Erik Widén

The paper develops population-specific polygenic risk scores for Han Chinese using genomic and electronic health record data, showing strong predictive performance for multiple diseases.

The paper introduces blockLASSO, a method for training polygenic scores that is computationally efficient and effective across multiple phenotypes and ancestry groups.

The paper evaluates sparse machine learning algorithms for polygenic prediction, showing LASSO performs well and provides projections for new biobanks.

The paper compares sibling and population genotype and phenotype variation using UK Biobank and IVF data, showing reduced polygenic score variation among siblings and developing a method to detect recombination breaks.

The paper constructs a polygenic health index using disease risk scores and validates it in the UK Biobank, finding higher scores correlate with reduced disease risk and longer life expectancy.

The paper trains machine learning models to predict blood/urine biomarkers from SNPs and disease risk from biomarkers using UK Biobank data, achieving high accuracy for several diseases.