High-density linkage mapping of yield components and epistatic interactions in maize with doubled haploid lines from four crosses

Grain yield (GY) is a genetically complex and physiologically multiplicative trait which can be decomposed into the components kernel number (KN) and 100-kernel weight (HKW). Genetic analysis of these less complex yield component traits may give insights into the genetic architecture and predictive ability of complex traits. Here, we investigated how the incorporation of component traits and epistasis in quantitative trait locus (QTL) mapping approaches influences the accuracy of GY prediction. High-density genetic maps with 7,000–10,000 polymorphic single nucleotide polymorphisms were constructed for four biparental populations. The populations comprised between 99 and 227 doubled haploid maize lines which were phenotyped in field trials in two environments. Heritability was highest for HKW (88–89 %), intermediate for KN (72–80 %), and lowest for GY (64–83 %). Mapped QTL explained in total 21–55 %, 22–67 %, and 24–75 % of the genotypic variance for GY, KN, and HKW, respectively. Support intervals of QTL were short, indicating that QTL were located with high precision. Co-located QTLs with same parental origin of favorable alleles were detected within populations for different traits and between populations for the same traits. Using GY predictions based on the detected QTL, prediction accuracies (r) determined by cross validation ranged from 0.18 to 0.52. Epistatic models did not outperform the corresponding additive models. In conclusion, models based on QTL positions of component traits support the identification of favorable alleles for multiplicative traits and provide a basis to select superior inbred lines by marker-assisted breeding.     

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

Quantitative trait locus (QTL) mapping has been used to dissect the genetic architecture of complex traits and predict phenotypes for marker-assisted selection. Many QTL mapping studies in plants have been limited to one biparental family population. Joint analysis of multiple biparental families offers an alternative approach to QTL mapping with a wider scope of inference. Joint-multiple population analysis should have higher power to detect QTL shared among multiple families, but may have lower power to detect rare QTL. We compared prediction ability of single-family and joint-family QTL analysis methods with fivefold cross-validation for 6 diverse traits using the maize nested association mapping population, which comprises 25 biparental recombinant inbred families. Joint-family QTL analysis had higher mean prediction abilities than single-family QTL analysis for all traits at most significance thresholds, and was always better at more stringent significance thresholds. Most robust QTL (detected in >50% of data samples) were restricted to one family and were often not detected at high frequency by joint-family analysis, implying substantial genetic heterogeneity among families for complex traits in maize. The superior predictive ability of joint-family QTL models despite important genetic differences among families suggests that joint-family models capture sufficient smaller effect QTL that are shared across families to compensate for missing some rare large-effect QTL.     

Phenotypic Plasticity Contributes to Maize Adaptation and Heterosis

Plant phenotypic plasticity describes altered phenotypic performance of an individual when grown in different environments. Exploring genetic architecture underlying plant plasticity variation may help mitigate the detrimental effects of a rapidly changing climate on agriculture, but little research has been done in this area to date. In the present study, we established a population of 976 maize F₁ hybrids by crossing 488 diverse inbred lines with two elite testers. Genome-wide association study identified hundreds of quantitative trait loci associated with phenotypic plasticity variation across diverse F₁ hybrids, the majority of which contributed very little variance, in accordance with the polygenic nature of these traits. We identified several quantitative trait locus regions that may have been selected during the tropical-temperate adaptation process. We also observed heterosis in terms of phenotypic plasticity, in addition to the traditional genetic value differences measured between hybrid and inbred lines, and the pattern of which was affected by genetic background. Our results demonstrate a landscape of phenotypic plasticity in maize, which will aid in the understanding of its genetic architecture, its contribution to adaptation and heterosis, and how it may be exploited for future maize breeding in a rapidly changing environment.     

