Joint-multiple family linkage analysis predicts within-family variation better than single-family analysis of the maize nested association mapping population

Mapping QTLs for blight resistance and morpho-phenological traits in inter-species hybrid families of chestnut (Castanea spp.)

Chestnut blight (caused by Cryphonectria parasitica), together with Phytophthora root rot (caused by Phytophthora cinnamomi), has nearly extirpated American chestnut (Castanea dentata) from its native range. In contrast to the susceptibility of American chestnut, many Chinese chestnut (C. mollissima) genotypes are resistant to blight. In this research, we performed a series of genome-wide association studies for blight resistance originating from three unrelated Chinese chestnut trees (Mahogany, Nanking and M16) and a Quantitative Trait Locus (QTL) study on a Mahogany-derived inter-species F2 family. We evaluated trees for resistance to blight after artificial inoculation with two fungal strains and scored nine morpho-phenological traits that are the hallmarks of species differentiation between American and Chinese chestnuts. Results support a moderately complex genetic architecture for blight resistance, as 31 QTLs were found on 12 chromosomes across all studies. Additionally, although most morpho-phenological trait QTLs overlap or are adjacent to blight resistance QTLs, they tend to aggregate in a few genomic regions. Finally, comparison between QTL intervals for blight resistance and those previously published for Phytophthora root rot resistance, revealed five common disease resistance regions on chromosomes 1, 5, and 11. Our results suggest that it will be difficult, but still possible to eliminate Chinese chestnut alleles for the morpho-phenological traits while achieving relatively high blight resistance in a backcross hybrid tree. We see potential for a breeding scheme that utilizes marker-assisted selection early for relatively large effect QTLs followed by genome selection in later generations for smaller effect genomic regions.

Identification of loci conferring resistance to 4 foliar diseases of maize

Foliar diseases of maize are among the most important diseases of maize worldwide. This study focused on 4 major foliar diseases of maize: Goss's wilt, gray leaf spot, northern corn leaf blight, and southern corn leaf blight. QTL mapping for resistance to Goss's wilt was conducted in 4 disease resistance introgression line populations with Oh7B as the common recurrent parent and Ki3, NC262, NC304, and NC344 as recurrent donor parents. Mapping results for Goss's wilt resistance were combined with previous studies for gray leaf spot, northern corn leaf blight, and southern corn leaf blight resistance in the same 4 populations. We conducted (1) individual linkage mapping analysis to identify QTL specific to each disease and population; (2) Mahalanobis distance analysis to identify putative multiple disease resistance regions for each population; and 3) joint linkage mapping to identify QTL across the 4 populations for each disease. We identified 3 lines that were resistant to all 4 diseases. We mapped 13 Goss's wilt QTLs in the individual populations and an additional 6 using joint linkage mapping. All Goss's wilt QTL had small effects, confirming that resistance to Goss's wilt is highly quantitative. We report several potentially important chromosomal bins associated with multiple disease resistance including 1.02, 1.03, 3.04, 4.06, 4.08, and 9.03. Together, these findings indicate that disease QTL distribution is not random and that there are locations in the genome that confer resistance to multiple diseases. Furthermore, resistance to bacterial and fungal diseases is not entirely distinct, and we identified lines resistant to both fungi and bacteria, as well as loci that confer resistance to both bacterial and fungal diseases.

Two teosintes made modern maize

The origins of maize were the topic of vigorous debate for nearly a century, but neither the current genetic model nor earlier archaeological models account for the totality of available data, and recent work has highlighted the potential contribution of a wild relative, Zea mays ssp. mexicana. Our population genetic analysis reveals that the origin of modern maize can be traced to an admixture between ancient maize and Zea mays ssp. mexicana in the highlands of Mexico some 4000 years after domestication began. We show that variation in admixture is a key component of maize diversity, both at individual loci and for additive genetic variation underlying agronomic traits. Our results clarify the origin of modern maize and raise new questions about the anthropogenic mechanisms underlying dispersal throughout the Americas.

Exploring the Relationship Between Pattern-Triggered Immunity and Quantitative Resistance to Xanthomonas vasicola pv. vasculorum in Maize

Bacterial leaf streak (BLS) of maize is an emerging foliar disease of maize in the Americas. It is caused by the gram-negative nonvascular bacterium Xanthomonas vasicola pv. vasculorum. There are no chemical controls available for BLS, and thus, host resistance is crucial for managing X. vasicola pv. vasculorum. The objective of this study was to examine the genetic determinants of resistance to X. vasicola pv. vasculorum in maize, as well as the relationship between other defense-related traits and BLS resistance. Specifically, we examined the correlations among BLS severity, severity for three fungal diseases, flg-22 response, hypersensitive response, and auricle color. We conducted quantitative trait locus (QTL) mapping for X. vasicola pv. vasculorum resistance using the maize recombinant inbred line population Z003 (B73 × CML228). We detected three QTLs for BLS resistance. In addition to the disease resistance QTL, we detected a single QTL for auricle color. We observed significant, yet weak, correlations among BLS severity, levels of pattern-triggered immunity response and leaf flecking. These results will be useful for understanding resistance to X. vasicola pv. vasculorum and mitigating the impact of BLS on maize yields.

Molecular characterization of doubled haploid lines derived from different cycles of the Iowa Stiff Stalk Synthetic (BSSS) maize population

Molecular characterization of a given set of maize germplasm could be useful for understanding the use of the assembled germplasm for further improvement in a breeding program, such as analyzing genetic diversity, selecting a parental line, assigning heterotic groups, creating a core set of germplasm and/or performing association analysis for traits of interest. In this study, we used single nucleotide polymorphism (SNP) markers to assess the genetic variability in a set of doubled haploid (DH) lines derived from the unselected Iowa Stiff Stalk Synthetic (BSSS) maize population, denoted as C0 (BSSS(R)C0), the seventeenth cycle of reciprocal recurrent selection in BSSS (BSSS(R)C17), denoted as C17 and the cross between BSSS(R)C0 and BSSS(R)C17 denoted as C0/C17. With the aim to explore if we have potentially lost diversity from C0 to C17 derived DH lines and observe whether useful genetic variation in C0 was left behind during the selection process since C0 could be a reservoir of genetic diversity that could be untapped using DH technology. Additionally, we quantify the contribution of the BSSS progenitors in each set of DH lines. The molecular characterization analysis confirmed the apparent separation and the loss of genetic variability from C0 to C17 through the recurrent selection process. Which was observed by the degree of differentiation between the C0_DHL versus C17_DHL groups by Wright's F-statistics (FST). Similarly for the population structure based on principal component analysis (PCA) revealed a clear separation among groups of DH lines. Some of the progenitors had a higher genetic contribution in C0 compared with C0/C17 and C17 derived DH lines. Although genetic drift can explain most of the genetic structure genome-wide, phenotypic data provide evidence that selection has altered favorable allele frequencies in the BSSS maize population through the reciprocal recurrent selection program.

