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ScienceDirect The crop QTLome comes of age Silvio Salvi and Roberto Tuberosa Recent progress in genomics and phenomics allows for a more accurate and comprehensive characterization of the Quantitative Trait Loci (QTLs) — hereafter defined ‘QTLome’ as a whole — that govern the variation targeted in breeding programs. High-density genotyping now provides unambiguous identification of QTL alleles, and for several traits beneficial alleles at major QTLs have already been deployed in marker-assisted breeding. However, the amount of QTLome information is enormous and approaches to distill and translate this information to breeders remain to be refined. Improved QTL meta-analyses, better estimation of QTL effects, improved crop modelling and full sharing of raw QTL data will enable a more effective exploitation of the QTLome. Addresses Department of Agricultural Sciences, University of Bologna, Viale Fanin 44, 40127 Bologna, Italy Corresponding author: Salvi, Silvio ([email protected])
Current Opinion in Biotechnology 2015, 32:179–185 This review comes from a themed issue on Plant biotechnology Edited by Inge Broer and George N Skaracis
http://dx.doi.org/10.1016/j.copbio.2015.01.001 0958-1669/# 2015 Elsevier Ltd. All rights reserved.
of the main factors contributing to the limited impact of QTLs in plant breeding, as already recognized by Bernardo: ‘QTLs are on papers rather than on cultivars’ [7]. The difficulty or lack of this synthesis also prevents us from accessing emerging properties that often arise when the results of single experiments are integrated in larger sets. Here, we briefly review where we stand as to QTL mapping and exploitation in crops. Additionally, we advocate for a more comprehensive and effective synthesis of QTL information with the belief that such synthesis will enhance our understanding of the genetic and functional basis of quantitative traits and the effectiveness of marker-assisted breeding (MAB) programs targeting major effect QTLs. We will not deal with general QTL analysis methodologies, (linkage or genome-wide association (GWA) mapping) as they are well established and have been thoroughly reviewed [8,9]. Recently, genomic selection (GS), a different type of MAB, has been introduced [10,11]. In GS, selection for complex traits follows the computation of an individual breeding value based on multi-marker genotype, ignoring information on single QTLs, which are usually modelled as virtually undetectable small effect QTLs. Conversely, our discussion will focus on QTLs characterized by effects large enough to be detected via mapping approaches.
Definition of QTLome Introduction The latest genomic technologies, including next-generation sequencing (NGS), enable us to acquire information on the genetic make-up of plant genetic resources — either experimental or natural populations as well as collections of unrelated accessions — at continuously lower costs [1,2]. Moreover, remarkable improvements in phenotyping technologies allow us to measure agronomically-relevant phenotypes in many individuals at unprecedented accuracy, speed and costs, both in controlled and field conditions [3,4]. Consequently, the number of plant studies reporting on Quantitative Trait Loci (QTLs) is growing at an impressive pace, with several thousands of studies reporting on countless QTLs [5]. A similar trend of growing QTL information is being observed in animal genetics, both in farm and model species [6]. Cataloguing, summarizing and making the plethora of QTL information readily accessible is a daunting undertaking and is likely one www.sciencedirect.com
A trait QTLome is defined as the set of information describing all experimentally supported QTLs for that trait in one species. For each QTL, the QTLome will report on the map position, allele identity and the genetic effect in terms of magnitude and type (additive vs. dominant) (Graphical abstract). The QTLome concept could also be utilized in a more restricted perspective to specify QTL alleles accessible to breeders in a germplasm collection. For example, the ‘plant-height QTLome’ of a wheat breeding program would report on the genetic makeup (i.e. QTL alleles/haplotypes) of each breeding line at all known plant-height QTLs. More in general, a crop’s QTLome would encompass the entire collection of genetically mapped loci and their allelic variation shown to influence any quantitative traits. Ultimately, the QTLome should inform about the expected effect on a trait caused by the substitution of haplotypes in any given genomic region. Importantly, QTLome alleles are — or should be — identified in terms of molecular haplotypes based on Single Nucleotide Polymorphism (SNP) markers or plain DNA sequences. This is arguably the most important impact of NGS Current Opinion in Biotechnology 2015, 32:179–185
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technologies in QTL mapping as it provides (i) straightforward connection of markers to the reference genome sequence and among different QTL maps and (ii) unambiguous identification of QTL alleles. Additionally, a correct trait ontology should be utilized [12], which in turn would encourage and facilitate cross-references and comparative studies, particularly important to evaluate the applicative value of QTLs. Eventually, QTLome information including all raw datasets (genotype, phenotype, and analytical protocols by the original authors) and exhaustive information about the experimental environment should be fully deposited and made available in public databases (e.g. www.datadryad. org/or others), as already suggested [5].
Capturing and characterizing a crop QTLome QTLs have now been mapped in large number for many traits. Based on a literature survey, yield, disease tolerance and seed quality largely dominate QTL studies in wheat (Figure 1), maize, rice and soybean, reflecting their agronomic and economic importance. What is missing from this list? For example, very few studies have so far addressed anatomical traits at the organ/tissue level. It is likely that a vast genetic variability hides in these traits, likely crucial for the response to environmental constraints, hence crop plasticity and adaptation to climate change. Initial studies have addressed anatomical features of roots in maize and tomato [13,14]. Rather Figure 1
Dormancy Others 7% 2% Pests 3% Sprouting 4%
Yield and yield components 14%
Phenology 5%
Quality 14%
Abiotic stresses 7%
Plant architecture 9%
Other diseases 10%
Fusarium 14%
Rusts 11% Current Opinion in Biotechnology
The wheat QTLome in terms of number of published studies for trait type. The collection was obtained by searching the Web of Science (Thomson ReutersTM) database with ‘wheat or Triticum’ as keywords in title field and ‘QTL and map’ in topic field, followed by manual inspection. The total number of studies was 950. Updated to November 5, 2014. Current Opinion in Biotechnology 2015, 32:179–185
unexplored are also the so-called dynamic traits (i.e. changing across developmental phases) such as leaf area index, plant biomass, metabolite concentration and others which have been shown to be informative for crop modelling [15,16]. QTL studies also addressed different ‘-omics’ by profiling and quantifying transcripts, proteins, and/or metabolites, resulting in expression-QTLs (eQTLs), protein-QTLs (pQTLs) and metabolite-QTLs (mQTLs), respectively. Because of the increasing automation in data collection and analysis, eQTL and mQTL studies are now more popular than pQTL studies, which were introduced first [17,18], Recently, mQTL for hundreds of different metabolites were mapped in maize and rice, showing that (i) the genetic architecture of metabolites concentration may vary from simple to very complex and (ii) mQTLs can help ascertaining the genetic and functional basis of agronomic traits [19,20]. QTLs have also been mapped for rather ‘exotic’ features such as frequency of recombination in maize [21] or trait homeostasis in barley [22], all of which may offer novel breeding opportunities. The first step to rationalize and interpret the plethora of QTL information comes from QTL meta-analysis [23–25], a statistical framework to project QTLs on a consensus map and to identify coincident QTLs among independent experiments. The resulting meta-QTLs are also expected to better define the boundaries of the causative genomic intervals [24]. QTL meta-analyses are now very popular in literature and generally are used to (i) summarize QTL information for one trait [26–29], (ii) locally verify the co-location of QTLs between different populations as a first step towards QTL validation [30,31] and/or (iii) prioritize candidate genes [32]. This notwithstanding, current meta-analysis has notable shortcomings such as lack of integration of QTL haplotype information and no obvious protocol able to integrate QTL results from experimental populations with those from GWA studies. Further developments in this direction will be very useful. A different approach towards a better QTLome synthesis is the ‘connected models’ approach, which exploits marker-based identity-by-descent (IBD) estimates at local genomic positions among population founders [33,34]. This approach has recently evolved by including a precomputation haplotype estimation step along the genome map using high-density SNP profiles. Because of the high coancestry in breeding materials, the number of haplotypes is usually lower than the number of parents, enabling for a QTL analysis across populations with fewer parameters, enhanced statistical power and more accurate estimates of QTL location and effects [34,35]. Additionally, QTLs can be associated with SNP-based haplotypes. An alternative mathematical approach aiming at the same objective is the Bayesian ‘pedigree-map’ www.sciencedirect.com
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approach, first applied in pedigree-related apple breeding populations [36]. While current meta-analysis needs to be improved, and ‘connected models’ and ‘pedigree map’ are now just two of the many QTL mapping approaches, they could represent the prototypes instrumental for a comprehensive QTLome synthesis. Importantly, these approaches have the potential to be applied retrospectively to QTL analyses carried out in the past, once the parents of the crosses will be sequenced or SNP-profiled at high density.
