CALANGO: A phylogeny-aware comparative genomics tool for discovering quantitative genotype-phenotype associations across species

Jorge Augusto Hongo, Giovanni Marques de Castro, Alison Pelri Albuquerque Menezes, Agnello César Rios Picorelli, Thieres Tayroni Martins da Silva, Eddie Luidy Imada, Luigi Marchionni, Luiz Eduardo Del-Bem, Anderson Vieira Chaves, Gabriel Magno de Freitas Almeida, Felipe Campelo, Francisco Pereira Lobo*

*Corresponding author for this work

Research output: Contribution to journalArticle (Academic Journal)peer-review

3 Citations (Scopus)

Abstract

Living species vary significantly in phenotype and genomic content. Sophisticated statistical methods linking genes with phenotypes within a species have led to breakthroughs in complex genetic diseases and genetic breeding. Despite the abundance of genomic and phenotypic data available for thousands of species, finding genotype-phenotype associations across species is challenging due to the non-independence of species data resulting from common ancestry. To address this, we present CALANGO (comparative analysis with annotation-based genomic components), a phylogeny-aware comparative genomics tool to find homologous regions and biological roles associated with quantitative phenotypes across species. In two case studies, CALANGO identified both known and previously unidentified genotype-phenotype associations. The first study revealed unknown aspects of the ecological interaction between Escherichia coli, its integrated bacteriophages, and the pathogenicity phenotype. The second identified an association between maximum height in angiosperms and the expansion of a reproductive mechanism that prevents inbreeding and increases genetic diversity, with implications for conservation biology and agriculture.

Original languageEnglish
Article number100728
JournalPatterns
Volume4
Issue number6
DOIs
Publication statusPublished - 9 Jun 2023

Bibliographical note

Publisher Copyright:
© 2023 The Author(s)

Keywords

  • comparative genomics
  • comparative methods
  • DSML2: Proof-of-concept: Data science output has been formulated, implemented, and tested for one domain/problem
  • evolution of quantitative phenotypes
  • genotype-phenotype association
  • molecular functional convergence
  • quantitative trait
  • species data

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