Research

Our research directions
Role of Structural Variants in Human Health

Genome-wide association studies (GWAS) have revealed countless links between genetic variants and human traits, but common structural variants (SVs)—large rearrangements of DNA that can reshape the genome—remain mostly underexplored. These variants hold the key to explaining complex genetic architectures, uncovering molecular mechanisms, and identifying new causal genes at GWAS loci.

Advancing Haplotype-Informed Methods for SV detection

In our group we focus on developing haplotype-informed methods to analyze SVs across hundreds of thousands of genomes. By integrating signals such as read depth, split reads, and discordant reads with haplotype-sharing patterns, we can extract and denoise SV genotypes with remarkable precision. These tools not only detect known SVs but also discover previously unrecognized ones, producing comprehensive genotype data that can be directly linked to phenotypes.

We apply these approaches to datasets like the UK Biobank, revealing novel SV associations with complex traits and diseases. Our results show how SVs significantly influence human phenotypes, often in ways not captured by SNPs alone. We are expanding our analyses to the FinnGen cohort, leveraging its unique population structure and high levels of identity-by-descent to further explore the role of SVs in human health and disease.

Statistical and popolation genetics

Our research focuses on developing and applying statistical methods to analyze large-scale genomic datasets, with an emphasis on phasing and genotype imputation. We work with cost-effective sequencing technologies, such as low-coverage whole-genome sequencing and SNP arrays, to uncover meaningful insights from noisy data. A key aspect of our work is haplotype-based analysis—identifying shared chromosomal segments and studying how genetic variation influences population diversity and contributes to disease.

Phased genomic data are central to what we do. Our efforts include the phasing the whole-genome sequencing version of the UK Biobank, one of the most comprehensive biobank datasets to date. By combining these data with proteomics and metabolomics, we aim to connect genetic discovery to clinical application.