A gene regulatory network model for floral transition of the shoot apex in maize and its dynamic modeling

The transition from the vegetative to reproductive development is a critical event in the plant life cycle. The accurate prediction of flowering time in elite germplasm is important for decisions in maize breeding programs and best agronomic practices. The understanding of the genetic control of flowering time in maize has significantly advanced in the past decade. Through comparative genomics, mutant analysis, genetic analysis and QTL cloning, and transgenic approaches, more than 30 flowering time candidate genes in maize have been revealed and the relationships among these genes have been partially uncovered. Based on the knowledge of the flowering time candidate genes, a conceptual gene regulatory network model for the genetic control of flowering time in maize is proposed. To demonstrate the potential of the proposed gene regulatory network model, a first attempt was made to develop a dynamic gene network model to predict flowering time of maize genotypes varying for specific genes. The dynamic gene network model is composed of four genes and was built on the basis of gene expression dynamics of the two late flowering id1 and dlf1 mutants, the early flowering landrace Gaspe Flint and the temperate inbred B73. The model was evaluated against the phenotypic data of the id1 dlf1 double mutant and the ZMM4 overexpressed transgenic lines. The model provides a working example that leverages knowledge from model organisms for the utilization of maize genomic information to predict a whole plant trait phenotype, flowering time, of maize genotypes.     

Mapping QTL for agronomic traits in breeding populations

Detection of quantitative trait loci (QTL) in breeding populations offers the advantage that these QTL are of direct relevance for the improvement of crops via knowledge-based breeding. As phenotypic data are routinely generated in breeding programs and the costs for genotyping are constantly decreasing, it is tempting to exploit this information to unravel the genetic architecture underlying important agronomic traits in crops. This review characterizes the germplasm from breeding populations available for QTL detection, provides a classification of the different QTL mapping approaches that are available, and highlights important considerations concerning study design and biometrical models suitable for QTL analysis.     

Using mating designs to uncover QTL and the genetic architecture of complex traits

Analysis of quantitative trait loci (QTL) affecting complex traits is often pursued in single-cross experiments. For most purposes, including breeding, some assessment is desired of the generalizability of the QTL findings and of the overall genetic architecture of the trait. Single-cross experiments provide a poor basis for these purposes, as comparison across experiments is hampered by segregation of different allelic combinations among different parents and by context-dependent effects of QTL. To overcome this problem, we combined the benefits of QTL analysis (to identify genomic regions affecting trait variation) and classic diallel analysis (to obtain insight into the general inheritance of the trait) by analyzing multiple mapping families that are connected via shared parents. We first provide a theoretical derivation of main (general combining ability (GCA)) and interaction (specific combining ability (SCA)) effects on F(2) family means relative to variance components in a randomly mating reference population. Then, using computer simulations to generate F(2) families derived from 10 inbred parents in different partial-diallel designs, we show that QTL can be detected and that the residual among-family variance can be analyzed. Standard diallel analysis methods are applied in order to reveal the presence and mode of action (in terms of GCA and SCA) of undetected polygenes. Given a fixed experiment size (total number of individuals), we demonstrate that QTL detection and estimation of the genetic architecture of polygenic effects are competing goals, which should be explicitly accounted for in the experimental design. Our approach provides a general strategy for exploring the genetic architecture, as well as the QTL underlying variation in quantitative traits.     

Validation of mega-environment universal and specific QTL associated with seed yield and agronomic traits in soybeans

The value of quantitative trait loci (QTL) is dependant on the strength of association with the traits of interest, allelic diversity at the QTL and the effect of the genetic background on the expression of the QTL. A number of recent studies have identified QTL associated with traits of interest that appear to be independent of the environment but dependant on the genetic background in which they are found. Therefore, the objective of this study was to validate universal and/or mega-environment-specific seed yield QTL that have been previously reported in an independent recombinant inbred line (RIL) population derived from the cross between an elite Chinese and Canadian parent. The population was evaluated at two field environments in China and in five environments in Canada in 2005 and 2006. Of the seven markers linked to seed yield QTL reported by our group in a previous study, four were polymorphic between the two parents. No association between seed yield and QTL was observed. The result could imply that seed yield QTL were either not stable in this particular genetic background or harboured different alleles than the ones in the original mapping population. QTL_U Satt162 was associated with several agronomic traits of which lodging was validated. Both the non-adapted and adapted parent contributed favourable alleles to the progeny. Therefore, plant introductions have been validated as a source of favourable alleles that could increase the genetic variability of the soybean germplasm pool and lead to further improvements in seed yield and other agronomic traits.     

