Stefan Grünewald

The paper shows guide-tree bias in whole-genome alignment can mislead phylogenomic analyses and proposes a consensus alignment strategy to reduce this bias.

TriMouNet infers level-1 phylogenetic networks from multi-locus gene tree distributions, identifying reticulations with low false positive rates when gene trees are accurate.

This paper introduces MLRE, a maximum-likelihood method for species tree reconstruction using low-homoplasy markers, showing improved performance on simulated data and better agreement with published Neoaves phylogenies.

Lpnet reconstructs phylogenetic networks from distances using integer linear programming, improving approximation accuracy over Neighbor-net for most datasets.

This study identifies five novel genetic loci associated with facial morphology traits in Han Chinese through a genome-wide association analysis of 3D facial data.

This paper develops a statistical framework for connexel-wise analysis in brain-wide association studies, improving power and reducing false positives using spatial information from fMRI data.

This paper introduces sKPCR, a method for identifying voxel-phenotype associations in dense connectomes, showing better reproducibility and overlap with existing schizophrenia findings.