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1. Association mapping across a multitude of traits collected in diverse environments in maize

   https://doi.org/10.1093/gigascience/giac080  

   Mural, Ravi V; Sun, Guangchao; Grzybowski, Marcin; Tross, Michael C; Jin, Hongyu; Smith, Christine; Newton, Linsey; Andorf, Carson M; Woodhouse, Margaret R; Thompson, Addie M; et al (January 2022, GigaScience)

   Abstract Classical genetic studies have identified many cases of pleiotropy where mutations in individual genes alter many different phenotypes. Quantitative genetic studies of natural genetic variants frequently examine one or a few traits, limiting their potential to identify pleiotropic effects of natural genetic variants. Widely adopted community association panels have been employed by plant genetics communities to study the genetic basis of naturally occurring phenotypic variation in a wide range of traits. High-density genetic marker data—18M markers—from 2 partially overlapping maize association panels comprising 1,014 unique genotypes grown in field trials across at least 7 US states and scored for 162 distinct trait data sets enabled the identification of of 2,154 suggestive marker-trait associations and 697 confident associations in the maize genome using a resampling-based genome-wide association strategy. The precision of individual marker-trait associations was estimated to be 3 genes based on a reference set of genes with known phenotypes. Examples were observed of both genetic loci associated with variation in diverse traits (e.g., above-ground and below-ground traits), as well as individual loci associated with the same or similar traits across diverse environments. Many significant signals are located near genes whose functions were previously entirely unknown or estimated purely via functional data on homologs. This study demonstrates the potential of mining community association panel data using new higher-density genetic marker sets combined with resampling-based genome-wide association tests to develop testable hypotheses about gene functions, identify potential pleiotropic effects of natural genetic variants, and study genotype-by-environment interaction. 

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2. A maize mutant in the glutamate receptor-like dwarf13 is modified by cis-acting natural variation and a cornichon homolog

   https://doi.org/10.1371/journal.pgen.1012006  

   Kaur, Amanpreet; Khangura, Rajdeep S; Dilkes, Brian P (December 2025, PLOS Genetics)

   Giraud, Tatiana (Ed.)

   Deciphering the molecular basis of complex traits requires understanding how natural genetic variation interacts with underlying biological pathways. In this study, we explored how natural genetic variation influences traits in maize affected by a semi-dominant maize dwarfing allele,Dwarf13–1 (D13-1)which encodes a defective ionotropic glutamate receptor (GLR). This allowed us to investigate natural genetic variation in the genome affecting GLR signaling in maize. We implemented an F1 association mapping (FOAM) approach, where heterozygous mutants carrying the semi-dominantD13-1allele were crossed with a maize association panel. The resulting F1 families segregated 1:1 for mutant and wild-type phenotypes allowing comparisons between the congenic F1 hybrid siblings to identify and map natural alleles that interact with theD13-1mutant allele. FOAM mapping detected two loci that modify the expression of theD13-1/+ mutant phenotype. The phenotypic impacts of both loci were epistatically controlled byD13-1, and only affected the phenotypes of mutant F1 hybrids. One,tropotriskaideka1(tod1), encoded a maize homolog of the GLR-interactingcornichongene and modifiedD13-1/+mutant severity. A second, encoded by thed13locus itself, affected the severity of theD13-1/+phenotype via variation in the wild-type allele in the heterozygous mutants. By integrating gene expression analyses, these epistatic interactions, and SNP linkage information we identified multiple, unlinked, alleles affecting expression of the wild-type D13 transcript that modify mutant trait expression. Greater expression of the wild-type D13 allele increased plant height and suppressedD13-1/+ mutant severity, consistent with a multi-subunit complex GLR structure and complex-poisoning mode-of-action for the semi-dominantD13-1allele. This approach identifies natural alleles affecting the GLR pathway in maize and establishes GLRs and their interactors as dose-dependent regulators of plant architecture. Our pathway-focused framework and epistasis testing of natural variants provides greater confidence in identifying genes contributing to complex traits. 

