Genome-Wide Association Study and Genomic Selection for Plant Growth Habit in Peanuts Using the USDA Public Data

Abstract

Peanut ( Arachis hypogaea L.) production is valued at $1.28 billion annually in the USA. Plant growth habit can be used to determine plant population density and cultivation practices a given farmer uses. Erect plants are generally more compact and can be more densely planted unlike plants with more prostrate growth. The objectives of this study were to analyze publicly available datasets to identify single-nucleotide polymorphism (SNP) markers associated with plant growth habit in peanuts and to conduct genomic selection. A genome-wide association study (GWAS) was used to identify SNPs for growth habit type among 775 USDA peanut accessions. A total of 13,306 SNPs were used to conduct GWAS using five statistical models. The models used were single-marker regression, generalized linear model (PCA), generalized linear model (Q), mixed linear model (PCA), and mixed linear model (Q) and a total of 181, 1, 108, 1, and 10 SNPs were found associated with growth habit respectively. Based on this dataset, results showed that genomic selection can achieve up to 61% accuracy, depending on the training population size being used for the prediction. SNP AX-176821681 was found in all models. Gene ontology for this location shows an annotated gene, Araip.0F3YM, found 2485 bp upstream of this SNP and encodes for a peptidyl-prolyl cis-trans isomerase. To the best of our knowledge, this is the first report identifying molecular markers linked to plant growth habit type in peanuts. This finding suggests that a molecular marker can be developed to identify specific plant growth habits in peanuts, enabling early generation selection by peanut breeders.