Overview of QTL detection in plants and tests for synergistic epistatic interactions

Detecting directional and non-directional epistasis in bi-parental populations using genomic data

Epistasis, commonly defined as interaction effects between alleles of different loci, is an important genetic component of the variation of phenotypic traits in natural and breeding populations. In addition to its impact on variance, epistasis can also affect the expected performance of a population and is then referred to as directional epistasis. Before the advent of genomic data, the existence of epistasis (both directional and non-directional) was investigated based on complex and expensive mating schemes involving several generations evaluated for a trait of interest. In this study, we propose a methodology to detect the presence of epistasis based on simple inbred biparental populations, both genotyped and phenotyped, ideally along with their parents. Thanks to genomic data, parental proportions as well as shared parental proportions between inbred individuals can be estimated. They allow the evaluation of epistasis through a test of the expected performance for directional epistasis or the variance of genetic values. This methodology was applied to two large multiparental populations, i.e. the American maize and soybean nested association mapping populations, evaluated for different traits. Results showed significant epistasis, especially for the test of directional epistasis, e.g. the increase in anthesis to silking interval observed in most maize inbred progenies or the decrease in grain yield observed in several soybean inbred progenies. In general, the effects detected suggested that shuffling allelic associations of both elite parents had a detrimental effect on the performance of their progeny. This methodology is implemented in the EpiTest R-package and can be applied to any bi/multiparental inbred population evaluated for a trait of interest.

QTL mapping for resistance against cereal cyst nematode (Heterodera avenae Woll.) in wheat (Triticum aestivum L.)

The resistance to cereal cyst nematode (Heterodera avenae Woll.) in wheat (Triticum aestivum L.) was studied using 114 doubled haploid lines from a novel ITMI mapping population. These lines were screened for nematode infestation in a controlled environment for two years. QTL-mapping analyses were performed across two years (Y1 and Y2) as well as combining two years (CY) data. On the 114 lines that were screened, a total of 2,736 data points (genotype, batch or years, and replication combinations) were acquired. For QTL analysis, 12,093 markers (11,678 SNPs and 415 SSRs markers) were used, after filtering the genotypic data, for the QTL mapping. Composite interval mapping, using Haley-Knott regression (hk) method in R/QTL, was used for QTL analysis. In total, 19 QTLs were detected out of which 13 were novel and six were found to be colocalized or nearby to previously reported Cre genes, QTLs or MTAs for H. avenae or H. filipjevi. Nine QTLs were detected across all three groups (Y1, Y2 and CY) including a significant QTL "QCcn.ha-2D" on chromosome 2D that explains 23% of the variance. This QTL colocalized with a previously identified Cre3 locus. Novel QTL, QCcn.ha-2A, detected in the present study could be the possible unreported homeoloci to QCcn.ha-2D, QCcn.ha-2B.1 and QCcn.ha-2B.2. Six significant digenic epistatic interactions were also observed. In addition, 26 candidate genes were also identified including genes known for their involvement in PPNs (plant parasitic nematodes) resistance in different plant species. In-silico expression of putative candidate genes showed differential expression in roots during specific developmental stages. Results obtained in the present study are useful for wheat breeding to generate resistant genetic resources against H. avenae.

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.

Identification and Validation of Major QTLs, Epistatic Interactions, and Candidate Genes for Soybean Seed Shape and Weight Using Two Related RIL Populations

