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Phenotypic variation in natural populations results from a combination of genetic effects, environmental effects, and gene-by-environment interactions. Despite the vast amount of genomic data becoming available, many pressing questions remain about the nature of genetic mutations that underlie functional variation. We present the results of combining genome-wide association analysis of 41 different phenotypes in ∼ 5,000 inbred maize lines to analyze patterns of high-resolution genetic association among of 28.9 million single-nucleotide polymorphisms (SNPs) and ∼ 800,000 copy-number variants (CNVs). We show that genic and intergenic regions have opposite patterns of enrichment, minor allele frequencies, and effect sizes, implying tradeoffs among the probability that a given polymorphism will have an effect, the detectable size of that effect, and its frequency in the population. We also find that genes tagged by GWAS are enriched for regulatory functions and are ∼ 50% more likely to have a paralog than expected by chance, indicating that gene regulation and gene duplication are strong drivers of phenotypic variation. These results will likely apply to many other organisms, especially ones with large and complex genomes like maize.

Quantitative resistance to plant pathogens, controlled by multiple loci of small effect is important for food production, food security, and food safety but is poorly understood. To gain insights into the genetic architecture of quantitative resistance in maize, we evaluated a 5,000-inbred-line nested association mapping population for resistance to northern leaf blight, a maize disease of global economic importance, twenty-nine quantitative trait loci were identified, and most had multiple alleles. The large variation in resistance phenotypes could be attributed to the accumulation of numerous loci of small additive effects. Genome-wide nested association mapping, using 1.6 million SNPs, identified multiple candidate genes related to plant defense, including receptor-like kinase genes similar to those involved in basal defense. These results are consistent with the hypothesis that quantitative disease resistance in plants is conditioned by a range of mechanisms and could have considerable mechanistic overlap with basal resistance.

Ed Buckler and colleagues report a genome-wide association study for leaf architecture in the maize nested association mapping population. Genetic variation at the ligueless genes is associated with leaf angle, an important agronomic trait. US maize yield has increased eight-fold in the past 80 years, with half of the gain attributed to selection by breeders. During this time, changes in maize leaf angle and size have altered plant architecture, allowing more efficient light capture as planting density has increased. Through a genome-wide association study (GWAS) of the maize nested association mapping panel, we determined the genetic basis of important leaf architecture traits and identified some of the key genes. Overall, we demonstrate that the genetic architecture of the leaf traits is dominated by small effects, with little epistasis, environmental interaction or pleiotropy. In particular, GWAS results show that variations at the liguleless genes have contributed to more upright leaves. These results demonstrate that the use of GWAS with specially designed mapping populations is effective in uncovering the basis of key agronomic traits.

We compared the genetic architecture of thirteen maize morphological traits in a large population of recombinant inbred lines. Four traits from the male inflorescence (tassel) and three traits from the female inflorescence (ear) were measured and studied using linkage and genome-wide association analyses and compared to three flowering and three leaf traits previously studied in the same population. Inflorescence loci have larger effects than flowering and leaf loci, and ear effects are larger than tassel effects. Ear trait models also have lower predictive ability than tassel, flowering, or leaf trait models. Pleiotropic loci were identified that control elongation of ear and tassel, consistent with their common developmental origin. For these pleiotropic loci, the ear effects are larger than tassel effects even though the same causal polymorphisms are likely involved. This implies that the observed differences in genetic architecture are not due to distinct features of the underlying polymorphisms. Our results support the hypothesis that genetic architecture is a function of trait stability over evolutionary time, since the traits that changed most during the relatively recent domestication of maize have the largest effects.

Zhiwu Zhang and colleagues report a mixed linear model approach for correcting for population structure and family relatedness in genome-wide association studies. Mixed linear model (MLM) methods have proven useful in controlling for population structure and relatedness within genome-wide association studies. However, MLM-based methods can be computationally challenging for large datasets. We report a compression approach, called 'compressed MLM', that decreases the effective sample size of such datasets by clustering individuals into groups. We also present a complementary approach, 'population parameters previously determined' (P3D), that eliminates the need to re-compute variance components. We applied these two methods both independently and combined in selected genetic association datasets from human, dog and maize. The joint implementation of these two methods markedly reduced computing time and either maintained or improved statistical power. We used simulations to demonstrate the usefulness in controlling for substructure in genetic association datasets for a range of species and genetic architectures. We have made these methods available within an implementation of the software program TASSEL.