How large a trait QTLome really is? The identification of all genes and non-coding sequences influencing a complex trait while being a key objective in evolutionary biology and plant breeding is far from being reached. Functional genomics and system biology addressing cause-effect networks mediated by organism physiology [37] appear more suitable approaches than QTL analysis to address this issue and have already suggested a high complexity. Gene targeting or knockouts experiments in model organisms invariably showed that a relatively large proportion (from 4 to 20%, i.e. from 1000 to 6000 genes in a 30,000-gene species) of genes appeared to affect the expression of a complex trait [38,39]. Additionally, the proportion and role of functional elements outside coding regions is still under debate although it seems substantial [40]. However, the question ‘how large a trait QTLome really is?’ has a slightly different meaning in a crop breeding perspective. It more precisely points to the number of QTLs actually segregating in cultivated germplasm (as opposed to the total number of loci influencing a trait in a species) and to the distribution of QTL effects. Of course the presence of QTLs of sizeable and stable genetic effect is the key factor for a more effective exploitation of the QTLome to accelerate crop genetic improvement. Biometric approaches suggested that in single experiments the number of QTLs is generally underestimated [41] whereas the average QTL effect is overestimated [42]. Thanks to the vast QTL literature, we can attempt an empirical estimation of QTLome size. For the first two most popular trait types in wheat, yield and disease resistance, from 1992 to October 2014 a total of 133 and 361 QTL studies were published, respectively (additional details in Figure 1), for an estimated number of approximately 1600 and 4300 QTLs, assuming four QTLs per trait and three traits per analysis [7]. QTL meta-analysis, by accounting for QTL overlaps might contribute to reduce this list up to approx. 70% of the initial number [28]. Based on parallel GWAS or multiparental cross-based QTL analyses, QTLome size for traits with high heritability such as flowering time or plant height appears to be in the range of several tens of QTLs in both maize [43,44] and rice [45,46]. Of course, QTLome size is also constrained by the portion of www.sciencedirect.com
species’ genetic diversity surveyed in QTL studies, which is only a fraction of the total; therefore, estimates of number of QTLs should be scaled up accordingly. Addressing this issue, Joseph et al. [47] showed that the number of metabolome QTLs in Arabidopsis increased with the number of populations although it was not possible to tell whether the relationship was linear or log-linear. In conclusion, based on the above considerations, a complex trait QTLome in a crop species appears to conservatively range from several ten to one hundred or more non-overlapping QTLs. Empirical analyses have repeatedly shown an L-shaped distribution of QTL genetic effects, that is, a small number of large effect QTLs and an increasingly high number of lower effect ones [38,48]. This supports the idea of the QTLome resembling an iceberg, with the vast majority of small-effects QTLs below the detection power of most studies, which breeders can only capture by GS [10]. This is in accordance with population genetics theories, which predict that loci segregating for strong alleles become quickly fixed and therefore tend to ‘disappear’ from the population (Fisher–Orr model [44,49]). Therefore, for complex traits such as yield, it is likely that only minor effect QTLs are left segregating in elite cultivars, because these types of materials approach agricultural desirability [44]. Indeed, in maize, large QTL mapping studies for flowering time, plant height and leaf architecture have shown that small-effect QTLs dominate the scene [50]. However, slightly different results have been reported in rice, where both meta-analysis and large GWA studies found a higher portion of major-effect QTLs even for typically highly complex traits such as yield, plant height or flowering [45,51]. Also in wheat, large GWA studies and meta-analysis of several biparentals found a number of major QTLs with strong effect on plant height and heading date, both in coincidence with well-known genes (e.g. Ppd and Vrn) or as novel loci [27,52,53]. Zanke et al. [53] concluded that in elite bread wheat there is a wealth of plant height loci available besides the well-known Rht Green Revolution genes that could be used for modulating plant height in variety development. The levels of epistasis and pleiotropy should also be considered in charting QTLomes. The role of epistasis, that is the proportion of phenotypic variance due to inter-locus interactions, is still debated in view of the controversial results so far reported. Considering only relatively large experiments, epistasis was found negligible in maize [54], sizeable in wheat and sugar beet [55– 57] and remarkably pervasive in rice [58]. An emerging possible explanation is that an important fraction of current QTLs identified under simple additive models may have underlying epistatic genetic architecture because epistasis can indeed generate beneficial additive variance for quantitative traits, although Current Opinion in Biotechnology 2015, 32:179–185
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experiments size is usually inadequate for its detection [59]. Even less is known about QTL pleiotropy, that is to what extent different quantitative traits share QTLs, although the accumulation of multi-trait analysis on the same mapping population is starting to shed some light on this issue [3,60].
Harnessing the QTLome for plant breeding Although QTLs with strong effect are rare in crop germplasm, a number of major-effect QTLs have been mapped, validated, cloned and in many cases also been successfully utilized in MAB. The breeding frameworks within which QTLome information has impacted and will continue to impact are the so-called ‘backcross breeding’ and ‘breeding by design’ or ‘haplotype breeding’ [61,62,63,64]. These approaches address the development of new cultivars by recruiting beneficial alleles at known key loci by means of marker-assisted selection. Importantly, once a QTL is cloned, information can also be utilized to produce genetically modified crops (Graphical abstract). Examples of strong effect QTLs already (or close to be) exploited in cultivars are QTLs for drought tolerance in chickpea [65], stay green in sorghum [66], cyst nematode resistance in soybean [67] and Fusarium or salt tolerance in wheat [68,69]. Examples of traits for which major QTLs were cloned are flowering time [70] and carotenoid content in maize [45], yield components in rice [51], submergence tolerance in rice [71], aluminium tolerance in rice, sorghum and maize [72], phosphorus starvation and root architecture in rice [73,74] and boron toxicity in wheat [75]. Unfortunately, because a quantitative trait phenotype in one individual is typically the result of non-linear responses to a large number of factors (genetic and/or environmental), the correct prediction of the resulting effect of new combinations of alleles and QTL-haplotypes may not be easy. Progress is being made in this direction by integrating QTL effects and environment parameters in the model, also considering climate-change scenarios [76–78]. Clearly, the accumulation of QTLome information should come along with the collection of environmental parameters under which the QTL alleles were detected or tested. In conclusion, the exhaustive description of crop genomic diversity coupled with the accumulation of QTL information, have the potential to strongly enhance the impact of QTLome analysis on plant breeding via: - Analysis of SNP or sequence-based QTL haplotypes, which will (i) unambiguously identify QTL alleles, (ii) resolve the linkage phase between marker and target QTL alleles and (iii) simplify QTL cloning; Current Opinion in Biotechnology 2015, 32:179–185
- Production of comprehensive QTL databases, which will valorize QTL results available from previous experiments or obtained in single or less powerful studies, maximizing the information returned and reducing the need for QTL validation; - Shared raw (source) datasets (genotypes, phenotypes), which will enable community-based testing and/or updating of results and along with the availability of molecular haplotypes, will allow for accurate metaanalysis and a more comprehensive synthesis of QTL information; - Increased integration of QTLome information and environmental parameters in crop modelling which in turn should inform breeders about ‘priority’ traits and accelerate the selection of agronomically desirable QTL alleles. More than three decades have elapsed since the publication of the first isozyme marker-based study reporting QTLs in a crop [79]. The remarkable progress in understanding the functional complexity underpinning quantitative traits and the first tangible applications of such knowledge clearly indicate that the crop QTLome has come of age. It is expected that locus-specific, genomicsassisted breeding will increasingly contribute to raise yield potential and stability while reducing the environmental footprint of agricultural practices. This notwithstanding, it is difficult to predict to what extent these expectations will be fulfilled. While GS will likely provide most of the incremental yield increases required to ensure food security in the next decade, in the longer term a well-informed, targeted exploitation of the QTLome will allow breeders to more effectively harness natural allelic diversity and, upon its exhaustion/fixation, to manipulate (e.g. through genome editing and/or genetic engineering) QTLome expression in order to optimize the expression of loci that determine yield potential.
