Reducing herbivory in mixed planting by genomic prediction of neighbor effects in the field

• Published in: Nature communications Vol. 15; no. 1; pp. 8467 - 14
• Main Authors: Sato, Yasuhiro, Shimizu-Inatsugi, Rie, Takeda, Kazuya, Schmid, Bernhard, Nagano, Atsushi J., Shimizu, Kentaro K.
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 07.10.2024; Nature Publishing Group; Nature Portfolio
• Subjects: 38/43; 45/23; 631/208/205/2138; 631/449/2668; 704/158/2458; 704/158/2464; 704/158/670; Animals; Arabidopsis - genetics; Arabidopsis thaliana; Biodiversity; Crop yield; Damage detection; Gene polymorphism; Genome, Plant; Genome-wide association studies; Genome-Wide Association Study; Genomes; Genomics; Genomics - methods; Genotype; Genotype & phenotype; Genotypes; Herbivory; Humanities and Social Sciences; Ising model; Mixtures; Monoculture; multidisciplinary; Natural enemies; Plant Defense Against Herbivory - genetics; Plant resistance; Plant species; Planting; Polymorphism; Polymorphism, Single Nucleotide; Population genetics; Predictions; Science; Science (multidisciplinary)
• ISSN: 2041-1723; 2041-1723
• DOI: 10.1038/s41467-024-52374-7

Genetically diverse populations can increase plant resistance to natural enemies. Yet, beneficial genotype pairs remain elusive due to the occurrence of positive or negative effects of mixed planting on plant resistance, respectively called associational resistance or susceptibility. Here, we identify key genotype pairs responsible for associational resistance to herbivory using the genome-wide polymorphism data of the plant species Arabidopsis thaliana . To quantify neighbor interactions among 199 genotypes grown in a randomized block design, we employ a genome-wide association method named “Neighbor GWAS” and genomic prediction inspired by the Ising model of magnetics. These analyses predict that 823 of the 19,701 candidate pairs can reduce herbivory in mixed planting. We planted three pairs with the predicted effects in mixtures and monocultures, and detected 18–30% reductions in herbivore damage in the mixed planting treatment. Our study shows the power of genomic prediction to assemble genotype mixtures with positive biodiversity effects. Identifying pairs of genotypes that perform better in mixture than monoculture is important for increasing crop yields. Using the model species Arabidopsis thaliana , this study provides a proof of principle of how such beneficial genotype pairs could be found using genome-wide association studies.