Sharon Marie Lutz

The paper introduces ROMY, a framework for analyzing multi-omics data with robust association testing, variance-covariance effects, and interaction testing using double machine learning.

This paper develops polygenic hazard score models using lasso for Cox's proportional hazards to predict Alzheimer's free survival, showing improved accuracy and personalized survival curves.

This study performs a GWAS on asthma exacerbations in the UK Biobank, identifying two novel genetic loci associated with exacerbation risk in individuals with asthma.

RITSS identifies gene-environment interactions in quantitative traits using sample splitting and robust statistics, finding significant interactions in lung function and height data.

This paper introduces fast algorithms for computing principal components of genomic similarity matrices, including exact methods for specific matrices and an approximate Jaccard matrix with theoretical guarantees.

This paper examines how unmeasured confounding affects the MR Steiger method's ability to determine causal direction between phenotypes.

This paper introduces a method to combine genotypes from genotyping arrays and whole genome sequencing to expand public controls for GWAS, improving power for non-European ancestries.

The paper introduces a smoothed Lasso method for high-dimensional regression and applies it to compute polygenic risk scores for COPD, showing comparable accuracy to FISTA with similar runtime.

This paper introduces a smoothed version of the Lassosum penalty to improve integrated risk models, showing increased accuracy in predicting Alzheimer's and COPD risks compared to the original Lassosum and LDpred2.

The paper identifies two SARS-CoV-2 genome loci associated with increased COVID-19 mortality risk using logistic regression on whole-genome sequencing data.

This paper introduces a smoothing framework for L1 penalized regression that provides accurate predictions with theoretical guarantees and comparable runtime to existing methods.

locStra efficiently analyzes regional and global population stratification in WGS studies using sparse matrix algebra and sliding window approaches to improve association testing.

The paper develops a method to handle missing data in genetic meta-analysis, identifying novel low-frequency variants in nicotine dependence genes with improved accuracy.

The paper develops methods to analyze multi-allelic variants in large sequence datasets, showing improved power and unbiased estimates in association tests.