A high-throughput skim-sequencing approach for genotyping, dosage estimation and identifying translocations.

The development of next-generation sequencing (NGS) enabled a shift from array-based genotyping to directly sequencing genomic libraries for high-throughput genotyping. Even though whole-genome sequencing was initially too costly for routine analysis in large populations such as breeding or genetic studies, continued advancements in genome sequencing and bioinformatics have provided the opportunity to capitalize on whole-genome information. As new sequencing platforms can routinely provide high-quality sequencing data for sufficient genome coverage to genotype various breeding populations, a limitation comes in the time and cost of library construction when multiplexing a large number of samples. Here we describe a high-throughput whole-genome skim-sequencing (skim-seq) approach that can be utilized for a broad range of genotyping and genomic characterization. Using optimized low-volume Illumina Nextera chemistry, we developed a skim-seq method and combined up to 960 samples in one multiplex library using dual index barcoding. With the dual-index barcoding, the number of samples for multiplexing can be adjusted depending on the amount of data required, and could be extended to 3,072 samples or more. Panels of doubled haploid wheat lines (Triticum aestivum, CDC Stanley x CDC Landmark), wheat-barley (T. aestivum x Hordeum vulgare) and wheat-wheatgrass (Triticum durum x Thinopyrum intermedium) introgression lines as well as known monosomic wheat stocks were genotyped using the skim-seq approach. Bioinformatics pipelines were developed for various applications where sequencing coverage ranged from 1 × down to 0.01 × per sample. Using reference genomes, we detected chromosome dosage, identified aneuploidy, and karyotyped introgression lines from the skim-seq data. Leveraging the recent advancements in genome sequencing, skim-seq provides an effective and low-cost tool for routine genotyping and genetic analysis, which can track and identify introgressions and genomic regions of interest in genetics research and applied breeding programs.

Domestication reshaped the genetic basis of inbreeding depression in a maize landrace compared to its wild relative, teosinte.

Inbreeding depression is the reduction in fitness and vigor resulting from mating of close relatives observed in many plant and animal species. The extent to which the genetic load of mutations contributing to inbreeding depression is due to large-effect mutations versus variants with very small individual effects is unknown and may be affected by population history. We compared the effects of outcrossing and self-fertilization on 18 traits in a landrace population of maize, which underwent a population bottleneck during domestication, and a neighboring population of its wild relative teosinte. Inbreeding depression was greater in maize than teosinte for 15 of 18 traits, congruent with the greater segregating genetic load in the maize population that we predicted from sequence data. Parental breeding values were highly consistent between outcross and selfed offspring, indicating that additive effects determine most of the genetic value even in the presence of strong inbreeding depression. We developed a novel linkage scan to identify quantitative trait loci (QTL) representing large-effect rare variants carried by only a single parent, which were more important in teosinte than maize. Teosinte also carried more putative juvenile-acting lethal variants identified by segregation distortion. These results suggest a mixture of mostly polygenic, small-effect partially recessive effects in linkage disequilibrium underlying inbreeding depression, with an additional contribution from rare larger-effect variants that was more important in teosinte but depleted in maize following the domestication bottleneck. Purging associated with the maize domestication bottleneck may have selected against some large effect variants, but polygenic load is harder to purge and overall segregating mutational burden increased in maize compared to teosinte.

A conserved genetic architecture among populations of the maize progenitor, teosinte, was radically altered by domestication.

Very little is known about how domestication was constrained by the quantitative genetic architecture of crop progenitors and how quantitative genetic architecture was altered by domestication. Yang et al. [C. J. Yang et al., Proc. Natl. Acad. Sci. U.S.A. 116, 5643-5652 (2019)] drew multiple conclusions about how genetic architecture influenced and was altered by maize domestication based on one sympatric pair of teosinte and maize populations. To test the generality of their conclusions, we assayed the structure of genetic variances, genetic correlations among traits, strength of selection during domestication, and diversity in genetic architecture within teosinte and maize. Our results confirm that additive genetic variance is decreased, while dominance genetic variance is increased, during maize domestication. The genetic correlations are moderately conserved among traits between teosinte and maize, while the genetic variance-covariance matrices (G-matrices) of teosinte and maize are quite different, primarily due to changes in the submatrix for reproductive traits. The inferred long-term selection intensities during domestication were weak, and the neutral hypothesis was rejected for reproductive and environmental response traits, suggesting that they were targets of selection during domestication. The G-matrix of teosinte imposed considerable constraint on selection during the early domestication process, and constraint increased further along the domestication trajectory. Finally, we assayed variation among populations and observed that genetic architecture is generally conserved among populations within teosinte and maize but is radically different between teosinte and maize. While selection drove changes in essentially all traits between teosinte and maize, selection explains little of the difference in domestication traits among populations within teosinte or maize.

