Biology subjects

Burns, M. J.

Publications and source records attributed to Burns, M. J..

3 recordsLinked to original sources

bioRxiv2025

Maize kernel composition and morphology influences pericarp retention during nixtamalization

Background and ObjectivesPericarp retention during nixtamalization directly influences masa quality, affecting texture, machinability, and nutritional content of staple foods such as tortillas and chips. Despite its industrial relevance, the underlying kernel traits that govern pericarp retention remain poorly characterized. This study aimed to assess an existing staining method to quantify pericarp retention based on visual evaluation, and identify the compositional and morphological characteristics of maize kernels that most strongly predict pericarp retention during nixtamalization. FindingsStain-based scoring of pericarp retention showed moderate correlation with directly measured pericarp mass, achieving a Pearsons correlation coefficient of 0.77 in the rapid cook test and 0.56 in the benchtop cook test. Pearson correlation coefficients of 18 compositional and morphological traits range from -0.294 to 0.538. Ground kernel ash content and initial pericarp quantity were the most correlated and predictive variables associated with pericarp retention. ConclusionsStain-based methods lack the resolution necessary for quantitative pericarp assessment. Initial pericarp quantity and ground kernel ash content are likely key determinants of nixtamalization pericarp retention. Significance and NoveltyThis study provides a first assessment on the impacts of morphological and compositional variation for pericarp retention, which can be built upon to develop improved varieties and cooking methods for product optimization.

plant biology↗

bioRxiv2025

Optimizing population simulations to accurately parallel empirical data for digital breeding

The use of computational and data-driven approaches to accelerate and optimize breeding programs is becoming common practice among plant breeders. Simulations allow breeders to evaluate potential changes in breeding schemes in a time- and cost-efficient manner. However, accurately simulating traits that match empirical trait data remains a challenge. Here we tested if incorporating information about the genetic architecture from genome-wide association studies (GWAS) of maize agronomic traits with varying heritabilities into simulations can improve the concordance between simulated and empirical data in a population of hybrids developed from crosses of 333 maize recombinant inbred lines grown in four to eleven environments. Using at least 200 non-redundant top GWAS hits as causative variants, regardless of statistical significance, resulted in mean correlations between simulated and empirical trait data of 0.397 to 0.616 within environments and 0.610 to 0.915 across environments. Reducing the GWAS estimated marker effect sizes in the simulations further improved concordance with empirical data. This study provides valuable insights into methods for simulating more realistic phenotypes for digital breeding to parallel empirical trait distributions, and that these simulated traits are highly concordant with observed variance partitioning (i.e. genotype, environment, etc.), and genomic prediction performance. PLAIN LANGUAGE SUMMARYPlant breeders need to evaluate many possible changes to breeding schemes and resource allocation. One method of optimizing these changes is through computer-based simulations, which can be time- and cost-efficient. The genetic architecture of randomly simulated traits rarely parallels the genetic architecture of real-world traits, which can mislead the interpretation of such simulations. In this study, the genetic architecture of real-world traits was used to inform the simulation of digital traits to develop an optimized simulation pipeline for creating and assessing simulated breeding programs. The utility of these informed simulations is demonstrated through genomic prediction assessment, in which we show that the rank order of individuals and the distribution of traits is similar between simulated and real-world data. CORE IDEAS[bullet] Simulations allow breeders to evaluate potential changes in breeding schemes in a time- and cost-efficient manner. [bullet]Incorporating the genetic architecture of traits in simulations can improve the concordance with empirical data. [bullet]Reducing GWAS marker effect size improves correlation and distribution concordance of simulated and empirical data. [bullet]Increasing the number of causative variants beyond GWAS significance thresholds improves simulation performance. [bullet]Simulated data used in genomic prediction produces results similar to empirical genomic predicted data.

plant biology↗

bioRxiv2025

Genomic insights and breeding strategies for nixtamalization moisture content in hybrid maize

CORE IDEASO_LINixtamalization moisture content can be selected early in breeding programs using NIR spectroscopy. C_LIO_LIYield does not significantly correlate with nixtamalization moisture content in diverse or elite populations. C_LIO_LIAdditive and dominance gene action impact nixtamalization moisture content in hybrid maize. C_LIO_LIGenomic prediction can be used to assess nixtamalization moisture content early in hybrid maize breeding. C_LI Nixtamalization moisture content, a measure of the quantity of water absorbed during the nixtamalization of a grain such as maize, has a large impact on the end-quality of masa-based products. An application to predict nixtamalization moisture content from raw inbred and hybrid maize grain was recently developed, but its utility in a breeding context has not been assessed. Important breeding considerations for nixtamalization moisture content were assessed in diverse maize hybrids, modern commercial hybrids, and historically high-acreage hybrids grown in up to three environments across two years. This study demonstrated that nixtamalization moisture content is heavily influenced by growing conditions, but sufficient genetic variance is present to allow breeders to make gains from selection. Contrary to prior theory, there was no substantial correlation between nixtamalization moisture content and yield suggesting breeders can select for both traits without negatively impacting either trait. Both additive and dominant genetic action was observed and genomic prediction was able to predict nixtamalization moisture content in hybrids with an average Spearmans rank correlation coefficient greater than 0.441 and a root mean square error below 0.006. The findings here suggest that nixtamalization moisture content can be selected for early in breeding cycles, allowing breeders to develop improved food-grade maize germplasm without negatively impacting important traits such as yield. PLAIN LANGUAGE SUMMARYPlant breeders need to understand the biological mechanisms underlying a trait of interest to maximize the efficiency of their efforts. Nixtamalization moisture content is a highly complex trait that is determined by both genetic and environmental factors. In this study, nixtamalization moisture content was assessed in a diverse set of hybrid and inbred maize to understand the biological mechanisms underlying nixtamalization moisture content. The relationship between nixtamalization moisture content and yield was assessed, the genetic architecture and mode of gene action underlying nixtamalization moisture content were evaluated, and the efficacy of genomic prediction in assessing nixtamalization moisture content was determined. The findings of this study will allow breeders to create optimized breeding strategies for nixtamalization moisture content, thus improving the raw materials that are used to produce globally consumed products such as tortillas and tortilla chips.

plant biology↗