Mikko Arvas

Genome-Wide Association Study Identifies Protective Genetic Factors in Active Blood Donors Against Multiple Diseases

This study identifies 2,973 genetic loci associated with health outcomes in blood donors, suggesting genetic factors contribute to the healthier donor effect.

Population-scale multiome immune cell atlas reveals complex disease drivers

The paper uses population-scale single-nucleus ATAC-seq and RNA-seq to identify regulatory variants and their disease associations in immune cells.

Blood donor biobank pipeline to collect genome-based samples for research

The paper describes a pipeline for collecting high-quality blood samples from donors for multi-omics research, demonstrating their suitability for genetic and phenotypic studies.

Large-scale genome-wide analyses with proteomics integration reveal novel loci and biological insights into frailty

This study identifies 1,242 novel genetic variants and 31 novel genes associated with frailty through genome-wide association and proteomics integration.

Genome-wide meta-analysis conducted in three large biobanks expands the genetic landscape of lumbar disc herniations

This study conducts a genome-wide meta-analysis across three biobanks to identify new genetic loci associated with lumbar disc herniation, expanding the known genetic landscape.

Direct and INdirect effects analysis of Genetic lOci (DINGO): A software package to increase the power of locus discovery in GWAS meta-analyses of perinatal phenotypes and traits influenced by indirect genetic effects

DINGO improves locus discovery in GWAS of perinatal traits by combining maternal and fetal GWAS data through meta-analysis and novel statistical tests, identifying more genetic loci than separate analyses.

Image-based profiling of healthy donor immune cells with Blood Cell Painting reveals novel genotype-phenotype associations

The paper develops Blood Cell Painting (BCP) to profile immune cell morphology in healthy donors, identifying genetic and clinical associations with immune cell features.

DeepIFC: virtual fluorescent labeling of blood cells in imaging flow cytometry data with deep learning

DeepIFC uses deep learning to generate fluorescent marker images from imaging flow cytometry data, enabling accurate cell typing and recognition of unseen cell types.

GWAS of preeclampsia and hypertensive disorders of pregnancy uncovers genes related to cardiometabolic, endothelial and placental function

This study conducts a GWAS of preeclampsia and hypertensive disorders of pregnancy, identifying 18 genome-wide significant loci, including novel associations with blood pressure and placental function.

FinnGen: Unique genetic insights from combining isolated population and national health register data

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.

An expanded analysis framework for multivariate GWAS connects inflammatory biomarkers to functional variants and disease

The paper introduces a multivariate GWAS framework that identifies functional variants and disease associations by transforming multivariate results into univariate phenotypes for follow-up analysis.

Predicting Protein Producibility In Filamentous Fungi

The paper predicts protein producibility in filamentous fungi using machine learning, finding that models trained on one species can generalize to others with limited accuracy loss.