Multiple trait analysis of genetic mapping for quantitative trait loci

Major Gene with Polygene Inheritance Analysis of Prostrate Growth Habit in Hybrids of Chrysanthemum yantaiense × C. indicum

Plant architecture is a crucial trait for ornamental plants. Chrysanthemum with prostrate growth habit is a novel cultivar group of ground-cover chrysanthemum, which have high ornamental value, strong lodging resistance, and outstanding landscape greening capability. To explore the genetic mechanism underlying the prostrate growth habit in chrysanthemum, we used tetraploid prostrate-type Chrysanthemum yantaiense as the female parent and erect-type Chrysanthemum indicum as the male parent to produce four generations (P1, P2, F1, F2). Five traits related to prostrate growth habit in chrysanthemum were investigated including plant height (PH), crown width of the plant (CP), creeping index (CI), gravitropic set-point angle (GSA), and growth habit (GH). The major gene plus polygene mixed inheritance analysis was conducted on five traits across four generations over two years. For the five traits, the coefficients of variation (CVs) were wide-ranging and high (16.64-42.75%), with the PH having the highest CV among them. Genetic analysis revealed that PH conformed to the additive-dominant-epistatic polygene model (C-0) and the model of two equally dominant major genes plus additive-dominant polygene (E-5). The most suitable genetic model for CI was an additive-dominant major gene plus additive-dominant-epistatic polygene model (D-0). The best-fit models for CP and GH were both C-0. For GSA, the best-fit models were E-4 and C-0. Additionally, it appeared that both genetic and environmental factors influenced the prostrate growth habit, as the heritability of major genes and polygenes was less than 50%. This study can serve as a theoretical foundation for the mapping of quantitative trait loci (QTLs) and further exploration of the genetic mechanisms underlying plant architecture in chrysanthemum.

Novel candidate genes and genetic basis analysis of kernel starch content in tropical maize

BACKGROUND

Starch is the most abundant carbohydrate in maize grains, serving as a primary energy source for both humans and animals, and playing a crucial role in various industrial applications. Increasing the starch content of maize grains is beneficial for improving the grain yield and quality. To gain insight into the genetic basis of starch content in maize kernels, a multiparent population (MPP) was constructed and evaluated for starch content in three different environments.

RESULTS

The integration of QTL mapping and genome-wide association analysis (GWAS) identified two SNPs, 8_166371888 and 8_178656036, which overlapped the QTL interval of qSC8-1, identified in the tropical maize line YML46. The phenotypic variance explained (PVE) by the QTL qSC8-1 was12.17%, while the SNPs 8_166371888 and 8_178656036 explained 10.19% and 5.72% of the phenotypic variance. Combined GWAS and QTL analyses led to the identification of two candidate genes, Zm00001d012005 and Zm00001d012687 located on chromosome 8.

CONCLUSIONS

The candidate gene Zm00001d012005 encodes histidine kinase, which is known to play a role in starch accumulation in rice spikes. Related histidine kinases, such as AHK1, are involved in endosperm transfer cell development in barley, which affects grain quality. Zm00001d012687 encodes triacylglycerol lipase, which reduces seed oil content. Since oil content in cereal kernels is negatively correlated with starch content, this gene is likely involved in regulating the starch content in maize kernels. These findings provide insights into the genetic mechanisms underlying kernel starch content and establish a theoretical basis for breeding maize varieties with high starch content.

Epistasis and pleiotropy-induced variation for plant breeding

Epistasis refers to nonallelic interaction between genes that cause bias in estimates of genetic parameters for a phenotype with interactions of two or more genes affecting the same trait. Partitioning of epistatic effects allows true estimation of the genetic parameters affecting phenotypes. Multigenic variation plays a central role in the evolution of complex characteristics, among which pleiotropy, where a single gene affects several phenotypic characters, has a large influence. While pleiotropic interactions provide functional specificity, they increase the challenge of gene discovery and functional analysis. Overcoming pleiotropy-based phenotypic trade-offs offers potential for assisting breeding for complex traits. Modelling higher order nonallelic epistatic interaction, pleiotropy and non-pleiotropy-induced variation, and genotype × environment interaction in genomic selection may provide new paths to increase the productivity and stress tolerance for next generation of crop cultivars. Advances in statistical models, software and algorithm developments, and genomic research have facilitated dissecting the nature and extent of pleiotropy and epistasis. We overview emerging approaches to exploit positive (and avoid negative) epistatic and pleiotropic interactions in a plant breeding context, including developing avenues of artificial intelligence, novel exploitation of large-scale genomics and phenomics data, and involvement of genes with minor effects to analyse epistatic interactions and pleiotropic quantitative trait loci, including missing heritability.

Single- and multiple-trait quantitative trait locus analyses for seed oil and protein contents of soybean populations with advanced breeding line background

Soybean seed oil and protein contents are negatively correlated, posing challenges to enhance both traits simultaneously. Previous studies have identified numerous oil and protein QTLs via single-trait QTL analysis. Multiple-trait QTL methods were shown to be superior but have not been applied to seed oil and protein contents. Our study aimed to evaluate the effectiveness of single- and multiple-trait multiple interval mapping (ST-MIM and MT-MIM, respectively) for these traits using three recombinant inbred line populations from advanced breeding line crosses tested in four environments. Using original and simulated data, we found that MT-MIM did not outperform ST-MIM for our traits with high heritability (H2 > 0.84). Empirically, MT-MIM confirmed only five out of the seven QTLs detected by ST-MIM, indicating single-trait analysis was sufficient for these traits. All QTLs exerted opposite effects on oil and protein contents with varying protein-to-oil additive effect ratios (-0.4 to -4.8). We calculated the economic impact of the allelic variations via estimated processed values (EPV) using the National Oilseed Processors Association (NOPA) and High Yield + Quality (HY + Q) methods. Oil-increasing alleles had positive effects on both EPVNOPA and EPVHY+Q when the protein-to-oil ratio was low (-0.4 to -0.7). However, when the ratio was high (-4.1 to -4.8), oil-increasing alleles increased EPVNOPA and decreased EPVHY+Q, which penalizes low protein meal. In conclusion, single-trait QTL analysis is adequately effective for high heritability traits like seed oil and protein contents. Additionally, the populations' elite pedigrees and varying protein-to-oil ratios provide potential lines for further yield assessment and direct integration into breeding programs.

Supplementary Information

The online version contains supplementary material available at 10.1007/s11032-024-01489-2.

Multivariate genome-wide associations for immune traits in two maternal pig lines

BACKGROUND

Immune traits are considered to serve as potential biomarkers for pig's health. Medium to high heritabilities have been observed for some of the immune traits suggesting genetic variability of these phenotypes. Consideration of previously established genetic correlations between immune traits can be used to identify pleiotropic genetic markers. Therefore, genome-wide association study (GWAS) approaches are required to explore the joint genetic foundation for health biomarkers. Usually, GWAS explores phenotypes in a univariate (uv), trait-by-trait manner. Besides two uv GWAS methods, four multivariate (mv) GWAS approaches were applied on combinations out of 22 immune traits for Landrace (LR) and Large White (LW) pig lines.

RESULTS

In total 433 (LR: 351, LW: 82) associations were identified with the uv approach implemented in PLINK and a Bayesian linear regression uv approach (BIMBAM) software. Single Nucleotide Polymorphisms (SNPs) that were identified with both uv approaches (n = 32) were mostly associated with immune traits such as haptoglobin, red blood cell characteristics and cytokines, and were located in protein-coding genes. Mv GWAS approaches detected 647 associations for different mv immune trait combinations which were summarized to 133 Quantitative Trait Loci (QTL). SNPs for different trait combinations (n = 66) were detected with more than one mv method. Most of these SNPs are associated with red blood cell related immune trait combinations. Functional annotation of these QTL revealed 453 immune-relevant protein-coding genes. With uv methods shared markers were not observed between the breeds, whereas mv approaches were able to detect two conjoint SNPs for LR and LW. Due to unmapped positions for these markers, their functional annotation was not clarified.

CONCLUSIONS

This study evaluated the joint genetic background of immune traits in LR and LW piglets through the application of various uv and mv GWAS approaches. In comparison to uv methods, mv methodologies identified more significant associations, which might reflect the pleiotropic background of the immune system more accurately. In genetic research of complex traits, the SNP effects are generally small. Furthermore, one genetic variant can affect several correlated immune traits at the same time, termed pleiotropy. As mv GWAS methods consider strong dependencies among traits, the power to detect SNPs can be boosted. Both methods revealed immune-relevant potential candidate genes. Our results indicate that one single test is not able to detect all the different types of genetic effects in the most powerful manner and therefore, the methods should be applied complementary.

