Andrés Ingason

The paper develops a convolutional neural network to automate CNV validation, achieving high accuracy comparable to human visual inspection.

This study examines how rare copy number variants and polygenic scores jointly affect psychiatric disorder risk in a large population sample, finding no significant interaction between the two factors.

This paper uses ancient genomes to trace the spread of Germanic languages in Europe, identifying a Baltic origin and migration patterns that challenge traditional timelines.

The paper estimates population-based risks of psychiatric disorders associated with 34 recurrent CNVs using a large case-cohort sample, finding moderate risks for ADHD, autism, and schizophrenia, but not depression.

The paper introduces PA-FGRS, a method to estimate genetic liability from family records, and shows it improves MDD classification and GWAS power when combined with genotypes.

The paper evaluates how haplotype estimation and whole genome imputation accuracy vary with data integration protocols and phasing methods in a large psychiatric biobank dataset.

This paper introduces a framework to enhance polygenic prediction by combining multiple polygenic scores, showing significant accuracy improvements for psychiatric disorders and other traits.

This study analyzes ancient genomes from the Viking Age to understand genetic structure and migration patterns, finding significant foreign gene flow and population differentiation.

Impute.me provides an open-source tool for calculating and interpreting polygenic risk scores from consumer genetic data to better communicate genetic risk for complex diseases.

The paper identifies pQTLs for cardiovascular proteins and uses them to evaluate drug targets and suggest new candidates for precision medicine.

This study shows common genetic variants significantly contribute to migraine family clustering, with higher burden in familial cases compared to population-based cases.