Kaur Alasoo

VIDRA models genetic dose-response relationships to predict drug mechanisms, therapeutic potential, and adverse events using phenotypically associated variants.

This paper introduces gpu-coloc, a fast colocalisation method for genetic signals, showing it can efficiently analyse millions of traits and detect colocalisations with high accuracy.

This study performs a large-scale GWAS on 249 metabolic traits in 619,372 individuals, identifying 88,604 associations and using MR to explore causal links between metabolites and diseases.

This paper analyzes 100,526 GWAS to identify pleiotropic genes and their therapeutic implications, finding that highly pleiotropic genes are associated with safety liabilities and that combining PAV support with intermediate pleiotropy improves therapeutic success.

The paper validates an Alzheimer's disease-associated SNP in PTK2B that alters microglial function through changes in chromatin accessibility and gene expression.

The paper explores challenges in Mendelian randomisation with proxy exposures, demonstrating how variant effects on downstream metabolites can infer causal roles in biological processes.

The paper integrates QTL mapping and CRISPR screening in iPSC-derived microglia to identify genetic drivers of neurodegenerative risk by linking variants to phenotypes and gene expression.

GAME provides a standardized framework for benchmarking genomics models across tasks, enabling consistent evaluation and community contributions.

Learning and removing Tn5/DNase-I sequence bias lets a small base-resolution CNN (ChromBPNet) recover TF motifs and footprints from accessibility data and rival Enformer at predicting chromatin QTL effects.

The paper benchmarks methods for prioritizing GWAS target genes using eQTLs and finds that assigning pQTLs to closest protein-coding genes outperforms colocalisation methods.

The paper predicts the mechanistic impacts of human protein missense variants on stability, protein-protein interactions, and small-molecule binding using AlphaFold2 structures.

This study identifies genetic determinants of plasma protein levels in the Estonian population, including common, rare, and copy number variants, and provides fine-mapped credible sets for 151 proteins.

The paper visualises molecular QTLs to better understand genetic effects on gene expression and identifies causal genes at GWAS loci.

The paper improves detection of promoter usage effects by integrating CAGE data into RNA-seq analysis, increasing puQTL detection power by ~30%.

Finnish-enriched low-frequency variants, often coding, underlie many GWAS signals across 1,932 register endpoints in 218,792 Finns; a 15-disease meta-analysis with Estonian and UK biobanks adds 30 unreported associations.

Fine-mapping 148 complex traits in three biobanks with combined n = 811,261 yields 4,518 causal variant-trait pairs, 285 of them repeating in more than one population.

The paper presents the eQTL Catalogue, a resource containing uniformly processed eQTLs from 21 studies, enabling integrative analysis and identification of cell-type-specific eQTLs and disease colocalisations.

The paper identifies trans-acting genetic variants that control gene modules through co-expression analysis in blood cell types.

The paper identifies immune disease variants affecting Treg gene expression and highlights potential drug targets by linking GWAS signals with Treg QTLs.

The paper identifies context-specific genetic effects on promoter usage and shows they contribute to complex traits more than canonical splicing QTLs.

The paper identifies genetic effects on chromatin and gene expression in immune response conditions, revealing enhancer priming in naive cells.

The paper maps regulatory variants in iPSC-derived sensory neurons, identifying genetic influences on gene expression, chromatin accessibility, and RNA splicing, with overlaps to disease associations.

The paper compares transcriptomes of macrophages derived from monocytes and iPSCs to identify conserved and novel LPS responses.