An a-maize-ing set of genomes: Maize is an important crop cultivated worldwide. As maize spread across the world, selection for local environments resulted in variation, but the impact on differences between the genome has not been quantified. By producing high-quality genomic sequences of the 26 lines used in the maize nested association mapping panel, Hufford et al . map important traits and demonstrate the diversity of maize. Examining RNA and methylation of genes across accessions, the authors identified a core set of maize genes. Beyond this core set, comparative analysis across lines identified high levels of variation in the total set of genes, the maize pan-genome. The value of this resource was further exemplified by mapping quantitative traits of interest, including those related to pathogen resistance. —LMZ

Once considered nonfunctional, pseudogene transcripts are now known to provide valuable information for cancer susceptibility, including head and neck cancer (HNC), a serious health problem worldwide, with about 50% unimproved overall survival over the last decades. The present review focuses on the role of pseudogene transcripts involved in HNC risk and prognosis. We combined current literature and in silico analyses from The Cancer Genome Atlas (TCGA) database to identify the most deregulated pseudogene transcripts in HNC and their genetic variations. We then built a co-expression network and performed gene ontology enrichment analysis to better understand the pseudogenes’ interactions and pathways in HNC. In the literature, few pseudogenes have been studied in HNC. Our in silico analysis identified 370 pseudogene transcripts associated with HNC, where SPATA31D5P, HERC2P3, SPATA31C2, MAGEB6P1, SLC25A51P1, BAGE2, DNM1P47, SPATA31C1, ZNF733P and OR2W5 were found to be the most deregulated and presented several genetic alterations. NBPF25P, HSP90AB2P, ZNF658B and DPY19L2P3 pseudogenes were predicted to interact with 12 genes known to participate in HNC, DNM1P47 was predicted to interact with the TP53 gene, and HLA-H pseudogene was predicted to interact with HLA-A and HLA-B genes. The identified pseudogenes were associated with cancer biology pathways involving cell communication, response to stress, cell death, regulation of the immune system, regulation of gene expression, and Wnt signaling. Finally, we assessed the prognostic values of the pseudogenes with the Kaplan–Meier Plotter database, and found that expression of SPATA31D5P, SPATA31C2, BAGE2, SPATA31C1, ZNF733P and OR2W5 pseudogenes were associated with patients’ survival. Due to pseudogene transcripts’ potential for cancer diagnosis, progression, and as therapeutic targets, our study can guide new research to HNC understanding and development of new target therapies.

Maize exhibits tremendous gene expression variation between different lines. Complementation of diverse gene expression patterns in hybrids could play an important role in the manifestation of heterosis. In this study, we used transcriptome data of five different tissues from 33 maize inbreds and 89 hybrids (430 samples in total) to survey the global gene expression landscape of F ₁ -hybrids relative to their inbred parents. Analysis of this data set revealed that single parent expression (SPE), which is defined as gene expression in only one of the two parents, while commonly observed, is highly genotype- and tissue-specific. Genes that have SPE in at least one pair of inbreds also tend to be tissue-specific. Genes with SPE caused by genomic presence/absence variation (PAV SPE) are much more frequently expressed in hybrids than genes that are present in the genome of both inbreds, but expressed in only a single-parent (non-PAV SPE) (74.7% vs. 59.7%). For non-PAV SPE genes, allele specific expression was used to investigate whether parental alleles not expressed in the inbred line (“silent allele”) can be actively transcribed in the hybrid. We found that expression of the silent allele in the hybrid is relatively rare (∼6.3% of non-PAV SPE genes), but is observed in almost all hybrids and tissues. Non-PAV SPE genes with expression of the silent allele in the hybrid are more likely to exhibit above high-parent expression level in the hybrid than those that do not express the silent allele. Finally, both PAV SPE and non-PAV SPE genes are highly enriched for being classified as non-syntenic, but depleted for curated genes with experimentally determined functions. This study provides a more comprehensive understanding of the potential role of non-PAV SPE and PAV SPE genes in heterosis.

Breeders commonly use genetic markers to predict the performance of untested individuals as a way to improve the efficiency of breeding programs. These genomic prediction models have almost exclusively used single nucleotide polymorphisms (SNPs) as their source of genetic information, even though other types of markers exist, such as structural variants (SVs). Given that SVs are associated with environmental adaptation and not all of them are in linkage disequilibrium to SNPs, SVs have the potential to bring additional information to multi-environment prediction models that are not captured by SNPs alone. Here, we evaluated different marker types (SNPs and/or SVs) on prediction accuracy across a range of genetic architectures for simulated traits across multiple environments. Our results show that SVs can improve prediction accuracy by up to 19%, but it is highly dependent on the genetic architecture of the trait. Differences in prediction accuracy across marker types were more pronounced for traits with high heritability, high number of QTLs, and SVs as causative variants. In these scenarios, using SV markers resulted in better prediction accuracies than SNP markers, especially when predicting untested genotypes across environments, likely due to more predictors being in linkage disequilibrium with causative variants. The simulations revealed little impact of different effect sizes between SNPs and SVs as causative variants on prediction accuracy. This study demonstrates the importance of knowing the genetic architecture of a trait in deciding what markers and marker types to use in large scale genomic prediction modeling in a breeding program. Key message We demonstrate potential for improved multi-environment genomic prediction accuracy using structural variant markers. However, the degree of observed improvement is highly dependent on the genetic architecture of the trait.

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 remain a challenge. Here we tested if incorporating information about the genetic architecture from genome‐wide association studies (GWASs) of maize ( Zea mays L.) 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 4–11 environments. Using at least 200 nonredundant top GWAS hits as causative variants, regardless of statistical significance, resulted in mean correlations between simulated and empirical trait data of 0.397–0.616 within environments and 0.610–0.915 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 shows that these simulated traits are highly concordant with observed variance partitioning (i.e., genotype, environment, etc.) and genomic prediction performance.