Exploration of High-Throughput Data for Heat Tolerance Selection in Capsicum annuum

International audience

Inglés. Recently there has been a substantial increase in high-throughput technologies which generate highly complex large datasets for use in the sciences. Plant breeding and genetics is one field that has benefited from this data explosion where many public and private institutions now implement genomic and phenomic data to predict performance thus informing germplasm selection. However, the multitude of methodologies and data generate a situation of strategic uncertainty. We set out to compare different methods of genomic and phenomic selection in the Capsicum annuum core collection, developed through the G2P-Sol project, producing a combination of unique and similar selected genotypes for heat tolerance. Combined, the methods tested identified a total of 33 genotypes that show tremendous promise for use as parents in heat tolerance breeding; with 13 of these being present in more than one selection method. Combining classical and multispectral phenotyping methods produced better selection results and when separated, comparable results are obtained. Our weighted rank sum index identified 10 genotypes across environments that show heat tolerance, 8 of which are also selected within heat environments. This suggests that different breeding programs can reach similar results despite having different logistical constraints. Therefore, our case study into selection of pepper germplasm with phenomic and genomic data exhibits the potential to compensate for dearth of germplasm knowledge with high-throughput data as well as the converse, to compensate for logistical or financial constraint to new technologies with breeder knowledge.