Complex genetic architecture underlying the plasticity of maize agronomic traits

Phenotypic plasticity is the ability of a given genotype to produce multiple phenotypes in response to changing environmental conditions. Understanding the genetic basis of phenotypic plasticity and establishing a predictive model is highly relevant to future agriculture under a changing climate. Here we report findings on the genetic basis of phenotypic plasticity for 23 complex traits using a diverse maize population planted at five sites with distinct environmental conditions. We found that latitude-related environmental factors were the main drivers of across-site variation in flowering time traits but not in plant architecture or yield traits. For the 23 traits, we detected 109 quantitative trait loci (QTLs), 29 for mean values, 66 for plasticity, and 14 for both parameters, and 80% of the QTLs interacted with latitude. The effects of several QTLs changed in magnitude or sign, driving variation in phenotypic plasticity. We experimentally validated one plastic gene, ZmTPS14.1, whose effect was likely mediated by the compensation effect of ZmSPL6 from a downstream pathway. By integrating genetic diversity, environmental variation, and their interaction into a joint model, we could provide site-specific predictions with increased accuracy by as much as 9.9%, 2.2%, and 2.6% for days to tassel, plant height, and ear weight, respectively. This study revealed a complex genetic architecture involving multiple alleles, pleiotropy, and genotype-by-environment interaction that underlies variation in the mean and plasticity of maize complex traits. It provides novel insights into the dynamic genetic architecture of agronomic traits in response to changing environments, paving a practical way toward precision agriculture.

A clustering linear combination method for multiple phenotype association studies based on GWAS summary statistics

There is strong evidence showing that joint analysis of multiple phenotypes in genome-wide association studies (GWAS) can increase statistical power when detecting the association between genetic variants and human complex diseases. We previously developed the Clustering Linear Combination (CLC) method and a computationally efficient CLC (ceCLC) method to test the association between multiple phenotypes and a genetic variant, which perform very well. However, both of these methods require individual-level genotypes and phenotypes that are often not easily accessible. In this research, we develop a novel method called sCLC for association studies of multiple phenotypes and a genetic variant based on GWAS summary statistics. We use the LD score regression to estimate the correlation matrix among phenotypes. The test statistic of sCLC is constructed by GWAS summary statistics and has an approximate Cauchy distribution. We perform a variety of simulation studies and compare sCLC with other commonly used methods for multiple phenotype association studies using GWAS summary statistics. Simulation results show that sCLC can control Type I error rates well and has the highest power in most scenarios. Moreover, we apply the newly developed method to the UK Biobank GWAS summary statistics from the XIII category with 70 related musculoskeletal system and connective tissue phenotypes. The results demonstrate that sCLC detects the most number of significant SNPs, and most of these identified SNPs can be matched to genes that have been reported in the GWAS catalog to be associated with those phenotypes. Furthermore, sCLC also identifies some novel signals that were missed by standard GWAS, which provide new insight into the potential genetic factors of the musculoskeletal system and connective tissue phenotypes.

Covariation of brain and skull shapes as a model to understand the role of crosstalk in development and evolution

Covariation among discrete phenotypes can arise due to selection for shared functions, and/or shared genetic and developmental underpinnings. The consequences of such phenotypic integration are far-reaching and can act to either facilitate or limit morphological variation. The vertebrate brain is known to act as an "organizer" of craniofacial development, secreting morphogens that can affect the shape of the growing neurocranium, consistent with roles for pleiotropy in brain-neurocranium covariation. Here, we test this hypothesis in cichlid fishes by first examining the degree of shape integration between the brain and the neurocranium using three-dimensional geometric morphometrics in an F5 hybrid population, and then genetically mapping trait covariation using quantitative trait loci (QTL) analysis. We observe shape associations between the brain and the neurocranium, a pattern that holds even when we assess associations between the brain and constituent parts of the neurocranium: the rostrum and braincase. We also recover robust genetic signals for both hard- and soft-tissue traits and identify a genomic region where QTL for the brain and braincase overlap, implicating a role for pleiotropy in patterning trait covariation. Fine mapping of the overlapping genomic region identifies a candidate gene, notch1a, which is known to be involved in patterning skeletal and neural tissues during development. Taken together, these data offer a genetic hypothesis for brain-neurocranium covariation, as well as a potential mechanism by which behavioral shifts may simultaneously drive rapid change in neuroanatomy and craniofacial morphology.

Fruit growth and development in apple: a molecular, genomics and epigenetics perspective

Fruit growth and development are physiological processes controlled by several internal and external factors. This complex regulatory mechanism comprises a series of events occurring in a chronological order over a growing season. Understanding the underlying mechanism of fruit development events, however, requires consideration of the events occurring prior to fruit development such as flowering, pollination, fertilization, and fruit set. Such events are interrelated and occur in a sequential order. Recent advances in high-throughput sequencing technology in conjunction with improved statistical and computational methods have empowered science to identify some of the major molecular components and mechanisms involved in the regulation of fruit growth and have supplied encouraging successes in associating genotypic differentiation with phenotypic observations. As a result, multiple approaches have been developed to dissect such complex regulatory machinery and understand the genetic basis controlling these processes. These methods include transcriptomic analysis, quantitative trait loci (QTLs) mapping, whole-genome approach, and epigenetics analyses. This review offers a comprehensive overview of the molecular, genomic and epigenetics perspective of apple fruit growth and development that defines the final fruit size and provides a detailed analysis of the mechanisms by which fruit growth and development are controlled. Though the main emphasis of this article is on the molecular, genomic and epigenetics aspects of fruit growth and development, we will also deliver a brief overview on events occurring prior to fruit growth.

Dissecting Complex Traits Using Omics Data: A Review on the Linear Mixed Models and Their Application in GWAS

Genome-wide association study (GWAS) is the most popular approach to dissecting complex traits in plants, humans, and animals. Numerous methods and tools have been proposed to discover the causal variants for GWAS data analysis. Among them, linear mixed models (LMMs) are widely used statistical methods for regulating confounding factors, including population structure, resulting in increased computational proficiency and statistical power in GWAS studies. Recently more attention has been paid to pleiotropy, multi-trait, gene-gene interaction, gene-environment interaction, and multi-locus methods with the growing availability of large-scale GWAS data and relevant phenotype samples. In this review, we have demonstrated all possible LMMs-based methods available in the literature for GWAS. We briefly discuss the different LMM methods, software packages, and available open-source applications in GWAS. Then, we include the advantages and weaknesses of the LMMs in GWAS. Finally, we discuss the future perspective and conclusion. The present review paper would be helpful to the researchers for selecting appropriate LMM models and methods quickly for GWAS data analysis and would benefit the scientific society.

A multi-trait multi-locus stepwise approach for conducting GWAS on correlated traits

The ability to accurately quantify the simultaneous effect of multiple genomic loci on multiple traits is now possible due to current and emerging high-throughput genotyping and phenotyping technologies. To date, most efforts to quantify these genotype-to-phenotype relationships have focused on either multi-trait models that test a single marker at a time or multi-locus models that quantify associations with a single trait. Therefore, the purpose of this study was to compare the performance of a multi-trait, multi-locus stepwise (MSTEP) model selection procedure we developed to (a) a commonly used multi-trait single-locus model and (b) a univariate multi-locus model. We used real marker data in maize (Zea mays L.) and soybean (Glycine max L.) to simulate multiple traits controlled by various combinations of pleiotropic and nonpleiotropic quantitative trait nucleotides (QTNs). In general, we found that both multi-trait models outperformed the univariate multi-locus model, especially when analyzing a trait of low heritability. For traits controlled by either a combination of pleiotropic and nonpleiotropic QTNs or a large number of QTNs (i.e., 50), our MSTEP model often outperformed at least one of the two alternative models. When applied to the analysis of two tocochromanol-related traits in maize grain, MSTEP identified the same peak-associated marker that has been reported in a previous study. We therefore conclude that MSTEP is a useful addition to the suite of statistical models that are commonly used to gain insight into the genetic architecture of agronomically important traits.