A divide-and-conquer approach for genomic prediction in rubber tree using machine learning

Rubber tree (Hevea brasiliensis) is the main feedstock for commercial rubber; however, its long vegetative cycle has hindered the development of more productive varieties via breeding programs. With the availability of H. brasiliensis genomic data, several linkage maps with associated quantitative trait loci have been constructed and suggested as a tool for marker-assisted selection. Nonetheless, novel genomic strategies are still needed, and genomic selection (GS) may facilitate rubber tree breeding programs aimed at reducing the required cycles for performance assessment. Even though such a methodology has already been shown to be a promising tool for rubber tree breeding, increased model predictive capabilities and practical application are still needed. Here, we developed a novel machine learning-based approach for predicting rubber tree stem circumference based on molecular markers. Through a divide-and-conquer strategy, we propose a neural network prediction system with two stages: (1) subpopulation prediction and (2) phenotype estimation. This approach yielded higher accuracies than traditional statistical models in a single-environment scenario. By delivering large accuracy improvements, our methodology represents a powerful tool for use in Hevea GS strategies. Therefore, the incorporation of machine learning techniques into rubber tree GS represents an opportunity to build more robust models and optimize Hevea breeding programs.

Modeling allelic diversity of multiparent mapping populations affects detection of quantitative trait loci

The search for quantitative trait loci that explain complex traits such as yield and drought tolerance has been ongoing in all crops. Methods such as biparental quantitative trait loci mapping and genome-wide association studies each have their own advantages and limitations. Multiparent advanced generation intercross populations contain more recombination events and genetic diversity than biparental 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 multiparent advanced generation intercross population of doubled haploid maize lines created from 16 diverse founders to perform quantitative trait loci mapping. We compare 3 models that assume bi-allelic, founder, and ancestral haplotype allelic states for quantitative trait loci. The 3 methods have differing power to detect quantitative trait loci for a variety of agronomic traits. Although the founder approach finds the most quantitative trait loci, all methods are able to find unique quantitative trait loci, suggesting that each model has advantages for traits with different genetic architectures. A closer look at a well-characterized flowering time quantitative trait loci, 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 multiallelic quantitative trait loci.

An IBD-based mixed model approach for QTL mapping in multiparental populations

The identity-by-descent (IBD)-based mixed model approach introduced in this study can detect quantitative trait loci (QTLs) referring to the parental origin and simultaneously account for multilevel relatedness of individuals within and across families. This unified approach is proved to be a powerful approach for all kinds of multiparental population (MPP) designs. Multiparental populations (MPPs) have become popular for quantitative trait loci (QTL) detection. Tools for QTL mapping in MPPs are mostly developed for specific MPPs and do not generalize well to other MPPs. We present an IBD-based mixed model approach for QTL mapping in all kinds of MPP designs, e.g., diallel, Nested Association Mapping (NAM), and Multiparental Advanced Generation Intercross (MAGIC) designs. The first step is to compute identity-by-descent (IBD) probabilities using a general Hidden Markov model framework, called reconstructing ancestry blocks bit by bit (RABBIT). Next, functions of IBD information are used as design matrices, or genetic predictors, in a mixed model approach to estimate variance components for multiallelic genetic effects associated with parents. Family-specific residual genetic effects are added, and a polygenic effect is structured by kinship relations between individuals. Case studies of simulated diallel, NAM, and MAGIC designs proved that the advanced IBD-based multi-QTL mixed model approach incorporating both kinship relations and family-specific residual variances (IBD.MQMkin_F) is robust across a variety of MPP designs and allele segregation patterns in comparison to a widely used benchmark association mapping method, and in most cases, outperformed or behaved at least as well as other tools developed for specific MPP designs in terms of mapping power and resolution. Successful analyses of real data cases confirmed the wide applicability of our IBD-based mixed model methodology.

The genomic ecosystem of transposable elements in maize

Transposable elements (TEs) constitute the majority of flowering plant DNA, reflecting their tremendous success in subverting, avoiding, and surviving the defenses of their host genomes to ensure their selfish replication. More than 85% of the sequence of the maize genome can be ascribed to past transposition, providing a major contribution to the structure of the genome. Evidence from individual loci has informed our understanding of how transposition has shaped the genome, and a number of individual TE insertions have been causally linked to dramatic phenotypic changes. Genome-wide analyses in maize and other taxa have frequently represented TEs as a relatively homogeneous class of fragmentary relics of past transposition, obscuring their evolutionary history and interaction with their host genome. Using an updated annotation of structurally intact TEs in the maize reference genome, we investigate the family-level dynamics of TEs in maize. Integrating a variety of data, from descriptors of individual TEs like coding capacity, expression, and methylation, as well as similar features of the sequence they inserted into, we model the relationship between attributes of the genomic environment and the survival of TE copies and families. In contrast to the wholesale relegation of all TEs to a single category of junk DNA, these differences reveal a diversity of survival strategies of TE families. Together these generate a rich ecology of the genome, with each TE family representing the evolution of a distinct ecological niche. We conclude that while the impact of transposition is highly family- and context-dependent, a family-level understanding of the ecology of TEs in the genome can refine our ability to predict the role of TEs in generating genetic and phenotypic diversity.

Selective sorting of ancestral introgression in maize and teosinte along an elevational cline

