Biology subjects

Bornowski, N.

Publications and source records attributed to Bornowski, N..

3 recordsLinked to original sources

bioRxiv2022

Leveraging prior biological knowledge improves prediction of tocochromanols in maize grain

With an essential role in human health, tocochromanols are mostly obtained by consuming seed oils; however, the vitamin E content of the most abundant tocochromanols in maize grain is low. Several large-effect genes with cis-acting variants affecting mRNA expression are mostly responsible for tocochromanol variation in maize grain, with other relevant associated quantitative trait loci (QTL) yet to be fully resolved. Leveraging existing genomic and transcriptomic information for maize inbreds could improve prediction when selecting for higher vitamin E content. Here, we first evaluated a multikernel genomic best linear unbiased prediction (MK-GBLUP) approach for modeling known QTL in the prediction of nine tocochromanol grain phenotypes (12-21 QTL per trait) within and between two panels of 1,462 and 242 maize inbred lines. On average, MK-GBLUP models improved predictive abilities by 7.0 to 13.6% when compared to GBLUP. In a second approach with a subset of 545 lines from the larger panel, the highest average improvement in predictive ability relative to GBLUP was achieved with a multi-trait GBLUP model (15.4%) that had a tocochromanol phenotype and transcript abundances in developing grain for a few large-effect candidate causal genes (1-3 genes per trait) as multiple response variables. Taken together, our study illustrates the enhancement of prediction models when informed by existing biological knowledge pertaining to QTL and candidate causal genes. Core IdeasO_LIWith varying levels of vitamin E activity, tocochromanols found in maize grain are essential for human health C_LIO_LISelecting for higher vitamin E content in maize grain can be enhanced with genomic prediction C_LIO_LIPrediction models leveraging existing biological knowledge were evaluated in two panels of maize inbred lines C_LIO_LIMultikernel prediction models based on previously identified QTL improved predictive ability C_LIO_LIA multi-trait prediction model that had transcript abundances of a few large-effect causal genes performed the best C_LI

genetics↗

bioRxiv2022

Combining GWAS and TWAS to identify candidate causal genes for tocochromanol levels in maize grain

Tocochromanols (tocopherols and tocotrienols, collectively vitamin E) are lipid-soluble antioxidants important for both plant fitness and human health. The main dietary sources of vitamin E are seed oils that often accumulate high levels of tocopherol isoforms with lower vitamin E activity. The tocochromanol biosynthetic pathway is conserved across plant species but an integrated view of the genes and mechanisms underlying natural variation of tocochromanol levels in seed of most cereal crops remains limited. To address this issue, we utilized the high mapping resolution of the maize Ames panel of [~]1,500 inbred lines scored with 12.2 million single-nucleotide polymorphisms to generate metabolomic (mature grain tocochromanols) and transcriptomic (developing grain) data sets for genetic mapping. By combining results from genome- and transcriptome-wide association studies, we identified a total of 13 candidate causal gene loci, including five that had not been previously associated with maize grain tocochromanols: four biosynthetic genes (arodeH2 paralog, dxs1, vte5, and vte7) and a plastid S-adenosyl methionine transporter (samt1). Expression quantitative trait locus (eQTL) mapping of these 13 gene loci revealed that they are predominantly regulated by cis-eQTL. Through a joint statistical analysis, we implicated cis-acting variants as responsible for co-localized eQTL and GWAS association signals. Our multi-omics approach provided increased statistical power and mapping resolution to enable a detailed characterization of the genetic and regulatory architecture underlying tocochromanol accumulation in maize grain and provided insights for ongoing biofortification efforts to breed and/or engineer vitamin E and antioxidant levels in maize and other cereals.

genetics↗

bioRxiv2020

Composite modeling of leaf shape across shoots discriminates Vitis species better than individual leaves

Premise of studyLeaf morphology is dynamic, continuously deforming during leaf expansion and among leaves within a shoot. We measured leaf morphology from over 200 vines over four years, and modeled changes in leaf shape along the shoot to determine if a composite "shape of shapes" can better capture variation and predict species identity compared to individual leaves. MethodsUsing homologous universal landmarks found in grapevine leaves, we modeled various morphological features as a polynomial function of leaf node. The resulting functions are used to reconstruct modeled leaf shapes across shoots, generating composite leaves that comprehensively capture the spectrum of possible leaf morphologies. ResultsWe found that composite leaves are better predictors of species identity than individual leaves from the same plant. We were able to use composite leaves to predict species identity of previously unassigned vines, which were verified with genotyping. DiscussionObservations of individual leaf shape fail to capture the true diversity between species. Composite leaf shape--an assemblage of modeled leaf snapshots across the shoot--is a better representation of the dynamic and essential shapes of leaves, as well as serving as a better predictor of species identity than individual leaves.

plant biology↗