Acknowledgements We apologize for the many other valuable research works which could not be cited due to space limitations. We acknowledge the financial support by the EU FP7 Project Grants DROPS (Grant Agreement 244374), EUROOT (Grant Agreement 289300) and WATER4CROPS (Grant Agreement 311933).
References and recommended reading Papers of particular interest, published within the period of review, have been highlighted as: of special interest of outstanding interest 1.
He J, Zhao X, Laroche A, Lu ZX, Liu H, Li Z: Genotyping-bysequencing (GBS), an ultimate marker-assisted selection (MAS) tool to accelerate plant breeding. Front Plant Sci 2014, 5:484.
2.
Varshney RK, Terauchi R, McCouch SR: Harvesting the promising fruits of genomics: applying genome sequencing technologies to crop breeding. PLoS Biol 2014, 12:e1001883.
3.
Granier C, Vile D: Phenotyping and beyond: modelling the relationships between traits. Curr Opin Plant Biol 2014, 18:96-102. www.sciencedirect.com
The crop QTLome Salvi and Tuberosa 183
4.
Houle D, Govindaraju DR, Omholt S: Phenomics: the next challenge. Nat Rev Genet 2010, 11:855-866.
5. Zamir D: Where have all the crop phenotypes gone? PLoS Biol 2013, 11:e1001595. This perspective informs on the large amount of produced QTL (including phenotypic) information and loudly calls for the necessity to deposit in publicly available databases the raw datasets (genotypes, phenotypes and environmental information) of published experiments. 6.
Drinkwater NR, Gould MN: The long path from QTL to gene. PLoS Genet 2012, 8:e1002975.
7.
Bernardo R: Molecular markers and selection for complex traits in plants: learning from the last 20 years. Crop Sci 2008, 48:1649-1664.
8.
Wurschum T: Mapping QTL for agronomic traits in breeding populations. Theor Appl Genet 2012, 125:201-210.
9.
Korte A, Farlow A: The advantages and limitations of trait analysis with GWAS: a review. Plant Methods 2013, 9:29.
10. Jannink JL, Lorenz AJ, Iwata H: Genomic selection in plant breeding: from theory to practice. Brief Funct Genom 2010, 9:166-177. 11. Nakaya A, Isobe SN:: Will genomic selection be a practical method for plant breeding? Ann Bot 2012, 110:1303-1316. A clear and concise comparison in a plant breeding context between marker-assisted selection based on major loci/QTLs and genomic selection. 12. Cooper L, Walls RL, Elser J, Gandolfo MA, Stevenson DW, Smith B, Preece J, Athreya B, Mungall CJ, Rensing S: The plant ontology as a tool for comparative plant anatomy and genomic analyses. Plant Cell Physiol 2013, 54:e1-e1. 13. Mano Y, Omori F, Takeda K: Construction of intraspecific linkage maps, detection of a chromosome inversion, and mapping of QTL for constitutive root aerenchyma formation in the teosinte Zea nicaraguensis. Mol Breed 2012, 29:137-146. 14. Ron M, Dorrity MW, de Lucas M, Toal T, Hernandez RI, Little SA, Maloof JN, Kliebenstein DJ, Brady SM: Identification of novel loci regulating interspecific variation in root morphology and cellular development in tomato. Plant Physiol 2013, 162:755-768. 15. Wu R, Lin M: Functional mapping — how to map and study the genetic architecture of dynamic complex traits. Nat Rev Genet 2006, 7:229-237. 16. Wu¨rschum T, Liu W, Alheit KV, Tucker MR, Gowda M, Weissmann EA, Hahn V, Maurer HP: Adult plant development in triticale ( triticosecale wittmack) is controlled by dynamic genetic patterns of regulation. G3 2014, 4:1585-1591. 17. Horvatovich P, Franke L, Bischoff R: Proteomic studies related to genetic determinants of variability in protein concentrations. J Proteome Res 2014, 13:5-14. 18. Feltus FA: Systems genetics: a paradigm to improve discovery of candidate genes and mechanisms underlying complex traits. Plant Sci 2014, 223:45-48. 19. Riedelsheimer C, Lisec J, Czedik-Eysenberg A, Sulpice R, Flis A, Grieder C, Altmann T, Stitt M, Willmitzer L, Melchinger AE: Genome-wide association mapping of leaf metabolic profiles for dissecting complex traits in maize. Proc Natl Acad Sci USA 2012, 109:8872-8877. 20. Chen W, Gao Y, Xie W, Gong L, Lu K, Wang W, Li Y, Liu X, Zhang H, Dong H et al.: Genome-wide association analyses provide genetic and biochemical insights into natural variation in rice metabolism. Nat Genet 2014, 46:714-721. 21. Esch E, Szymaniak JM, Yates H, Pawlowski WP, Buckler ES: Using crossover breakpoints in recombinant inbred lines to identify quantitative trait loci controlling the global recombination frequency. Genetics 2007, 177:1851-1858. 22. Lacaze X, Hayes PM, Korol A: Genetics of phenotypic plasticity: QTL analysis in barley, Hordeum vulgare. Heredity (Edinb) 2009, 102:163-173. www.sciencedirect.com
23. Goffinet B, Gerber S: Quantitative trait loci: a meta-analysis. Genetics 2000, 155:463-473. 24. Veyrieras JB, Goffinet B, Charcosset A: MetaQTL: a package of new computational methods for the meta-analysis of QTL mapping experiments. BMC Bioinform 2007, 8:49. 25. Sosnowski O, Charcosset A, Joets J: BioMercator V3: an upgrade of genetic map compilation and quantitative trait loci meta-analysis algorithms. Bioinformatics 2012, 28:2082-2083. 26. Salvi S, Castelletti S, Tuberosa R: An updated consensus map for flowering time QTLs in maize. Maydica 2009, 54:501. 27. Griffiths S, Simmonds J, Leverington M, Wang Y, Fish L, Sayers L, Alibert L, Orford S, Wingen L, Snape J: Meta-QTL analysis of the genetic control of crop height in elite European winter wheat germplasm. Mol Breed 2012, 29:159-171. 28. Semagn K, Beyene Y, Warburton ML, Tarekegne A, Mugo S, Meisel B, Sehabiague P, Prasanna BM: Meta-analyses of QTL for grain yield and anthesis silking interval in 18 maize populations evaluated under water-stressed and wellwatered environments. BMC Genomics 2013, 14:313. 29. Said JI, Lin Z, Zhang X, Song M, Zhang J: A comprehensive meta QTL analysis for fiber quality, yield, yield related and morphological traits, drought tolerance, and disease resistance in tetraploid cotton. BMC Genomics 2013, 14:776. 30. Trijatmiko KR, Prasetiyono J, Thomson MJ, Cruz CMV, Moeljopawiro S, Pereira A: Meta-analysis of quantitative trait loci for grain yield and component traits under reproductivestage drought stress in an upland rice population. Mol Breed 2014, 34:283-295. 31. Landi P, Giuliani S, Salvi S, Ferri M, Tuberosa R, Sanguineti MC: Characterization of root-yield-1.06, a major constitutive QTL for root and agronomic traits in maize across water regimes. J Exp Bot 2010, 61:3553-3562. 32. Zhang H, Uddin MS, Zou C, Xie C, Xu Y, Li WX: Meta-analysis and candidate gene mining of low-phosphorus tolerance in maize. J Integr Plant Biol 2014, 56:262-270. 33. Blanc G, Charcosset A, Mangin B, Gallais A, Moreau L: Connected populations for detecting quantitative trait loci and testing for epistasis: an application in maize. Theor Appl Genet 2006, 113:206-224. 34. Bardol N, Ventelon M, Mangin B, Jasson S, Loywick V, Couton F, Derue C, Blanchard P, Charcosset A, Moreau L: Combined linkage and linkage disequilibrium QTL mapping in multiple families of maize (Zea mays L.) line crosses highlights complementarities between models based on parental haplotype and single locus polymorphism. Theor Appl Genet 2013, 126:2717-2736. 35. Leroux D, Rahmani A, Jasson S, Ventelon M, Louis F, Moreau L, Mangin B: Clusthaplo: a plug-in for MCQTL to enhance QTL detection using ancestral alleles in multi-cross design. Theor Appl Genet 2014, 127:921-933. The article describes an integrated software enabling to estimate QTL haplotypes in parents of multiparental populations based on local marker genomic similarities. Simulations showed that this approach provides significant gain in power and in accuracy of QTL mapping. 36. Bink MC, Jansen J, Madduri M, Voorrips RE, Durel CE, Kouassi AB, Laurens F, Mathis F, Gessler C, Gobbin D et al.: Bayesian QTL analyses using pedigreed families of an outcrossing species, with application to fruit firmness in apple. Theor Appl Genet 2014, 127:1073-1090. The first large-scale application of pedigree-based QTL mapping exploiting marker-based IBD estimates across populations belonging to different apple breeding programs. 37. Rockman MV: Reverse engineering the genotype–phenotype map with natural genetic variation. Nature 2008, 456:738-744. 38. Flint J, Mackay TF: Genetic architecture of quantitative traits in mice, flies, and humans. Genome Res 2009, 19:723-733. 39. Wilson-Sanchez D, Rubio-Diaz S, Munoz-Viana R, PerezPerez JM, Jover-Gil S, Ponce MR, Micol JL: Leaf phenomics: a systematic reverse genetic screen for Arabidopsis leaf mutants. Plant J 2014, 79:878-891. Current Opinion in Biotechnology 2015, 32:179–185