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3. Regulatory variation controlling architectural pleiotropy in maize

   https://doi.org/10.1038/s41467-025-56884-w  

   Bertolini, Edoardo; Rice, Brian R; Braud, Max; Yang, Jiani; Hake, Sarah; Strable, Josh; Lipka, Alexander E; Eveland, Andrea L (March 2025, Nature Communications)

   Abstract An early event in plant organogenesis is establishment of a boundary between the stem cell containing meristem and differentiating lateral organ. In maize (Zea mays), evidence suggests a common gene network functions at boundaries of distinct organs and contributes to pleiotropy between leaf angle and tassel branch number, two agronomic traits. To uncover regulatory variation at the nexus of these two traits, we use regulatory network topologies derived from specific developmental contexts to guide multivariate genome-wide association analyses. In addition to defining network plasticity around core pleiotropic loci, we identify new transcription factors that contribute to phenotypic variation in canopy architecture, and structural variation that contributes tocis-regulatory control of pleiotropy between tassel branching and leaf angle across maize diversity. Results demonstrate the power of informing statistical genetics with context-specific developmental networks to pinpoint pleiotropic loci and theircis-regulatory components, which can be used to fine-tune plant architecture for crop improvement. 

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4. Local adaptation contributes to gene expression divergence in maize

   https://doi.org/10.1093/g3journal/jkab004  

   Blanc, Jennifer; Kremling, Karl A; Buckler, Edward; Josephs, Emily B (February 2021, G3 Genes|Genomes|Genetics)

   Hernandez, R (Ed.)

   Abstract Gene expression links genotypes to phenotypes, so identifying genes whose expression is shaped by selection will be important for understanding the traits and processes underlying local adaptation. However, detecting local adaptation for gene expression will require distinguishing between divergence due to selection and divergence due to genetic drift. Here, we adapt a QST−FST framework to detect local adaptation for transcriptome-wide gene expression levels in a population of diverse maize genotypes. We compare the number and types of selected genes across a wide range of maize populations and tissues, as well as selection on cold-response genes, drought-response genes, and coexpression clusters. We identify a number of genes whose expression levels are consistent with local adaptation and show that genes involved in stress response show enrichment for selection. Due to its history of intense selective breeding and domestication, maize evolution has long been of interest to researchers, and our study provides insight into the genes and processes important for in local adaptation of maize. 

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5. Haplotype associated RNA expression (HARE) improves prediction of complex traits in maize

   https://doi.org/10.1371/journal.pgen.1009568  

   Giri, Anju; Khaipho-Burch, Merritt; Buckler, Edward S.; Ramstein, Guillaume P. (October 2021, PLOS Genetics)

   Hake, Sarah (Ed.)

   Genomic prediction typically relies on associations between single-site polymorphisms and traits of interest. This representation of genomic variability has been successful for predicting many complex traits. However, it usually cannot capture the combination of alleles in haplotypes and it has generated little insight about the biological function of polymorphisms. Here we present a novel and cost-effective method for imputingcishaplotype associated RNA expression (HARE), studied their transferability across tissues, and evaluated genomic prediction models within and across populations. HARE focuses on tightly linkedcisacting causal variants in the immediate vicinity of the gene, while excludingtranseffects from diffusion and metabolism. Therefore, HARE estimates were more transferrable across different tissues and populations compared to measured transcript expression. We also showed that HARE estimates captured one-third of the variation in gene expression. HARE estimates were used in genomic prediction models evaluated within and across two diverse maize panels–a diverse association panel (Goodman Association panel) and a large half-sib panel (Nested Association Mapping panel)–for predicting 26 complex traits. HARE resulted in up to 15% higher prediction accuracy than control approaches that preserved haplotype structure, suggesting that HARE carried functional information in addition to information about haplotype structure. The largest increase was observed when the model was trained in the Nested Association Mapping panel and tested in the Goodman Association panel. Additionally, HARE yielded higher within-population prediction accuracy as compared to measured expression values. The accuracy achieved by measured expression was variable across tissues, whereas accuracy by HARE was more stable across tissues. Therefore, imputing RNA expression of genes by haplotype is stable, cost-effective, and transferable across populations. 

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