Joint analysis of days to flowering reveals independent temperate adaptations in maize.

Domesticates are an excellent model for understanding biological consequences of rapid climate change. Maize (Zea mays ssp. mays) was domesticated from a tropical grass yet is widespread across temperate regions today. We investigate the biological basis of temperate adaptation in diverse structured nested association mapping (NAM) populations from China, Europe (Dent and Flint) and the United States as well as in the Ames inbred diversity panel, using days to flowering as a proxy. Using cross-population prediction, where high prediction accuracy derives from overall genomic relatedness, shared genetic architecture, and sufficient diversity in the training population, we identify patterns in predictive ability across the five populations. To identify the source of temperate adapted alleles in these populations, we predict top associated genome-wide association study (GWAS) identified loci in a Random Forest Classifier using independent temperate-tropical North American populations based on lines selected from Hapmap3 as predictors. We find that North American populations are well predicted (AUC equals 0.89 and 0.85 for Ames and USNAM, respectively), European populations somewhat well predicted (AUC equals 0.59 and 0.67 for the Dent and Flint panels, respectively) and that the Chinese population is not predicted well at all (AUC is 0.47), suggesting an independent adaptation process for early flowering in China. Multiple adaptations for the complex trait days to flowering in maize provide hope for similar natural systems under climate change.

Genotyping by sequencing for SNP marker development in onion.

Onion (Allium cepa) is not highly tractable for development of molecular markers due to its large (16 gigabases per 1C) nuclear genome. Single nucleotide polymorphisms (SNPs) are useful for genetic characterization and marker-aided selection of onion because of codominance and common occurrence in elite germplasm. We completed genotyping by sequencing (GBS) to identify SNPs in onion using 46 F2 plants, parents of the F2 plants (Ailsa Craig 43 and Brigham Yellow Globe 15-23), two doubled haploid (DH) lines (DH2107 and DH2110), and plants from 94 accessions in the USDA National Plant Germplasm System (NPGS). SNPs were called using the TASSEL 3.0 Universal Network Enabled Analysis (UNEAK) bioinformatics pipeline. Sequences from the F2 and DH plants were used to construct a pseudo-reference genome against which genotypes from all accessions were scored. Quality filters were used to identify a set of 284 high quality SNPs, which were placed onto an existing genetic map for the F2 family. Accessions showed a moderate level of diversity (mean He = 0.341) and evidence of inbreeding (mean F = 0.592). GBS is promising for SNP discovery in onion, although lack of a reference genome required extensive custom scripts for bioinformatics analyses to identify high quality markers.

Genomic prediction across years in a maize doubled haploid breeding program to accelerate early-stage testcross testing.

KEY MESSAGE: Genomic selection with a multiple-year training population dataset could accelerate early-stage testcross testing by skipping the first-stage yield testing, which significantly saves the time and cost of early-stage testcross testing. With the development of doubled haploid (DH) technology, the main task for a maize breeder is to estimate the breeding values of thousands of DH lines annually. In early-stage testcross testing, genomic selection (GS) offers the opportunity of replacing expensive multiple-environment phenotyping and phenotypic selection with lower-cost genotyping and genomic estimated breeding value (GEBV)-based selection. In the present study, a total of 1528 maize DH lines, phenotyped in multiple-environment trials in three consecutive years and genotyped with a low-cost per-sample genotyping platform of rAmpSeq, were used to explore how to implement GS to accelerate early-stage testcross testing. Results showed that the average prediction accuracy estimated from the cross-validation schemes was above 0.60 across all the scenarios. The average prediction accuracies estimated from the independent validation schemes ranged from 0.23 to 0.32 across all the scenarios, when the one-year datasets were used as training population (TRN) to predict the other year data as testing population (TST). The average prediction accuracies increased to a range from 0.31 to 0.42 across all the scenarios, when the two-years datasets were used as TRN. The prediction accuracies increased to a range from 0.50 to 0.56, when the TRN consisted of two-years of breeding data and 50% of third year's data converted from TST to TRN. This information showed that GS with a multiple-year TRN set offers the opportunity to accelerate early-stage testcross testing by skipping the first-stage yield testing, which significantly saves the time and cost of early-stage testcross testing.

