Tuuli Lappalainen

This paper evaluates the clinical use of long-read genome sequencing in Sweden, showing it provides molecular diagnoses and reduces diagnostic costs.

The paper decomposes polygenic risk of coronary artery disease into distinct biological processes using latent factor decomposition of genetic variants.

This study integrates CAD GWAS with transcriptomic data to identify genes and networks associated with CAD and subclinical atherosclerosis.

This paper evaluates isoform-level resolution in single-cell CRISPR screens using long-read sequencing, finding that it captures splicing changes missed by gene-level analysis.

This paper identifies challenges in GWAS integration and fine-mapping for variant interpretation and suggests a multi-modal approach for prioritizing causal variants.

The paper identifies target genes and regulatory networks for bone mineral density GWAS loci using STING-seq, linking noncoding variants to gene function in osteoblasts.

The paper integrates multi-omic QTLs and predictive models to identify regulatory variants in CD4+ T cells and their role in immune-related GWAS loci.

This study modulates gene dosage using CRISPR and examines nonlinear transcriptional responses to dosage changes in three master transcription factors, revealing non-monotonic effects and links to disease genes.

ANEVA-h improves detection of rare regulatory variants by analyzing haplotype-level ASE data, increasing testable genes and reducing false positives in GTEx and clinical cohorts.

This paper recalibrates differential gene expression by genetic dosage variance to prioritize functionally relevant genes, showing enrichment in metabolic and regulatory pathways.

The paper compares CRISPRi perturbation screens and eQTLs in identifying target genes for GWAS-associated loci, finding they often identify distinct genes and complement each other.

The paper integrates spatial transcriptomics data with genetic disease genes to identify tissue structures linked to complex traits and suggests spatially informed drug repurposing.

The paper quantifies protein abundance across human tissues and identifies genetic factors influencing protein levels, revealing both shared and tissue-specific genetic architectures.

The paper develops a method to define cell-specific transcription factor regulons and demonstrates their utility in analyzing transcriptional dysregulation in cancer.

The paper maps interaction QTLs (iQTLs) for cell type, age, and other variables in blood multi-omic data to identify context-specific genetic regulatory effects.

This study evaluates noninvasive biospecimens for transcriptome studies, finding hair follicles and urine cell pellets to be most promising for disease-relevant applications.

The paper uses interventional data to infer causal networks for blood traits, revealing regulatory relationships among 788 genes.

The paper identifies impactful rare variants by integrating multi-omic data from MESA, showing that combining expression and protein data increases the detection of rare stop and frameshift variants.

The paper identifies genetic variants affecting transcription factor activity by analyzing RNA-seq data from GTEx and using a beta-binomial GLM to map aQTLs.

This paper introduces MCFA, an unsupervised integration method for multi-omic data, and applies it to five omics types across 614 samples to infer shared and private factors.

This study identifies genetic variants associated with sputum mucin concentrations and links one variant to COPD phenotypes like chronic bronchitis and acute exacerbations.

This study shows that common splice-regulatory variants can reduce the pathogenicity of rare exonic variants by modulating mRNA splicing.

The paper identifies target genes and pathways of blood trait loci using CRISPR screens and single-cell RNA sequencing, revealing cis and trans regulatory effects of noncoding variants.

This study catalogs sQTLs using PSI scores and uses AlphaFold2 to predict protein structure changes linked to GWAS traits.

The paper links LIPA risk alleles to increased monocyte/macrophage LIPA expression, which exacerbates atherosclerosis through enhanced PU.1 binding and altered macrophage function.

This paper improves multi-omics integration by enhancing orthogonality in canonical vectors and demonstrates transferability across cohorts for proteomics and methylomics data.

The paper uses long-read RNA-seq data to identify new transcripts and develop a tool to analyze genetic effects on transcript structure, revealing insights into splicing regulation and variant interpretation.

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 identifies 6,262 TF-eQTL interactions across 1,598 genes using GTEx data, linking genetic variants to transcription factor regulation and GWAS loci.

The paper identifies SLC6A20 and CXCR6 as putative causal genes for the 3p21.31 GWAS signal in COVID-19 using an integrative approach combining CRISPR screens and eQTL data.