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Single primer enrichment technology (SPET) is a novel high-throughput genotyping method based on short-read sequencing of specific genomic regions harboring polymorphisms. SPET provides an efficient and reproducible method for genotyping target loci, overcoming the limits associated with other reduced representation library sequencing methods that are based on a random sampling of genomic loci. The possibility to sequence regions surrounding a target SNP allows the discovery of thousands of closely linked, novel SNPs. In this work, we report the design and application of the first SPET panel in lettuce, consisting of 41,547 probes spanning the whole genome and designed to target both coding (~96%) and intergenic (~4%) regions. A total of 81,531 SNPs were surveyed in 160 lettuce accessions originating from a total of 10 countries in Europe, America, and Asia and representing 10 horticultural types. Model ancestry population structure clearly separated the cultivated accessions (Lactuca sativa) from accessions of its presumed wild progenitor (L. serriola), revealing a total of six genetic subgroups that reflected a differentiation based on cultivar typology. Phylogenetic relationships and principal component analysis revealed a clustering of butterhead types and a general differentiation between germplasm originating from Western and Eastern Europe. To determine the potentiality of SPET for gene discovery, we performed genome-wide association analysis for main agricultural traits in L. sativa using six models (GLM naive, MLM, MLMM, CMLM, FarmCPU, and BLINK) to compare their strength and power for association detection. Robust associations were detected for seed color on chromosome 7 at 50 Mbp. Colocalization of association signals was found for outer leaf color and leaf anthocyanin content on chromosome 9 at 152 Mbp and on chromosome 5 at 86 Mbp. The association for bolting time was detected with the GLM, BLINK, and FarmCPU models on chromosome 7 at 164 Mbp. Associati

Investigating crop diversity through genome-wide association studies (GWAS) on core collections helps in deciphering the genetic determinants of complex quantitative traits. Using the G2P-SOL project world collection of 10 038 wild and cultivated Capsicum accessions from 10 major genebanks, we assembled a core collection of 423 accessions representing the known genetic diversity. Since complex traits are often highly dependent upon environmental variables and genotype-by-environment (G × E) interactions, multi-environment GWAS with a 10 195-marker genotypic matrix were conducted on a highly diverse subset of 350 Capsicum annuum accessions, extensively phenotyped in up to six independent trials from five climatically differing countries. Environment-specific and multi-environment quantitative trait loci (QTLs) were detected for 23 diverse agronomic traits. We identified 97 candidate genes potentially implicated in 53 of the most robust and high-confidence QTLs for fruit flavor, color, size, and shape traits, and for plant productivity, vigor, and earliness traits. Investigating the genetic architecture of agronomic traits in this way will assist the development of genetic markers and pave the way for marker-assisted selection. The G2P-SOL pepper core collection will be available upon request as a unique and universal resource for further exploitation in future gene discovery and marker-assisted breeding efforts by the pepper community.

Recently, there has been a substantial increase in high-throughput technologies that generate highly complex large datasets for use in the sciences. Plant breeding and genetics have 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 generates a situation of strategic uncertainty. We set out to compare different methods of genomic and phenomic selection in the Capsicum core collection, devel-oped 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 1 selection method. Combin-ing classical and multispectral phenotyping methods produced better selection results than either method alone. When each method was conducted without being informed by the other, similar results were obtained. Our weighted rank-sum selection index identified 10 entries across environments that show heat tolerance, 8 of which are also selected within heat environments. This suggests that different breeding pro-grams can reach similar results despite having different logistical constraints. Our case study within pepper germplasm using phenomic and genomic data exhibits the potential to compensate for the 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.