Tracking crop varieties using genotyping-by-sequencing markers: a case study using cassava (Manihot esculenta Crantz).

BACKGROUND: Accurate identification of crop cultivars is crucial in assessing the impact of crop improvement research outputs. Two commonly used identification approaches, elicitation of variety names from farmer interviews and morphological plant descriptors, have inherent uncertainty levels. Genotyping-by-sequencing (GBS) was used in a case study as an alternative method to track released varieties in farmers' fields, using cassava, a clonally propagated root crop widely grown in the tropics, and often disseminated through extension services and informal seed systems. A total of 917 accessions collected from 495 farming households across Ghana were genotyped at 56,489 SNP loci along with a "reference library" of 64 accessions of released varieties and popular landraces. RESULTS: Accurate cultivar identification and ancestry estimation was accomplished through two complementary clustering methods: (i) distance-based hierarchical clustering; and (ii) model-based maximum likelihood admixture analysis. Subsequently, 30% of the identified accessions from farmers' fields were matched to specific released varieties represented in the reference library. ADMIXTURE analysis revealed that the optimum number of major varieties was 11 and matched the hierarchical clustering results. The majority of the accessions (69%) belonged purely to one of the 11 groups, while the remaining accessions showed two or more ancestries. Further analysis using subsets of SNP markers reproduced results obtained from the full-set of markers, suggesting that GBS can be done at higher DNA multiplexing, thereby reducing the costs of variety fingerprinting. A large proportion of discrepancy between genetically unique cultivars as identified by markers and variety names as elicited from farmers were observed. Clustering results from ADMIXTURE analysis was validated using the assumption-free Discriminant Analysis of Principal Components (DAPC) method. CONCLUSION: We show that genome-wide SNP markers from increasingly affordable GBS methods coupled with complementary cluster analysis is a powerful tool for fine-scale population structure analysis and variety identification. Moreover, the ancestry estimation provides a framework for quantifying the contribution of exotic germplasm or older improved varieties to the genetic background of contemporary improved cultivars.

The groundnut improvement network for Africa (GINA) germplasm collection: a unique genetic resource for breeding and gene discovery.

Cultivated peanut or groundnut (Arachis hypogaea L.) is a grain legume grown in many developing countries by smallholder farmers for food, feed, and/or income. The speciation of the cultivated species, that involved polyploidization followed by domestication, greatly reduced its variability at the DNA level. Mobilizing peanut diversity is a prerequisite for any breeding program for overcoming the main constraints that plague production and for increasing yield in farmer fields. In this study, the Groundnut Improvement Network for Africa assembled a collection of 1,049 peanut breeding lines, varieties, and landraces from 9 countries in Africa. The collection was genotyped with the Axiom_Arachis2 48K SNP array and 8,229 polymorphic single nucleotide polymorphism (SNP) markers were used to analyze the genetic structure of this collection and quantify the level of genetic diversity in each breeding program. A supervised model was developed using dapc to unambiguously assign 542, 35, and 172 genotypes to the Spanish, Valencia, and Virginia market types, respectively. Distance-based clustering of the collection showed a clear grouping structure according to subspecies and market types, with 73% of the genotypes classified as fastigiata and 27% as hypogaea subspecies. Using STRUCTURE, the global structuration was confirmed and showed that, at a minimum membership of 0.8, 76% of the varieties that were not assigned by dapc were actually admixed. This was particularly the case of most of the genotype of the Valencia subgroup that exhibited admixed genetic heritage. The results also showed that the geographic origin (i.e. East, Southern, and West Africa) did not strongly explain the genetic structure. The gene diversity managed by each breeding program, measured by the expected heterozygosity, ranged from 0.25 to 0.39, with the Niger breeding program having the lowest diversity mainly because only lines that belong to the fastigiata subspecies are used in this program. Finally, we developed a core collection composed of 300 accessions based on breeding traits and genetic diversity. This collection, which is composed of 205 genotypes of fastigiata subspecies (158 Spanish and 47 Valencia) and 95 genotypes of hypogaea subspecies (all Virginia), improves the genetic diversity of each individual breeding program and is, therefore, a unique resource for allele mining and breeding.