Understanding the genetic mechanism underlying seed size, shape, and weight is essential for enhancing soybean cultivars. High-density genetic maps of two recombinant inbred line (RIL) populations, LM6 and ZM6, were evaluated across multiple environments to identify and validate M-QTLs as well as identify candidate genes behind major and stable quantitative trait loci (QTLs). A total of 239 and 43 M-QTLs were mapped by composite interval mapping (CIM) and mixed-model-based composite interval mapping (MCIM) approaches, from which 180 and 18, respectively, are novel QTLs. Twenty-two QTLs including four novel major QTLs were validated in the two RIL populations across multiple environments. Moreover, 18 QTLs showed significant AE effects, and 40 pairwise of the identified QTLs exhibited digenic epistatic effects. Thirty-four QTLs associated with seed flatness index (FI) were identified and reported here for the first time. Seven QTL clusters comprising several QTLs for seed size, shape, and weight on genomic regions of chromosomes 3, 4, 5, 7, 9, 17, and 19 were identified. Gene annotations, gene ontology (GO) enrichment, and RNA-seq analyses of the genomic regions of those seven QTL clusters identified 47 candidate genes for seed-related traits. These genes are highly expressed in seed-related tissues and nodules, which might be deemed as potential candidate genes regulating the seed size, weight, and shape traits in soybean. This study provides detailed information on the genetic basis of the studied traits and candidate genes that could be efficiently implemented by soybean breeders for fine mapping and gene cloning, and for marker-assisted selection (MAS) targeted at improving these traits individually or concurrently.

Genetic components of root architecture and anatomy adjustments to water-deficit stress in spring barley

Roots perform vital roles for adaptation and productivity under water-deficit stress, even though their specific functions are poorly understood. In this study, the genetic control of the nodal-root architectural and anatomical response to water deficit were investigated among diverse spring barley accessions. Water deficit induced substantial variations in the nodal root traits. The cortical, stele, and total root cross-sectional areas of the main-shoot nodal roots decreased under water deficit, but increased in the tiller nodal roots. Root xylem density and arrested nodal roots increased under water deficit, with the formation of root suberization/lignification and large cortical aerenchyma. Genome-wide association study implicated 11 QTL intervals in the architectural and anatomical nodal root response to water deficit. Among them, three and four QTL intervals had strong effects across seasons and on both root architectural and anatomical traits, respectively. Genome-wide epistasis analysis revealed 44 epistatically interacting SNP loci. Further analyses showed that these QTL intervals contain important candidate genes, including ZIFL2, MATE, and PPIB, whose functions are shown to be related to the root adaptive response to water deprivation in plants. These results give novel insight into the genetic architectures of barley nodal root response to soil water deficit stress in the fields, and thus offer useful resources for root-targeted marker-assisted selection.

Homeologous Epistasis in Wheat: The Search for an Immortal Hybrid

Hybridization between related species results in the formation of an allopolyploid with multiple subgenomes. These subgenomes will each contain complete, yet evolutionarily divergent, sets of genes. Like a diploid hybrid, allopolyploids will have two versions, or homeoalleles, for every gene. Partial functional redundancy between homeologous genes should result in a deviation from additivity. These epistatic interactions between homeoalleles are analogous to dominance effects, but are fixed across subgenomes through self pollination. An allopolyploid can be viewed as an immortalized hybrid, with the opportunity to select and fix favorable homeoallelic interactions within inbred varieties. We present a subfunctionalization epistasis model to estimate the degree of functional redundancy between homeoallelic loci and a statistical framework to determine their importance within a population. We provide an example using the homeologous dwarfing genes of allohexaploid wheat, Rht-1, and search for genome-wide patterns indicative of homeoallelic subfunctionalization in a breeding population. Using the IWGSC RefSeq v1.0 sequence, 23,796 homeoallelic gene sets were identified and anchored to the nearest DNA marker to form 10,172 homeologous marker sets. Interaction predictors constructed from products of marker scores were used to fit the homeologous main and interaction effects, as well as estimate whole genome genetic values. Some traits displayed a pattern indicative of homeoallelic subfunctionalization, while other traits showed a less clear pattern or were not affected. Using genomic prediction accuracy to evaluate importance of marker interactions, we show that homeologous interactions explain a portion of the nonadditive genetic signal, but are less important than other epistatic interactions.

