Bioinformatic Extraction of Functional Genetic Diversity from Heterogeneous Germplasm Collections for Crop Improvement

Abstract

Efficient utilization of genetic variation in plant germplasm collections is impeded by large collection size, uneven characterization of traits, and unpredictable apportionment of allelic diversity among heterogeneous accessions. Distributing compact subsets of the complete collection that contain maximum allelic diversity at functional loci of interest could streamline conventional and precision breeding. Using heterogeneous population samples from Arabidopsis, Populus and sorghum, we show that genomewide single nucleotide polymorphism (SNP) data permits the capture of 3–78 fold more haplotypic diversity in subsets than geographic or environmental data, which are commonly used surrogate predictors of genetic diversity. Using a large genomewide SNP data set from landrace sorghum, we demonstrate three bioinformatic approaches to extract functional genetic diversity. First, in a “candidate gene” approach, we assembled subsets that maximized haplotypic diversity at 135 putative lignin biosynthetic loci, relevant to biomass breeding programs. Secondly, we applied a keyword search against the Gene Ontology to identify 1040 regulatory loci and assembled subsets capturing genomewide regulatory gene diversity, a general source of phenotypic variation. Third, we developed a machine-learning approach to rank semantic similarity between Gene Ontology term definitions and the textual content of scientific publications on crop adaptation to climate, a complex breeding objective. We identified 505 sorghum loci whose defined function is semantically-related to climate adaptation concepts. The assembled subsets could be used to address climatic pressures on sorghum production. To face impending agricultural challenges and foster rapid extraction and use of novel genetic diversity resident in heterogeneous germplasm collections, whole genome resequencing efforts should be prioritized.

A pan-genome data structure induced by pooled sequencing facilitates variant mining in heterogeneous germplasm

“…agronomic loci) can deviate substantially from the predominantly neutral loci used for population structure analyses (Reeves et al 2012). Poolseq pangenome data structures enable query and selection of accessions without explicit regard to population structure, accession provenance, or passport information, which may not be meaningful predictors of the occurrence of desirable sequence variation (Reeves and Richards 2018;Reeves et al 2020).…”

“…Additionally, for conventional breeding projects, the catalog could support prediction of the frequency of particular traits or trait values recoverable from accessions based on the frequency of haplotypic variants of de ned function segregating therein. For traits that are wellunderstood genetically, this could enable initial selection of accessions for integration into a breeding program bioinformatically, instead of via extensive growouts and phenotyping or imprecise suggestions from passport data and surrogate predictors of genetic diversity (Reeves et al 2020).…”

DNA Variation in a Diversity Panel of Tomato Genetic Resources

“…A similar approach would be appropriate for wild tomato species [e.g., Solanum chilense (B€ ondel et al, 2015)] or feral and landrace populations of cultivated tomato. Metabolomics represents another method to characterize germplasm based on functional diversity (Reeves et al, 2020). Many hundreds of tomato quantitative metabolic loci for fruit and yield traits have been documented (Schauer et al, 2006;Tohge et al, 2020).…”

Envisaging an Effective Global Long-Term Agrobiodiversity Conservation System That Promotes and Facilitates Use

“…Many genebanks have already identified traditional core or mini-core subsets intended simply to make the task of phenotyping large collections more manageable [30]. Alternatively, accessions have been selected based on specific user-defined criteria (usually combinations of passport, phenotypic and genetic data), including using machine learning software such as the Focused Identification of Germplasm Strategy (FIGS) developed by ICARDA to create subsets that are more likely to contain adaptive traits that users want [31,32]. One of the reasons for higher distribution figures from genebanks for some crops, such as rice from IRRI, over the past decade has been the increased demand for subsets of accessions that have been sequenced [33][34][35][36][37].…”