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Recently Accepted Publications (page 3) July 2021
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This addresses USDA-ARS Research Goal: New bioinformatic tools developed for data analysis and mining, and to manage high throughput phenotypic and genotypic data and knowledge

Sharma, S., Pinson, S.R.M., Gealy, D. R., Edwards, J.D. 2021. Genomic prediction and QTL mapping of root system architecture and above-ground agronomic traits in rice (Oryza sativa L.) with a multi-trait index and Bayesian networks.  G3 Genes|Genomes|Genetics  (goes outside USDA site)

Roots are the means by which plants acquire the necessary water and nutrients for growth. A vigorous root system is essential for a high yielding crop variety.  However, we know very little about how root structure or “architecture” contributes to above-ground shoot growth and grain yield.   In this study, we used two gene mapping analysis methods to discover genetic marker associations with root structure traits predicted to impact overall plant growth and yield.  We first identified genes using data on individual traits, then used a multi-trait, machine learning model (a Bayesian network) that considered trait-to-trait associations as it identified marker-trait associations.  Genes identified using the network model explained how roots affect above-ground growth, while the genes identified using the individual traits were not only fewer in number, but also less informative of above-ground plant growth.  In addition, we developed a multi-trait genomic selection model and validated its ability to identify progeny with improved root architecture.  This research demonstrates a new approach that can be used to simultaneously select for numerous below-ground and above-ground traits using genomic information that may benefit rice cultivar development.

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