Genome-Wide Detection of Major and Epistatic Effect QTLs for Seed Protein and Oil Content in Soybean Under Multiple Environments Using High-Density Bin Map

Seed protein and oil content are the two important traits determining the quality and value of soybean. Development of improved cultivars requires detailed understanding of the genetic basis underlying the trait of interest. However, it is prerequisite to have a high-density linkage map for precisely mapping genomic regions, and therefore the present study used high-density genetic map containing 2267 recombination bin markers distributed on 20 chromosomes and spanned 2453.79 cM with an average distance of 1.08 cM between markers using restriction-site-associated DNA sequencing (RAD-seq) approach. A recombinant inbred line (RIL) population of 104 lines derived from a cross between Linhefenqingdou and Meng 8206 cultivars was evaluated in six different environments to identify main- and epistatic-effect quantitative trait loci (QTLs)as well as their interaction with environments. A total of 44 main-effect QTLs for protein and oil content were found to be distributed on 17 chromosomes, and 15 novel QTL were identified for the first time. Out of these QTLs, four were major and stable QTLs, viz., qPro-7-1, qOil-8-3, qOil-10-2 and qOil-10-4, detected in at least two environments plus combined environment with R² values >10%. Within the physical intervals of these four QTLs, 111 candidate genes were screened for their direct or indirect involvement in seed protein and oil biosynthesis/metabolism processes based on gene ontology and annotation information. Based on RNA sequencing (RNA-seq) data analysis, 15 of the 111 genes were highly expressed during seed development stage and root nodules that might be considered as the potential candidate genes. Seven QTLs associated with protein and oil content exhibited significant additive and additive × environment interaction effects, and environment-independent QTLs revealed higher additive effects. Moreover, three digenic epistatic QTLs pairs were identified, and no main-effect QTLs showed epistasis. In conclusion, the use of a high-density map identified closely linked flanking markers, provided better understanding of genetic architecture and candidate gene information, and revealed the scope available for improvement of soybean quality through marker assisted selection (MAS).

Identification of quantitative trait loci underlying seed protein content of soybean including main, epistatic, and QTL × environment effects in different regions of Northeast China

The objective here was to identify QTL underlying soybean protein content (PC), and to evaluate the additive and epistatic effects of the QTLs. A mapping population, consisting of 129 recombinant inbred lines (RILs), was created by crossing 'Dongnong 46' and 'L-100'. Phenotypic data of the parents and RILs were collected for 4 years in three locations of Heilongjiang Province of China. A total of 213 SSR markers were used to construct a genetic linkage map. Eight QTLs, located on seven chromosomes (Chr), were identified to be associated with PC among the 10 tested environments. Of the seven QTLs, five QTLs, qPR-2 (Satt710, on Chr9), qPR-3 (Sat_122, on Chr12), qPR-5 (Satt543, on Chr17), qPR-7 (Satt163, on Chr18), and qPR-8 (Satt614, on Chr20), were detected in six, seven, seven, six, and seven environments, respectively, implying relatively stable QTLs. qPR-3 could explain 3.33%-11.26% of the phenotypic variation across eight tested environments. qPR-5 and qPR-8 explained 3.64%-10.1% and 11.86%-18.40% of the phenotypic variation, respectively, across seven tested environments. Eight QTLs associated with PC exhibited additive and (or) additive × environment interaction effects. The results showed that environment-independent QTLs often had higher additive effects. Moreover, five epistatic pairwise QTLs were identified in the 10 environments.

What is crop heterosis: new insights into an old topic

Heterosis (or hybrid vigor) refers to a natural phenomenon whereby hybrid offspring of genetically diverse individuals out-perform their parents in multiple traits including yield, adaptability and resistances to biotic and abiotic stressors. Innovations in technology and research continue to clarify the mechanisms underlying crop heterosis, however the intrinsic relationship between the biological basis of heterosis remain unclear. In this review, we aim to provide insight into the molecular genetic basis of heterosis by presenting recent advances in the 'omics' of heterosis and the role of non-coding regions, particularly in relation to energy-use efficiency. We propose that future research should focus on integrating the expanding datasets from different species and hybrid combinations, to mine key heterotic genes and unravel interactive 'omics' networks associated with heterosis. Improved understanding of heterosis and the biological basis for its manipulation in agriculture should help to streamline its use in enhancing crop productivity.

