TY - JOUR
T1 - Two-way mixed-effects methods for joint association analysis using both host and pathogen genomes
AU - Wang, Miaoyan
AU - Roux, Fabrice
AU - Bartoli, Claudia
AU - Huard-Chauveau, Carine
AU - Meyer, Christopher
AU - Lee, Hana
AU - Roby, Dominique
AU - McPeek, Mary Sara
AU - Bergelson, Joy
N1 - Funding Information:
ACKNOWLEDGMENTS. This study is supported in part by the Franco-American Fulbright Commission (Program Nord Pas de Calais), the Région Midi-Pyrénées (project Accueil de nouvelles équipes d’excellence), the Agence Nationale de la Recherche (ANR) projects RIPOSTE (ANR-14-CE19-0024-01) and the Laboratoire d’Excellence (LABEX) Towards a Unified Theory of Biotic Interactions: Role of Environmental Pertubations (TULIP) (ANR-10-LABX-41 and ANR-11-IDEX-0002-02) (to F.R., C.B., C.H.-C., and D.R.), and National Institutes of Health Grants R01 HG001645 (to M.S.M.) and R01 GM083068 (to J.B.).
Publisher Copyright:
© 2018 National Academy of Sciences. All rights reserved.
PY - 2018/6/12
Y1 - 2018/6/12
N2 - 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.
AB - 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.
KW - Genome-wide association studies
KW - Host-pathogen interaction
KW - Mixed-effect models
KW - Population structure
KW - Statistical genetics
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U2 - 10.1073/pnas.1710980115
DO - 10.1073/pnas.1710980115
M3 - Article
C2 - 29848634
AN - SCOPUS:85048595619
VL - 115
SP - E5440-E5449
JO - Proceedings of the National Academy of Sciences of the United States of America
JF - Proceedings of the National Academy of Sciences of the United States of America
SN - 0027-8424
IS - 24
ER -