A computationally efficient clustering linear combination approach to jointly analyze multiple phenotypes for GWAS

There has been an increasing interest in joint analysis of multiple phenotypes in genome-wide association studies (GWAS) because jointly analyzing multiple phenotypes may increase statistical power to detect genetic variants associated with complex diseases or traits. Recently, many statistical methods have been developed for joint analysis of multiple phenotypes in genetic association studies, including the Clustering Linear Combination (CLC) method. The CLC method works particularly well with phenotypes that have natural groupings, but due to the unknown number of clusters for a given data, the final test statistic of CLC method is the minimum p-value among all p-values of the CLC test statistics obtained from each possible number of clusters. Therefore, a simulation procedure needs to be used to evaluate the p-value of the final test statistic. This makes the CLC method computationally demanding. We develop a new method called computationally efficient CLC (ceCLC) to test the association between multiple phenotypes and a genetic variant. Instead of using the minimum p-value as the test statistic in the CLC method, ceCLC uses the Cauchy combination test to combine all p-values of the CLC test statistics obtained from each possible number of clusters. The test statistic of ceCLC approximately follows a standard Cauchy distribution, so the p-value can be obtained from the cumulative density function without the need for the simulation procedure. Through extensive simulation studies and application on the COPDGene data, the results demonstrate that the type I error rates of ceCLC are effectively controlled in different simulation settings and ceCLC either outperforms all other methods or has statistical power that is very close to the most powerful method with which it has been compared.

A compressed variance component mixed model framework for detecting small and linked QTL-by-environment interactions

Detecting small and linked quantitative trait loci (QTLs) and QTL-by-environment interactions (QEIs) for complex traits is a difficult issue in immortalized F2 and F2:3 design, especially in the era of global climate change and environmental plasticity research. Here we proposed a compressed variance component mixed model. In this model, a parametric vector of QTL genotype and environment combination effects replaced QTL effects, environmental effects and their interaction effects, whereas the combination effect polygenic background replaced the QTL and QEI polygenic backgrounds. Thus, the number of variance components in the mixed model was greatly reduced. The model was incorporated into our genome-wide composite interval mapping (GCIM) to propose GCIM-QEI-random and GCIM-QEI-fixed, respectively, under random and fixed models of genetic effects. First, potentially associated QTLs and QEIs were selected from genome-wide scanning. Then, significant QTLs and QEIs were identified using empirical Bayes and likelihood ratio test. Finally, known and candidate genes around these significant loci were mined. The new methods were validated by a series of simulation studies and real data analyses. Compared with ICIM, GCIM-QEI-random had 29.77 ± 18.20% and 24.33 ± 10.15% higher average power, respectively, in 0.5–3.0% QTL and QEI detection, 43.44 ± 9.53% and 51.47 ± 15.70% higher average power, respectively, in linked QTL and QEI detection, and identified 30 more known genes for four rice yield traits, because GCIM-QEI-random identified more small genes/loci, being 2.69 ± 2.37% for additional genes. GCIM-QEI-random was slightly better than GCIM-QEI-fixed. In addition, the new methods may be extended into backcross and genome-wide association studies. This study provides effective methods for detecting small-effect and linked QTLs and QEIs.

Genetic Bases of Complex Traits: From Quantitative Trait Loci to Prediction

Conceived as a general introduction to the book, this chapter is a reminder of the core concepts of genetic mapping and molecular marker-based prediction. It provides an overview of the principles and the evolution of methods for mapping the variation of complex traits, and methods for QTL-based prediction of human disease risk and animal and plant breeding value. The principles of linkage-based and linkage disequilibrium-based QTL mapping methods are described in the context of the simplest, single-marker, methods. Methodological evolutions are analysed in relation with their ability to account for the complexity of the genotype-phenotype relations. Main characteristics of the genetic architecture of complex traits, drawn from QTL mapping works using large populations of unrelated individuals, are presented. Methods combining marker-QTL association data into polygenic risk score that captures part of an individual's susceptibility to complex diseases are reviewed. Principles of best linear mixed model-based prediction of breeding value in animal- and plant-breeding programs using phenotypic and pedigree data, are summarized and methods for moving from BLUP to marker-QTL BLUP are presented. Factors influencing the additional genetic progress achieved by using molecular data and rules for their optimization are discussed.

Quantitative trait locus mapping analysis of multiple traits when using genotype data with potential errors

Background

Quantitative trait locus (QTL) analysis aims to locate and estimate the effects of the genes influencing quantitative traits and infer the relationship between gene variants and changes in phenotypic characteristics using statistical methods. Some methods have been developed to map QTLs of multiple traits in the case of no genotype error in a given dataset. However, practical genetic data that people use may contain some potential errors because of the limitations of biotechnology. Common genetic data correction methods can only reduce errors, but cannot calculate the degree of error. In this paper, we propose a QTL mapping strategy for multiple traits in the presence of genotype errors.

Methods

The additive effect, dominant effect, recombination rate, error rate, and other parameters of QTLs can be simultaneously obtained using this new method in the framework of multiple-interval mapping.

Results

Our simulation results show that the accuracy of parameter estimation can be improved by considering the errors of marker genotypes during the analysis of genetic data. Real data analysis also shows that the new method proposed in this paper can map the QTLs of multiple traits more accurately.

QTL Mapping and GWAS Reveal the Genetic Mechanism Controlling Soluble Solids Content in Brassica napus Shoots

Oilseed-vegetable-dual-purpose (OVDP) rapeseed can effectively alleviate the land contradiction between crops and it supplements vegetable supplies in winter or spring. The soluble solids content (SSC) is an important index that is used to evaluate the quality and sugar content of fruits and vegetables. However, the genetic architecture underlying the SSC in Brassica napus shoots is still unclear. Here, quantitative trait loci (QTLs) for the SSC in B. napus shoots were investigated by performing linkage mapping using a recombinant inbred line population containing 189 lines. A germplasm set comprising 302 accessions was also used to conduct a genome-wide association study (GWAS). The QTL mapping revealed six QTLs located on chromosomes A01, A04, A08, and A09 in two experiments. Among them, two major QTLs, qSSC/21GY.A04-1 and qSSC/21NJ.A08-1, accounted for 12.92% and 10.18% of the phenotypic variance, respectively. In addition, eight single-nucleotide polymorphisms with phenotypic variances between 5.62% and 10.18% were identified by the GWAS method. However, no locus was simultaneously identified by QTL mapping and GWAS. We identified AH174 (7.55 °Brix and 7.9 °Brix), L166 (8.9 °Brix and 8.38 °Brix), and L380 (8.9 °Brix and 7.74 °Brix) accessions can be used as superior parents. These results provide valuable information that increases our understanding of the genetic control of SSC and will facilitate the breeding of high-SSC B. napus shoots.

The cichlid oral and pharyngeal jaws are evolutionarily and genetically coupled

Evolutionary constraints may significantly bias phenotypic change, while "breaking" from such constraints can lead to expanded ecological opportunity. Ray-finned fishes have broken functional constraints by developing two jaws (oral-pharyngeal), decoupling prey capture (oral jaw) from processing (pharyngeal jaw). It is hypothesized that the oral and pharyngeal jaws represent independent evolutionary modules and this facilitated diversification in feeding architectures. Here we test this hypothesis in African cichlids. Contrary to our expectation, we find integration between jaws at multiple evolutionary levels. Next, we document integration at the genetic level, and identify a candidate gene, smad7, within a pleiotropic locus for oral and pharyngeal jaw shape that exhibits correlated expression between the two tissues. Collectively, our data show that African cichlid evolutionary success has occurred within the context of a coupled jaw system, an attribute that may be driving adaptive evolution in this iconic group by facilitating rapid shifts between foraging habitats, providing an advantage in a stochastic environment such as the East African Rift-Valley.

