Biology subjects

Grünewald, S.

Publications and source records attributed to Grünewald, S..

4 recordsLinked to original sources

bioRxiv2026

Guide-tree bias of whole genome alignment can mislead phylogenomic analyses

Whole-genome alignment (WGA) is widely used for genome-scale phylogenetic inference, and most scalable WGA pipelines rely on progressive alignment guided by a pre-specified tree. Among progressive whole-genome aligners, Progressive Cactus is a successful state-of-the-art method. However, analyses of real and simulated avian data indicate that guide-tree choice can influence downstream tree inference; star guide trees do not remove this effect and can exacerbate long-branch attraction artefacts. We have developed a consensus strategy based on the Progressive Cactus framework by generating a small set of alternative guide-tree alignments and retaining only homology relationships consistently recovered across all alignments. In simulation experiments, consensus alignments improve precision, bring inferred site-pattern frequency distributions closer to those of the true alignments, and recover more true splits than single guide-tree alignments. In a real landbird (Telluraves) dataset, we observe a strong bias towards single binary guide trees and long-branch attraction for less resolved trees. While the reconstructed tree still depends on the phylogenetic method and taxa sampling, our consensus alignment has no clear bias. We implemented a hierarchical consensus workflow that only locally resolves uncertainty in the guide tree. Therefore, the computational cost increases only moderately, for example by an estimated 68 percent for a recently published large-scale alignment of more than 300 modern birds (Neoaves) taxa.

evolutionary biology↗

bioRxiv2026

TriMouNet: An Algorithm for Inferring Level-1 Phylogenetic Networks from Multi-Locus Gene Tree Distributions.

With the availability of full genomes of an ever-increasing number of species, many recent phylogenetic analyses have focused on datasets with thousands of loci. In the presence of incomplete lineage sorting (ILS), many gene trees will be discordant with the species tree, which can then be estimated with a supertree method. In a separate stream of research, the reconstruction of phylogenetic networks, which aim to detect and visualize reticulations in addition to the dominant phylogenetic tree, has become common practice. Level-1 networks have the feature that every node can be interpreted as an ancestor of a subset of the taxa of interest and no two distinct cycles share a common node. TriLoNet is a method that constructs a level-1 network on all taxa from 3-taxa networks, so-called trinets. This approach is similar to modern supertree methods, but the trinets are assigned based on a single sequence alignment. Here we present TriMouNet (Trinet Multilocus Network), which uses the tree topology and branch length distribution in gene trees of a multilocus dataset to infer best-fitting trinets, together with scores quantifying their statistical support. These trinets are then puzzled together into a network on all taxa in a TriLoNet fashion. Experiments on simulated and real datasets show that TriMouNet can identify reticulations with low false positive rate, if the gene trees are accurate. On the other hand, TriLoNet applied to the concatenation of all loci, tends to predict wrong reticulations as the consequence of violations of model assumptions.

evolutionary biology↗

bioRxiv2021

Lpnet: Reconstructing Phylogenetic Networks from Distances Using Integer Linear Programming

We present Lpnet, a variant of the widely used Neighbor-net method that approximates pairwise distances between taxa by a circular phylogenetic network. We first apply standard methods to construct a binary phylogenetic tree and then use integer linear programming to compute an optimal circular orderings that agrees with all tree splits. This approach achieves an improved approximation of the input distance for the clear majority of experiments that we have run for simulated and real data. We release an implementation in R that can handle up to 94 taxa and usually needs about one minute on a standard computer for 80 taxa. For larger taxa sets, we include a top-down heuristic which also tends to perform better than Neighbor-net.

bioinformatics↗

bioRxiv2019

Genome-wide Association study of facial morphology identifies novel genetic loci in Han Chinese

Human face is a heritable surface with many complex sensory organs. In recent years, many genetic loci associated with facial features have been reported in different populations, yet there is a lack for the Han Chinese population. We report a genome-wide association analysis of 3D normal human faces in 2659 Han Chinese with two groups of phenotypes, the partial and whole face phenotypes and the distance and angle phenotypes. We found significant signals in five genomic regions with traits related to nose or eyes, including rs970797 in 2q31.1 near HOXD1 and MTX2, rs16897517 in 8q22.2 at intron of VPS13B, rs9995821 in 4q31.3 near DCHS2 and SFRP2, rs12636297 in 3q23 near PISRT1, and rs12948076 in 17q24.3 near SOX9 and CASC17. We visualized changes in facial morphology by comparing the volume of local areas and observed that these nose-related loci were associated with different features of the nose, including nose prominence, nasion height, and nostril shape, suggesting that the nose underlies precise genetic regulation. These results provide a more comprehensive understanding of the relationship between genetic loci and human facial morphology.\n\nAuthor SummaryHuman face as a combination of delicate sensory organs has a strong genetic component, as evidenced by the identical appearance in twins and shared facial features in close relatives. Although facial genetics have been studied in different populations, our knowledge between genetic markers with facial features is still limited. In this paper, we found genetic variants associated with nose and eyes through a large-scale high-resolution 3D facial genetic study on the Han Chinese population. We observed that these nose-related loci were associated with different features of the nose, including nose prominence, nasion height, and nostril shape, which suggests the nose underlies precise genetic regulation. Intriguingly, we noted that genes (DCHS2 and SFRP2) related to one of these loci are differentially expressed in human and chimp cranial neural crest cells, which plays a crucial role in the early formation of facial morphology. The ongoing genetic studies of facial morphology will improve our understanding of human craniofacial development, and provide potential evolution evidence of human facial features.

genetics↗