Performance prediction of F1 hybrids between recombinant inbred lines derived from two elite maize inbred lines

Selection of recombinant inbred lines (RILs) from elite hybrids is a key method in maize breeding especially in developing countries. The RILs are normally derived by repeated self-pollination and selection. In this study, we first investigated the accuracy of different models in predicting the performance of F₁ hybrids between RILs derived from two elite maize inbred lines Zong3 and 87-1, and then compared these models through simulation using a wider range of genetic models. Results indicated that appropriate prediction models depended on genetic architecture, e.g., combined model using breeding value and genome-wide prediction (BV+GWP) has the highest prediction accuracy for high V _D/V _A ratio (>0.5) traits. Theoretical studies demonstrated that different components of genetic variance were captured by different prediction models, which in turn explained the accuracy of these models in predicting the F₁ hybrid performance. Based on genome-wide prediction model (GWP), 114 untested F₁ hybrids possibly having higher grain yield than the original F₁ hybrid Yuyu22 (the single cross between Zong3 and 87-1) have been identified and recommended for further field test.     

On the use of mathematically-derived traits in QTL mapping

Mathematically-derived traits from two or more component traits, either by addition, subtraction, multiplication, or division, have been frequently used in genetics and breeding. When used in quantitative trait locus (QTL) mapping, derived traits sometimes show discrepancy with QTL identified for the component traits. We used three QTL distributions and three genetic effects models, and an actual maize mapping population, to investigate the efficiency of using derived traits in QTL mapping, and to understand the genetic and biological basis of derived-only QTL, i.e., QTL identified for a derived trait but not for any component trait. Results indicated that the detection power of the four putative QTL was consistently greater than 90% for component traits in simulated populations, each consisting of 200 recombinant inbred lines. Lower detection power and higher false discovery rate (FDR) were observed when derived traits were used. In an actual maize population, simulations were designed based on the observed QTL distributions and effects. When derived traits were used, QTL detected for both component and derived traits had comparable power, but those detected for component traits but not for derived traits had low detection power. The FDR from subtraction and division in the maize population were higher than the FDR from addition and multiplication. The use of derived traits increased the gene number, caused higher-order gene interactions than observed in component traits, and possibly complicated the linkage relationship between QTL as well. The increased complexity of the genetic architecture with derived traits may be responsible for the reduced detection power and the increased FDR. Derived-only QTL identified in practical genetic populations can be explained either as minor QTL that are not significant in QTL mapping of component traits, or as false positives.     

Epistatic interaction is an important genetic basis of grain yield and its components in maize

A population of 294 recombinant inbred lines (RIL) derived from Yuyu22, an elite maize hybrid extending broadly in China, has been constructed to investigate the genetic basis of grain yield, and associated yield components in maize. The main-effect quantitative trait loci (QTL), digenic epistatic interactions, and their interactions with the environment for grain yield and its three components were identified by using the mixed linear model approach. Thirty-two main-effect QTL and forty-four pairs of digenic epistatic interactions were detected for the four measured traits in four environments. Our results suggest that both additive effects and epistasis (additive × additive) effects are important genetic bases of grain yield and its components in the RIL population. Only 30.4% of main-effect QTL for ear length were involved in epistatic interactions. This implies that many loci in epistatic interactions may not have significant effects for traits alone but may affect trait expression by epistatic interaction with the other loci.     

