Colin M. Brand

This study uses non-invasive fecal samples and chromosome 21 target capture sequencing to analyze bonobo population structure, identifying three subpopulations and river barriers influencing genetic differentiation.

This paper uses genetic data to show that two distinct superarchaic populations contributed to modern human ancestry, with one contributing ~20% to African populations and the other to Eurasian archaics.

The paper creates a catalog of proxy variants linked to ancient genotyped variants to improve the study of genetic variants in ancient DNA.

The paper uses sequence-based machine learning to predict 3D genome contact maps in bonobos and chimpanzees, identifying divergent folding patterns and variants affecting genome structure.

The paper uses machine learning to infer 3D chromatin contact patterns across diverse human populations and identifies regions with significant 3D divergence not explained by sequence variation.

The paper uses machine learning to analyze Neanderthal and Denisovan genomes, identifying splicing variants and their potential impact on phenotypic differences.

The paper estimates the evolutionary history of bonobos and chimpanzees using whole genome sequences, finding introgression events and male-biased reproduction.

This study identifies soft sweeps as the dominant mode of recent positive selection in bonobos and chimpanzees using supervised machine learning on population genomic data.

This study estimates population size and density of African forest elephants in southwestern Gabon using genetic capture-recapture and analyzes social structure through network analysis, finding lower population estimates than previously reported.