Harnessing multivariate, penalized regression methods for genomic prediction and QTL detection of drought-related traits in grapevine

Viticulture has to cope with climate change and to decrease pesticide inputs, while maintaining yield and wine quality. Breeding is a key lever to meet this challenge, and genomic prediction a promising tool to accelerate breeding programs. Multivariate methods are potentially more accurate than univariate ones. Moreover, some prediction methods also provide marker selection, thus allowing quantitative trait loci (QTLs) detection and the identification of positional candidate genes. To study both genomic prediction and QTL detection for drought-related traits in grapevine, we applied several methods, interval mapping (IM) as well as univariate and multivariate penalized regression, in a bi-parental progeny. With a dense genetic map, we simulated two traits under four QTL configurations. The penalized regression method Elastic Net (EN) for genomic prediction, and controlling the marginal False Discovery Rate on EN selected markers to prioritize the QTLs. Indeed, penalized methods were more powerful than IM for QTL detection across various genetic architectures. Multivariate prediction did not perform better than its univariate counterpart, despite strong genetic correlation between traits. Using 14 traits measured in semi-controlled conditions under different watering conditions, penalized regression methods proved very efficient for intra-population prediction whatever the genetic architecture of the trait, with predictive abilities reaching 0.68. Compared to a previous study on the same traits, these methods applied on a denser map found new QTLs controlling traits linked to drought tolerance and provided relevant candidate genes. Overall, these findings provide a strong evidence base for implementing genomic prediction in grapevine breeding.

Robust regression based genome-wide multi-trait QTL analysis

In genome-wide quantitative trait locus (QTL) mapping studies, multiple quantitative traits are often measured along with the marker genotypes. Multi-trait QTL (MtQTL) analysis, which includes multiple quantitative traits together in a single model, is an efficient technique to increase the power of QTL identification. The two most widely used classical approaches for MtQTL mapping are Gaussian Mixture Model-based MtQTL (GMM-MtQTL) and Linear Regression Model-based MtQTL (LRM-MtQTL) analyses. There are two types of LRM-MtQTL approach known as least squares-based LRM-MtQTL (LS-LRM-MtQTL) and maximum likelihood-based LRM-MtQTL (ML-LRM-MtQTL). These three classical approaches are equivalent alternatives for QTL detection, but ML-LRM-MtQTL is computationally faster than GMM-MtQTL and LS-LRM-MtQTL. However, one major limitation common to all the above classical approaches is that they are very sensitive to outliers, which leads to misleading results. Therefore, in this study, we developed an LRM-based robust MtQTL approach, called LRM-RobMtQTL, for the backcross population based on the robust estimation of regression parameters by maximizing the β-likelihood function induced from the β-divergence with multivariate normal distribution. When β = 0, the proposed LRM-RobMtQTL method reduces to the classical ML-LRM-MtQTL approach. Simulation studies showed that both ML-LRM-MtQTL and LRM-RobMtQTL methods identified the same QTL positions in the absence of outliers. However, in the presence of outliers, only the proposed method was able to identify all the true QTL positions. Real data analysis results revealed that in the presence of outliers only our LRM-RobMtQTL approach can identify all the QTL positions as those identified in the absence of outliers by both methods. We conclude that our proposed LRM-RobMtQTL analysis approach outperforms the classical MtQTL analysis methods.

Connecting tree-ring phenotypes, genetic associations and transcriptomics to decipher the genomic architecture of drought adaptation in a widespread conifer

As boreal forests face significant threats from climate change, understanding evolutionary trajectories of coniferous species has become fundamental to adapting management and conservation to a drying climate. We examined the genomic architecture underlying adaptive variation related to drought tolerance in 43 populations of a widespread boreal conifer, white spruce (Picea glauca [Moench] Voss), by combining genotype-environment associations, genotype-phenotype associations, and transcriptomics. Adaptive genetic variation was identified by correlating allele frequencies for 6,153 single nucleotide polymorphisms from 2,606 candidate genes with temperature, precipitation and aridity gradients, and testing for significant associations between genotypes and 11 dendrometric and drought-related traits (i.e., anatomical, growth response and climate-sensitivity traits) using a polygenic model. We identified a set of 285 genes significantly associated with a climatic factor or a phenotypic trait, including 110 that were differentially expressed in response to drought under greenhouse-controlled conditions. The interlinked phenotype-genotype-environment network revealed eight high-confidence genes involved in white spruce adaptation to drought, of which four were drought-responsive in the expression analysis. Our findings represent a significant step toward the characterization of the genomic basis of drought tolerance and adaptation to climate in conifers, which is essential to enable the establishment of resilient forests in view of new climate conditions.

A statistical framework for QTL hotspot detection

Quantitative trait loci (QTL) hotspots (genomic locations enriched in QTL) are a common and notable feature when collecting many QTL for various traits in many areas of biological studies. The QTL hotspots are important and attractive since they are highly informative and may harbor genes for the quantitative traits. So far, the current statistical methods for QTL hotspot detection use either the individual-level data from the genetical genomics experiments or the summarized data from public QTL databases to proceed with the detection analysis. These methods may suffer from the problems of ignoring the correlation structure among traits, neglecting the magnitude of LOD scores for the QTL, or paying a very high computational cost, which often lead to the detection of excessive spurious hotspots, failure to discover biologically interesting hotspots composed of a small-to-moderate number of QTL with strong LOD scores, and computational intractability, respectively, during the detection process. In this article, we describe a statistical framework that can handle both types of data as well as address all the problems at a time for QTL hotspot detection. Our statistical framework directly operates on the QTL matrix and hence has a very cheap computational cost and is deployed to take advantage of the QTL mapping results for assisting the detection analysis. Two special devices, trait grouping and top γn,α profile, are introduced into the framework. The trait grouping attempts to group the traits controlled by closely linked or pleiotropic QTL together into the same trait groups and randomly allocates these QTL together across the genomic positions separately by trait group to account for the correlation structure among traits, so as to have the ability to obtain much stricter thresholds and dismiss spurious hotspots. The top γn,α profile is designed to outline the LOD-score pattern of QTL in a hotspot across the different hotspot architectures, so that it can serve to identify and characterize the types of QTL hotspots with varying sizes and LOD-score distributions. Real examples, numerical analysis, and simulation study are performed to validate our statistical framework, investigate the detection properties, and also compare with the current methods in QTL hotspot detection. The results demonstrate that the proposed statistical framework can effectively accommodate the correlation structure among traits, identify the types of hotspots, and still keep the notable features of easy implementation and fast computation for practical QTL hotspot detection.

QTL Mapping for Grain Zinc and Iron Concentrations in Bread Wheat

Deficiency of micronutrient elements, such as zinc (Zn) and iron (Fe), is called “hidden hunger,” and bio-fortification is the most effective way to overcome the problem. In this study, a high-density Affymetrix 50K single-nucleotide polymorphism (SNP) array was used to map quantitative trait loci (QTL) for grain Zn (GZn) and grain Fe (GFe) concentrations in 254 recombinant inbred lines (RILs) from a cross Jingdong 8/Bainong AK58 in nine environments. There was a wide range of variation in GZn and GFe concentrations among the RILs, with the largest effect contributed by the line × environment interaction, followed by line and environmental effects. The broad sense heritabilities of GZn and GFe were 0.36 ± 0.03 and 0.39 ± 0.03, respectively. Seven QTL for GZn on chromosomes 1DS, 2AS, 3BS, 4DS, 6AS, 6DL, and 7BL accounted for 2.2–25.1% of the phenotypic variances, and four QTL for GFe on chromosomes 3BL, 4DS, 6AS, and 7BL explained 2.3–30.4% of the phenotypic variances. QTL on chromosomes 4DS, 6AS, and 7BL might have pleiotropic effects on both GZn and GFe that were validated on a germplasm panel. Closely linked SNP markers were converted to high-throughput KASP markers, providing valuable tools for selection of improved Zn and Fe bio-fortification in breeding.

Lessons from a GWAS study of a wheat pre-breeding program: pyramiding resistance alleles to Fusarium crown rot

Much has been published on QTL detection for complex traits using bi-parental and multi-parental crosses (linkage analysis) or diversity panels (GWAS studies). While successful for detection, transferability of results to real applications has proven more difficult. Here, we combined a QTL detection approach using a pre-breeding populations which utilized intensive phenotypic selection for the target trait across multiple plant generations, combined with rapid generation turnover (i.e. "speed breeding") to allow cycling of multiple plant generations each year. The reasoning is that QTL mapping information would complement the selection process by identifying the genome regions under selection within the relevant germplasm. Questions to answer were the location of the genomic regions determining response to selection and the origin of the favourable alleles within the pedigree. We used data from a pre-breeding program that aimed at pyramiding different resistance sources to Fusarium crown rot into elite (but susceptible) wheat backgrounds. The population resulted from a complex backcrossing scheme involving multiple resistance donors and multiple elite backgrounds, akin to a MAGIC population (985 genotypes in total, with founders, and two major offspring layers within the pedigree). A significant increase in the resistance level was observed (i.e. a positive response to selection) after the selection process, and 17 regions significantly associated with that response were identified using a GWAS approach. Those regions included known QTL as well as potentially novel regions contributing resistance to Fusarium crown rot. In addition, we were able to trace back the sources of the favourable alleles for each QTL. We demonstrate that QTL detection using breeding populations under selection for the target trait can identify QTL controlling the target trait and that the frequency of the favourable alleles was increased as a response to selection, thereby validating the QTL detected. This is a valuable opportunistic approach that can provide QTL information that is more easily transferred to breeding applications.