High-Density Genetic Linkage Mapping of Lepidium Based on Genotyping-by-Sequencing SNPs and Segregating Contig Tag Haplotypes.

Lepidium campestre has been targeted for domestication as future oilseed and catch crop. Three hundred eighty plants comprising genotypes of L. campestre, Lepidium heterophyllum, and their interspecific F2 mapping population were genotyped using genotyping by sequencing (GBS), and the generated polymorphic markers were used for the construction of high-density genetic linkage map. TASSEL-GBS, a reference genome-based pipeline, was used for this analysis using a draft L. campestre whole genome sequence. The analysis resulted in 120,438 biallelic single-nucleotide polymorphisms (SNPs) with minor allele frequency (MAF) above 0.01. The construction of genetic linkage map was conducted using MSTMap based on phased SNPs segregating in 1:2:1 ratio for the F2 individuals, followed by genetic mapping of segregating contig tag haplotypes as dominant markers against the linkage map. The final linkage map consisted of eight linkage groups (LGs) containing 2,330 SNP markers and spanned 881 Kosambi cM. Contigs (10,302) were genetically mapped to the eight LGs, which were assembled into pseudomolecules that covered a total of ∼120.6 Mbp. The final size of the pseudomolecules ranged from 9.4 Mbp (LG-4) to 20.4 Mpb (LG-7). The following major correspondence between the eight Lepidium LGs (LG-1 to LG-8) and the five Arabidopsis thaliana (At) chromosomes (Atx-1-Atx-5) was revealed through comparative genomics analysis: LG-1&2_Atx-1, LG-3_Atx-2&3, LG-4_Atx-2, LG-5_Atx-2&Atx-3, LG-6_Atx-4&5, LG-7_Atx-4, and LG-8_Atx-5. This analysis revealed that at least 66% of the sequences of the LGs showed high collinearity with At chromosomes. The sequence identity between the corresponding regions of the LGs and At chromosomes ranged from 80.6% (LG-6) to 86.4% (LG-8) with overall mean of 82.9%. The map positions on Lepidium LGs of the homologs of 24 genes that regulate various traits in A. thaliana were also identified. The eight LGs revealed in this study confirm the previously reported (1) haploid chromosome number of eight in L. campestre and L. heterophyllum and (2) chromosomal fusion, translocation, and inversion events during the evolution of n = 8 karyotype in ancestral species shared by Lepidium and Arabidopsis to n = 5 karyotype in A. thaliana. This study generated highly useful genomic tools and resources for Lepidium that can be used to accelerate its domestication.

Species-Specific Duplication Event Associated with Elevated Levels of Nonstructural Carbohydrates in Sorghum bicolor.

Simple sugars are the essential foundation to plant life, and thus, their production, utilization, and storage are highly regulated processes with many complex genetic controls. Despite their importance, many of the genetic and biochemical mechanisms remain unknown or uncharacterized. Sorghum, a highly productive, diverse C4 grass important for both industrial and subsistence agricultural systems, has considerable phenotypic diversity in the accumulation of nonstructural sugars in the stem. We use this crop species to examine the genetic controls of high levels of sugar accumulation, identify genetic mechanisms for the accumulation of nonstructural sugars, and link carbon allocation with iron transport. We identify a species-specific tandem duplication event controlling sugar accumulation using genome-wide association analysis, characterize multiple allelic variants causing increased sugar content, and provide further evidence of a putative neofunctionalization event conferring adaptability in Sorghum bicolor Comparative genomics indicate that this event is unique to sorghum which may further elucidate evolutionary mechanisms for adaptation and divergence within the Poaceae. Furthermore, the identification and characterization of this event was only possible with the continued advancement and improvement of the reference genome. The characterization of this region and the process in which it was discovered serve as a reminder that any reference genome is imperfect and is in need of continual improvement.

Haplotyping the Vitis collinear core genome with rhAmpSeq improves marker transferability in a diverse genus.

