Julian Hecker

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.

The paper identifies DNA-methylation markers at birth and childhood associated with lung function and reveals sex- and genetics-related pathways linked to asthma and lung development.

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.

The paper introduces miRAIDD, a database of human miRNA-disease causal associations generated using large language models, and quantifies miRNA pleiotropy and its biological characteristics.

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 study identifies two novel Alzheimer's disease-associated genes, DTNB and DLG2, by analyzing rare variants in whole-genome sequencing data from AD family datasets.

The paper uses unsupervised cluster analysis to show that omicron variant SARS-CoV-2 sequences are most similar to early 2020 sequences, suggesting long-term circulation.

The paper introduces a framework for region-based association testing in family-based studies, showing improved performance over existing methods in simulations and a real dataset.

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

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

This paper describes a sequential testing method for permutation-based association testing in whole-genome sequencing that controls error rates and minimizes required permutations.

The paper identifies an error in VEGAS2's top-percentage test that leads to deflated p-values and false positives, providing corrected code for accurate p-value computation.

This paper extends LD Score regression using generalized estimating equations to better quantify polygenic effects in GWAS by incorporating external reference panel information.