QTL mapping and validation of bread wheat flag leaf morphology across multiple environments in different genetic backgrounds

Eight major and stably expressed QTL for flag leaf morphology across eleven environments were identified and validated using newly developed KASP markers in seven biparental populations with different genetic backgrounds. Flag leaf morphology is a determinant trait influencing plant architecture and yield potential in wheat (Triticum aestivum L.). A recombinant inbred line (RIL) population with a 55 K SNP-based constructed genetic map was used to map quantitative trait loci (QTL) for flag leaf length (FLL), width (FLW), area (FLA), angle (FLANG), opening angle (FLOA), and bend angle (FLBA) in eleven environments. Eight major QTL were detected in 11 environments with 5.73-54.38% of explained phenotypic variation. These QTL were successfully verified using the newly developed Kompetitive Allele Specific PCR (KASP) markers in six biparental populations with different genetic backgrounds. Among these 8 major QTL, two co-located intervals were identified. Significant interactions for both FLL- and FLW-related QTL were detected. Comparison analysis showed that QFll.sau-SY-2B and QFla.sau-SY-2B are likely new loci. Significant relationships between flag leaf- and yield-related traits were observed and discussed. Several genes associated with leaf development including the ortholog of maize ZmRAVL1, a B3-domain transcription factor involved in regulation of leaf angle, were predicted in physical intervals harboring these major QTL on reference genomes of bread wheat 'Chinese spring', T. turgidum, and Aegilops tauschii. Taken together, these results broaden our understanding on genetic basis of flag leaf morphology and provide clues for fine mapping and marker-assisted breeding wheat with optimized plant architecture for promising loci.

Responses of Soybean Genes in the Substituted Segments of Segment Substitution Lines Following a Xanthomonas Infection

Bacterial blight, which is one of the most common soybean diseases, is responsible for considerable yield losses. In this study, a novel Xanthomonas vasicola strain was isolated from the leaves of soybean plants infected with bacterial blight under field conditions. Sequencing the X. vasicola genome revealed type-III effector-coding genes. Moreover, the hrpG deletion mutant was constructed. To identify the soybean genes responsive to HrpG, two chromosome segment substitution lines (CSSLs) carrying the wild soybean genome, but with opposite phenotypes following Xanthomonas inoculations, were used to analyze gene expression networks based on RNA sequencing at three time points after inoculations with wild-type Xanthomonas or the hrpG deletion mutant. To further identify the hub genes underlying soybean responses to HrpG, the genes located on the substituted chromosome segments were examined. Finally, a combined analysis with the QTLs for resistance to Xanthomonas identified 35 hub genes in the substituted chromosomal segments that may help regulate soybean responses to Xanthomonas and HrpG. Furthermore, two candidate genes in the CSSLs might play pivotal roles in response to Xanthomonas.

Detecting Shared Genetic Architecture Among Multiple Phenotypes by Hierarchical Clustering of Gene-Level Association Statistics

Emerging large-scale biobanks pairing genotype data with phenotype data present new opportunities to prioritize shared genetic associations across multiple phenotypes for molecular validation. Past research, by our group and others, has shown gene-level tests of association produce biologically interpretable characterization of the genetic architecture of a given phenotype. Here, we present a new method, Ward clustering to identify Internal Node branch length outliers using Gene Scores (WINGS), for identifying shared genetic architecture among multiple phenotypes. The objective of WINGS is to identify groups of phenotypes, or "clusters," sharing a core set of genes enriched for mutations in cases. We validate WINGS using extensive simulation studies and then combine gene-level association tests with WINGS to identify shared genetic architecture among 81 case-control and seven quantitative phenotypes in 349,468 European-ancestry individuals from the UK Biobank. We identify eight prioritized phenotype clusters and recover multiple published gene-level associations within prioritized clusters.

Mapping Stem Rust (Puccinia graminis f. sp. secalis) Resistance in Self-Fertile Winter Rye Populations

Rye stem rust caused by Puccinia graminis f. sp. secalis can be found in all European rye growing regions. When the summers are warm and dry, the disease can cause severe yield losses over large areas. To date only little research was done in Europe to trigger resistance breeding. To our knowledge, all varieties currently registered in Germany are susceptible. In this study, three biparental populations of inbred lines and one testcross population developed for mapping resistance were investigated. Over 2 years, 68-70 genotypes per population were tested, each in three locations. Combining the phenotypic data with genotyping results of a custom 10k Infinium iSelect single nucleotide polymorphism (SNP) array, we identified both quantitatively inherited adult plant resistance and monogenic all-stage resistance. A single resistance gene, tentatively named Pgs1, located at the distal end of chromosome 7R, could be identified in two independently developed populations. With high probability, it is closely linked to a nucleotide-binding leucine-rich repeat (NB-LRR) resistance gene homolog. A marker for a competitive allele-specific polymerase chain reaction (KASP) genotyping assay was designed that could explain 73 and 97% of the genetic variance in each of both populations, respectively. Additional investigation of naturally occurring rye leaf rust (caused by Puccinia recondita ROEBERGE) revealed a gene complex on chromosome 7R. The gene Pgs1 and further identified quantitative trait loci (QTL) have high potential to be used for breeding stem rust resistant rye.

Genome-Wide Gene-Based Multi-Trait Analysis

Genome-wide association studies focusing on a single phenotype have been broadly conducted to identify genetic variants associated with a complex disease. The commonly applied single variant analysis is limited by failing to consider the complex interactions between variants, which motivated the development of association analyses focusing on genes or gene sets. Moreover, when multiple correlated phenotypes are available, methods based on a multi-trait analysis can improve the association power. However, most currently available multi-trait analyses are single variant-based analyses; thus have limited power when disease variants function as a group in a gene or a gene set. In this work, we propose a genome-wide gene-based multi-trait analysis method by considering genes as testing units. For a given phenotype, we adopt a rapid and powerful kernel-based testing method which can evaluate the joint effect of multiple variants within a gene. The joint effect, either linear or nonlinear, is captured through kernel functions. Given a series of candidate kernel functions, we propose an omnibus test strategy to integrate the test results based on different candidate kernels. A p-value combination method is then applied to integrate dependent p-values to assess the association between a gene and multiple correlated phenotypes. Simulation studies show a reasonable type I error control and an excellent power of the proposed method compared to its counterparts. We further show the utility of the method by applying it to two data sets: the Human Liver Cohort and the Alzheimer Disease Neuroimaging Initiative data set, and novel genes are identified. Our method has broad applications in other fields in which the interest is to evaluate the joint effect (linear or nonlinear) of a set of variants.

Linkage mapping and genome-wide association study reveals conservative QTL and candidate genes for Fusarium rot resistance in maize

BACKGROUND

Fusarium ear rot (FER) caused by Fusarium verticillioides is a major disease of maize that reduces grain yield and quality globally. However, there have been few reports of major loci for FER were verified and cloned.

RESULT

To gain a comprehensive understanding of the genetic basis of natural variation in FER resistance, a recombinant inbred lines (RIL) population and one panel of inbred lines were used to map quantitative trait loci (QTL) for resistance. As a result, a total of 10 QTL were identified by linkage mapping under four environments, which were located on six chromosomes and explained 1.0-7.1% of the phenotypic variation. Epistatic mapping detected four pairs of QTL that showed significant epistasis effects, explaining 2.1-3.0% of the phenotypic variation. Additionally, 18 single nucleotide polymorphisms (SNPs) were identified across the whole genome by genome-wide association study (GWAS) under five environments. Compared linkage and association mapping revealed five common intervals located on chromosomes 3, 4, and 5 associated with FER resistance, four of which were verified in different near-isogenic lines (NILs) populations. GWAS identified three candidate genes in these consistent intervals, which belonged to the Glutaredoxin protein family, actin-depolymerizing factors (ADFs), and AMP-binding proteins. In addition, two verified FER QTL regions were found consistent with Fusarium cob rot (FCR) and Fusarium seed rot (FSR).

