BackgroundThe history of maize has been characterized by major demographic events, including population size changes associated with domestication and range expansion, and gene flow with wild relatives. The interplay between demographic history and selection has shaped diversity across maize populations and genomes.ResultsWe investigate these processes using high-depth resequencing data from 31 maize landraces spanning the pre-Columbian distribution of maize, and four wild teosinte individuals (Zea mays ssp. parviglumis). Genome-wide demographic analyses reveal that maize experienced pronounced declines in effective population size due to both a protracted domestication bottleneck and serial founder effects during post-domestication spread, while parviglumis in the Balsas River Valley experienced population growth. The domestication bottleneck and subsequent spread led to an increase in deleterious alleles in the domesticate compared to the wild progenitor. This cost is particularly pronounced in Andean maize, which has experienced a more dramatic founder event compared to other maize populations. Additionally, we detect introgression from the wild teosinte Zea mays ssp. mexicana into maize in the highlands of Mexico, Guatemala, and the southwestern USA, which reduces the prevalence of deleterious alleles likely due to the higher long-term effective population size of teosinte.ConclusionsThese findings underscore the strong interaction between historical demography and the efficiency of selection and illustrate how domesticated species are particularly useful for understanding these processes. The landscape of deleterious alleles and therefore evolutionary potential is clearly influenced by recent demography, a factor that could bear importantly on many species that have experienced recent demographic shifts.Electronic supplementary materialThe online version of this article (doi:10.1186/s13059-017-1346-4) contains supplementary material, which is available to authorized users.

Genotyping-by-sequencing (GBS) approaches provide low-cost, high-density genotype information. However, GBS has unique technical considerations, including a substantial amount of missing data and a nonuniform distribution of sequence reads. The goal of this study was to characterize technical variation using this method and to develop methods to optimize read depth to obtain desired marker coverage. To empirically assess the distribution of fragments produced using GBS, ∼8.69 Gb of GBS data were generated on the Zea mays reference inbred B73, utilizing ApeKI for genome reduction and single-end reads between 75 and 81 bp in length. We observed wide variation in sequence coverage across sites. Approximately 76% of potentially observable cut site-adjacent sequence fragments had no sequencing reads whereas a portion had substantially greater read depth than expected, up to 2369 times the expected mean. The methods described in this article facilitate determination of sequencing depth in the context of empirically defined read depth to achieve desired marker density for genetic mapping studies.

Association mapping (AM) is a powerful tool for fine mapping complex trait variation down to nucleotide sequences by exploiting historical recombination events. A major problem in AM is controlling false positives that can arise from population structure and family relatedness. False positives are often controlled by incorporating covariates for structure and kinship in mixed linear models (MLM). These MLM-based methods are single locus models and can introduce false negatives due to over fitting of the model. In this study, eight different statistical models, ranging from single-locus to multilocus, were compared for AM for three traits differing in heritability in two crop species: soybean (Glycine max L.) and maize (Zea mays L.). Soybean and maize were chosen, in part, due to their highly differentiated rate of linkage disequilibrium (LD) decay, which can influence false positive and false negative rates. The fixed and random model circulating probability unification (FarmCPU) performed better than other models based on an analysis of Q-Q plots and on the identification of the known number of quantitative trait loci (QTLs) in a simulated data set. These results indicate that the FarmCPU controls both false positives and false negatives. Six qualitative traits in soybean with known published genomic positions were also used to compare these models, and results indicated that the FarmCPU consistently identified a single highly significant SNP closest to these known published genes. Multiple comparison adjustments (Bonferroni, false discovery rate, and positive false discovery rate) were compared for these models using a simulated trait having 60% heritability and 20 QTLs. Multiple comparison adjustments were overly conservative for MLM, CMLM, ECMLM, and MLMM and did not find any significant markers; in contrast, ANOVA, GLM, and SUPER models found an excessive number of markers, far more than 20 QTLs. The FarmCPU model, using less conservative methods (false discovery rate, and positive false discovery rate) identified 10 QTLs, which was closer to the simulated number of QTLs than the number found by other models.

The history of maize has been characterized by major demographic events including changes in population size associated with domestication and range expansion as well as gene flow with wild relatives. The interplay between demographic history and selection has shaped diversity across maize populations and genomes. Here, we investigate these processes based on high-depth resequencing data from 31 maize landraces spanning the pre-Columbian distribution of maize as well as four wild progenitor individuals (Zea mays ssp. parviglumis) from the Balsas River Valley in Mexico. Genome-wide demographic analyses reveal that maize domestication and spread resulted in pronounced declines in effective population size due to both a protracted bottleneck and serial founder effects, while, concurrently, parviglumis experienced population growth. The cost of maize domestication and spread was an increase in deleterious alleles in the domesticate relative to its wild progenitor. This cost is particularly pronounced in Andean maize, which appears to have experienced a more dramatic founder event when compared to other maize populations. Introgression from the wild teosinte Zea mays ssp. mexicana into maize in the highlands of Mexico and Guatemala is found found to decrease the prevalence of deleterious alleles, likely due to the higher long-term effective population size of wild maize. These findings underscore the strong interaction between historical demography and the efficiency of selection species-and genome-wide and suggest domesticated species with well-characterized histories may be particularly useful for understanding this interplay.

