Regev Schweiger

The paper identifies and resolves false population size peaks in PSMC analyses, showing they result from parameter settings and not true population dynamics.

The paper extends coalescent-based inference to deep time scales using high-quality genome assemblies, revealing a population size peak in humans, chimpanzees, and bonobos around 3-6 million years ago.

The paper uses long-read sperm sequencing to analyze non-crossover recombination events, finding variation in recombination rates and distinct mechanisms for non-crossover gene conversions.

This study performs a GWAS and evaluates polygenic risk scores for AMD in Israeli populations, finding consistent associations with known loci and demonstrating predictive power of international PRS.

The paper introduces Gamma-SMC, a method for fast genome-wide inference of pairwise coalescence times, showing it is >14 times faster than existing methods.

The paper benchmarks two methods for detecting cell-type-specific differential DNA methylation, finding TCA superior to CellDMC and discussing best practices for cell-type resolution analysis.

This study shows that low coverage sequencing can effectively match relatives with genotyping arrays, achieving similar accuracy for close relatives and strong correlation for distant ones.

The paper demonstrates a method to infer cell-type-specific DNA methylation from bulk data, revealing novel associations with leukocyte composition and rheumatoid arthritis.

FactorialHMM provides fast exact inference for factorial hidden Markov models, enabling efficient state sequence and likelihood computation with linearithmic time and space complexity.

This paper introduces a Bayesian method to estimate cell type composition from DNA methylation without requiring a methylation reference, showing it can identify individual cell types rather than just linear combinations.

RL-SKAT improves the accuracy of score tests for heritability and set-based genetic association by calculating exact p-values, avoiding the inflation or deflation of p-values seen in traditional methods.

This paper shows that DNA methylation data can capture ancestry information and introduces a method to infer ancestry from methylation data without genetic data.

This paper introduces ALBI, a method for constructing accurate confidence intervals for heritability estimates using linear mixed models, addressing biases in existing approaches.