We report de novo genome assemblies, transcriptomes, annotations, and methylomes for the 26 inbreds that serve as the founders for the maize nested association mapping population. The number of pan-genes in these diverse genomes exceeds 103,000, with approximately a third found across all genotypes. The results demonstrate that the ancient tetraploid character of maize continues to degrade by fractionation to the present day. Excellent contiguity over repeat arrays and complete annotation of centromeres revealed additional variation in major cytological landmarks. We show that combining structural variation with single-nucleotide polymorphisms can improve the power of quantitative mapping studies. We also document variation at the level of DNA methylation and demonstrate that unmethylated regions are enriched for cis-regulatory elements that contribute to phenotypic variation.

We report de novo genome assemblies, transcriptomes, annotations, and methylomes for the 26 inbreds that serve as the founders for the maize nested association mapping population. The data indicate that the number of pan-genes exceeds 103,000 and that the ancient tetraploid character of maize continues to degrade by fractionation to the present day. Excellent contiguity over repeat arrays and complete annotation of centromeres further reveal the locations and internal structures of major cytological landmarks. We show that combining structural variation with SNPs can improve the power of quantitative mapping studies. Finally, we document variation at the level of DNA methylation, and demonstrate that unmethylated regions are enriched for cis-regulatory elements that overlap QTL and contribute to changes in gene expression.One sentence summaryA multi-genome analysis of maize reveals previously unknown variation in gene content, genome structure, and methylation.

Genotype-by-genotype (G×G) interactions are an essential requirement for the coevolution of hosts and parasites, but have only been documented in a small number of animal model systems. G×G effects arise from interactions between host and pathogen genotypes, such that some pathogen strains are more infectious in certain hosts and some hosts are more susceptible to certain pathogen strains. We tested for G×G interactions in the gypsy moth (Lymantria dispar) and its baculovirus. We infected 21 full-sib families of gypsy moths with each of 16 isolates of baculovirus and measured the between-isolate correlations of infection rate across host families for all pairwise combinations of isolates. Mean infectiousness varied among isolates and disease susceptibility varied among host families. Between-isolate correlations of infection rate were generally less than one, indicating nonadditive effects of host and pathogen type consistent with G×G interactions. Our results support the presence of G×G effects in the gypsy moth-baculovirus interaction and provide empirical evidence that correlations in infection rates between field-collected isolates are consistent with values that mathematical models have previously shown to increase the likelihood of pathogen polymorphism.

The search for quantitative trait loci (QTL) that explain complex traits such as yield and drought tolerance has been ongoing in all crops. Methods such as bi-parental QTL mapping and genome-wide association studies (GWAS) each have their own advantages and limitations. Multi-parent advanced generation inter-crossing (MAGIC) populations contain more recombination events and genetic diversity than bi-parental mapping populations and are better able to estimate effect sizes of rare alleles than association mapping populations. Here we discuss the results of using a MAGIC population of doubled haploid (DH) maize lines created from 16 diverse founders to perform QTL mapping. We compare three models that assume bi-allelic, founder, and ancestral haplotype allelic states for QTL. The three methods have differing power to detect QTL for a variety of agronomic traits. Although the founder approach finds the most QTL, all methods are able to find unique QTL, suggesting that each model has advantages for traits with different genetic architectures. A closer look at a well-characterized flowering time QTL, qDTA8, which contains vgt1, highlights the strengths and weaknesses of each method and suggests a potential epistatic interaction. Overall, our results reinforce the importance of considering different approaches to analyzing genotypic datasets, and shows the limitations of binary SNP data for identifying multi-allelic QTL.

Genotype by environment interactions are a significant challenge for crop breeding as well as being important for understanding the genetic basis of environmental adaptation. In this study, we analyzed genotype by environment interactions in a maize multi-parent advanced generation intercross population grown across five environments. We found that genotype by environment interactions contributed as much as genotypic effects to the variation in some agronomically important traits. In order to understand how genetic correlations between traits change across environments, we estimated the genetic variance-covariance matrix in each environment. Changes in genetic covariances between traits across environments were common, even among traits that show low genotype by environment variance. We also performed a genome-wide association study to identify markers associated with genotype by environment interactions but found only a small number of significantly associated markers, possibly due to the highly polygenic nature of genotype by environment interactions in this population.