Genome-wide association for agro-morphological traits in a triploid banana population with large chromosome rearrangements

Abstract

Banana breeding is hampered by the very low fertility of domesticated bananas and the lack of knowledge about the genetic determinism of agronomic traits. We analysed a breeding population of 2723 triploid hybrids resulting from crosses between diploid and tetraploid Musa acuminata parents, which was evaluated over three successive crop cycles for 24 traits relating to yield components and plant, bunch, and fruit architectures. A subset of 1129 individuals was genotyped by sequencing, revealing 205 612 single-nucleotide polymorphisms (SNPs). Most parents were heterozygous for one or several large reciprocal chromosomal translocations, which are known to impact recombination and chromosomal segregation. We applied two linear mixed models to detect associations between markers and traits: (i) a standard model with a kinship calculated using all SNPs and (ii) a model with chromosome-specific kinships that aims at recovering statistical power at alleles carried by long non-recombined haplotypic segments. For 23 of the 24 traits, we identified one to five significant quantitative trait loci (QTLs) for which the origin of favourable alleles could often be determined amongst the main ancestral contributors to banana cultivars. Several QTLs, located in the rearranged regions, were only detected using the second model. The resulting QTL landscape represents an important resource to support breeding programmes. The proposed strategy for recovering power at SNPs carried by long non-recombined rearranged haplotypic segments is an important methodological advance for future association studies in banana and other species affected by chromosomal rearrangements.

Acknowledgements

This research was supported by the Centre de Coopération Internationale en Recherche Agronomique pour le Développement (CIRAD) and Genoscope (from French Alternative Energies and Atomic Energy Commission (CEA)), the European Agricultural Fund for Rural Development (FEADER) and Région Guadeloupe through ‘Plan Banane Durable 1′ and ‘Plan Banane Durable 2′ programmes, the France Génomique (ANR-10-INBS-09-08) project DYNAMO, the CGIAR Research Programme on Roots, Tubers and Bananas and the Agropolis Fondation (ID 1504-006) ‘GenomeHarvest’ project through the French Investissements d’Avenir programme (Labex Agro: ANR- 10-LABX-0001-01). This work has been realized with the support of MESO@LR-Platform at the University of Montpellier and the technical support of the bioinformatics group of the UMR AGAP Institute, member of the French Institute of Bioinformatics (IFB) - South Green Bioinformatics Platform. We thank Sébastien Ricci for his thoughts on the experimental design. We thank Christian Vingadassalon, Frédéric Vingadassalon, Raymond Crispin, Alexin Clotaire, Ginot Karramkan, Gérard Numitor, and Nathanaelle Leclerc for their contributions to the maintenance of the experimental set-up and phenotyping. We thank Franc-Christophe Baurens for his biomolecular support. We thank the GPTR (Great regional technical platform) of Montpellier core facility for its technical support.

Author contributions

J.Y.H., G.M., F.S., and A.D. conceived the study and contributed to funding acquisition. F.S., C.G., F.M., Cl.M., J.M.D., F.L., Ca.M., and J.C.E., generated breeding material, implemented the experimental design, and acquired phenotypic data. C.H., C.C., and GENOSCOPE acquired the genotypic data. S.R., L.T., J.Y.H., and G.M. performed GWAS analyses, interpreted results, and wrote the first draft, which was reviewed and edited by all authors. All authors read and approved the final manuscript.

Data availability statement

All phenotypic and genotypic data underlying this study are available from the following CIRAD Dataverse repository: https://doi.org/10.18167/DVN1/FTM7GC, including the vcf file ‘Genotyping_1129hybrids_200Ksnps.vcf.gz’, the pedigree information and BLUPs for all traits in ‘Phenotyping_2727hybrids_24traits.tsv’, and GWAS summary statistics for the K and Kc in archives ‘GWAS_summary_stats_K_model.tar.gz’ and ‘GWAS_summary_stats_Kc_model.tar.gz’, respectively. The vcf file is also available on the exploration and visualization tool Gigwa: https://gigwa.cgiar.org/gigwa/?module=GWAS_agromorphotraits. The Illumina data of the progenies are available in the SRA database (PRJNA1106767).

Conflict of interests

The authors declare no conflict of interest.

Supplementary Data

Supplementary data is available at Horticulture Research online.

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