CONCLUSIONS

These results revealed that multi pathways were involved in FER resistance, which was a complex trait that was controlled by multiple genes with minor effects, and provided important QTL and genes, which could be used in molecular breeding for resistance.

Bayesian multivariate reanalysis of large genetic studies identifies many new associations

Genome-wide association studies (GWAS) have now been conducted for hundreds of phenotypes of relevance to human health. Many such GWAS involve multiple closely-related phenotypes collected on the same samples. However, the vast majority of these GWAS have been analyzed using simple univariate analyses, which consider one phenotype at a time. This is despite the fact that, at least in simulation experiments, multivariate analyses have been shown to be more powerful at detecting associations. Here, we conduct multivariate association analyses on 13 different publicly-available GWAS datasets that involve multiple closely-related phenotypes. These data include large studies of anthropometric traits (GIANT), plasma lipid traits (GlobalLipids), and red blood cell traits (HaemgenRBC). Our analyses identify many new associations (433 in total across the 13 studies), many of which replicate when follow-up samples are available. Overall, our results demonstrate that multivariate analyses can help make more effective use of data from both existing and future GWAS.

Genome-wide association studies (GWAS) have become a common and powerful tool for identifying significant correlations between markers of genetic variation and physical traits of interest. Often these studies are conducted by comparing genetic variation against single traits one at a time (‘univariate’); however, it has previously been shown that it is possible to increase your power to detect significant associations by comparing genetic variation against multiple traits simultaneously (‘multivariate’). Despite this apparent increase in power though, researchers still rarely conduct multivariate GWAS, even when studies have multiple traits readily available. Here, we reanalyze 13 previously published GWAS using a multivariate method and find >400 additional associations. Our method makes use of univariate GWAS summary statistics and is available as a software package, thus making it accessible to other researchers interested in conducting the same analyses. We also show, using studies that have multiple releases, that our new associations have high rates of replication. Overall, we argue multivariate approaches in GWAS should no longer be overlooked and how, often, there is low-hanging fruit in the form of new associations by running these methods on data already collected.

Tightly linked antagonistic-effect loci underlie polygenic phenotypic variation in C. elegans

Recent work has provided strong empirical support for the classic polygenic model for trait variation. Population-based findings suggest that most regions of genome harbor variation affecting most traits. Here, we use the approach of experimental genetics to show that, indeed, most genomic regions carry variants with detectable effects on growth and reproduction in Caenorhabditis elegans populations sensitized by nickel stress. Nine of 15 adjacent intervals on the X chromosome, each encompassing ∼0.001 of the genome, have significant effects when tested individually in near-isogenic lines (NILs). These intervals have effects that are similar in magnitude to those of genome-wide significant loci that we mapped in a panel of recombinant inbred advanced intercross lines (RIAILs). If NIL-like effects were randomly distributed across the genome, the RIAILs would exhibit phenotypic variance that far exceeds the observed variance. However, the NIL intervals are arranged in a pattern that significantly reduces phenotypic variance relative to a random arrangement; adjacent intervals antagonize one another, cancelling each other's effects. Contrary to the expectation of small additive effects, our findings point to large-effect variants whose effects are masked by epistasis or linkage disequilibrium between alleles of opposing effect.

Utilizing trait networks and structural equation models as tools to interpret multi-trait genome-wide association studies

BACKGROUND

Plant breeders seek to develop cultivars with maximal agronomic value, which is often assessed using numerous, often genetically correlated traits. As intervention on one trait will affect the value of another, breeding decisions should consider the relationships among traits in the context of putative causal structures (i.e., trait networks). While multi-trait genome-wide association studies (MTM-GWAS) can infer putative genetic signals at the multivariate scale, standard MTM-GWAS does not accommodate the network structure of phenotypes, and therefore does not address how the traits are interrelated. We extended the scope of MTM-GWAS by incorporating trait network structures into GWAS using structural equation models (SEM-GWAS). Here, we illustrate the utility of SEM-GWAS using a digital metric for shoot biomass, root biomass, water use, and water use efficiency in rice.

RESULTS

A salient feature of SEM-GWAS is that it can partition the total single nucleotide polymorphism (SNP) effects acting on a trait into direct and indirect effects. Using this novel approach, we show that for most QTL associated with water use, total SNP effects were driven by genetic effects acting directly on water use rather that genetic effects originating from upstream traits. Conversely, total SNP effects for water use efficiency were largely due to indirect effects originating from the upstream trait, projected shoot area.

CONCLUSIONS

We describe a robust framework that can be applied to multivariate phenotypes to understand the interrelationships between complex traits. This framework provides novel insights into how QTL act within a phenotypic network that would otherwise not be possible with conventional multi-trait GWAS approaches. Collectively, these results suggest that the use of SEM may enhance our understanding of complex relationships among agronomic traits.

Testing Pleiotropy vs. Separate QTL in Multiparental Populations

The high mapping resolution of multiparental populations, combined with technology to measure tens of thousands of phenotypes, presents a need for quantitative methods to enhance understanding of the genetic architecture of complex traits. When multiple traits map to a common genomic region, knowledge of the number of distinct loci provides important insight into the underlying mechanism and can assist planning for subsequent experiments. We extend the method of Jiang and Zeng (1995), for testing pleiotropy with a pair of traits, to the case of more than two alleles. We also incorporate polygenic random effects to account for population structure. We use a parametric bootstrap to determine statistical significance. We apply our methods to a behavioral genetics data set from Diversity Outbred mice. Our methods have been incorporated into the R package qtl2pleio.

qtl2pleio: Testing pleiotropy vs. separate QTL in multiparental populations

Modern quantitative trait locus (QTL) studies in multiparental populations offer opportunities to identify causal genes for thousands of clinical and molecular traits. Traditional analyses examine each trait by itself. However, to fully leverage this vast number of measured traits, the systems genetics community needs statistical tools to analyze multiple traits simultaneously (Jiang & Zeng, 1995; Korol, Ronin, & Kirzhner, 1995). A test of pleiotropy vs. separate QTL is one such tool that will aid dissection of complex trait genetics and enhance understanding of genetic architecture.

Integration of lodging resistance QTL in soybean

Poor lodging resistance could limit increases in soybean yield. Previously, a considerable number of observations of quantitative trait loci (QTL) for lodging resistance have been reported by independent studies. The integration of these QTL into a consensus map will provide further evidence of their usefulness in soybean improvement. To improve informative QTL in soybean, a mapping population from a cross between the Harosoy and Clark cultivars, which inherit major U.S. soybean genetic backgrounds, was used along with previous mapping populations to identify QTL for lodging resistance. Together with 78 QTL for lodging collected from eighteen independent studies, a total of 88 QTL were projected onto the soybean consensus map. A total of 16 significant QTL clusters were observed; fourteen of them were confirmed in either two or more mapping populations or a single population subjected to different environmental conditions. Four QTL (one on chromosome 7 and three on 10) were newly identified in the present study. Further, meta-analysis was used to integrate QTL across different studies, resulting in two significant meta-QTL each on chromosomes 6 and 19. Our results provide deeper knowledge of valuable lodging resistance QTL in soybean, and these QTL could be used to increase lodging resistance.

Dissecting the genetics underlying the relationship between protein content and grain yield in a large hybrid wheat population

Additive and dominance effect QTL for grain yield and protein content display antagonistic pleiotropic effects, making genomic selection based on the index grain protein deviation a promising method to alleviate the negative correlation between these traits in wheat breeding. Grain yield and quality-related traits such as protein content and sedimentation volume are key traits in wheat breeding. In this study, we used a large population of 1604 hybrids and their 135 parental components to investigate the genetics and metabolomics underlying the negative relationship of grain yield and quality, and evaluated approaches for their joint improvement. We identified a total of nine trait-associated metabolites and show that prediction using genomic data alone resulted in the highest prediction ability for all traits. We dissected the genetic architecture of grain yield and quality-determining traits and show results of the first mapping of the derived trait grain protein deviation. Further, we provide a genetic analysis of the antagonistic relation of grain yield and protein content and dissect the mode of gene action (pleiotropy vs linkage) of identified QTL. Lastly, we demonstrate that the composition of the training set for genomic prediction is crucial when considering different quality classes in wheat breeding.