Genetic analysis of two new quantitative trait loci for ear weight in maize inbred line Huangzao4

Ear weight is one of the most important agronomic traits considered necessary in maize (Zea mays L.) breeding projects. To determine its genetic basis, a population consisting of 239 recombinant inbred lines, derived from the cross Mo17 x Huangzao4, was used to detect quantitative trait loci (QTLs) for ear weight under two nitrogen regimes. Under a high nitrogen fertilization regime, one QTL was identified in chromosome bin 2.08-2.09, which explained 7.46% of phenotypic variance and an increase in ear weight of about 5.79 g, owing to an additive effect. Under a low nitrogen regime, another QTL was identified in chromosome bin 1.10-1.11; it accounted for 7.11% of phenotypic variance and a decrease of 5.24 g in ear weight, due to an additive effect. Based on comparisons with previous studies, these two QTLs are new loci associated with ear weight in maize. These findings contribute to our knowledge about the genetic basis of ear weight in maize.     

The relationship between parental genetic or phenotypic divergence and progeny variation in the maize nested association mapping population

Appropriate selection of parents for the development of mapping populations is pivotal to maximizing the power of quantitative trait loci detection. Trait genotypic variation within a family is indicative of the family's informativeness for genetic studies. Accurate prediction of the most useful parental combinations within a species would help guide quantitative genetics studies. We tested the reliability of genotypic and phenotypic distance estimators between pairs of maize inbred lines to predict genotypic variation for quantitative traits within families derived from biparental crosses. We developed 25 families composed of ~200 random recombinant inbred lines each from crosses between a common reference parent inbred, B73, and 25 diverse maize inbreds. Parents and families were evaluated for 19 quantitative traits across up to 11 environments. Genetic distances (GDs) among parents were estimated with 44 simple sequence repeat and 2303 single-nucleotide polymorphism markers. GDs among parents had no predictive value for progeny variation, which is most likely due to the choice of neutral markers. In contrast, we observed for about half of the traits measured a positive correlation between phenotypic parental distances and within-family genetic variance estimates. Consequently, the choice of promising segregating populations can be based on selecting phenotypically diverse parents. These results are congruent with models of genetic architecture that posit numerous genes affecting quantitative traits, each segregating for allelic series, with dispersal of allelic effects across diverse genetic material. This architecture, common to many quantitative traits in maize, limits the predictive value of parental genotypic or phenotypic values on progeny variance.     

Detection of QTL for flowering time in multiple families of elite maize

Flowering time is a fundamental quantitative trait in maize that has played a key role in the postdomestication process and the adaptation to a wide range of climatic conditions. Flowering time has been intensively studied and recent QTL mapping results based on diverse founders suggest that the genetic architecture underlying this trait is mainly based on numerous small-effect QTL. Here, we used a population of 684 progenies from five connected families to investigate the genetic architecture of flowering time in elite maize. We used a joint analysis and identified nine main effect QTL explaining approximately 50?% of the genotypic variation of the trait. The QTL effects were small compared with the observed phenotypic variation and showed strong differences between families. We detected no epistasis with the genetic background but four digenic epistatic interactions in a full 2-dimensional genome scan. Our results suggest that flowering time in elite maize is mainly controlled by main effect QTL with rather small effects but that epistasis may also contribute to the genetic architecture of the trait.     

Mapping quantitative trait loci using naturally occurring genetic variance among commercial inbred lines of maize (Zea mays L.)

