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Open AccessArticle

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

1
United States Department of Agriculture, Agricultural Research Service, National Laboratory for Genetic Resources Preservation, 1111 South Mason Street, Fort Collins, CO 80521, USA
2
United States Department of Agriculture, Agricultural Research Service, Wheat, Sorghum, and Forage Research Unit, 251 Filley Hall, University of Nebraska, Lincoln, NE 68583, USA
*
Author to whom correspondence should be addressed.
Agronomy 2020, 10(4), 593; https://doi.org/10.3390/agronomy10040593
Received: 12 March 2020 / Revised: 9 April 2020 / Accepted: 15 April 2020 / Published: 22 April 2020
(This article belongs to the Special Issue Bioinformatics Applied to Genetic Improvement of Crop Species)
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. View Full-Text
Keywords: ex situ conservation; core collection; Gene Ontology; genome wide association; machine learning; natural language processing; SNP ex situ conservation; core collection; Gene Ontology; genome wide association; machine learning; natural language processing; SNP
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MDPI and ACS Style

Reeves, P.A.; Tetreault, H.M.; Richards, C.M. Bioinformatic Extraction of Functional Genetic Diversity from Heterogeneous Germplasm Collections for Crop Improvement. Agronomy 2020, 10, 593. https://doi.org/10.3390/agronomy10040593

AMA Style

Reeves PA, Tetreault HM, Richards CM. Bioinformatic Extraction of Functional Genetic Diversity from Heterogeneous Germplasm Collections for Crop Improvement. Agronomy. 2020; 10(4):593. https://doi.org/10.3390/agronomy10040593

Chicago/Turabian Style

Reeves, Patrick A.; Tetreault, Hannah M.; Richards, Christopher M. 2020. "Bioinformatic Extraction of Functional Genetic Diversity from Heterogeneous Germplasm Collections for Crop Improvement" Agronomy 10, no. 4: 593. https://doi.org/10.3390/agronomy10040593

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