184 Plant biotechnology
40. Kellis M, Wold B, Snyder MP, Bernstein BE, Kundaje A, Marinov GK, Ward LD, Birney E, Crawford GE, Dekker J et al.: Defining functional DNA elements in the human genome. Proc Natl Acad Sci USA 2014, 111:6131-6138. 41. Otto SP, Jones CD: Detecting the undetected: estimating the total number of loci underlying a quantitative trait. Genetics 2000, 156:2093-2107. 42. Beavis WD: The power and deceit of QTL experiments: lessons from comparative QTL studies. In Proceedings of the FortyNinth Annual Corn and Sorghum Industry Research Conference. 1994:250-266. 43. Romay MC, Millard MJ, Glaubitz JC, Peiffer JA, Swarts KL, Casstevens TM, Elshire RJ, Acharya CB, Mitchell SE, FlintGarcia SA et al.: Comprehensive genotyping of the USA national maize inbred seed bank. Genome Biol 2013, 14:R55. An impressive genotyping-by-sequencing screening effort (almost 3000 public elite maize inbred lines analysed with 680,000 SNPs). The study showed that length and number of haplotypes vary enormously across genomic regions and germplasm groups, and that regions with low LD require even higher marker density for exhaustive GWA-based QTL mapping. 44. Peiffer JA, Romay MC, Gore MA, Flint-Garcia SA, Zhang Z, Millard MJ, Gardner CA, McMullen MD, Holland JB, Bradbury PJ et al.: The genetic architecture of maize height. Genetics 2014, 196:1337-1356. 45. Huang X, Wei X, Sang T, Zhao Q, Feng Q, Zhao Y, Li C, Zhu C, Lu T, Zhang Z et al.: Genome-wide association studies of 14 agronomic traits in rice landraces. Nat Genet 2010, 42:961-967. 46. Zhao K, Tung CW, Eizenga GC, Wright MH, Ali ML, Price AH, Norton GJ, Islam MR, Reynolds A, Mezey J et al.: Genome-wide association mapping reveals a rich genetic architecture of complex traits in Oryza sativa. Nat Commun 2011, 2:467. 47. Joseph B, Atwell S, Corwin JA, Li B, Kliebenstein DJ:: Meta-analysis of metabolome QTLs in Arabidopsis: trying to estimate the network size controlling genetic variation of the metabolome. Front Plant Sci 2014, 5:461. An attempt to empirically estimate the true number of QTLs affecting quantitative traits in plants. The article emphasizes the importance of considering how much of the species diversity was included in QTL studies. Results showed that the genetic complexity might be much higher than expected. 48. Kliebenstein DJ: Systems biology uncovers the foundation of natural genetic diversity. Plant Physiol 2010, 152:480-486. 49. Orr HA: The genetic theory of adaptation: a brief history. Nat Rev Genet 2005, 6:119-127. 50. Wallace JG, Larsson SJ, Buckler ES: Entering the second century of maize quantitative genetics. Heredity 2014, 112:30-38. An informed review which discusses many of the key topics in quantitative genetics relevant for maize breeding, including an assessment of the main current challenges, namely searching for the rare beneficial QTL alleles, controlling recombination rates, understanding the role of epigenetics, and others. 51. Xing Y, Zhang Q: Genetic and molecular bases of rice yield. Annu Rev Plant Biol 2010, 61:421-442. 52. Maccaferri M, Sanguineti MC, Demontis A, El-Ahmed A, Garcia del Moral L, Maalouf F, Nachit M, Nserallah N, Ouabbou H, Rhouma S et al.: Association mapping in durum wheat grown across a broad range of water regimes. J Exp Bot 2011, 62:409-438. 53. Zanke CD, Ling J, Plieske J, Kollers S, Ebmeyer E, Korzun V, Argillier O, Stiewe G, Hinze M, Neumann K et al.: Whole genome association mapping of plant height in winter wheat (Triticum aestivum L.). PLoS One 2014, 9:113287. Straightforward GWAS in wheat identified approx. 100 plant height QTLs, including Green Revolution genes but also apparently equally effective additional unexploited loci. High congruency with former QTL metaanalysis and candidate genes was noted. 54. McMullen MD, Kresovich S, Villeda HS, Bradbury P, Li H, Sun Q, Flint-Garcia S, Thornsberry J, Acharya C, Bottoms C et al.: Genetic properties of the maize nested association mapping population. Science 2009, 325:737-740. Current Opinion in Biotechnology 2015, 32:179–185
55. Maccaferri M, Sanguineti MC, Corneti S, Ortega JL, Salem MB, Bort J, DeAmbrogio E, del Moral LF, Demontis A, El-Ahmed A et al.: Quantitative trait loci for grain yield and adaptation of durum wheat (Triticum durum Desf.) across a wide range of water availability. Genetics 2008, 178:489-511. 56. Wurschum T, Maurer HP, Schulz B, Mohring J, Reif JC: Genomewide association mapping reveals epistasis and genetic interaction networks in sugar beet. Theor Appl Genet 2011, 123:109-118. 57. Quarrie SA, Steed A, Calestani C, Semikhodskii A, Lebreton C, Chinoy C, Steele N, Pljevljakusic D, Waterman E, Weyen J et al.: A high-density genetic map of hexaploid wheat (Triticum aestivum L.) from the cross Chinese Spring SQ1 and its use to compare QTLs for grain yield across a range of environments. Theor Appl Genet 2005, 110:865-880. 58. Huang A, Xu S, Cai X: Whole-genome quantitative trait locus mapping reveals major role of epistasis on yield of rice. PLoS One 2014, 9:e87330. Using immortalized F2-like populations from RILs of a highly productive hybrid cultivar, the authors thoroughly mapped additive and epistatic (digenic) QTLs. The latter ones were found to be the majority. 59. Mackay TF: Epistasis and quantitative traits: using model organisms to study gene–gene interactions. Nat Rev Genet 2014, 15:22-33. 60. Cane` MA, Maccaferri M, Nazemi G, Salvi S, Francia R, Colalongo C, Tuberosa R:: Association mapping for root architectural traits in durum wheat seedlings as related to agronomic performance. Mol Breed 2014, 34:1629-1645. 61. Peleman JD, van der Voort JR: Breeding by design. Trends Plant Sci 2003, 8:330-334. 62. Yamamoto T, Nagasaki H, Yonemaru J, Ebana K, Nakajima M, Shibaya T, Yano M: Fine definition of the pedigree haplotypes of closely related rice cultivars by means of genome-wide discovery of single-nucleotide polymorphisms. BMC Genomics 2010, 11:267. 63. Yonemaru J, Mizobuchi R, Kato H, Yamamoto T, Yamamoto E, Matsubara K, Hirabayashi H, Takeuchi Y, Tsunematsu H, Ishii T et al.: Genomic regions involved in yield potential detected by genome-wide association analysis in Japanese high-yielding rice cultivars. BMC Genomics 2014, 15:346. This paper reports on the use of genomewide SNP-defined haplotypes to describe genetic variation for heading date, blast resistance and other yield related quantitative traits in rice. 64. Chen H, He H, Zhou F, Yu H, Deng XW: Development of genomics-based genotyping platforms and their applications in rice breeding. Curr Opin Plant Biol 2013, 16:247-254. 65. Varshney RK, Gaur PM, Chamarthi SK, Krishnamurthy L, Tripathi S, Kashiwagi J, Samineni S, Singh VK, Thudi M, Jaganathan D: Fast-Track introgression of ‘‘QTL hotspot’’ for root traits and other drought tolerance traits in JG 11, an elite and leading variety of chickpea. Plant Genome 2013:6. 66. Borrell AK, Mullet JE, George-Jaeggli B, van Oosterom EJ, Hammer GL, Klein PE, Jordan DR: Drought adaptation of stay-green sorghum is associated with canopy development, leaf anatomy, root growth, and water uptake. J Exp Bot 2014, 65:6251-6263. 67. Concibido VC, Diers BW, Arelli PR: A decade of QTL mapping for cyst nematode resistance in soybean. Crop Sci 2004, 44:1121-1131. 68. Anderson JA, Chao S, Liu S: Molecular breeding using a major QTL for Fusarium head blight resistance in wheat. Crop Sci 2007, 47:S-112-S-119. 69. James RA, Blake C, Byrt CS, Munns R: Major genes for Na+ exclusion, Nax1 and Nax2 (wheat HKT1;4 and HKT1;5), decrease Na+ accumulation in bread wheat leaves under saline and waterlogged conditions. J Exp Bot 2011, 62:2939-2947. 70. Salvi S, Sponza G, Morgante M, Tomes D, Niu X, Fengler KA, Meeley R, Ananiev EV, Svitashev S, Bruggemann E et al.: Conserved noncoding genomic sequences associated with a www.sciencedirect.com