Many commercial inbred lines are available in crops. A large amount of genetic variation is preserved among these lines. The genealogical history of the inbred lines is usually well documented. However, quantitative trait loci (QTL) responsible for the genetic variances among the lines are largely unexplored due to lack of statistical methods. In this study, we show that the pedigree information of the lines along with the trait values and marker information can be used to map QTL without the need of further crossing experiments. We develop a Monte Carlo method to estimate locus-specific identity-by-descent (IBD) matrices. These IBD matrices are further incorporated into a mixed-model equation for variance component analysis. QTL variance is estimated and tested at every putative position of the genome. The actual QTL are detected by scanning the entire genome. Applying this new method to a well-documented pedigree of maize (Zea mays L.) that consists of 404 inbred lines, we mapped eight QTL for the maize male flowering trait, growing degree day heat units to pollen shedding (GDUSHD). These detected QTL contributed >80% of the variance observed among the inbred lines. The QTL were then used to evaluate all the inbred lines using the best linear unbiased prediction (BLUP) technique. Superior lines were selected according to the estimated QTL allelic values, a technique called marker-assisted selection (MAS). The MAS procedure implemented via BLUP may be routinely used by breeders to select superior lines and line combinations for development of new cultivars.     

Genomic Selection and Association Mapping in Rice (Oryza sativa): Effect of Trait Genetic Architecture,Training Population Composition,Marker Number and Statistical Model on Accuracy of Rice Genomic Selection in Elite,Tropical Rice Breeding Lines

Genomic Selection (GS) is a new breeding method in which genome-wide markers are used to predict the breeding value of individuals in a breeding population. GS has been shown to improve breeding efficiency in dairy cattle and several crop plant species, and here we evaluate for the first time its efficacy for breeding inbred lines of rice. We performed a genome-wide association study (GWAS) in conjunction with five-fold GS cross-validation on a population of 363 elite breeding lines from the International Rice Research Institute''s (IRRI) irrigated rice breeding program and herein report the GS results. The population was genotyped with 73,147 markers using genotyping-by-sequencing. The training population, statistical method used to build the GS model, number of markers, and trait were varied to determine their effect on prediction accuracy. For all three traits, genomic prediction models outperformed prediction based on pedigree records alone. Prediction accuracies ranged from 0.31 and 0.34 for grain yield and plant height to 0.63 for flowering time. Analyses using subsets of the full marker set suggest that using one marker every 0.2 cM is sufficient for genomic selection in this collection of rice breeding materials. RR-BLUP was the best performing statistical method for grain yield where no large effect QTL were detected by GWAS, while for flowering time, where a single very large effect QTL was detected, the non-GS multiple linear regression method outperformed GS models. For plant height, in which four mid-sized QTL were identified by GWAS, random forest produced the most consistently accurate GS models. Our results suggest that GS, informed by GWAS interpretations of genetic architecture and population structure, could become an effective tool for increasing the efficiency of rice breeding as the costs of genotyping continue to decline.     

Maize association population: a high-resolution platform for quantitative trait locus dissection

Crop improvement and the dissection of complex genetic traits require germplasm diversity. Although this necessary phenotypic variability exists in diverse maize, most research is conducted using a small subset of inbred lines. An association population of 302 lines is now available--a valuable research tool that captures a large proportion of the alleles in cultivated maize. Provided that appropriate statistical models correcting for population structure are included, this tool can be used in association analyses to provide high-resolution evaluation of multiple alleles. This study describes the population structure of the 302 lines, and investigates the relationship between population structure and various measures of phenotypic and breeding value. On average, our estimates of population structure account for 9.3% of phenotypic variation, roughly equivalent to a major quantitative trait locus (QTL), with a high of 35%. Inclusion of population structure in association models is critical to meaningful analyses. This new association population has the potential to identify QTL with small effects, which will aid in dissecting complex traits and in planning future projects to exploit the rich diversity present in maize.     

