Two-way mixed-effects methods for joint association analysis using both host and pathogen genomes

Miaoyan Wang, Fabrice Roux, Claudia Bartoli, Carine Huard-Chauveau, Christopher Meyer, Hana Lee, Dominique Roby, Mary Sara McPeek, Joy Bergelson

Research output: Contribution to journalArticlepeer-review


Infectious diseases are often affected by specific pairings of hosts and pathogens and therefore by both of their genomes. The integration of a pair of genomes into genome-wide association mapping can provide an exquisitely detailed view of the genetic landscape of complex traits. We present a statistical method, ATOMM (Analysis with a Two-Organism Mixed Model), that maps a trait of interest to a pair of genomes simultaneously; this method makes use of whole-genome sequence data for both host and pathogen organisms. ATOMM uses a two-way mixed-effect model to test for genetic associations and cross-species genetic interactions while accounting for sample structure including interactions between the genetic backgrounds of the two organisms. We demonstrate the applicability of ATOMM to a joint association study of quantitative disease resistance (QDR) in the Arabidopsis thaliana-Xanthomonas arboricola pathosystem. Our method uncovers a clear host-strain specificity in QDR and provides a powerful approach to identify genetic variants on both genomes that contribute to phenotypic variation.

Original languageEnglish (US)
Pages (from-to)E5440-E5449
JournalProceedings of the National Academy of Sciences of the United States of America
Issue number24
StatePublished - Jun 12 2018


  • Genome-wide association studies
  • Host-pathogen interaction
  • Mixed-effect models
  • Population structure
  • Statistical genetics

ASJC Scopus subject areas

  • General


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