Fine mapping and discovery of candidate genes for seed size in watermelon by genome survey sequencing

Fine mapping and discovery of candidate genes underlying seed size are important for modern watermelon breeding. Here, by using a high-resolution genetic map and whole-genome genetic variation detection aided by genome survey sequencing, we fine mapped and discovered candidate genes for seed size in watermelon. QTL (quantitative trait locus) mapping identified two pleiotropic QTLs for seed size, namely, qSS4 and qSS6, using a high-density genetic map constructed by specific length amplified fragment sequencing. qSS6 explained 93.00%, 94.11% and 95.26% of the phenotypic variation in thousand-seed weight, seed length and seed width, respectively, and was defined as a major QTL. Then, high-coverage re-sequencing of two parental lines detected a total of 193,395 SNPs (single nucleotide polymorphisms) and 45,065 indels (insertions/deletions), which corresponded to a frequency of 534 SNPs/Mb and 124 indels/Mb. Based on the genetic variation in the two parental lines, newly developed PCR-based markers allowed the region of qSS6 to be narrowed to 55.5 kb. Three potential candidates were identified, including a known seed size regulator in rice, SRS3. Taken together, our results reveal successful rapid fine mapping and discovery of candidate genes for seed size in watermelon, which could be applied to many traits of interest in plants.

Genetic architecture of quantitative flower and leaf traits in a pair of sympatric sister species of Primulina

Flowers and leaves each represent suites of functionally interrelated traits that are often involved in species divergence and local adaptation. However, a major unresolved issue is how the individual component traits that make up a complex trait such as a flower evolve in a coordinated fashion to retain a high degree of functionality. We use a quantitative trait loci (QTL) approach to elucidate the genetic architecture of divergence in flower and leaf traits between the sister species Primulina depressa and Primulina danxiaensis, which grow sympatrically but in contrasting microhabitats. We found that flower traits were controlled by multiple QTL of small effect, while leaf physiological and morphological traits tended to be controlled by QTL of larger effect. The observed floral integration, manifested by a high degree overlap in both individual trait QTL and QTL for principal component scores (PCA QTL), may have been critical for evolutionary divergence of floral morphology in relation to their pollinators. This overlap suggests that direct selection on only one or a few of the component traits could have caused substantial divergence in other floral traits due to genetic correlations, while the low QTL overlap between floral and vegetative traits suggests that these trait suites are genetically unlinked and can evolve independently in response to different selective pressures corresponding to their distinct functions.

Dissection of the molecular bases of genotype x environment interactions: a study of phenotypic plasticity of Saccharomyces cerevisiae in grape juices

Background

The ability of a genotype to produce different phenotypes according to its surrounding environment is known as phenotypic plasticity. Within different individuals of the same species, phenotypic plasticity can vary greatly. This contrasting response is caused by gene-by-environment interactions (GxE). Understanding GxE interactions is particularly important in agronomy, since selected breeds and varieties may have divergent phenotypes according to their growing environment. Industrial microbes such as Saccharomyces cerevisiae are also faced with a large range of fermentation conditions that affect their technological properties. Finding the molecular determinism of such variations is a critical task for better understanding the genetic bases of phenotypic plasticity and can also be helpful in order to improve breeding methods.

Results

In this study we implemented a QTL mapping program using two independent cross (~ 100 progeny) in order to investigate the molecular basis of yeast phenotypic response in a wine fermentation context. Thanks to whole genome sequencing approaches, both crosses were genotyped, providing saturated genetic maps of thousands of markers. Linkage analyses allowed the detection of 78 QTLs including 21 with significant interaction with the environmental conditions. Molecular dissection of a major QTL demonstrated that the sulfite pump Ssu1p has a pleiotropic effect and impacts the phenotypic plasticity of several traits.

Conclusions

The detection of QTLs and their interactions with environment emphasizes the complexity of yeast industrial traits. The validation of the interaction of SSU1 allelic variants with the nature of the fermented juice increases knowledge about the impact of the sulfite pump during fermentation. All together these results pave the way for exploiting and deciphering the genetic determinism of phenotypic plasticity.

Electronic supplementary material

The online version of this article (10.1186/s12864-018-5145-4) contains supplementary material, which is available to authorized users.

Study of QTLs linked to awn length and their relationships with chloroplasts under control and saline environments in bread wheat

INTRODUCTION

Some studies in wheat showed that awns may have a useful effect on yield, especially under drought stress. Up to this time few researches has identified the awn length QTLs with different effect in salinity stress.

OBJECTIVE

The primary objective of this study was to examine the additive (a) and the epistatic (aa) QTLs involve in wheat awns length in control and saline environments.

METHODS

A F7 RIL population consisting of 319 sister lines, derived from a cross between wheat cultivars Roshan and Falat (seri82), and the two parents were grown in two environments (control and Saline) based on an alpha lattice design with two replications in each environment. At flowering, awn length was measured for each line. For QTL analysis, the linkage map of the ''Roshan × Falat'' population was used, which included 748 markers including 719 DArT, 29 simple sequenced repeats (SSRs). Additive and pleiotropic QTLs were identified. In order to reveal the relationship between the identified QTL for awns length and the role of the gene or genes that it expresses, the awns length locus location and characteristics of its related CDS, gene, UTRs, ORF, exons and Introns were studied using ensemble plant ( http://plants.ensembl.org/Triticum_aestivum ). Furthermore, the promoter analysis has been done using NSITE-PL.

RESULTS

We identified 6 additive QTLs for awn length by QTL Cartographer program using single-environment phenotypical values. Also, we detected three additive and two epistatic QTLs for awn length by the QTLNetwork program using multi-environment phenotypical values. Our results showed that none of the additive and epistatic QTLs had interactions with environment. One of the additive QTLs located on chromosome 4A was co-located with QTLs for number of sterile spikelet per spike in both environment and number of seed per spike in control environment.

COCLUSION

Studies of the locus linked to the awns length QTL revealed the role of awn and its chloroplasts in grain filing during abiotic stress could be enhanced by over expression of some genes like GTP-Binding proteins which are enriched in chloroplasts encoded by genes included wPt-5730 locus.

Wild GWAS-association mapping in natural populations

The increasing affordability of sequencing and genotyping technologies has transformed the field of molecular ecology in recent decades. By correlating marker variants with trait variation using association analysis, large-scale genotyping and phenotyping of individuals from wild populations has enabled the identification of genomic regions that contribute to phenotypic differences among individuals. Such "gene mapping" studies are enabling us to better predict evolutionary potential and the ability of populations to adapt to challenges, such as changing environment. These studies are also allowing us to gain insight into the evolutionary processes maintaining variation in natural populations, to better understand genotype-by-environment and epistatic interactions and to track the dynamics of allele frequency change at loci contributing to traits under selection. Gene mapping in the wild using genomewide association scans (GWAS) do, however, come with a number of methodological challenges, not least the population structure in space and time inherent to natural populations. We here provide an overview of these challenges, summarize the exciting methodological advances and applications of association mapping in natural populations reported in this special issue and provide some guidelines for future "wild GWAS" research.

Genomic prediction applied to high-biomass sorghum for bioenergy production

The increasing cost of energy and finite oil and gas reserves have created a need to develop alternative fuels from renewable sources. Due to its abiotic stress tolerance and annual cultivation, high-biomass sorghum (Sorghum bicolor L. Moench) shows potential as a bioenergy crop. Genomic selection is a useful tool for accelerating genetic gains and could restructure plant breeding programs by enabling early selection and reducing breeding cycle duration. This work aimed at predicting breeding values via genomic selection models for 200 sorghum genotypes comprising landrace accessions and breeding lines from biomass and saccharine groups. These genotypes were divided into two sub-panels, according to breeding purpose. We evaluated the following phenotypic biomass traits: days to flowering, plant height, fresh and dry matter yield, and fiber, cellulose, hemicellulose, and lignin proportions. Genotyping by sequencing yielded more than 258,000 single-nucleotide polymorphism markers, which revealed population structure between subpanels. We then fitted and compared genomic selection models BayesA, BayesB, BayesCπ, BayesLasso, Bayes Ridge Regression and random regression best linear unbiased predictor. The resulting predictive abilities varied little between the different models, but substantially between traits. Different scenarios of prediction showed the potential of using genomic selection results between sub-panels and years, although the genotype by environment interaction negatively affected accuracies. Functional enrichment analyses performed with the marker-predicted effects suggested several interesting associations, with potential for revealing biological processes relevant to the studied quantitative traits. This work shows that genomic selection can be successfully applied in biomass sorghum breeding programs.