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flowering-time quantitative trait locus in maize. Proc Natl Acad Sci USA 2007, 104:11376-11381. 71. Septiningsih EM, Pamplona AM, Sanchez DL, Neeraja CN, Vergara GV, Heuer S, Ismail AM, Mackill DJ: Development of submergence-tolerant rice cultivars: the Sub1 locus and beyond. Ann Bot 2009, 103:151-160. 72. Famoso AN, Zhao K, Clark RT, Tung CW, Wright MH, Bustamante C, Kochian LV, McCouch SR: Genetic architecture of aluminum tolerance in rice (Oryza sativa) determined through genome-wide association analysis and QTL mapping. PLoS Genet 2011, 7:e1002221. 73. Gamuyao R, Chin JH, Pariasca-Tanaka J, Pesaresi P, Catausan S, Dalid C, Slamet-Loedin I, Tecson-Mendoza EM, Wissuwa M, Heuer S: The protein kinase Pstol1 from traditional rice confers tolerance of phosphorus deficiency. Nature 2012, 488:535-539. 74. Steele KA, Price AH, Witcombe JR, Shrestha R, Singh BN, Gibbons JM, Virk DS: QTLs associated with root traits increase yield in upland rice when transferred through marker-assisted selection. Theor Appl Genet 2013, 126:101-108.
www.sciencedirect.com
75. Pallotta M, Schnurbusch T, Hayes J, Hay A, Baumann U, Paull J, Langridge P, Sutton T:: Molecular basis of adaptation to high soil boron in wheat landraces and elite cultivars. Nature 2014, 514:88-91. 76. Tardieu F, Tuberosa R:: Dissection and modelling of abiotic stress tolerance in plants. Curr Opin Plant Biol 2010, 13:206-212. 77. Cooper M, van Eeuwijk FA, Hammer GL, Podlich DW, Messina CQTL: Modeling for complex traits: detection and context for plant breeding. Curr Opin Plant Biol 2009, 12:231240. 78. Harrison MT, Tardieu F, Dong Z, Messina CD, Hammer GL: Characterizing drought stress and trait influence on maize yield under current and future conditions. Glob Chang Biol 2014, 20:867-878. 79. Tanksley SD, Medina-Filho H, Rick CM: Use of naturallyoccurring enzyme variation to detect and map genes controlling quantitative traits in an interspecific backcross of tomato. Heredity 1982, 49:11-25.
Current Opinion in Biotechnology 2015, 32:179–185
ScienceDirect The crop QTLome comes of age Silvio Salvi and Roberto Tuberosa Recent progress in genomics and phenomics allows for a more accurate and comprehensive characterization of the Quantitative Trait Loci (QTLs) — hereafter defined ‘QTLome’ as a whole — that govern the variation targeted in breeding programs. High-density genotyping now provides unambiguous identification of QTL alleles, and for several traits beneficial alleles at major QTLs have already been deployed in marker-assisted breeding. However, the amount of QTLome information is enormous and approaches to distill and translate this information to breeders remain to be refined. Improved QTL meta-analyses, better estimation of QTL effects, improved crop modelling and full sharing of raw QTL data will enable a more effective exploitation of the QTLome. Addresses Department of Agricultural Sciences, University of Bologna, Viale Fanin 44, 40127 Bologna, Italy Corresponding author: Salvi, Silvio ([email protected])
Current Opinion in Biotechnology 2015, 32:179–185 This review comes from a themed issue on Plant biotechnology Edited by Inge Broer and George N Skaracis
http://dx.doi.org/10.1016/j.copbio.2015.01.001 0958-1669/# 2015 Elsevier Ltd. All rights reserved.
of the main factors contributing to the limited impact of QTLs in plant breeding, as already recognized by Bernardo: ‘QTLs are on papers rather than on cultivars’ [7]. The difficulty or lack of this synthesis also prevents us from accessing emerging properties that often arise when the results of single experiments are integrated in larger sets. Here, we briefly review where we stand as to QTL mapping and exploitation in crops. Additionally, we advocate for a more comprehensive and effective synthesis of QTL information with the belief that such synthesis will enhance our understanding of the genetic and functional basis of quantitative traits and the effectiveness of marker-assisted breeding (MAB) programs targeting major effect QTLs. We will not deal with general QTL analysis methodologies, (linkage or genome-wide association (GWA) mapping) as they are well established and have been thoroughly reviewed [8,9]. Recently, genomic selection (GS), a different type of MAB, has been introduced [10,11]. In GS, selection for complex traits follows the computation of an individual breeding value based on multi-marker genotype, ignoring information on single QTLs, which are usually modelled as virtually undetectable small effect QTLs. Conversely, our discussion will focus on QTLs characterized by effects large enough to be detected via mapping approaches.
Definition of QTLome Introduction The latest genomic technologies, including next-generation sequencing (NGS), enable us to acquire information on the genetic make-up of plant genetic resources — either experimental or natural populations as well as collections of unrelated accessions — at continuously lower costs [1,2]. Moreover, remarkable improvements in phenotyping technologies allow us to measure agronomically-relevant phenotypes in many individuals at unprecedented accuracy, speed and costs, both in controlled and field conditions [3,4]. Consequently, the number of plant studies reporting on Quantitative Trait Loci (QTLs) is growing at an impressive pace, with several thousands of studies reporting on countless QTLs [5]. A similar trend of growing QTL information is being observed in animal genetics, both in farm and model species [6]. Cataloguing, summarizing and making the plethora of QTL information readily accessible is a daunting undertaking and is likely one www.sciencedirect.com
A trait QTLome is defined as the set of information describing all experimentally supported QTLs for that trait in one species. For each QTL, the QTLome will report on the map position, allele identity and the genetic effect in terms of magnitude and type (additive vs. dominant) (Graphical abstract). The QTLome concept could also be utilized in a more restricted perspective to specify QTL alleles accessible to breeders in a germplasm collection. For example, the ‘plant-height QTLome’ of a wheat breeding program would report on the genetic makeup (i.e. QTL alleles/haplotypes) of each breeding line at all known plant-height QTLs. More in general, a crop’s QTLome would encompass the entire collection of genetically mapped loci and their allelic variation shown to influence any quantitative traits. Ultimately, the QTLome should inform about the expected effect on a trait caused by the substitution of haplotypes in any given genomic region. Importantly, QTLome alleles are — or should be — identified in terms of molecular haplotypes based on Single Nucleotide Polymorphism (SNP) markers or plain DNA sequences. This is arguably the most important impact of NGS Current Opinion in Biotechnology 2015, 32:179–185
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technologies in QTL mapping as it provides (i) straightforward connection of markers to the reference genome sequence and among different QTL maps and (ii) unambiguous identification of QTL alleles. Additionally, a correct trait ontology should be utilized [12], which in turn would encourage and facilitate cross-references and comparative studies, particularly important to evaluate the applicative value of QTLs. Eventually, QTLome information including all raw datasets (genotype, phenotype, and analytical protocols by the original authors) and exhaustive information about the experimental environment should be fully deposited and made available in public databases (e.g. www.datadryad. org/or others), as already suggested [5].