BackgroundHigh-density genomic data is often analyzed by combining information over windows of adjacent markers. Interpretation of data grouped in windows versus at individual locations may increase statistical power, simplify computation, reduce sampling noise, and reduce the total number of tests performed. However, use of adjacent marker information can result in over- or under-smoothing, undesirable window boundary specifications, or highly correlated test statistics. We introduce a method for defining windows based on statistically guided breakpoints in the data, as a foundation for the analysis of multiple adjacent data points. This method involves first fitting a cubic smoothing spline to the data and then identifying the inflection points of the fitted spline, which serve as the boundaries of adjacent windows. This technique does not require prior knowledge of linkage disequilibrium, and therefore can be applied to data collected from individual or pooled sequencing experiments. Moreover, in contrast to existing methods, an arbitrary choice of window size is not necessary, since these are determined empirically and allowed to vary along the genome.ResultsSimulations applying this method were performed to identify selection signatures from pooled sequencing FST data, for which allele frequencies were estimated from a pool of individuals. The relative ratio of true to false positives was twice that generated by existing techniques. A comparison of the approach to a previous study that involved pooled sequencing FST data from maize suggested that outlying windows were more clearly separated from their neighbors than when using a standard sliding window approach.ConclusionsWe have developed a novel technique to identify window boundaries for subsequent analysis protocols. When applied to selection studies based on FST data, this method provides a high discovery rate and minimizes false positives. The method is implemented in the R package GenWin, which is publicly available from CRAN.

A genome-wide scan to detect evidence of selection was conducted in the Golden Glow maize long-term selection population. The population had been subjected to selection for increased number of ears per plant for 30 generations, with an empirically estimated effective population size ranging from 384 to 667 individuals and an increase of more than threefold in the number of ears per plant. Allele frequencies at .1.2 million single-nucleotide polymorphism loci were estimated from pooled wholegenome resequencing data, and F ST values across sliding windows were employed to assess divergence between the population preselection and the population postselection. Twenty-eight highly divergent regions were identified, with half of these regions providing gene-level resolution on potentially selected variants. Approximately 93% of the divergent regions do not demonstrate a significant decrease in heterozygosity, which suggests that they are not approaching fixation. Also, most regions display a pattern consistent with a soft-sweep model as opposed to a hard-sweep model, suggesting that selection mostly operated on standing genetic variation. For at least 25% of the regions, results suggest that selection operated on variants located outside of currently annotated coding regions. These results provide insights into the underlying genetic effects of long-term artificial selection and identification of putative genetic elements underlying number of ears per plant in maize.

Genetic diversity is shaped by the interaction of drift and selection, but the details of this interaction are not well understood. The impact of genetic drift in a population is largely determined by its demographic history, typically summarized by its long-term effective population size (Ne). Rapidly changing population demographics complicate this relationship, however. To better understand how changing demography impacts selection, we used whole-genome sequencing data to investigate patterns of linked selection in domesticated and wild maize (teosinte). We produce the first whole-genome estimate of the demography of maize domestication, showing that maize was reduced to approximately 5% the population size of teosinte before it experienced rapid expansion post-domestication to population sizes much larger than its ancestor. Evaluation of patterns of nucleotide diversity in and near genes shows little evidence of selection on beneficial amino acid substitutions, and that the domestication bottleneck led to a decline in the efficiency of purifying selection in maize. Young alleles, however, show evidence of much stronger purifying selection in maize, reflecting the much larger effective size of present day populations. Our results demonstrate that recent demographic change-a hall-mark of many species including both humans and crops-can have immediate and wide-ranging impacts on diversity that conflict with expectations based on long-term Ne alone.

The interaction between genetic drift and selection in shaping genetic diversity is not fully understood. In particular, a population's propensity to drift is typically summarized by its long-term effective population size (N e ), but rapidly changing population demographics may complicate this relationship. To better understand how changing demography impacts selection, we investigated linked selection in the genomes of 23 domesticated maize and 13 wild maize (teosinte) individuals. We show that maize went through a domestication bottleneck with a population size of approximately 5% that of teosinte before it experienced rapid expansion post-domestication. We observe that hard sweeps on genic mutations are not the primary force driving maize evolution. As expected, a reduced population size during domestication decreased the efficiency of purifying selection to purge deleterious alleles from maize, but rapid expansion after domestication has since increased the efficiency of purifying selection to levels exceeding those seen in teosinte. This final observation demonstrates that rapid demographic change can have wide-ranging impacts on diversity that conflict with would-be expectations based on long-term N e .