Why epistasis is important for selection and adaptation

Organisms are built from thousands of genes that interact in complex ways. Still, the mathematical theory of evolution is dominated by a gene-by-gene perspective in which genes are assumed to have the same effects regardless of genetic background. Gene interaction, or epistasis, plays a role in some theoretical developments such as the evolution of recombination, reproductive isolation, and canalization, but is strikingly missing from our standard accounts of phenotypic adaptation. This absence is most puzzling within the field of quantitative genetics, which, despite its polygenic perspective and elaborate statistical representation of epistasis, has not found a single important role for gene interaction in evolution. To the contrary, there is a widespread consensus that epistasis is evolutionary inert, and that all we need to know to predict evolutionary dynamics is the additive component of the genetic variance. This view may have roots in convenience, but also in theoretical results showing that the response to selection derived from epistatic variance components is not permanent and will decay when selection is relaxed. I show that these results are tied to a conceptual confusion, and are misleading as general statements about the significance of epistasis for the selection response and adaptation.

Metabolomics: creating new potentials for unraveling the mechanisms in response to salt and drought stress and for the biotechnological improvement of xero-halophytes

Breeders have long been interested in understanding the biological function and mechanism of xero-halophytes and their ability for growth in drought-stricken and salinized environments. However, the mechanisms in response to stress have been difficult to unravel because their defenses require regulatory changes to the activation of multiple genes and pathways. Metabolomics is becoming a key tool in comprehensively understanding the cellular response to abiotic stress and represents an important addition to the tools currently employed in genomics-assisted selection for plant improvement. In this review, we highlight the applications of plant metabolomics in characterizing metabolic responses to salt and drought stress, and identifying metabolic quantitative trait loci (QTLs). We also discuss the potential of metabolomics as a tool to unravel stress response mechanisms, and as a viable option for the biotechnological improvement of xero-halophytes when no other genetic information such as linkage maps and QTLs are available, by combining with germplasm-regression-combined marker-trait association identification.

QTL, additive and epistatic effects for SCN resistance in PI 437654

PI 437654 is a unique accession because of its resistance to nearly all HG types (races) of soybean cyst nematode (Heterodera glycines Ichinohe; SCN). Objectives of this study were to confirm and refine the locations and gene action associated with SCN resistance previously discovered in PI 437654, and to identify new QTLs that may have been missed because of low coverage with genetic markers used in previous studies. Using 205 F(7:9) RILs and 276 SSR and AFLP molecular markers covering 2,406.5 cM of 20 linkage groups (LGs), we confirmed and refined the locations of major SCN resistance QTLs on LG-A2, -B1, and -G previously identified in PI 437654 or other resistant sources. We found that these major QTLs have epistatic effects among them or with other loci for SCN resistance. We also detected some new QTLs with additive or epistatic effects for SCN resistance to different HG types (races) on all LGs except LGs-B2 and -D1b. The QTL on LG-G was associated with resistance to HG types 2.5.7, 1.2.5.7, 0, and 2.7 (races 1, 2, 3, and 5), and it contributed a large proportion of the additive effects. The QTL on LG-A2 was associated with resistance to HG types 2.5.7 and 0 (races 1 and 3). The QTL on LG-B1, associated with resistance to HG types 2.5.7, 0, 2.7 (races 1, 3, and 5), was the similar QTL found in PI 90763 and PI 404198B. In addition to QTL on LGs-A2, -B1 and -G, a novel additive QTL associated with SCN resistance to HG types 0, 2.7, and 1.3.5.6.7 (race 3, 5, and 14) was identified on LG-I flanked by Sat_299 and Sat_189. Several minor QTLs on LGs-C1, D1a, H, and K were also found to be associated with SCN resistance. Confirmation of the new resistance QTL is underway by evaluating another RIL population with a different genetic background.