Height is one of the most heritable and easily measured traits in maize (Zea mays L.). Given a pedigree or estimates of the genomic identity-by-state (IBS) among related plants, height is also accurately predictable. But, mapping alleles explaining natural variation in maize height remains a formidable challenge. To address this challenge, we measured the plant height, ear height, flowering time, and node counts of plants grown in >64,500 plots across 13 environments. These plots contained >7,300 inbreds representing most publically available maize inbreds in the U.S.A. as well as families of the maize Nested Association Mapping (NAM) panel. Joint-linkage mapping of quantitative trait loci (QTL), fine mapping in near isogenic lines (NILs), genome wide association studies (GWAS), and genomic best linear unbiased prediction (GBLUP) were performed. The heritability of plant height was estimated to be over 90%. Mapping of NAM family-nested QTL revealed the largest explained about 2.1 ± 0.9% of height variation. The effects of two tropical alleles at this QTL were independently validated by fine mapping. Several significant associations found by GWAS co-localized with established height loci including brassinosteroid-deficient dwarf1, dwarf plant1, and semi-dwarf2. GBLUP explained >80% of plant height variation in the observed panels and outperformed bootstrap aggregation of family-nested QTL models in evaluations of prediction accuracy. These results revealed maize height was under strong genetic control and had a highly polygenic genetic architecture. They also showed that multiple models of genetic architecture differing in polygenicity and effect sizes can plausibly explain a population’s variation in maize height, but they may vary in predictive efficacy.

Significance Meiotic recombination is known to vary over 1,000-fold in many eukaryotic organisms, including maize. This regional genomic variation has enormous consequences for plant breeders, who rely on meiotic cross-overs to fine-map quantitative traits and introgress favorable alleles. Deleterious mutations are also predicted to accumulate preferentially within low-recombination regions, particularly within historically outcrossing species, such as maize. Here, we show that meiotic recombination is predictable across diverse crosses based on several genomic features of the reference genome. We demonstrate that the extant patterns of recombination are historically stable and tied to variation in the number of deleterious mutations. The ability of plant breeders to exploit recombination to purge segregating deleterious alleles will determine the efficacy of future crop improvement. 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.

James Holland and colleagues report a genome-wide association study for resistance to Southern Leaf Blight (SLB) in the maize nested association mapping population. Linkage mapping identified 32 QTLs linked to SLB resistance, and association tests showed that 51 SNPs, many located within the QTL intervals, are significantly associated with SLB resistance. Nested association mapping (NAM) offers power to resolve complex, quantitative traits to their causal loci. The maize NAM population, consisting of 5,000 recombinant inbred lines (RILs) from 25 families representing the global diversity of maize, was evaluated for resistance to southern leaf blight (SLB) disease. Joint-linkage analysis identified 32 quantitative trait loci (QTLs) with predominantly small, additive effects on SLB resistance. Genome-wide association tests of maize HapMap SNPs were conducted by imputing founder SNP genotypes onto the NAM RILs. SNPs both within and outside of QTL intervals were associated with variation for SLB resistance. Many of these SNPs were within or near sequences homologous to genes previously shown to be involved in plant disease resistance. Limited linkage disequilibrium was observed around some SNPs associated with SLB resistance, indicating that the maize NAM population enables high-resolution mapping of some genome regions.

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. Structural variations in crop genomes are thought to be responsible for significant differences in phenotype and they can be well-represented by a ‘pan-genome’. Here, Lu et al. develop an approach to genetically map pan-genome sequence anchors using 14,129 inbred lines of maize, showing structural variation is a significant source of adaptive variation.

Teosinte, the progenitor of maize, is restricted to tropical environments in Mexico and Central America. The pre-Columbian spread of maize from its center of origin in tropical Southern Mexico to the higher latitudes of the Americas required postdomestication selection for adaptation to longer day lengths. Flowering time of teosinte and tropical maize is delayed under long day lengths, whereas temperate maize evolved a reduced sensitivity to photoperiod. We measured flowering time of the maize nested association and diverse association mapping panels in the field under both short and long day lengths, and of a maize-teosinte mapping population under long day lengths. Flowering time in maize is a complex trait affected by many genes and the environment. Photoperiod response is one component of flowering time involving a subset of flowering time genes whose effects are strongly influenced by day length. Genome-wide association and targeted high-resolution linkage mapping identified ZmCCT , a homologue of the rice photoperiod response regulator Ghd7, as the most important gene affecting photoperiod response in maize. Under long day lengths ZmCCT alleles from diverse teosintes are consistently expressed at higher levels and confer later flowering than temperate maize alleles. Many maize inbred lines, including some adapted to tropical regions, carry ZmCCT alleles with no sensitivity to day length. Indigenous farmers of the Americas were remarkably successful at selecting on genetic variation at key genes affecting the photoperiod response to create maize varieties adapted to vastly diverse environments despite the hindrance of the geographic axis of the Americas and the complex genetic control of flowering time.