Whereas breeders have exploited diversity in maize for yield improvements, there has been limited progress in using beneficial alleles in undomesticated varieties. Characterizing standing variation in this complex genome has been challenging, with only a small fraction of it described to date. Using a population genetics scoring model, we identified 55 million SNPs in 103 lines across pre-domestication and domesticated Zea mays varieties, including a representative from the sister genus Tripsacum. We find that structural variations are pervasive in the Z. mays genome and are enriched at loci associated with important traits. By investigating the drivers of genome size variation, we find that the larger Tripsacum genome can be explained by transposable element abundance rather than an allopolyploid origin. In contrast, intraspecies genome size variation seems to be controlled by chromosomal knob content. There is tremendous overlap in key gene content in maize and Tripsacum, suggesting that adaptations from Tripsacum (for example, perennialism and frost and drought tolerance) can likely be integrated into maize.

Switchgrass (Panicum virgatum L.) is a perennial grass that has been designated as an herbaceous model biofuel crop for the United States of America. To facilitate accelerated breeding programs of switchgrass, we developed both an association panel and linkage populations for genome-wide association study (GWAS) and genomic selection (GS). All of the 840 individuals were then genotyped using genotyping by sequencing (GBS), generating 350 GB of sequence in total. As a highly heterozygous polyploid (tetraploid and octoploid) species lacking a reference genome, switchgrass is highly intractable with earlier methodologies of single nucleotide polymorphism (SNP) discovery. To access the genetic diversity of species like switchgrass, we developed a SNP discovery pipeline based on a network approach called the Universal Network-Enabled Analysis Kit (UNEAK). Complexities that hinder single nucleotide polymorphism discovery, such as repeats, paralogs, and sequencing errors, are easily resolved with UNEAK. Here, 1.2 million putative SNPs were discovered in a diverse collection of primarily upland, northern-adapted switchgrass populations. Further analysis of this data set revealed the fundamentally diploid nature of tetraploid switchgrass. Taking advantage of the high conservation of genome structure between switchgrass and foxtail millet (Setaria italica (L.) P. Beauv.), two parent-specific, synteny-based, ultra high-density linkage maps containing a total of 88,217 SNPs were constructed. Also, our results showed clear patterns of isolation-by-distance and isolation-by-ploidy in natural populations of switchgrass. Phylogenetic analysis supported a general south-to-north migration path of switchgrass. In addition, this analysis suggested that upland tetraploid arose from upland octoploid. All together, this study provides unparalleled insights into the diversity, genomic complexity, population structure, phylogeny, phylogeography, ploidy, and evolutionary dynamics of switchgrass.

The goal of many plant scientists' research is to explain natural phenotypic variation in terms of simple changes in DNA sequence. Traditionally, linkage mapping has been the most commonly employed method to reach this goal: experimental crosses are made to generate a family with known relatedness, and attempts are made to identify cosegregation of genetic markers and phenotypes within this family. In vertebrate systems, association mapping (also known as linkage disequilibrium mapping) is increasingly being adopted as the mapping method of choice. Association mapping involves searching for genotype-phenotype correlations in unrelated individuals and often is more rapid and cost-effective than traditional linkage mapping. We emphasize here that linkage and association mapping are complementary approaches and are more similar than is often assumed. Unlike in vertebrates, where controlled crosses can be expensive or impossible (e.g., in humans), the plant scientific community can exploit the advantages of both controlled crosses and association mapping to increase statistical power and mapping resolution. While the time and money required for the collection of genotype data were critical considerations in the past, the increasing availability of inexpensive DNA sequencing and genotyping methods should prompt researchers to shift their attention to experimental design. This review provides thoughts on finding the optimal experimental mix of association mapping using unrelated individuals and controlled crosses to identify the genes underlying phenotypic variation. GENETIC MAPPING: IT'S ALL ABOUT RECOMBINATIONThe aim of many genetic mapping studies is to identify quantitative trait loci (QTL) that are responsible for phenotypic variation. Although often viewed as fundamentally different, linkage and association mapping share a common strategy that exploits recombination's ability to break up the genome into fragments that can be correlated with phenotypic variation. The key difference between the two methods is the control the experimenter has over recombination. On the one hand, linkage mapping is a highly controlled experiment: individuals are crossed to generate a mapping population in which relatedness is known. In plants, these are generally biparental crosses, while in humans these populations may be extended pedigrees. The experimenter thereby creates a closed system and uses a small number of genetic markers to infer the locations of the relatively few recombination breakpoints. With genotype data from across the genome, the experimenter can then determine if a chromosomal fragment between two specific breakpoints is associated with a phenotype. Association mapping, on the other hand, is not a controlled experiment, but rather a natural experiment. Genotype and phenotype data are collected from a population in which relatedness is not controlled by the experimenter, and correlations between genetic markers and phenotypes are sought within this population. This open system design provides higher mapping resol...