Combining data sets for maize root seedling traits increases the power of GWAS and genomic prediction accuracies
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2022-05-24
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Oxford University Press
Abstract
The identification of genomic regions associated with root traits and the genomic prediction of untested genotypes can increase the rate of genetic gain in maize breeding programs targeting roots traits. Here, we combined two maize association panels with different genetic backgrounds to identify SNPs significantly associated with root traits and through of genome-wide association study (GWAS) and to assess the potential of genomic prediction these traits in maize. For this, we evaluated 377 lines from the Ames and 302 from the BGEM (Backcrossed Germplasm Enhancement of Maize) panels in a Combined panel of 679 lines. The lines were genotyped with 232,460 SNPs, and four root traits were collected from 14-day old seedlings. We identified 30 SNPs significantly associated with root traits in the Combined panel, whereas only two and six SNPs were detected in the Ames and BGEM panels, respectively. Those 38 SNPs were in linkage disequilibrium with 35 candidate genes. In addition, we found higher prediction accuracy in the Combined panel than in the Ames or BGEM panels. We concluded that combining association panels appear to be a useful strategy to identify candidate genes associated with root traits in maize and improve the efficiency of the genomic prediction.
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This is a pre-copyedited, author-produced version of an article accepted for publication in Journal of Experimental Botany following peer review. The version of record: Tonello Zuffo, Leandro, Rodrigo Oliveira DeLima, and Thomas Lübberstedt. "Combining data sets for maize root seedling traits increases the power of GWAS and genomic prediction accuracies." Journal of Experimental Botany (2022) is available online at DOI: 10.1093/jxb/erac236. Copyright 2022 The Author(s). Posted with permission.