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L Moritz Blumer

Publications and source records attributed to L Moritz Blumer.

2 recordsLinked to original sources

Adaptation across an extreme elevational gradient in Andean leaf-eared mice, the world's highest-dwelling mammal.

Andean leaf-eared mice (Phyllotis vaccarum) live at the highest elevations of any mammal, and they also have the broadest elevational range, from sea level to mountain summits of >6700 meters. Highland populations have evolved an enhanced thermogenic capacity in hypoxia relative to lowland conspecifics, and this improved physiological performance is associated with an increased mitochondrial respiratory capacity in skeletal muscle. Population genomic analyses identified mechanisms of hypoxia adaptation and revealed an unanticipated dimension of environmental adaptation in P. vaccarum because selection on biotransformation pathways suggests an evolved capacity to metabolize plant-derived dietary toxins. The world's highest-dwelling mammal has adapted to habitats at both the low- and high-elevation limits of its range, and much of the elevation-related selection relates to previously unappreciated aspects of feeding ecology.

Animals

WinPCA: a package for windowed principal component analysis.

SUMMARY: With chromosomal reference genomes and population-scale whole genome-sequencing becoming increasingly accessible, contemporary studies often include characterizations of the genomic landscape as it varies along chromosomes, commonly termed genome scans. While traditional summary statistics like FST and dXY between pre-assigned populations remain integral to characterizing the genomic divergence profile, PCA differs by providing single-sample resolution, thereby supporting the identification of polymorphic inversions, introgression and other types of divergent sequence that may not be fully aligned with global population structure. Here, we introduce WinPCA, a user-friendly package to compute, polarize and visualize genetic principal components in windows along the genome. To accommodate low-coverage whole genome-sequencing datasets, WinPCA can optionally make use of PCAngsd methods to compute principal components in a genotype likelihood framework. WinPCA accepts variant data in either VCF or BEAGLE format and can generate rich plots for interactive data exploration and downstream presentation. AVAILABILITY AND IMPLEMENTATION: WinPCA is implemented in Python and freely available at https://github.com/MoritzBlumer/winpca and https://doi.org/10.5281/zenodo.15614979.

Software