR improves transcriptome prediction across populations, leading to more replicable discoveries in multi-ethnic transcriptome-wide association studies.

This paper introduces a tissue-aware machine learning framework to improve understanding and diagnosis of Mendelian and rare diseases by leveraging tissue-specific gene mechanisms.

This study performs a joint transcriptome-wide association study across multiple tissues to identify new candidate susceptibility genes for breast cancer, finding 380 genes at 129 loci with 22 remaining significant after conditioning on known variants.

This paper introduces a probabilistic framework to integrate TWAS and colocalization analysis, improving gene implication and pathway identification for complex traits.

The paper develops genetic predictors of plasma protein abundance in diverse populations and demonstrates their utility in proteome-wide association studies (PWAS) for complex traits.

The paper uses multi-ancestry biobank data and integrative methods to identify gene-disease associations across different ancestry groups, finding shared and ancestry-specific genetic links.

This study improves transcriptome-wide association studies by integrating pleiotropy-guided methods with GWAS data to identify new cancer susceptibility genes.

The paper introduces a scalable framework integrating total and allele-specific gene expression to improve detection of genetic effects on gene expression, showing a 29% power gain equivalent to increased sample size.

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 evaluates colocalization methods for genetic variants, identifying limitations and recommending best practices for accurate analysis of complex traits and molecular QTLs.

This study develops polygenic transcriptome risk scores (PTRS) to improve the portability of polygenic risk scores across ancestries, showing better performance in African populations compared to traditional PRS.

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.

PhenomeXcan integrates GWAS and transcriptomic data to identify gene-trait associations, providing a resource for translational research with biological context.

The paper assesses the impact of admixture and ancestry on eQTL analysis and GWAS colocalization in GTEx, finding that local ancestry adjustment improves power but has limited effect on colocalization probabilities.

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.

Integrating expression, allelic expression and splicing across tissues identifies rare variants with relevance to traits.