Päivi Pajukanta

Comprehensive gene heritability estimation reveals the genetic architecture of rare coding variants underlying complex traits

This paper introduces FLEX, a method for estimating gene-level heritability from whole-exome sequencing data, showing rare coding variants contribute significantly to complex trait heritability.

Integrated ambient modeling and genetic demultiplexing of single-cell RNA+ATAC multiome experiments with Ambimux

The paper introduces ambimux, a method to estimate and demultiplex ambient molecule contamination in single-cell RNA+ATAC multiome data, improving the detection of biologically meaningful signals.

Single-cell DNA methylome and 3D genome atlas of the human subcutaneous adipose tissue

The paper characterizes the epigenomic and 3D genomic landscape of human subcutaneous adipose tissue at the cell-type level and links epigenomic variation to abdominal obesity risk.

Longitudinal adipose tissue single cell transcriptomics reveals genes and variants regulating weight loss after bariatric surgery

This study uses longitudinal single-cell adipose transcriptomics to identify genes and variants associated with weight loss after bariatric surgery and shows that BMI polygenic risk scores predict weight loss outcomes.

Genome-wide Association Study of Long COVID

This study performs a genome-wide association study for Long COVID, identifying a significant association with the FOXP4 locus and highlighting the role of pulmonary dysfunction and COVID-19 severity.

Twin pair analysis uncovers novel links between DNA methylation, mitochondrial DNA quantity and obesity

The paper identifies a locus, SH3BP4, where DNA methylation correlates with mtDNAq and obesity-related traits in adipose tissue, suggesting potential causal relationships.

Integration of single cell omics with biobank data discovers trans effects of SREBF1 abdominal obesity risk variants on adipocyte expression of more than 100 genes

The paper integrates single-cell omics with biobank data to show SREBF1 risk variants affect adipocyte gene expression in trans, impacting abdominal obesity.

Deep Learning-based Phenotype Imputation on Population-scale Biobank Data Increases Genetic Discoveries

AutoComplete improves phenotype imputation in biobank data, enhancing GWAS power and genetic discovery.

Adipose tissue eQTL meta-analysis reveals the contribution of allelic heterogeneity to gene expression regulation and cardiometabolic traits

The paper identifies additional eQTL signals in adipose tissue and shows they contribute to cardiometabolic traits through gene dosage effects.

ACE2 expression in adipose tissue is associated with COVID-19 cardio-metabolic risk factors and cell type composition

The paper examines the association between adipose tissue ACE2 expression and cardio-metabolic risk factors, finding lower ACE2 levels linked to T2D, obesity, and inflammation-related cell type changes.

Fast estimation of genetic correlation for Biobank-scale data

The paper introduces SCORE, a scalable and accurate method for estimating genetic correlation in large datasets, achieving higher precision than existing methods.

Enhancing droplet-based single-nucleus RNA-seq resolution using the semi-supervised machine learning classifier DIEM

The paper introduces DIEM, a semi-supervised machine learning classifier to remove debris-contaminated droplets in snRNA-seq, improving clustering quality across three datasets.

Accurate estimation of cell composition in bulk expression through robust integration of single-cell information

Bisque estimates cell composition in bulk RNA-seq using single-cell data, improving accuracy and efficiency over existing methods.

A comprehensive study of metabolite genetics reveals strong pleiotropy and heterogeneity across time and context

This study identifies 588 SNP-metabolite associations using the METSIM cohort, revealing pleiotropy, temporal changes, and age-related genetic control shifts.

Reverse GWAS: Using Genetics to Identify and Model Phenotypic Subtypes

This paper introduces Reverse GWAS (RGWAS) to identify and validate phenotypic subtypes using genetics, finding three novel metabolic subtypes and showing differential treatment effects.

Genetic and environmental perturbations lead to regulatory decoherence

This study identifies genetic and environmental factors that disrupt trait correlations, showing environmental perturbations reduce correlations and genetic factors influence correlation patterns.

Efficient estimation and applications of cross-validated genetic predictions

This paper introduces a reference-free method for generating polygenic risk scores (PRS) that avoids over-fitting and computational inefficiencies, and demonstrates applications in pleiotropy detection and mixed-model association testing.

Phenotype-specific enrichment of Mendelian disorder genes near GWAS regions across 62 complex traits

The paper quantifies the overlap between GWAS genes for 62 complex traits and Mendelian disorder genes, finding significant enrichment and elevated effect sizes for phenotypically-matched genes.