Christoph Lange

This study uses polygenic risk scores and proteomic analysis to identify genetic and molecular associations with asthma in two family-based cohorts, highlighting IL1R1's role in asthma pathogenesis.

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 quantifies individual dataset contributions to prediction accuracy using cooperative learning in the CAMP study.

This study identifies molecular and cellular signatures of cognitive resilience in Alzheimer's disease by integrating multi-omics data from brain tissues.

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.

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 develops a trans-ancestry polygenic risk score for type 2 diabetes and validates it across diverse populations to improve equitable risk prediction.

This paper develops a genome-wide polygenic score for chronic kidney disease that incorporates APOL1 risk genotypes and shows consistent performance across four major continental ancestries.

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.

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

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 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 uses unsupervised clustering of SARS-CoV-2 genomes to show geographic progression and identify genetic subgroups.

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.

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 analyzes SARS-CoV-2 genomes from Beijing to determine their genetic origin, finding they cluster with European and South(east) Asian samples from 2020.

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.