Genomic prediction of monolignol composition in maize hybrids

Maize (Zea mays L.) is cultivated on over 93 million acres in the U.S. and produces substantial crop residues, with maize stover— the biomass left after grain harvest—emerging as a potential resource for bioethanol and other chemical products. Lignin, a complex polymer that contributes to abiotic and biotic stress resistance, presents a bottleneck in the conversion of stover to valuable products due to its resistance to breakdown. Given the importance of lignin for both plant health and economic value, we aim to test the accuracy of genomic prediction in a hybrid population for lignin content and monolignol ratios. We analyzed stover samples from 443 hybrids in the 2023 Genomes to Fields project using pyrolysis-molecular beam mass spectroscopy, a high-throughput method of quantifying lignin and monolignol composition. Two genomic prediction models—BayesB and GBLUP—were evaluated using five-fold cross-validation. Our results indicate that genomic prediction can be used with moderate accuracy to select for lignin composition. The inclusion of a priori knowledge in genomic prediction models did not improve the accuracy. This suggests that lignin may be more of a quantitative trait than a qualitative one, with many markers each having a very small effect on the phenotype, rather than a few markers having a large effect each. More research will be needed to better understand the genetic architecture of lignin composition in maize stover. Changes in lignin traits did not negatively impact yield, suggesting potential for optimizing lignin content without compromising productivity. These findings provide valuable insights for breeding programs focused on enhancing maize stover utility.

Working in the Gore Lab has been a transformative experience for me this summer. I have gained a deep understanding of data analysis techniques that are crucial for my ongoing research in plant genetics. This experience has not only expanded my skill set but also provided me with a new perspective on how computational tools can be leveraged to solve complex biological problems. The mentorship and support I received in the lab were invaluable, fostering both my professional and personal growth. This experience has reaffirmed my passion for research and strengthened my resolve to pursue a PhD, where I can further explore genetics, genomics, and the plant sciences.