While often deleterious, hybridization can also be a key source of genetic variation and pre-adapted haplotypes, enabling rapid evolution and niche expansion. Here we evaluate these opposing selection forces on introgressed ancestry between maize (Zea mays ssp. mays) and its wild teosinte relative, mexicana (Zea mays ssp. mexicana). Introgression from ecologically diverse teosinte may have facilitated maize's global range expansion, in particular to challenging high elevation regions (> 1500 m). We generated low-coverage genome sequencing data for 348 maize and mexicana individuals to evaluate patterns of introgression in 14 sympatric population pairs, spanning the elevational range of mexicana, a teosinte endemic to the mountains of Mexico. While recent hybrids are commonly observed in sympatric populations and mexicana demonstrates fine-scale local adaptation, we find that the majority of mexicana ancestry tracts introgressed into maize over 1000 generations ago. This mexicana ancestry seems to have maintained much of its diversity and likely came from a common ancestral source, rather than contemporary sympatric populations, resulting in relatively low FST between mexicana ancestry tracts sampled from geographically distant maize populations. Introgressed mexicana ancestry in maize is reduced in lower-recombination rate quintiles of the genome and around domestication genes, consistent with pervasive selection against introgression. However, we also find mexicana ancestry increases across the sampled elevational gradient and that high introgression peaks are most commonly shared among high-elevation maize populations, consistent with introgression from mexicana facilitating adaptation to the highland environment. In the other direction, we find patterns consistent with adaptive and clinal introgression of maize ancestry into sympatric mexicana at many loci across the genome, suggesting that maize also contributes to adaptation in mexicana, especially at the lower end of its elevational range. In sympatric maize, in addition to high introgression regions we find many genomic regions where selection for local adaptation maintains steep gradients in introgressed mexicana ancestry across elevation, including at least two inversions: the well-characterized 14 Mb Inv4m on chromosome 4 and a novel 3 Mb inversion Inv9f surrounding the macrohairless1 locus on chromosome 9. Most outlier loci with high mexicana introgression show no signals of sweeps or local sourcing from sympatric populations and so likely represent ancestral introgression sorted by selection, resulting in correlated but distinct outcomes of introgression in different contemporary maize populations.

The genetic architecture of leaf stable carbon isotope composition in Zea mays and the effect of transpiration efficiency on leaf elemental accumulation

With increased demand on freshwater resources for agriculture, it is imperative that more water-use efficient crops are developed. Leaf stable carbon isotope composition, δ13C, is a proxy for transpiration efficiency and a possible tool for breeders, but the underlying mechanisms effecting δ13C in C4 plants are not known. It has been suggested that differences in specific leaf area (SLA), which potentially reflects variation in internal CO2 diffusion, can impact leaf δ13C. Furthermore, although it is known that water movement is important for elemental uptake, it is not clear how manipulation of transpiration for increased water-use efficiency may impact nutrient accumulation. Here, we characterize the genetic architecture of leaf δ13C and test its relationship to SLA and the ionome in five populations of maize. Five significant QTL for leaf δ13C were identified, including novel QTL as well as some that were identified previously in maize kernels. One of the QTL regions contains an Erecta-like gene, the ortholog of which has been shown to regulate transpiration efficiency and leaf δ13C in Arabidopsis. QTL for δ13C were located in the same general chromosome region, but slightly shifted, when comparing data from two different years. Our data does not support a relationship between δ13C and SLA, and of the 19 elements analyzed, only a weak correlation between molybdenum and δ13C was detected. Together these data add to the genetic understanding of leaf δ13C in maize and suggest that improvements to plant water use may be possible without significantly influencing elemental homeostasis.

Molecular Parallelism Underlies Convergent Highland Adaptation of Maize Landraces

Convergent phenotypic evolution provides some of the strongest evidence for adaptation. However, the extent to which recurrent phenotypic adaptation has arisen via parallelism at the molecular level remains unresolved, as does the evolutionary origin of alleles underlying such adaptation. Here, we investigate genetic mechanisms of convergent highland adaptation in maize landrace populations and evaluate the genetic sources of recurrently selected alleles. Population branch excess statistics reveal substantial evidence of parallel adaptation at the level of individual single-nucleotide polymorphism (SNPs), genes, and pathways in four independent highland maize populations. The majority of convergently selected SNPs originated via migration from a single population, most likely in the Mesoamerican highlands, while standing variation introduced by ancient gene flow was also a contributor. Polygenic adaptation analyses of quantitative traits reveal that alleles affecting flowering time are significantly associated with elevation, indicating the flowering time pathway was targeted by highland adaptation. In addition, repeatedly selected genes were significantly enriched in the flowering time pathway, indicating their significance in adapting to highland conditions. Overall, our study system represents a promising model to study convergent evolution in plants with potential applications to crop adaptation across environmental gradients.

De novo assembly, annotation, and comparative analysis of 26 diverse maize genomes

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.

Eleven biosynthetic genes explain the majority of natural variation in carotenoid levels in maize grain

Vitamin A deficiency remains prevalent in parts of Asia, Latin America, and sub-Saharan Africa where maize (Zea mays) is a food staple. Extensive natural variation exists for carotenoids in maize grain. Here, to understand its genetic basis, we conducted a joint linkage and genome-wide association study of the US maize nested association mapping panel. Eleven of the 44 detected quantitative trait loci (QTL) were resolved to individual genes. Six of these were correlated expression and effect QTL (ceeQTL), showing strong correlations between RNA-seq expression abundances and QTL allelic effect estimates across six stages of grain development. These six ceeQTL also had the largest percentage of phenotypic variance explained, and in major part comprised the three to five loci capturing the bulk of genetic variation for each trait. Most of these ceeQTL had strongly correlated QTL allelic effect estimates across multiple traits. These findings provide an in-depth genome-level understanding of the genetic and molecular control of carotenoids in plants. In addition, these findings provide a roadmap to accelerate breeding for provitamin A and other priority carotenoid traits in maize grain that should be readily extendable to other cereals.

A Multi-Locus Association Model Framework for Nested Association Mapping With Discriminating QTL Effects in Various Subpopulations

Nested association mapping (NAM) has been an invaluable approach for plant genetics community and can dissect the genetic architecture of complex traits. As the most popular NAM analysis strategy, joint multifamily mapping can combine all information from diverse genetic backgrounds and increase population size. However, it is influenced by the genetic heterogeneity of quantitative trait locus (QTL) across various subpopulations. Multi-locus association mapping has been proven to be powerful in many cases of QTL mapping and genome-wide association studies. Therefore, we developed a multi-locus association model of multiple families in the NAM population, which could discriminate the effects of QTLs in all subpopulations. A series of simulations with a real maize NAM genomic data were implemented. The results demonstrated that the new method improves the statistical power in QTL detection and the accuracy in QTL effect estimation. The new approach, along with single-family linkage mapping, was used to identify QTLs for three flowering time traits in the maize NAM population. As a result, most QTLs detected in single family linkage mapping were identified by the new method. In addition, the new method also mapped some new QTLs with small effects, although their functions need to be identified in the future.