Capturing and characterizing a crop QTLome QTLs have now been mapped in large number for many traits. Based on a literature survey, yield, disease tolerance and seed quality largely dominate QTL studies in wheat (Figure 1), maize, rice and soybean, reflecting their agronomic and economic importance. What is missing from this list? For example, very few studies have so far addressed anatomical traits at the organ/tissue level. It is likely that a vast genetic variability hides in these traits, likely crucial for the response to environmental constraints, hence crop plasticity and adaptation to climate change. Initial studies have addressed anatomical features of roots in maize and tomato [13,14]. Rather Figure 1
Dormancy Others 7% 2% Pests 3% Sprouting 4%
Yield and yield components 14%
Phenology 5%
Quality 14%
Abiotic stresses 7%
Plant architecture 9%
Other diseases 10%
Fusarium 14%
Rusts 11% Current Opinion in Biotechnology
The wheat QTLome in terms of number of published studies for trait type. The collection was obtained by searching the Web of Science (Thomson ReutersTM) database with ‘wheat or Triticum’ as keywords in title field and ‘QTL and map’ in topic field, followed by manual inspection. The total number of studies was 950. Updated to November 5, 2014. Current Opinion in Biotechnology 2015, 32:179–185
unexplored are also the so-called dynamic traits (i.e. changing across developmental phases) such as leaf area index, plant biomass, metabolite concentration and others which have been shown to be informative for crop modelling [15,16]. QTL studies also addressed different ‘-omics’ by profiling and quantifying transcripts, proteins, and/or metabolites, resulting in expression-QTLs (eQTLs), protein-QTLs (pQTLs) and metabolite-QTLs (mQTLs), respectively. Because of the increasing automation in data collection and analysis, eQTL and mQTL studies are now more popular than pQTL studies, which were introduced first [17,18], Recently, mQTL for hundreds of different metabolites were mapped in maize and rice, showing that (i) the genetic architecture of metabolites concentration may vary from simple to very complex and (ii) mQTLs can help ascertaining the genetic and functional basis of agronomic traits [19,20]. QTLs have also been mapped for rather ‘exotic’ features such as frequency of recombination in maize [21] or trait homeostasis in barley [22], all of which may offer novel breeding opportunities. The first step to rationalize and interpret the plethora of QTL information comes from QTL meta-analysis [23–25], a statistical framework to project QTLs on a consensus map and to identify coincident QTLs among independent experiments. The resulting meta-QTLs are also expected to better define the boundaries of the causative genomic intervals [24]. QTL meta-analyses are now very popular in literature and generally are used to (i) summarize QTL information for one trait [26–29], (ii) locally verify the co-location of QTLs between different populations as a first step towards QTL validation [30,31] and/or (iii) prioritize candidate genes [32]. This notwithstanding, current meta-analysis has notable shortcomings such as lack of integration of QTL haplotype information and no obvious protocol able to integrate QTL results from experimental populations with those from GWA studies. Further developments in this direction will be very useful. A different approach towards a better QTLome synthesis is the ‘connected models’ approach, which exploits marker-based identity-by-descent (IBD) estimates at local genomic positions among population founders [33,34]. This approach has recently evolved by including a precomputation haplotype estimation step along the genome map using high-density SNP profiles. Because of the high coancestry in breeding materials, the number of haplotypes is usually lower than the number of parents, enabling for a QTL analysis across populations with fewer parameters, enhanced statistical power and more accurate estimates of QTL location and effects [34,35]. Additionally, QTLs can be associated with SNP-based haplotypes. An alternative mathematical approach aiming at the same objective is the Bayesian ‘pedigree-map’ www.sciencedirect.com
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approach, first applied in pedigree-related apple breeding populations [36]. While current meta-analysis needs to be improved, and ‘connected models’ and ‘pedigree map’ are now just two of the many QTL mapping approaches, they could represent the prototypes instrumental for a comprehensive QTLome synthesis. Importantly, these approaches have the potential to be applied retrospectively to QTL analyses carried out in the past, once the parents of the crosses will be sequenced or SNP-profiled at high density.
How large a trait QTLome really is? The identification of all genes and non-coding sequences influencing a complex trait while being a key objective in evolutionary biology and plant breeding is far from being reached. Functional genomics and system biology addressing cause-effect networks mediated by organism physiology [37] appear more suitable approaches than QTL analysis to address this issue and have already suggested a high complexity. Gene targeting or knockouts experiments in model organisms invariably showed that a relatively large proportion (from 4 to 20%, i.e. from 1000 to 6000 genes in a 30,000-gene species) of genes appeared to affect the expression of a complex trait [38,39]. Additionally, the proportion and role of functional elements outside coding regions is still under debate although it seems substantial [40]. However, the question ‘how large a trait QTLome really is?’ has a slightly different meaning in a crop breeding perspective. It more precisely points to the number of QTLs actually segregating in cultivated germplasm (as opposed to the total number of loci influencing a trait in a species) and to the distribution of QTL effects. Of course the presence of QTLs of sizeable and stable genetic effect is the key factor for a more effective exploitation of the QTLome to accelerate crop genetic improvement. Biometric approaches suggested that in single experiments the number of QTLs is generally underestimated [41] whereas the average QTL effect is overestimated [42]. Thanks to the vast QTL literature, we can attempt an empirical estimation of QTLome size. For the first two most popular trait types in wheat, yield and disease resistance, from 1992 to October 2014 a total of 133 and 361 QTL studies were published, respectively (additional details in Figure 1), for an estimated number of approximately 1600 and 4300 QTLs, assuming four QTLs per trait and three traits per analysis [7]. QTL meta-analysis, by accounting for QTL overlaps might contribute to reduce this list up to approx. 70% of the initial number [28]. Based on parallel GWAS or multiparental cross-based QTL analyses, QTLome size for traits with high heritability such as flowering time or plant height appears to be in the range of several tens of QTLs in both maize [43,44] and rice [45,46]. Of course, QTLome size is also constrained by the portion of www.sciencedirect.com
species’ genetic diversity surveyed in QTL studies, which is only a fraction of the total; therefore, estimates of number of QTLs should be scaled up accordingly. Addressing this issue, Joseph et al. [47] showed that the number of metabolome QTLs in Arabidopsis increased with the number of populations although it was not possible to tell whether the relationship was linear or log-linear. In conclusion, based on the above considerations, a complex trait QTLome in a crop species appears to conservatively range from several ten to one hundred or more non-overlapping QTLs. Empirical analyses have repeatedly shown an L-shaped distribution of QTL genetic effects, that is, a small number of large effect QTLs and an increasingly high number of lower effect ones [38,48]. This supports the idea of the QTLome resembling an iceberg, with the vast majority of small-effects QTLs below the detection power of most studies, which breeders can only capture by GS [10]. This is in accordance with population genetics theories, which predict that loci segregating for strong alleles become quickly fixed and therefore tend to ‘disappear’ from the population (Fisher–Orr model [44,49]). Therefore, for complex traits such as yield, it is likely that only minor effect QTLs are left segregating in elite cultivars, because these types of materials approach agricultural desirability [44]. Indeed, in maize, large QTL mapping studies for flowering time, plant height and leaf architecture have shown that small-effect QTLs dominate the scene [50]. However, slightly different results have been reported in rice, where both meta-analysis and large GWA studies found a higher portion of major-effect QTLs even for typically highly complex traits such as yield, plant height or flowering [45,51]. Also in wheat, large GWA studies and meta-analysis of several biparentals found a number of major QTLs with strong effect on plant height and heading date, both in coincidence with well-known genes (e.g. Ppd and Vrn) or as novel loci [27,52,53]. Zanke et al. [53] concluded that in elite bread wheat there is a wealth of plant height loci available besides the well-known Rht Green Revolution genes that could be used for modulating plant height in variety development. The levels of epistasis and pleiotropy should also be considered in charting QTLomes. The role of epistasis, that is the proportion of phenotypic variance due to inter-locus interactions, is still debated in view of the controversial results so far reported. Considering only relatively large experiments, epistasis was found negligible in maize [54], sizeable in wheat and sugar beet [55– 57] and remarkably pervasive in rice [58]. An emerging possible explanation is that an important fraction of current QTLs identified under simple additive models may have underlying epistatic genetic architecture because epistasis can indeed generate beneficial additive variance for quantitative traits, although Current Opinion in Biotechnology 2015, 32:179–185
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experiments size is usually inadequate for its detection [59]. Even less is known about QTL pleiotropy, that is to what extent different quantitative traits share QTLs, although the accumulation of multi-trait analysis on the same mapping population is starting to shed some light on this issue [3,60].