Advantages and limitations of multiple-trait genomic prediction for Fusarium head blight severity in hybrid wheat (Triticum aestivum L.)

Predictabilities for wheat hybrids less related to the estimation set were improved by shifting from single- to multiple-trait genomic prediction of Fusarium head blight severity. Breeding for improved Fusarium head blight resistance (FHBr) of wheat is a very laborious and expensive task. FHBr complexity is mainly due to its highly polygenic nature and because FHB severity (FHBs) is greatly influenced by the environment. Associated traits plant height and heading date may provide additional information related to FHBr, but this is ignored in single-trait genomic prediction (STGP). The aim of our study was to explore the benefits in predictabilities of multiple-trait genomic prediction (MTGP) over STGP of target trait FHBs in a population of 1604 wheat hybrids using information on 17,372 single nucleotide polymorphism markers along with indicator traits plant height and heading date. The additive inheritance of FHBs allowed accurate hybrid performance predictions using information on general combining abilities or average performance of both parents without the need of markers. Information on molecular markers and indicator trait(s) improved FHBs predictabilities for hybrids less related to the estimation set. Indicator traits must be observed on the predicted individuals to benefit from MTGP. Magnitudes of genetic and phenotypic correlations along with improvements in predictabilities made plant height a better indicator trait for FHBs than heading date. Thus, MTGP having only plant height as indicator trait already maximized FHBs predictabilities. Provided a good indicator trait was available, MTGP could reduce the impacts of genotype environment [Formula: see text] interaction on STGP for hybrids less related to the estimation set.

Simultaneous estimation of QTL parameters for mapping multiple traits

The analysis of quantitative trait loci (QTLs) aims at mapping and estimating the positions and effects of the genes that may affect the quantitative trait, and evaluating the relationship between the gene variation and the phenotype. In existing studies, most methods mainly focus on the association/linkage between multiple gene loci and one trait, in which some useful joint information of multiple traits may be ignored. In this paper, we proposed a method of simultaneously estimating all QTL parameters in the framework of multiple-trait multiple-interval mapping. Simulation results show that in accuracy aspect, the proposed method outperforms an existing method for mapping multiple traits. A real example is also provided to validate the performance of the new method.

Quantitative Trait Loci Mapping of Western Corn Rootworm (Coleoptera: Chrysomelidae) Host Plant Resistance in Two Populations of Doubled Haploid Lines in Maize (Zea mays L.)

Over the last 70 yr, more than 12,000 maize accessions have been screened for their level of resistance to western corn rootworm, Diabrotica virgifera virgifera (LeConte; Coleoptera: Chrysomelidae), larval feeding. Less than 1% of this germplasm was selected for initiating recurrent selection or other breeding programs. Selected genotypes were mostly characterized by large root systems and superior root regrowth after root damage caused by western corn rootworm larvae. However, no hybrids claiming native (i.e., host plant) resistance to western corn rootworm larval feeding are currently commercially available. We investigated the genetic basis of western corn rootworm resistance in maize materials with improved levels of resistance using linkage disequilibrium mapping approaches. Two populations of topcrossed doubled haploid maize lines (DHLs) derived from crosses between resistant and susceptible maize lines were evaluated for their level of resistance in three to four different environments. For each DHL topcross an average root damage score was estimated and used for quantitative trait loci (QTL) analysis. We found genomic regions contributing to western corn rootworm resistance on all maize chromosomes, except for chromosome 4. Models fitting all QTL simultaneously explained about 30 to 50% of the genotypic variance for root damage scores in both mapping populations. Our findings confirm the complex genetic structure of host plant resistance against western corn rootworm larval feeding in maize. Interestingly, three of these QTL regions also carry genes involved in ascorbate biosynthesis, a key compound we hypothesize is involved in the expression of western corn rootworm resistance.

QTL Landscape for Oil Content in Brassica juncea: Analysis in Multiple Bi-Parental Populations in High and "0" Erucic Background

Increasing oil content in oilseed mustard (Brassica juncea) is a major breeding objective-more so, in the lines that have "0" erucic acid content (< 2% of the seed oil) as earlier studies have shown negative pleiotropic effect of erucic acid loci on the oil content, both in oilseed mustard and rapeseed. We report here QTL analysis of oil content in eight different mapping populations involving seven different parents-including a high oil content line J8 (~49%). The parental lines of the mapping populations contained wide variation in oil content and erucic acid content. The eight mapping populations were categorized into two sets-five populations with individuals segregating for erucic acid (SE populations) and the remaining three with zero erucic acid segregants (ZE populations). Meta-analysis of QTL mapped in individual SE populations identified nine significant C-QTL, with two of these merging most of the major oil QTL that colocalized with the erucic acid loci on the linkage groups A08 and B07. QTL analysis of oil content in ZE populations revealed a change in the landscape of the oil QTL compared to the SE populations, in terms of altered allelic effects and phenotypic variance explained by ZE QTL at the "common" QTL and observation of "novel" QTL in the ZE background. The important loci contributing to oil content variation, identified in the present study could be used in the breeding programmes for increasing the oil content in high erucic and "0" erucic backgrounds.

QTL-By-Environment Interaction in the Response of Maize Root and Shoot Traits to Different Water Regimes

Drought is a major abiotic stress factor limiting maize production, and elucidating the genetic control of root system architecture and plasticity to water-deficit stress is a crucial problem to improve drought adaptability. In this study, 13 root and shoot traits and genetic plasticity were evaluated in a recombinant inbred line (RIL) population under well-watered (WW) and water stress (WS) conditions. Significant phenotypic variation was observed for all observed traits both under WW and WS conditions. Most of the measured traits showed significant genotype-environment interaction (GEI) in both environments. Strong correlations were observed among traits in the same class. Multi-environment (ME) and multi-trait (MT) QTL analyses were conducted for all observed traits. A total of 48 QTLs were identified by ME, including 15 QTLs associated with 9 traits showing significant QTL-by-Environment interactions (QEI). QTLs associated with crown root angle (CRA2) and crown root length (CRL1) were identified as having antagonistic pleiotropic effects, while 13 other QTLs showed signs of conditional neutrality (CN), including 9 and 4 QTLs detected under WW and WS conditions, respectively. MT analysis identified 14 pleiotropic QTLs for 13 traits, SNP20 (1@79.2 cM) was associated with the length of crown root (CR), primary root (PR), and seminal root (SR) and might contribute to increases in root length under WS condition. Taken together, these findings contribute to our understanding of the phenotypic and genotypic patterns of root plasticity in response to water deficiency, which will be useful to improve drought tolerance in maize.

MVQTLCIM: composite interval mapping of multivariate traits in a hybrid F1 population of outbred species

Background

With the plummeting cost of the next-generation sequencing technologies, high-density genetic linkage maps could be constructed in a forest hybrid F₁ population. However, based on such genetic maps, quantitative trait loci (QTL) mapping cannot be directly conducted with traditional statistical methods or tools because the linkage phase and segregation pattern of molecular markers are not always fixed as in inbred lines.

Results

We implemented the traditional composite interval mapping (CIM) method to multivariate trait data in forest trees and developed the corresponding software, mvqtlcim. Our method not only incorporated the various segregations and linkage phases of molecular markers, but also applied Takeuchi’s information criterion (TIC) to discriminate the QTL segregation type among several possible alternatives. QTL mapping was performed in a hybrid F₁ population of Populus deltoides and P. simonii, and 12 QTLs were detected for tree height over 6 time points. The software package allowed many options for parameters as well as parallel computing for permutation tests. The features of the software were demonstrated with the real data analysis and a large number of Monte Carlo simulations.

Conclusions

We provided a powerful tool for QTL mapping of multiple or longitudinal traits in an outbred F₁ population, in which the traditional software for QTL mapping cannot be used. This tool will facilitate studying of QTL mapping and thus will accelerate molecular breeding programs especially in forest trees. The tool package is freely available from https://github.com/tongchf /mvqtlcim.

Electronic supplementary material

The online version of this article (10.1186/s12859-017-1908-1) contains supplementary material, which is available to authorized users.