Selective Loss of Diversity in Doubled-Haploid Lines from European Maize Landraces

Maize landraces are well adapted to their local environments and present valuable sources of genetic diversity for breeding and conservation. But the maintenance of open-pollinated landraces in ex-situ programs is challenging, as regeneration of seed can often lead to inbreeding depression and the loss of diversity due to genetic drift. Recent reports suggest that the production of doubled-haploid (DH) lines from landraces may serve as a convenient means to preserve genetic diversity in a homozygous form that is immediately useful for modern breeding. The production of doubled-haploid (DH) lines presents an extreme case of inbreeding which results in instantaneous homozygosity genome-wide. Here, we analyzed the effect of DH production on genetic diversity, using genome-wide SNP data from hundreds of individuals of five European landraces and their related DH lines. In contrast to previous findings, we observe a dramatic loss of diversity at both the haplotype level and that of individual SNPs. We identify thousands of SNPs that exhibit allele frequency differences larger than expected under models of neutral genetic drift and document losses of shared haplotypes. We find evidence consistent with selection at functional sites that are potentially involved in the diversity differences between landrace and DH populations. Although we were unable to uncover more details about the mode of selection, we conclude that landrace DH lines may be a valuable tool for the introduction of variation into maize breeding programs but come at the cost of decreased genetic diversity.

Quantitative Genomic Dissection of Soybean Yield Components

Soybean is a crop of major economic importance with low rates of genetic gains for grain yield compared to other field crops. A deeper understanding of the genetic architecture of yield components may enable better ways to tackle the breeding challenges. Key yield components include the total number of pods, nodes and the ratio pods per node. We evaluated the SoyNAM population, containing approximately 5600 lines from 40 biparental families that share a common parent, in 6 environments distributed across 3 years. The study indicates that the yield components under evaluation have low heritability, a reasonable amount of epistatic control, and partially oligogenic architecture: 18 quantitative trait loci were identified across the three yield components using multi-approach signal detection. Genetic correlation between yield and yield components was highly variable from family-to-family, ranging from -0.2 to 0.5. The genotype-by-environment correlation of yield components ranged from -0.1 to 0.4 within families. The number of pods can be utilized for indirect selection of yield. The selection of soybean for enhanced yield components can be successfully performed via genomic prediction, but the challenging data collections necessary to recalibrate models over time makes the introgression of QTL a potentially more feasible breeding strategy. The genomic prediction of yield components was relatively accurate across families, but less accurate predictions were obtained from within family predictions and predicting families not observed included in the calibration set.

Transposable elements contribute to dynamic genome content in maize

Transposable elements (TEs) are ubiquitous components of eukaryotic genomes and can create variation in genome organization and content. Most maize genomes are composed of TEs. We developed an approach to define shared and variable TE insertions across genome assemblies and applied this method to four maize genomes (B73, W22, Mo17 and PH207) with uniform structural annotations of TEs. Among these genomes we identified approximately 400 000 TEs that are polymorphic, encompassing 1.6 Gb of variable TE sequence. These polymorphic TEs include a combination of recent transposition events as well as deletions of older TEs. There are examples of polymorphic TEs within each of the superfamilies of TEs and they are found distributed across the genome, including in regions of recent shared ancestry among individuals. There are many examples of polymorphic TEs within or near maize genes. In addition, there are 2380 gene annotations in the B73 genome that are located within variable TEs, providing evidence for the role of TEs in contributing to the substantial differences in annotated gene content among these genotypes. TEs are highly variable in our survey of four temperate maize genomes, highlighting the major contribution of TEs in driving variation in genome organization and gene content. OPEN RESEARCH BADGES: This article has earned an Open Data Badge for making publicly available the digitally-shareable data necessary to reproduce the reported results. The data is available at https://github.com/SNAnderson/maizeTE_variation; https://mcstitzer.github.io/maize_TEs.

A large nested association mapping population for breeding and quantitative trait locus mapping in Ethiopian durum wheat

The Ethiopian plateau hosts thousands of durum wheat (Triticum turgidum subsp. durum) farmer varieties (FV) with high adaptability and breeding potential. To harness their unique allelic diversity, we produced a large nested association mapping (NAM) population intercrossing fifty Ethiopian FVs with an international elite durum wheat variety (Asassa). The Ethiopian NAM population (EtNAM) is composed of fifty interconnected bi-parental families, totalling 6280 recombinant inbred lines (RILs) that represent both a powerful quantitative trait loci (QTL) mapping tool, and a large pre-breeding panel. Here, we discuss the molecular and phenotypic diversity of the EtNAM founder lines, then we use an array featuring 13 000 single nucleotide polymorphisms (SNPs) to characterize a subset of 1200 EtNAM RILs from 12 families. Finally, we test the usefulness of the population by mapping phenology traits and plant height using a genome wide association (GWA) approach. EtNAM RILs showed high allelic variation and a genetic makeup combining genetic diversity from Ethiopian FVs with the international durum wheat allele pool. EtNAM SNP data were projected on the fully sequenced AB genome of wild emmer wheat, and were used to estimate pairwise linkage disequilibrium (LD) measures that reported an LD decay distance of 7.4 Mb on average, and balanced founder contributions across EtNAM families. GWA analyses identified 11 genomic loci individually affecting up to 3 days in flowering time and more than 1.6 cm in height. We argue that the EtNAM is a powerful tool to support the production of new durum wheat varieties targeting local and global agriculture.

Detecting Adaptive Differentiation in Structured Populations with Genomic Data and Common Gardens

Adaptation in quantitative traits often occurs through subtle shifts in allele frequencies at many loci-a process called polygenic adaptation. While a number of methods have been developed to detect polygenic adaptation in human populations, we lack clear strategies for doing so in many other systems. In particular, there is an opportunity to develop new methods that leverage datasets with genomic data and common garden trait measurements to systematically detect the quantitative traits important for adaptation. Here, we develop methods that do just this, using principal components of the relatedness matrix to detect excess divergence consistent with polygenic adaptation, and using a conditional test to control for confounding effects due to population structure. We apply these methods to inbred maize lines from the United States Department of Agriculture germplasm pool and maize landraces from Europe. Ultimately, these methods can be applied to additional domesticated and wild species to give us a broader picture of the specific traits that contribute to adaptation and the overall importance of polygenic adaptation in shaping quantitative trait variation.

The genetic architecture of genome-wide recombination rate variation in allopolyploid wheat revealed by nested association mapping

Recombination affects the fate of alleles in populations by imposing constraints on the reshuffling of genetic information. Understanding the genetic basis of these constraints is critical for manipulating the recombination process to improve the resolution of genetic mapping, and reducing the negative effects of linkage drag and deleterious genetic load in breeding. Using sequence-based genotyping of a wheat nested association mapping (NAM) population of 2,100 recombinant inbred lines created by crossing 29 diverse lines, we mapped QTL affecting the distribution and frequency of 102 000 crossovers (CO). Genome-wide recombination rate variation was mostly defined by rare alleles with small effects together explaining up to 48.6% of variation. Most QTL were additive and showed predominantly trans-acting effects. The QTL affecting the proximal COs also acted additively without increasing the frequency of distal COs. We showed that the regions with decreased recombination carry more single nucleotide polymorphisms (SNPs) with possible deleterious effects than the regions with a high recombination rate. Therefore, our study offers insights into the genetic basis of recombination rate variation in wheat and its effect on the distribution of deleterious SNPs across the genome. The identified trans-acting additive QTL can be utilized to manipulate CO frequency and distribution in the large polyploid wheat genome opening the possibility to improve the efficiency of gene pyramiding and reducing the deleterious genetic load in the low-recombining pericentromeric regions of chromosomes.