Harnessing the QTLome for plant breeding Although QTLs with strong effect are rare in crop germplasm, a number of major-effect QTLs have been mapped, validated, cloned and in many cases also been successfully utilized in MAB. The breeding frameworks within which QTLome information has impacted and will continue to impact are the so-called ‘backcross breeding’ and ‘breeding by design’ or ‘haplotype breeding’ [61,62,63,64]. These approaches address the development of new cultivars by recruiting beneficial alleles at known key loci by means of marker-assisted selection. Importantly, once a QTL is cloned, information can also be utilized to produce genetically modified crops (Graphical abstract). Examples of strong effect QTLs already (or close to be) exploited in cultivars are QTLs for drought tolerance in chickpea [65], stay green in sorghum [66], cyst nematode resistance in soybean [67] and Fusarium or salt tolerance in wheat [68,69]. Examples of traits for which major QTLs were cloned are flowering time [70] and carotenoid content in maize [45], yield components in rice [51], submergence tolerance in rice [71], aluminium tolerance in rice, sorghum and maize [72], phosphorus starvation and root architecture in rice [73,74] and boron toxicity in wheat [75]. Unfortunately, because a quantitative trait phenotype in one individual is typically the result of non-linear responses to a large number of factors (genetic and/or environmental), the correct prediction of the resulting effect of new combinations of alleles and QTL-haplotypes may not be easy. Progress is being made in this direction by integrating QTL effects and environment parameters in the model, also considering climate-change scenarios [76–78]. Clearly, the accumulation of QTLome information should come along with the collection of environmental parameters under which the QTL alleles were detected or tested. In conclusion, the exhaustive description of crop genomic diversity coupled with the accumulation of QTL information, have the potential to strongly enhance the impact of QTLome analysis on plant breeding via: - Analysis of SNP or sequence-based QTL haplotypes, which will (i) unambiguously identify QTL alleles, (ii) resolve the linkage phase between marker and target QTL alleles and (iii) simplify QTL cloning; Current Opinion in Biotechnology 2015, 32:179–185
- Production of comprehensive QTL databases, which will valorize QTL results available from previous experiments or obtained in single or less powerful studies, maximizing the information returned and reducing the need for QTL validation; - Shared raw (source) datasets (genotypes, phenotypes), which will enable community-based testing and/or updating of results and along with the availability of molecular haplotypes, will allow for accurate metaanalysis and a more comprehensive synthesis of QTL information; - Increased integration of QTLome information and environmental parameters in crop modelling which in turn should inform breeders about ‘priority’ traits and accelerate the selection of agronomically desirable QTL alleles. More than three decades have elapsed since the publication of the first isozyme marker-based study reporting QTLs in a crop [79]. The remarkable progress in understanding the functional complexity underpinning quantitative traits and the first tangible applications of such knowledge clearly indicate that the crop QTLome has come of age. It is expected that locus-specific, genomicsassisted breeding will increasingly contribute to raise yield potential and stability while reducing the environmental footprint of agricultural practices. This notwithstanding, it is difficult to predict to what extent these expectations will be fulfilled. While GS will likely provide most of the incremental yield increases required to ensure food security in the next decade, in the longer term a well-informed, targeted exploitation of the QTLome will allow breeders to more effectively harness natural allelic diversity and, upon its exhaustion/fixation, to manipulate (e.g. through genome editing and/or genetic engineering) QTLome expression in order to optimize the expression of loci that determine yield potential.
Acknowledgements We apologize for the many other valuable research works which could not be cited due to space limitations. We acknowledge the financial support by the EU FP7 Project Grants DROPS (Grant Agreement 244374), EUROOT (Grant Agreement 289300) and WATER4CROPS (Grant Agreement 311933).
References and recommended reading Papers of particular interest, published within the period of review, have been highlighted as: of special interest of outstanding interest 1.
He J, Zhao X, Laroche A, Lu ZX, Liu H, Li Z: Genotyping-bysequencing (GBS), an ultimate marker-assisted selection (MAS) tool to accelerate plant breeding. Front Plant Sci 2014, 5:484.
2.
Varshney RK, Terauchi R, McCouch SR: Harvesting the promising fruits of genomics: applying genome sequencing technologies to crop breeding. PLoS Biol 2014, 12:e1001883.
3.
Granier C, Vile D: Phenotyping and beyond: modelling the relationships between traits. Curr Opin Plant Biol 2014, 18:96-102. www.sciencedirect.com
The crop QTLome Salvi and Tuberosa 183
4.
Houle D, Govindaraju DR, Omholt S: Phenomics: the next challenge. Nat Rev Genet 2010, 11:855-866.
5. Zamir D: Where have all the crop phenotypes gone? PLoS Biol 2013, 11:e1001595. This perspective informs on the large amount of produced QTL (including phenotypic) information and loudly calls for the necessity to deposit in publicly available databases the raw datasets (genotypes, phenotypes and environmental information) of published experiments. 6.
Drinkwater NR, Gould MN: The long path from QTL to gene. PLoS Genet 2012, 8:e1002975.
7.
Bernardo R: Molecular markers and selection for complex traits in plants: learning from the last 20 years. Crop Sci 2008, 48:1649-1664.
8.
Wurschum T: Mapping QTL for agronomic traits in breeding populations. Theor Appl Genet 2012, 125:201-210.
9.
Korte A, Farlow A: The advantages and limitations of trait analysis with GWAS: a review. Plant Methods 2013, 9:29.