Genetic basis of agronomically important traits in sugar beet (Beta vulgaris L.) investigated with joint linkage association mapping

Epistatic interactions may contribute substantially to the hybrid performance of sugar beet. The main goal of our study was to dissect the genetic basis of eight important physiological and agronomic traits using two different biometrical models for joint linkage association mapping. A total of 197 genotypes of an elite breeding population were evaluated in multi-location trials and fingerprinted with 194 SNP markers. Two different statistical models were used for the genome-wide scan for marker–trait associations: Model A, which corrects for the genetic background with markers as cofactors and Model B, which additionally models a population effect. Based on the extent of linkage disequilibrium in the parental population, we estimated that for a genome-wide scan at least 100 equally spaced markers are necessary. We mapped across the eight traits 39 QTL for Model A and 22 for Model B. Only 11% of the total number of QTL were identified based on Models A and B, which indicates that both models are complementary. Epistasis was detected only for two out of the eight traits, and contributed only to a minor extent to the genotypic variance. This low relevance of epistasis implies that in sugar beet breeding the prediction of performance of three-way hybrids is feasible with high accuracy based on the means of their single crosses.     

Quantitative trait loci affecting survival and fertility-related traits in Caenorhabditis elegans show genotype-environment interactions, pleiotropy and epistasis.

We have identified, using composite interval mapping, quantitative trait loci (QTL) affecting a variety of life history traits (LHTs) in the nematode Caenorhabditis elegans. Using recombinant inbred strains assayed on the surface of agar plates, we found QTL for survival, early fertility, age of onset of sexual maturity, and population growth rate. There was no overall correlation between survival on solid media and previous measures of survival in liquid media. Of the four survival QTL found in these two environments, two have genotype-environment interactions (GEIs). Epistatic interactions between markers were detected for four traits. A multiple regression approach was used to determine which single markers and epistatic interactions best explained the phenotypic variance for each trait. The amount of phenotypic variance accounted for by genetic effects ranged from 13% (for internal hatching) to 46% (for population growth). Epistatic effects accounted for 9-11% of the phenotypic variance for three traits. Two regions containing QTL that affected more than one fertility-related trait were found. This study serves as an example of the power of QTL mapping for dissecting the genetic architecture of a suite of LHTs and indicates the potential importance of environment and GEIs in the evolution of this architecture.     

A method to optimize selection on multiple identified quantitative trait loci

A mathematical approach was developed to model and optimize selection on multiple known quantitative trait loci (QTL) and polygenic estimated breeding values in order to maximize a weighted sum of responses to selection over multiple generations. The model allows for linkage between QTL with multiple alleles and arbitrary genetic effects, including dominance, epistasis, and gametic imprinting. Gametic phase disequilibrium between the QTL and between the QTL and polygenes is modeled but polygenic variance is assumed constant. Breeding programs with discrete generations, differential selection of males and females and random mating of selected parents are modeled. Polygenic EBV obtained from best linear unbiased prediction models can be accommodated. The problem was formulated as a multiple-stage optimal control problem and an iterative approach was developed for its solution. The method can be used to develop and evaluate optimal strategies for selection on multiple QTL for a wide range of situations and genetic models.     

A paternally imprinted QTL for mature body mass on mouse Chromosome 8

Body mass (BM) is a classic polygenic trait that has been extensively investigated to determine the underlying genetic architecture. Many previous studies looking at the genetic basis of variation in BM in murine animal models by quantitative trait loci (QTL) mapping have used crosses between two inbred lines. As a consequence it has not been possible to explore imprinting effects which have been shown to play an important role in the genetic basis of early growth with persistent effects throughout the growth curve. Here we use partially inbred mouse lines to identify QTL for mature BM by applying both Mendelian and Imprinting models. The analysis of an F₂ population (n ≈ 500) identified a number of QTL at 14, 16, and 18 weeks explaining in total 31.5%, 34.4%, and 30.5% of total phenotypic variation, respectively. On Chromosome 8 a QTL of large effect (14% of the total phenotypic variance at 14 weeks) was found to be explained by paternal imprinting. Although Chromosome 8 has not been previously associated with imprinting effects, features of candidate genes within the QTL confidence interval (CpG islands and direct clustered repeats) support the hypothesis that Insulin receptor substrate 2 may be associated with imprinting, but as yet is unidentified as being so.