Accounting for Genotype-by-Environment Interactions and Residual Genetic Variation in Genomic Selection for Water-Soluble Carbohydrate Concentration in Wheat

Abiotic stress tolerance traits are often complex and recalcitrant targets for conventional breeding improvement in many crop species. This study evaluated the potential of genomic selection to predict water-soluble carbohydrate concentration (WSCC), an important drought tolerance trait, in wheat under field conditions. A panel of 358 varieties and breeding lines constrained for maturity was evaluated under rainfed and irrigated treatments across two locations and two years. Whole-genome marker profiles and factor analytic mixed models were used to generate genomic estimated breeding values (GEBVs) for specific environments and environment groups. Additive genetic variance was smaller than residual genetic variance for WSCC, such that genotypic values were dominated by residual genetic effects rather than additive breeding values. As a result, GEBVs were not accurate predictors of genotypic values of the extant lines, but GEBVs should be reliable selection criteria to choose parents for intermating to produce new populations. The accuracy of GEBVs for untested lines was sufficient to increase predicted genetic gain from genomic selection per unit time compared to phenotypic selection if the breeding cycle is reduced by half by the use of GEBVs in off-season generations. Further, genomic prediction accuracy depended on having phenotypic data from environments with strong correlations with target production environments to build prediction models. By combining high-density marker genotypes, stress-managed field evaluations, and mixed models that model simultaneously covariances among genotypes and covariances of complex trait performance between pairs of environments, we were able to train models with good accuracy to facilitate genetic gain from genomic selection.

Bayesian Diallel Analysis Reveals Mx1-Dependent and Mx1-Independent Effects on Response to Influenza A Virus in Mice

Influenza A virus (IAV) is a respiratory pathogen that causes substantial morbidity and mortality during both seasonal and pandemic outbreaks. Infection outcomes in unexposed populations are affected by host genetics, but the host genetic architecture is not well understood. Here, we obtain a broad view of how heritable factors affect a mouse model of response to IAV infection using an 8 × 8 diallel of the eight inbred founder strains of the Collaborative Cross (CC). Expanding on a prior statistical framework for modeling treatment response in diallels, we explore how a range of heritable effects modify acute host response to IAV through 4 d postinfection. Heritable effects in aggregate explained ∼57% of the variance in IAV-induced weight loss. Much of this was attributable to a pattern of additive effects that became more prominent through day 4 postinfection and was consistent with previous reports of antiinfluenza myxovirus resistance 1 (Mx1) polymorphisms segregating between these strains; these additive effects largely recapitulated haplotype effects observed at the Mx1 locus in a previous study of the incipient CC, and are also replicated here in a CC recombinant intercross population. Genetic dominance of protective Mx1 haplotypes was observed to differ by subspecies of origin: relative to the domesticus null Mx1 allele, musculus acts dominantly whereas castaneus acts additively. After controlling for Mx1, heritable effects, though less distinct, accounted for ∼34% of the phenotypic variance. Implications for future mapping studies are discussed.

Genetic Architecture of Nitrogen-Deficiency Tolerance in Wheat Seedlings Based on a Nested Association Mapping (NAM) Population

Genetic divergence for nitrogen utilization in germplasms is important in wheat breeding programs, especially for low nitrogen input management. In this study, a nested association mapping (NAM) population, derived from "Yanzhan 1" (a Chinese domesticated cultivar) crossed with "Hussar" (a British domesticated cultivar) and another three semi-wild wheat varieties, namely, "Cayazheda 29" (Triticum aestivum ssp. tibetanum Shao), "Yunnan" (T. aestivum ssp. yunnanense King), and "Yutian" (T. aestivum petropavloski Udats et Migusch), was used to detect quantitative trait loci (QTLs) for nitrogen utilization at the seedling stage. An integrated genetic map was constructed using 2,059 single nucleotide polymorphism (SNP) markers from a 90 K SNP chip, with a total coverage of 2,355.75 cM and an average marker spacing of 1.13 cM. A total of 67 QTLs for RDW (root dry weight), SDW (shoot dry weight), TDW (total dry weight), and RSDW (root to shoot ratio) were identified under normal nitrogen conditions (N⁺) and nitrogen deficient conditions (N^-). Twenty-three of these QTLs were only detected under N^- conditions. Moreover, 23 favorable QTLs were identified in the domesticated cultivar Yanzhan 1, 15 of which were detected under N⁺ conditions, while only four were detected under N^- conditions. In contrast, the semi-wild cultivars contributed more favorable N^--specific QTLs (eight from Cayazheda 29; nine from Yunnan), which could be further explored for breeding cultivars adapted to nitrogen-deficient conditions. In particular, QRSDW-5A.1 from YN should be further evaluated using high-resolution mapping.

The interplay of demography and selection during maize domestication and expansion

BACKGROUND

The 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.

RESULTS

We 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.

CONCLUSIONS

These 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.

The interplay of demography and selection during maize domestication and expansion

BACKGROUND

The 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.

RESULTS

We 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.

CONCLUSIONS

These 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.

Novel Loci Underlie Natural Variation in Vitamin E Levels in Maize Grain

Tocopherols, tocotrienols, and plastochromanols (collectively termed tocochromanols) are lipid-soluble antioxidants synthesized by all plants. Their dietary intake, primarily from seed oils, provides vitamin E and other health benefits. Tocochromanol biosynthesis has been dissected in the dicot Arabidopsis thaliana, which has green, photosynthetic seeds, but our understanding of tocochromanol accumulation in major crops, whose seeds are nonphotosynthetic, remains limited. To understand the genetic control of tocochromanols in grain, we conducted a joint linkage and genome-wide association study in the 5000-line U.S. maize (Zea mays) nested association mapping panel. Fifty-two quantitative trait loci for individual and total tocochromanols were identified, and of the 14 resolved to individual genes, six encode novel activities affecting tocochromanols in plants. These include two chlorophyll biosynthetic enzymes that explain the majority of tocopherol variation, which was not predicted given that, like most major cereal crops, maize grain is nonphotosynthetic. This comprehensive assessment of natural variation in vitamin E levels in maize establishes the foundation for improving tocochromanol and vitamin E content in seeds of maize and other major cereal crops.

