Biology subjects

Kuderna, L.

Publications and source records attributed to Kuderna, L..

3 recordsLinked to original sources

bioRxiv2025

A phylogenetic protein-coding genome-phenome map of complex traits across 224 primate species.

Complex traits arise from networks of coding and regulatory loci, making it difficult to resolve their genetic basis. Macroevolutionary studies leverage tens of millions of years of divergence across species to uncover fixed genomic changes invisible to within-species approaches, such as GWAS, offering a complementary framework for generating hypotheses in biomedical research. Here, we present the first phylogenetic protein-coding primate-wide genome-phenome map (P3GMap), spanning 200 curated traits across 224 primate species, which we release through the Primate Genome-Phenome Archive (PGA, https://pgarchive.github.io). Using two complementary approaches, convergent amino acid substitutions and relative evolutionary rates, we linked protein-coding variation to complex phenotypes and identified thousands of candidate gene-trait associations, including lineage-specific signals related to diet, immunity, and lifespan. One sentence summaryCross-species genome-phenome mapping in primates reveals thousands of protein-coding variants linked to the evolution of complex traits.

evolutionary biology↗

bioRxiv2023

Enamel proteins reveal biological sex and genetic variability within southern African Paranthropus

The evolutionary relationships among extinct African hominin taxa are highly debated and largely unresolved, due in part to a lack of molecular data. Even within taxa, it is not always clear, based on morphology alone, whether ranges of variation are due to sexual dimorphism versus potentially undescribed taxonomic diversity. For Paranthropus robustus, a Pleistocene hominin found only in South Africa, both phylogenetic relationships to other taxa 1,2 and the nature of intraspecific variation 3-6 are still disputed. Here we report the mass spectrometric (MS) sequencing of enamel proteomes from four ca. 2 million year (Ma) old dental specimens attributed morphologically to P. robustus, from the site of Swartkrans. The identification of AMELY-specific peptides and semi-quantitative MS data analysis enabled us to determine the biological sex of all the specimens. Our combined molecular and morphometric data also provide compelling evidence of a significant degree of variation within southern African Paranthropus, as previously suggested based on morphology alone 6. Finally, the molecular data also confirm the taxonomic placement of Paranthropus within the hominin clade. This study demonstrates the feasibility of recovering informative Early Pleistocene hominin enamel proteins from Africa. Crucially, it also shows how the analysis of these proteins can contribute to understanding whether hominin morphological variation is due to sexual dimorphism or to taxonomic differences. We anticipate that this approach can be widely applied to geologically-comparable sites within South Africa, and possibly more broadly across the continent.

evolutionary biology↗

bioRxiv2023

The landscape of tolerated genetic variation in humans and primates

Personalized genome sequencing has revealed millions of genetic differences between individuals, but our understanding of their clinical relevance remains largely incomplete. To systematically decipher the effects of human genetic variants, we obtained whole genome sequencing data for 809 individuals from 233 primate species, and identified 4.3 million common protein-altering variants with orthologs in human. We show that these variants can be inferred to have non-deleterious effects in human based on their presence at high allele frequencies in other primate populations. We use this resource to classify 6% of all possible human protein-altering variants as likely benign and impute the pathogenicity of the remaining 94% of variants with deep learning, achieving state-of-the-art accuracy for diagnosing pathogenic variants in patients with genetic diseases. One Sentence SummaryDeep learning classifier trained on 4.3 million common primate missense variants predicts variant pathogenicity in humans.

genomics↗