Transferable DNA markers are essential for breeding and genetics. Grapevine (Vitis) breeders utilize disease resistance alleles from congeneric species ~20 million years divergent, but existing Vitis marker platforms have cross-species transfer rates as low as 2%. Here, we apply a marker strategy targeting the inferred Vitis core genome. Incorporating seven linked-read de novo assemblies and three existing assemblies, the Vitis collinear core genome is estimated to converge at 39.8 Mb (8.67% of the genome). Adding shotgun genome sequences from 40 accessions enables identification of conserved core PCR primer binding sites flanking polymorphic haplotypes with high information content. From these target regions, we develop 2,000 rhAmpSeq markers as a PCR multiplex and validate the panel in four biparental populations spanning the diversity of the Vitis genus, showing transferability increases to 91.9%. This marker development strategy should be widely applicable for genetic studies in many taxa, particularly those ~20 million years divergent.

The genetic architecture of teosinte catalyzed and constrained maize domestication.

The process of evolution under domestication has been studied using phylogenetics, population genetics-genomics, quantitative trait locus (QTL) mapping, gene expression assays, and archaeology. Here, we apply an evolutionary quantitative genetic approach to understand the constraints imposed by the genetic architecture of trait variation in teosinte, the wild ancestor of maize, and the consequences of domestication on genetic architecture. Using modern teosinte and maize landrace populations as proxies for the ancestor and domesticate, respectively, we estimated heritabilities, additive and dominance genetic variances, genetic-by-environment variances, genetic correlations, and genetic covariances for 18 domestication-related traits using realized genomic relationships estimated from genome-wide markers. We found a reduction in heritabilities across most traits, and the reduction is stronger in reproductive traits (size and numbers of grains and ears) than vegetative traits. We observed larger depletion in additive genetic variance than dominance genetic variance. Selection intensities during domestication were weak for all traits, with reproductive traits showing the highest values. For 17 of 18 traits, neutral divergence is rejected, suggesting they were targets of selection during domestication. Yield (total grain weight) per plant is the sole trait that selection does not appear to have improved in maize relative to teosinte. From a multivariate evolution perspective, we identified a strong, nonneutral divergence between teosinte and maize landrace genetic variance-covariance matrices (G-matrices). While the structure of G-matrix in teosinte posed considerable genetic constraint on early domestication, the maize landrace G-matrix indicates that the degree of constraint is more unfavorable for further evolution along the same trajectory.

Identification of genetic variants associated with maize flowering time using an extremely large multi-genetic background population.

Flowering time is one of the major adaptive traits in domestication of maize and an important selection criterion in breeding. To detect more maize flowering time variants we evaluated flowering time traits using an extremely large multi- genetic background population that contained more than 8000 lines under multiple Sino-United States environments. The population included two nested association mapping (NAM) panels and a natural association panel. Nearly 1 million single-nucleotide polymorphisms (SNPs) were used in the analyses. Through the parallel linkage analysis of the two NAM panels, both common and unique flowering time regions were detected. Genome wide, a total of 90 flowering time regions were identified. One-third of these regions were connected to traits associated with the environmental sensitivity of maize flowering time. The genome-wide association study of the three panels identified nearly 1000 flowering time-associated SNPs, mainly distributed around 220 candidate genes (within a distance of 1 Mb). Interestingly, two types of regions were significantly enriched for these associated SNPs - one was the candidate gene regions and the other was the approximately 5 kb regions away from the candidate genes. Moreover, the associated SNPs exhibited high accuracy for predicting flowering time.

Independent Molecular Basis of Convergent Highland Adaptation in Maize.

Convergent evolution is the independent evolution of similar traits in different species or lineages of the same species; this often is a result of adaptation to similar environments, a process referred to as convergent adaptation. We investigate here the molecular basis of convergent adaptation in maize to highland climates in Mesoamerica and South America, using genome-wide SNP data. Taking advantage of archaeological data on the arrival of maize to the highlands, we infer demographic models for both populations, identifying evidence of a strong bottleneck and rapid expansion in South America. We use these models to then identify loci showing an excess of differentiation as a means of identifying putative targets of natural selection and compare our results to expectations from recently developed theory on convergent adaptation. Consistent with predictions across a wide parameter space, we see limited evidence for convergent evolution at the nucleotide level in spite of strong similarities in overall phenotypes. Instead, we show that selection appears to have predominantly acted on standing genetic variation and that introgression from wild teosinte populations appears to have played a role in highland adaptation in Mexican maize.

High-resolution genetic mapping of maize pan-genome sequence anchors.