Incomplete dominance of deleterious alleles contributes substantially to trait variation and heterosis in maize

Deleterious alleles have long been proposed to play an important role in patterning phenotypic variation and are central to commonly held ideas explaining the hybrid vigor observed in the offspring of a cross between two inbred parents. We test these ideas using evolutionary measures of sequence conservation to ask whether incorporating information about putatively deleterious alleles can inform genomic selection (GS) models and improve phenotypic prediction. We measured a number of agronomic traits in both the inbred parents and hybrids of an elite maize partial diallel population and re-sequenced the parents of the population. Inbred elite maize lines vary for more than 350,000 putatively deleterious sites, but show a lower burden of such sites than a comparable set of traditional landraces. Our modeling reveals widespread evidence for incomplete dominance at these loci, and supports theoretical models that more damaging variants are usually more recessive. We identify haplotype blocks using an identity-by-decent (IBD) analysis and perform genomic prediction analyses in which we weigh blocks on the basis of complementation for segregating putatively deleterious variants. Cross-validation results show that incorporating sequence conservation in genomic selection improves prediction accuracy for grain yield and other fitness-related traits as well as heterosis for those traits. Our results provide empirical support for an important role for incomplete dominance of deleterious alleles in explaining heterosis and demonstrate the utility of incorporating functional annotation in phenotypic prediction and plant breeding.

Genome-Wide Linkage Analysis of Large Multiple Multigenerational Families Identifies Novel Genetic Loci for Coronary Artery Disease

Coronary artery disease (CAD) is the leading cause of death, and genetic factors contribute significantly to risk of CAD. This study aims to identify new CAD genetic loci through a large-scale linkage analysis of 24 large and multigenerational families with 433 family members (GeneQuest II). All family members were genotyped with markers spaced by every 10 cM and a model-free nonparametric linkage (NPL-all) analysis was carried out. Two highly significant CAD loci were identified on chromosome 17q21.2 (NPL score of 6.20) and 7p22.2 (NPL score of 5.19). We also identified four loci with significant NPL scores between 4.09 and 4.99 on 2q33.3, 3q29, 5q13.2 and 9q22.33. Similar analyses in individual families confirmed the six significant CAD loci and identified seven new highly significant linkages on 9p24.2, 9q34.2, 12q13.13, 15q26.1, 17q22, 20p12.3, and 22q12.1, and two significant loci on 2q11.2 and 11q14.1. Two loci on 3q29 and 9q22.33 were also successfully replicated in our previous linkage analysis of 428 nuclear families. Moreover, two published risk variants, SNP rs46522 in UBE2Z and SNP rs6725887 in WDR12 by GWAS, were found within the 17q21.2 and 2q33.3 loci. These studies lay a foundation for future identification of causative variants and genes for CAD.

Genetic Architecture of a Rice Nested Association Mapping Population

Describing the genetic diversity in the gene pool of crops will provide breeders with novel resources for varietal improvement. Nested Association Mapping (NAM) populations are uniquely suited for characterizing parental diversity through the shuffling and fixation of parental haplotypes. Here, we describe a set of 1879 rice NAM lines created through the selfing and single-seed descent of F1 hybrids derived from elite IR64 indica crossed with 10 diverse tropical japonica lines. Genotyping data indicated tropical japonica alleles were captured at every queried locus despite the presence of segregation distortion factors. Several distortion loci were mapped, both shared and unique, among the 10 populations. Using two-point and multi-point genetic map calculations, our datasets achieved the ∼1500 cM expected map size in rice. Finally, we highlighted the utility of the NAM lines for QTL mapping, including joint analysis across the 10 populations, by confirming known QTL locations for the trait days to heading.

Pedigree-Based Analysis in a Multiparental Population of Octoploid Strawberry Reveals QTL Alleles Conferring Resistance to Phytophthora cactorum

Understanding the genetic architecture of traits in breeding programs can be critical for making genetic progress. Important factors include the number of loci controlling a trait, allele frequencies at those loci, and allele effects in breeding germplasm. To this end, multiparental populations offer many advantages for quantitative trait locus (QTL) analyses compared to biparental populations. These include increased power for QTL detection, the ability to sample a larger number of segregating loci and alleles, and estimation of allele effects across diverse genetic backgrounds. Here, we investigate the genetic architecture of resistance to crown rot disease caused by Phytophthora cactorum in strawberry (Fragaria × ananassa), using connected full-sib families from a breeding population. Clonal replicates of > 1100 seedlings from 139 full-sib families arising from 61 parents were control-inoculated during two consecutive seasons. Subgenome-specific single nucleotide polymorphism (SNP) loci were mapped in allo-octoploid strawberry (2n = 8 × = 56), and FlexQTL software was utilized to perform a Bayesian, pedigree-based QTL analysis. A major locus on linkage group (LG) 7D, which we name FaRPc2, accounts for most of the genetic variation for resistance. Four predominant SNP haplotypes were detected in the FaRPc2 region, two of which are strongly associated with two different levels of resistance, suggesting the presence of multiple resistance alleles. The phenotypic effects of FaRPc2 alleles across trials and across numerous genetic backgrounds make this locus a highly desirable target for genetic improvement of resistance in cultivated strawberry.

High-throughput SNP genotyping of modern and wild emmer wheat for yield and root morphology using a combined association and linkage analysis

Durum wheat (Triticum turgidum var. durum Desf.) is a major world crop that is grown primarily in areas of the world that experience periodic drought, and therefore, breeding climate-resilient durum wheat is a priority. High-throughput single nucleotide polymorphism (SNP) genotyping techniques have greatly increased the power of linkage and association mapping analyses for bread wheat, but as yet there is no durum wheat-specific platform available. In this study, we evaluate the new 384HT Wheat Breeders Array for its usefulness in tetraploid wheat breeding by genotyping a breeding population of F₆ hybrids, derived from multiple crosses between T. durum cultivars and wild and cultivated emmer wheat accessions. Using a combined linkage and association mapping approach, we generated a genetic map including 1345 SNP markers, and identified markers linked to 6 QTLs for coleoptile length (2), heading date (1), anthocyanin accumulation (1) and osmotic stress tolerance (2). We also developed a straightforward approach for combining genetic data from multiple families of limited size that will be useful for evaluating and mapping pre-existing breeding material.

