bioRxivEvolutionary BiologyPreprint
BUSTED-PH: Isolating the genomic signatures of convergent phenotypes
Avery SelbergiD, Nathan L ClarkiD, Anton NekrutenkoiD, Maria D. ChikinaiD, Sergei L. Kosakovsky PondiD
Abstract
Convergent evolution offers a natural test for adaptive predictability, yet pinpointing its molecular basis remains difficult. Current methods often grapple with distinguishing adaptive convergence from background noise, either by demanding overly stringent identical substitutions or relying on low-resolution evolutionary rate shifts. Here we introduce BUSTED-PH (Branch-site Unrestricted Statistical Test for Episodic Diversification - Phenotype), a branch-site codon model that detects phenotype-associated episodic diversifying selection. Unlike standard approaches, BUSTED-PH explicitly contrasts selective regimes between phenotype-positive (foreground) and phenotype-negative (background) lineages, effectively winnowing out spurious associations driven by pervasive background adaptation. BUSTED-PH has already been applied in numerous independent studies, and here we rigorously validate it using canonical positive controls and simulations to confirm high power and strict false positive control. A genome-wide scan of 120 mammalian species using strict statistical criteria (FDR [≤] 0.01) identified 72 genes associated with echolocation; while recovering paradigmatic auditory drivers (e.g., Prestin, TMC1 ), we also uncover novel candidates in physiological support systems ranging from lipid homeostasis to neural development. A parallel analysis of mammalian gigantism identifies 91 genes linked to musculoskeletal reinforcement, organ size governance, and genomic integrity, characterizing the molecular adaptations required to support massive body size. As projects like Zoonomia and the Vertebrate Genomes Project expand comparative datasets, BUSTED-PH provides a robust framework for dissecting the genetic architecture of complex convergent traits.
- organism: mouse
- organism: human
- organism: rat
- assay: whole-genome sequencing
- research focus: chromatin & epigenetic regulation
- research focus: gene regulatory network inference
- approach: deep learning
- Citations
- 3
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