In addition to single-nucleotide polymorphisms, structural variation is abundant in many plant genomes. The structural variation across a species can be represented by a 'pan-genome', which is essential to fully understand the genetic control of phenotypes. However, the pan-genome's complexity hinders its accurate assembly via sequence alignment. Here we demonstrate an approach to facilitate pan-genome construction in maize. By performing 18 trillion association tests we map 26 million tags generated by reduced representation sequencing of 14,129 maize inbred lines. Using machine-learning models we select 4.4 million accurately mapped tags as sequence anchors, 1.1 million of which are presence/absence variations. Structural variations exhibit enriched association with phenotypic traits, indicating that it is a significant source of adaptive variation in maize. The ability to efficiently map ultrahigh-density pan-genome sequence anchors enables fine characterization of structural variation and will advance both genetic research and breeding in many crops.

Recombination in diverse maize is stable, predictable, and associated with genetic load.

Among the fundamental evolutionary forces, recombination arguably has the largest impact on the practical work of plant breeders. Varying over 1,000-fold across the maize genome, the local meiotic recombination rate limits the resolving power of quantitative trait mapping and the precision of favorable allele introgression. The consequences of low recombination also theoretically extend to the species-wide scale by decreasing the power of selection relative to genetic drift, and thereby hindering the purging of deleterious mutations. In this study, we used genotyping-by-sequencing (GBS) to identify 136,000 recombination breakpoints at high resolution within US and Chinese maize nested association mapping populations. We find that the pattern of cross-overs is highly predictable on the broad scale, following the distribution of gene density and CpG methylation. Several large inversions also suppress recombination in distinct regions of several families. We also identify recombination hotspots ranging in size from 1 kb to 30 kb. We find these hotspots to be historically stable and, compared with similar regions with low recombination, to have strongly differentiated patterns of DNA methylation and GC content. We also provide evidence for the historical action of GC-biased gene conversion in recombination hotspots. Finally, using genomic evolutionary rate profiling (GERP) to identify putative deleterious polymorphisms, we find evidence for reduced genetic load in hotspot regions, a phenomenon that may have considerable practical importance for breeding programs worldwide.

TASSEL-GBS: a high capacity genotyping by sequencing analysis pipeline.

Genotyping by sequencing (GBS) is a next generation sequencing based method that takes advantage of reduced representation to enable high throughput genotyping of large numbers of individuals at a large number of SNP markers. The relatively straightforward, robust, and cost-effective GBS protocol is currently being applied in numerous species by a large number of researchers. Herein we describe a bioinformatics pipeline, TASSEL-GBS, designed for the efficient processing of raw GBS sequence data into SNP genotypes. The TASSEL-GBS pipeline successfully fulfills the following key design criteria: (1) Ability to run on the modest computing resources that are typically available to small breeding or ecological research programs, including desktop or laptop machines with only 8-16 GB of RAM, (2) Scalability from small to extremely large studies, where hundreds of thousands or even millions of SNPs can be scored in up to 100,000 individuals (e.g., for large breeding programs or genetic surveys), and (3) Applicability in an accelerated breeding context, requiring rapid turnover from tissue collection to genotypes. Although a reference genome is required, the pipeline can also be run with an unfinished "pseudo-reference" consisting of numerous contigs. We describe the TASSEL-GBS pipeline in detail and benchmark it based upon a large scale, species wide analysis in maize (Zea mays), where the average error rate was reduced to 0.0042 through application of population genetic-based SNP filters. Overall, the GBS assay and the TASSEL-GBS pipeline provide robust tools for studying genomic diversity.

Grapevine powdery mildew resistance and susceptibility loci identified on a high-resolution SNP map.

Improved efficacy and durability of powdery mildew resistance can be enhanced via knowledge of the genetics of resistance and susceptibility coupled with the development of high-resolution maps to facilitate the stacking of multiple resistance genes and other desirable traits. We studied the inheritance of powdery mildew (Erysiphe necator) resistance and susceptibility of wild Vitis rupestris B38 and cultivated V. vinifera 'Chardonnay', finding evidence for quantitative variation. Molecular markers were identified using genotyping-by-sequencing, resulting in 16,833 single nucleotide polymorphisms (SNPs) based on alignment to the V. vinifera 'PN40024' reference genome sequence. With an average density of 36 SNPs/Mbp and uniform coverage of the genome, this 17K set was used to identify 11 SNPs on chromosome 7 associated with a resistance locus from V. rupestris B38 and ten SNPs on chromosome 9 associated with a locus for susceptibility from 'Chardonnay' using single marker association and linkage disequilibrium analysis. Linkage maps for V. rupestris B38 (1,146 SNPs) and 'Chardonnay' (1,215 SNPs) were constructed and used to corroborate the 'Chardonnay' locus named Sen1 (Susceptibility to Erysiphe necator 1), providing the first insight into the genetics of susceptibility to powdery mildew from V. vinifera. The identification of markers associated with a susceptibility locus in a V. vinifera background can be used for negative selection among breeding progenies. This work improves our understanding of the nature of powdery mildew resistance in V. rupestris B38 and 'Chardonnay', while applying next-generation sequencing tools to advance grapevine genomics and breeding.

