Biology subjects

Orkin, J.

Publications and source records attributed to Orkin, J..

2 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

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↗