AI Article Synopsis

  • - Tocochromanols, a key component of vitamin E, are primarily found in maize, prompting research into biofortifying maize to enhance nutrition through breeding practices that incorporate exotic germplasm.
  • - The study evaluated genomic prediction accuracy for tocochromanol traits using two maize populations, the adapted Ames Diversity Panel (AP) and the exotic-derived Backcrossed Germplasm Enhancement of Maize (BGEM), achieving high accuracies (up to 0.79) with gBLUP within populations but low accuracy when predicting BGEM from AP data.
  • - By employing optimal training population design methods, such as FURS and MaxCD, researchers improved prediction accuracies compared to random training sets, highlighting the necessity of

Article Abstract

Tocochromanols (vitamin E) are an essential part of the human diet. Plant products, including maize (Zea mays L.) grain, are the major dietary source of tocochromanols; therefore, breeding maize with higher vitamin content (biofortification) could improve human nutrition. Incorporating exotic germplasm in maize breeding for trait improvement including biofortification is a promising approach and an important research topic. However, information about genomic prediction of exotic-derived lines using available training data from adapted germplasm is limited. In this study, genomic prediction was systematically investigated for nine tocochromanol traits within both an adapted (Ames Diversity Panel [AP]) and an exotic-derived (Backcrossed Germplasm Enhancement of Maize [BGEM]) maize population. Although prediction accuracies up to 0.79 were achieved using genomic best linear unbiased prediction (gBLUP) when predicting within each population, genomic prediction of BGEM based on an AP training set resulted in low prediction accuracies. Optimal training population (OTP) design methods fast and unique representative subset selection (FURS), maximization of connectedness and diversity (MaxCD), and partitioning around medoids (PAM) were adapted for inbreds and, along with the methods mean coefficient of determination (CDmean) and mean prediction error variance (PEVmean), often improved prediction accuracies compared with random training sets of the same size. When applied to the combined population, OTP designs enabled successful prediction of the rest of the exotic-derived population. Our findings highlight the importance of leveraging genotype data in training set design to efficiently incorporate new exotic germplasm into a plant breeding program.

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Source
http://dx.doi.org/10.1002/tpg2.20286DOI Listing

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