Maize (Zea mays L.) has been a focus of scientific research and breeding for over a century. It is also one of the most economically important crops in the world, with a value of approximately US$50 billion per year in the United States alone. Additionally, maize has long been the model species of choice for the study and exploitation of hybrid vigor, and it continues to be one of the world's most efficient converters of photosynthetic energy into starch. This review discusses the history and future of maize predictive breeding in the context of both genotype centric methods, and those focusing on genotype × environment × management interactions. Current prediction challenges are highlighted, as well as important advances in technology, methods, datasets, interdisciplinary collaborations, and scientific culture that will enable accelerated progress in predictive maize (and other crop species) breeding for years to come.

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DNA sequencing technology has advanced so quickly, identifying key functional regions using evolutionary approaches is required to understand how those genomes work. This research develops a sensitive sequence alignment approach to identify functional constrained non-coding sequences in the Andropogoneae tribe. The grass tribe Andropogoneae contains several crop species descended from a common ancestor ~18 million years ago. Despite broadly similar phenotypes, they have tremendous genomic diversity with a broad range of ploidy levels and transposons. These features make Andropogoneae a powerful system for studying conserved non-coding sequence (CNS), here we used it to understand the function of CNS in maize. We find that 86% of CNS comprise known genomic elements e.g., cis-regulatory elements, chromosome interactions, introns, several transposable element superfamilies, and are linked to genomic regions related to DNA replication initiation, DNA methylation and histone modification. In maize, we show that CNSs regulate gene expression and variants in CNS are associated with phenotypic variance, and rare CNS absence contributes to loss of gene expression. Furthermore, we find the evolution of CNS is associated with the functional diversification of duplicated genes in the context of the maize subgenomes. Our results provide a quantitative understanding of constrained non-coding elements and identify functional non-coding variation in maize.