New Strategies and Tools in Quantitative Genetics: How to Go from the Phenotype to the Genotype

Quantitative genetics has a long history in plants: It has been used to study specific biological processes, identify the factors important for trait evolution, and breed new crop varieties. These classical approaches to quantitative trait locus mapping have naturally improved with technology. In this review, we show how quantitative genetics has evolved recently in plants and how new developments in phenotyping, population generation, sequencing, gene manipulation, and statistics are rejuvenating both the classical linkage mapping approaches (for example, through nested association mapping) as well as the more recently developed genome-wide association studies. These strategies are complementary in most instances, and indeed, one is often used to confirm the results of the other. Despite significant advances, an emerging trend is that the outcome and efficiency of the different approaches depend greatly on the genetic architecture of the trait in the genetic material under study.

Enhancing genomic prediction with genome-wide association studies in multiparental maize populations

Genome-wide association mapping using dense marker sets has identified some nucleotide variants affecting complex traits that have been validated with fine-mapping and functional analysis. However, many sequence variants associated with complex traits in maize have small effects and low repeatability. In contrast to genome-wide association study (GWAS), genomic prediction (GP) is typically based on models incorporating information from all available markers, rather than modeling effects of individual loci. We considered methods to integrate results of GWASs into GP models in the context of multiple interconnected families. We compared association tests based on a biallelic additive model constraining the effect of a single-nucleotide polymorphism (SNP) to be equal across all families in which it segregates to a model in which the effect of a SNP can vary across families. Association SNPs were then included as fixed effects into a GP model that also included the random effects of the whole genome background. Simulation studies revealed that the effectiveness of this joint approach depends on the extent of polygenicity of the traits. Congruent with this finding, cross-validation studies indicated that GP including the fixed effects of the most significantly associated SNPs along with the polygenic background was more accurate than the polygenic background model alone for moderately complex but not highly polygenic traits measured in the maize nested association mapping population. Individual SNPs with strong and robust association signals can effectively improve GP. Our approach provides a new integrative modeling approach for both reliable gene discovery and robust GP.

Estimating parent-specific QTL effects through cumulating linked identity-by-state SNP effects in multiparental populations

The emergence of multiparental mapping populations enabled plant geneticists to gain deeper insights into the genetic architecture of major agronomic traits and to map quantitative trait loci (QTLs) controlling the expression of these traits. Although the investigated mapping populations are similar, one open question is whether genotype data should be modelled as identical by state (IBS) or identical by descent (IBD). Whereas IBS simply makes use of raw genotype scores to distinguish alleles, IBD data are derived from parental offspring information. We report on comparing IBS and IBD by applying two multiple regression models on four traits studied in the barley nested association mapping (NAM) population HEB-25. We observed that modelling parent-specific IBD genotypes produced a lower number of significant QTLs with increased prediction abilities compared with modelling IBS genotypes. However, at lower trait heritabilities the IBS model produced higher prediction abilities. We developed a method to estimate multiallelic QTL effects in multiparental populations from simple biallelic IBS data. This method is based on cumulating IBS-derived single-nucleotide polymorphism (SNP) effect estimates in a defined genetic region surrounding a QTL. Comparing the resulting parent-specific QTL effects with those obtained from IBD approaches revealed high accordance that could be confirmed through simulations. The method turned out to be also applicable to a barley multiparent advanced generation inter-cross (MAGIC) population. The 'cumulation method' represents a universal approach to differentiate parent-specific QTL effects in multiparental populations, even if no IBD information is available. In future, the method could further benefit from the availability of much denser SNP maps.

Multiple across-strain and within-strain QTLs suggest highly complex genetic architecture for hypoxia tolerance in channel catfish

The ability to survive hypoxic conditions is important for various organisms, especially for aquatic animals. Teleost fish, representing more than 50 % of vertebrate species, are extremely efficient in utilizing low levels of dissolved oxygen in water. However, huge variations exist among various taxa of fish in their ability to tolerate hypoxia. In aquaculture, hypoxia tolerance is among the most important traits because hypoxia can cause major economic losses. Genetic enhancement for hypoxia tolerance in catfish is of great interest, but little was done with analysis of the genetic architecture of hypoxia tolerance. The objective of this study was to conduct a genome-wide association study to identify QTLs for hypoxia tolerance using the catfish 250K SNP array with channel catfish families from six strains. Multiple significant and suggestive QTLs were identified across and within strains. One significant QTL and four suggestive QTLs were identified across strains. Six significant QTLs and many suggestive QTLs were identified within strains. There were rare overlaps among the QTLs identified within the six strains, suggesting a complex genetic architecture of hypoxia tolerance. Overall, within-strain QTLs explained larger proportion of phenotypic variation than across-strain QTLs. Many of genes within these identified QTLs have known functions for regulation of oxygen metabolism and involvement in hypoxia responses. Pathway analysis indicated that most of these genes were involved in MAPK or PI3K/AKT/mTOR signaling pathways that were known to be important for hypoxia-mediated angiogenesis, cell proliferation, apoptosis and survival.

Genetic Architecture of Domestication-Related Traits in Maize

Strong directional selection occurred during the domestication of maize from its wild ancestor teosinte, reducing its genetic diversity, particularly at genes controlling domestication-related traits. Nevertheless, variability for some domestication-related traits is maintained in maize. The genetic basis of this could be sequence variation at the same key genes controlling maize-teosinte differentiation (due to lack of fixation or arising as new mutations after domestication), distinct loci with large effects, or polygenic background variation. Previous studies permit annotation of maize genome regions associated with the major differences between maize and teosinte or that exhibit population genetic signals of selection during either domestication or postdomestication improvement. Genome-wide association studies and genetic variance partitioning analyses were performed in two diverse maize inbred line panels to compare the phenotypic effects and variances of sequence polymorphisms in regions involved in domestication and improvement to the rest of the genome. Additive polygenic models explained most of the genotypic variation for domestication-related traits; no large-effect loci were detected for any trait. Most trait variance was associated with background genomic regions lacking previous evidence for involvement in domestication. Improvement sweep regions were associated with more trait variation than expected based on the proportion of the genome they represent. Selection during domestication eliminated large-effect genetic variants that would revert maize toward a teosinte type. Small-effect polygenic variants (enriched in the improvement sweep regions of the genome) are responsible for most of the standing variation for domestication-related traits in maize.