Comprehensive genotyping of the USA national maize inbred seed bank.

BACKGROUND: Genotyping by sequencing, a new low-cost, high-throughput sequencing technology was used to genotype 2,815 maize inbred accessions, preserved mostly at the National Plant Germplasm System in the USA. The collection includes inbred lines from breeding programs all over the world. RESULTS: The method produced 681,257 single-nucleotide polymorphism (SNP) markers distributed across the entire genome, with the ability to detect rare alleles at high confidence levels. More than half of the SNPs in the collection are rare. Although most rare alleles have been incorporated into public temperate breeding programs, only a modest amount of the available diversity is present in the commercial germplasm. Analysis of genetic distances shows population stratification, including a small number of large clusters centered on key lines. Nevertheless, an average fixation index of 0.06 indicates moderate differentiation between the three major maize subpopulations. Linkage disequilibrium (LD) decays very rapidly, but the extent of LD is highly dependent on the particular group of germplasm and region of the genome. The utility of these data for performing genome-wide association studies was tested with two simply inherited traits and one complex trait. We identified trait associations at SNPs very close to known candidate genes for kernel color, sweet corn, and flowering time; however, results suggest that more SNPs are needed to better explore the genetic architecture of complex traits. CONCLUSIONS: The genotypic information described here allows this publicly available panel to be exploited by researchers facing the challenges of sustainable agriculture through better knowledge of the nature of genetic diversity.

Population genomic and genome-wide association studies of agroclimatic traits in sorghum.

Accelerating crop improvement in sorghum, a staple food for people in semiarid regions across the developing world, is key to ensuring global food security in the context of climate change. To facilitate gene discovery and molecular breeding in sorghum, we have characterized ~265,000 single nucleotide polymorphisms (SNPs) in 971 worldwide accessions that have adapted to diverse agroclimatic conditions. Using this genome-wide SNP map, we have characterized population structure with respect to geographic origin and morphological type and identified patterns of ancient crop diffusion to diverse agroclimatic regions across Africa and Asia. To better understand the genomic patterns of diversification in sorghum, we quantified variation in nucleotide diversity, linkage disequilibrium, and recombination rates across the genome. Analyzing nucleotide diversity in landraces, we find evidence of selective sweeps around starch metabolism genes, whereas in landrace-derived introgression lines, we find introgressions around known height and maturity loci. To identify additional loci underlying variation in major agroclimatic traits, we performed genome-wide association studies (GWAS) on plant height components and inflorescence architecture. GWAS maps several classical loci for plant height, candidate genes for inflorescence architecture. Finally, we trace the independent spread of multiple haplotypes carrying alleles for short stature or long inflorescence branches. This genome-wide map of SNP variation in sorghum provides a basis for crop improvement through marker-assisted breeding and genomic selection.

Comparative population genomics of maize domestication and improvement.

Domestication and plant breeding are ongoing 10,000-year-old evolutionary experiments that have radically altered wild species to meet human needs. Maize has undergone a particularly striking transformation. Researchers have sought for decades to identify the genes underlying maize evolution, but these efforts have been limited in scope. Here, we report a comprehensive assessment of the evolution of modern maize based on the genome-wide resequencing of 75 wild, landrace and improved maize lines. We find evidence of recovery of diversity after domestication, likely introgression from wild relatives, and evidence for stronger selection during domestication than improvement. We identify a number of genes with stronger signals of selection than those previously shown to underlie major morphological changes. Finally, through transcriptome-wide analysis of gene expression, we find evidence both consistent with removal of cis-acting variation during maize domestication and improvement and suggestive of modern breeding having increased dominance in expression while targeting highly expressed genes.