Genomic dissection of plant development and its impact on thousand grain weight in barley through nested association mapping

Flowering time is a key agronomic trait that plays an important role in crop yield. There is growing interest in dissecting the developmental subphases of flowering to better understand and fine-tune plant development and maximize yield. To do this, we used the wild barley nested association mapping (NAM) population HEB-25, comprising 1420 BC1S3 lines, to map quantitative trait loci (QTLs) controlling five developmental traits, plant height, and thousand grain weight. Genome-wide association studies (GWAS) enabled us to locate a total of 89 QTLs that genetically regulate the seven investigated traits. Several exotic QTL alleles proved to be highly effective and potentially useful in barley breeding. For instance, thousand grain weight was increased by 4.5 g and flowering time was reduced by 9.3 days by substituting Barke elite QTL alleles for exotic QTL alleles at the denso/sdw1 and the Ppd-H1 loci, respectively. We showed that the exotic allele at the semi-dwarf locus denso/sdw1 can be used to increase grain weight since it uncouples the negative correlation between shoot elongation and the ripening phase. Our study demonstrates that nested association mapping of HEB-25 can help unravel the genetic regulation of plant development and yield formation in barley. Moreover, since we detected numerous useful exotic QTL alleles in HEB-25, we conclude that the introgression of these wild barley alleles into the elite barley gene pool may enable developmental phases to be specifically fine-tuned in order to maximize thousand grain weight and, potentially, yield in the long term.

Choice of models for QTL mapping with multiple families and design of the training set for prediction of Fusarium resistance traits in maize

KEY MESSAGE

QTL analysis for Fusarium resistance traits with multiple connected families detected more QTL than single-family analysis. Prediction accuracy was tightly associated with the kinship of the validation and training set.

ABSTRACT

QTL mapping has recently shifted from analysis of single families to multiple, connected families and several biometric models have been suggested. Using a high-density consensus map with 2472 marker loci, we performed QTL mapping with five connected bi-parental families with 639 doubled-haploid (DH) lines in maize for ear rot resistance and analyzed traits DON, Gibberella ear rot severity (GER), and days to silking (DS). Five biometric models differing in the assumption about the number and effects of alleles at QTL were compared. Model 2 to 5 performing joint analyses across all families and using linkage and/or linkage disequilibrium (LD) information identified all and even further QTL than Model 1 (single-family analyses) and generally explained a higher proportion pG of the genotypic variance for all three traits. QTL for DON and GER were mostly family specific, but several QTL for DS occurred in multiple families. Many QTL displayed large additive effects and most alleles increasing resistance originated from a resistant parent. Interactions between detected QTL and genetic background (family) occurred rarely and were comparatively small. Detailed analysis of three fully connected families yielded higher pG values for Model 3 or 4 than for Model 2 and 5, irrespective of the size NTS of the training set (TS). In conclusion, Model 3 and 4 can be recommended for QTL-based prediction with larger families. Including a sufficiently large number of full sibs in the TS helped to increase QTL-based prediction accuracy (rVS) for various scenarios differing in the composition of the TS.

Identification and Fine Mapping of a Stably Expressed QTL for Cold Tolerance at the Booting Stage Using an Interconnected Breeding Population in Rice

Cold stress is one of the major abiotic stresses that impede rice production. A interconnected breeding (IB) population consisted of 497 advanced lines developed using HHZ as the recurrent parent and eight diverse elite indica lines as the donors were used to identify stably expressed QTLs for CT at the booting stage. A total of 41,754 high-quality SNPs were obtained through re-sequencing of the IB population. Phenotyping was conducted under field conditions in two years and three locations. Association analysis identified six QTLs for CT on the chromosomes 3, 4 and 12. QTL qCT-3-2 that showed stable CT across years and locations was fine-mapped to an approximately 192.9 kb region. Our results suggested that GWAS applied to an IB population allows better integration of gene discovery and breeding. QTLs can be mapped in high resolution and quickly utilized in breeding.

Genetic properties of the MAGIC maize population: a new platform for high definition QTL mapping in Zea mays

BACKGROUND

Maize (Zea mays) is a globally produced crop with broad genetic and phenotypic variation. New tools that improve our understanding of the genetic basis of quantitative traits are needed to guide predictive crop breeding. We have produced the first balanced multi-parental population in maize, a tool that provides high diversity and dense recombination events to allow routine quantitative trait loci (QTL) mapping in maize.

RESULTS

We produced 1,636 MAGIC maize recombinant inbred lines derived from eight genetically diverse founder lines. The characterization of 529 MAGIC maize lines shows that the population is a balanced, evenly differentiated mosaic of the eight founders, with mapping power and resolution strengthened by high minor allele frequencies and a fast decay of linkage disequilibrium. We show how MAGIC maize may find strong candidate genes by incorporating genome sequencing and transcriptomics data. We discuss three QTL for grain yield and three for flowering time, reporting candidate genes. Power simulations show that subsets of MAGIC maize might achieve high-power and high-definition QTL mapping.

CONCLUSIONS

We demonstrate MAGIC maize's value in identifying the genetic bases of complex traits of agronomic relevance. The design of MAGIC maize allows the accumulation of sequencing and transcriptomics layers to guide the identification of candidate genes for a number of maize traits at different developmental stages. The characterization of the full MAGIC maize population will lead to higher power and definition in QTL mapping, and lay the basis for improved understanding of maize phenotypes, heterosis included. MAGIC maize is available to researchers.

Ensemble Learning of QTL Models Improves Prediction of Complex Traits

Quantitative trait locus (QTL) models can provide useful insights into trait genetic architecture because of their straightforward interpretability but are less useful for genetic prediction because of the difficulty in including the effects of numerous small effect loci without overfitting. Tight linkage between markers introduces near collinearity among marker genotypes, complicating the detection of QTL and estimation of QTL effects in linkage mapping, and this problem is exacerbated by very high density linkage maps. Here we developed a thinning and aggregating (TAGGING) method as a new ensemble learning approach to QTL mapping. TAGGING reduces collinearity problems by thinning dense linkage maps, maintains aspects of marker selection that characterize standard QTL mapping, and by ensembling, incorporates information from many more markers-trait associations than traditional QTL mapping. The objective of TAGGING was to improve prediction power compared with QTL mapping while also providing more specific insights into genetic architecture than genome-wide prediction models. TAGGING was compared with standard QTL mapping using cross validation of empirical data from the maize (Zea mays L.) nested association mapping population. TAGGING-assisted QTL mapping substantially improved prediction ability for both biparental and multifamily populations by reducing both the variance and bias in prediction. Furthermore, an ensemble model combining predictions from TAGGING-assisted QTL and infinitesimal models improved prediction abilities over the component models, indicating some complementarity between model assumptions and suggesting that some trait genetic architectures involve a mixture of a few major QTL and polygenic effects.