10. Jannink JL, Lorenz AJ, Iwata H: Genomic selection in plant breeding: from theory to practice. Brief Funct Genom 2010, 9:166-177. 11. Nakaya A, Isobe SN:: Will genomic selection be a practical method for plant breeding? Ann Bot 2012, 110:1303-1316. A clear and concise comparison in a plant breeding context between marker-assisted selection based on major loci/QTLs and genomic selection. 12. Cooper L, Walls RL, Elser J, Gandolfo MA, Stevenson DW, Smith B, Preece J, Athreya B, Mungall CJ, Rensing S: The plant ontology as a tool for comparative plant anatomy and genomic analyses. Plant Cell Physiol 2013, 54:e1-e1. 13. Mano Y, Omori F, Takeda K: Construction of intraspecific linkage maps, detection of a chromosome inversion, and mapping of QTL for constitutive root aerenchyma formation in the teosinte Zea nicaraguensis. Mol Breed 2012, 29:137-146. 14. Ron M, Dorrity MW, de Lucas M, Toal T, Hernandez RI, Little SA, Maloof JN, Kliebenstein DJ, Brady SM: Identification of novel loci regulating interspecific variation in root morphology and cellular development in tomato. Plant Physiol 2013, 162:755-768. 15. Wu R, Lin M: Functional mapping — how to map and study the genetic architecture of dynamic complex traits. Nat Rev Genet 2006, 7:229-237. 16. Wu¨rschum T, Liu W, Alheit KV, Tucker MR, Gowda M, Weissmann EA, Hahn V, Maurer HP: Adult plant development in triticale ( triticosecale wittmack) is controlled by dynamic genetic patterns of regulation. G3 2014, 4:1585-1591. 17. Horvatovich P, Franke L, Bischoff R: Proteomic studies related to genetic determinants of variability in protein concentrations. J Proteome Res 2014, 13:5-14. 18. Feltus FA: Systems genetics: a paradigm to improve discovery of candidate genes and mechanisms underlying complex traits. Plant Sci 2014, 223:45-48. 19. Riedelsheimer C, Lisec J, Czedik-Eysenberg A, Sulpice R, Flis A, Grieder C, Altmann T, Stitt M, Willmitzer L, Melchinger AE: Genome-wide association mapping of leaf metabolic profiles for dissecting complex traits in maize. Proc Natl Acad Sci USA 2012, 109:8872-8877. 20. Chen W, Gao Y, Xie W, Gong L, Lu K, Wang W, Li Y, Liu X, Zhang H, Dong H et al.: Genome-wide association analyses provide genetic and biochemical insights into natural variation in rice metabolism. Nat Genet 2014, 46:714-721. 21. Esch E, Szymaniak JM, Yates H, Pawlowski WP, Buckler ES: Using crossover breakpoints in recombinant inbred lines to identify quantitative trait loci controlling the global recombination frequency. Genetics 2007, 177:1851-1858. 22. Lacaze X, Hayes PM, Korol A: Genetics of phenotypic plasticity: QTL analysis in barley, Hordeum vulgare. Heredity (Edinb) 2009, 102:163-173. www.sciencedirect.com
23. Goffinet B, Gerber S: Quantitative trait loci: a meta-analysis. Genetics 2000, 155:463-473. 24. Veyrieras JB, Goffinet B, Charcosset A: MetaQTL: a package of new computational methods for the meta-analysis of QTL mapping experiments. BMC Bioinform 2007, 8:49. 25. Sosnowski O, Charcosset A, Joets J: BioMercator V3: an upgrade of genetic map compilation and quantitative trait loci meta-analysis algorithms. Bioinformatics 2012, 28:2082-2083. 26. Salvi S, Castelletti S, Tuberosa R: An updated consensus map for flowering time QTLs in maize. Maydica 2009, 54:501. 27. Griffiths S, Simmonds J, Leverington M, Wang Y, Fish L, Sayers L, Alibert L, Orford S, Wingen L, Snape J: Meta-QTL analysis of the genetic control of crop height in elite European winter wheat germplasm. Mol Breed 2012, 29:159-171. 28. Semagn K, Beyene Y, Warburton ML, Tarekegne A, Mugo S, Meisel B, Sehabiague P, Prasanna BM: Meta-analyses of QTL for grain yield and anthesis silking interval in 18 maize populations evaluated under water-stressed and wellwatered environments. BMC Genomics 2013, 14:313. 29. Said JI, Lin Z, Zhang X, Song M, Zhang J: A comprehensive meta QTL analysis for fiber quality, yield, yield related and morphological traits, drought tolerance, and disease resistance in tetraploid cotton. BMC Genomics 2013, 14:776. 30. Trijatmiko KR, Prasetiyono J, Thomson MJ, Cruz CMV, Moeljopawiro S, Pereira A: Meta-analysis of quantitative trait loci for grain yield and component traits under reproductivestage drought stress in an upland rice population. Mol Breed 2014, 34:283-295. 31. Landi P, Giuliani S, Salvi S, Ferri M, Tuberosa R, Sanguineti MC: Characterization of root-yield-1.06, a major constitutive QTL for root and agronomic traits in maize across water regimes. J Exp Bot 2010, 61:3553-3562. 32. Zhang H, Uddin MS, Zou C, Xie C, Xu Y, Li WX: Meta-analysis and candidate gene mining of low-phosphorus tolerance in maize. J Integr Plant Biol 2014, 56:262-270. 33. Blanc G, Charcosset A, Mangin B, Gallais A, Moreau L: Connected populations for detecting quantitative trait loci and testing for epistasis: an application in maize. Theor Appl Genet 2006, 113:206-224. 34. Bardol N, Ventelon M, Mangin B, Jasson S, Loywick V, Couton F, Derue C, Blanchard P, Charcosset A, Moreau L: Combined linkage and linkage disequilibrium QTL mapping in multiple families of maize (Zea mays L.) line crosses highlights complementarities between models based on parental haplotype and single locus polymorphism. Theor Appl Genet 2013, 126:2717-2736. 35. Leroux D, Rahmani A, Jasson S, Ventelon M, Louis F, Moreau L, Mangin B: Clusthaplo: a plug-in for MCQTL to enhance QTL detection using ancestral alleles in multi-cross design. Theor Appl Genet 2014, 127:921-933. The article describes an integrated software enabling to estimate QTL haplotypes in parents of multiparental populations based on local marker genomic similarities. Simulations showed that this approach provides significant gain in power and in accuracy of QTL mapping. 36. Bink MC, Jansen J, Madduri M, Voorrips RE, Durel CE, Kouassi AB, Laurens F, Mathis F, Gessler C, Gobbin D et al.: Bayesian QTL analyses using pedigreed families of an outcrossing species, with application to fruit firmness in apple. Theor Appl Genet 2014, 127:1073-1090. The first large-scale application of pedigree-based QTL mapping exploiting marker-based IBD estimates across populations belonging to different apple breeding programs. 37. Rockman MV: Reverse engineering the genotype–phenotype map with natural genetic variation. Nature 2008, 456:738-744. 38. Flint J, Mackay TF: Genetic architecture of quantitative traits in mice, flies, and humans. Genome Res 2009, 19:723-733. 39. Wilson-Sanchez D, Rubio-Diaz S, Munoz-Viana R, PerezPerez JM, Jover-Gil S, Ponce MR, Micol JL: Leaf phenomics: a systematic reverse genetic screen for Arabidopsis leaf mutants. Plant J 2014, 79:878-891. Current Opinion in Biotechnology 2015, 32:179–185
184 Plant biotechnology
40. Kellis M, Wold B, Snyder MP, Bernstein BE, Kundaje A, Marinov GK, Ward LD, Birney E, Crawford GE, Dekker J et al.: Defining functional DNA elements in the human genome. Proc Natl Acad Sci USA 2014, 111:6131-6138. 41. Otto SP, Jones CD: Detecting the undetected: estimating the total number of loci underlying a quantitative trait. Genetics 2000, 156:2093-2107. 42. Beavis WD: The power and deceit of QTL experiments: lessons from comparative QTL studies. In Proceedings of the FortyNinth Annual Corn and Sorghum Industry Research Conference. 1994:250-266. 43. Romay MC, Millard MJ, Glaubitz JC, Peiffer JA, Swarts KL, Casstevens TM, Elshire RJ, Acharya CB, Mitchell SE, FlintGarcia SA et al.: Comprehensive genotyping of the USA national maize inbred seed bank. Genome Biol 2013, 14:R55. An impressive genotyping-by-sequencing screening effort (almost 3000 public elite maize inbred lines analysed with 680,000 SNPs). The study showed that length and number of haplotypes vary enormously across genomic regions and germplasm groups, and that regions with low LD require even higher marker density for exhaustive GWA-based QTL mapping. 44. Peiffer JA, Romay MC, Gore MA, Flint-Garcia SA, Zhang Z, Millard MJ, Gardner CA, McMullen MD, Holland JB, Bradbury PJ et al.: The genetic architecture of maize height. Genetics 2014, 196:1337-1356. 45. Huang X, Wei X, Sang T, Zhao Q, Feng Q, Zhao Y, Li C, Zhu C, Lu T, Zhang Z et al.: Genome-wide association studies of 14 agronomic traits in rice landraces. Nat Genet 2010, 42:961-967. 46. Zhao K, Tung CW, Eizenga GC, Wright MH, Ali ML, Price AH, Norton GJ, Islam MR, Reynolds A, Mezey J et al.: Genome-wide association mapping reveals a rich genetic architecture of complex traits in Oryza sativa. Nat Commun 2011, 2:467. 47. Joseph B, Atwell S, Corwin JA, Li B, Kliebenstein DJ:: Meta-analysis of metabolome QTLs in Arabidopsis: trying to estimate the network size controlling genetic variation of the metabolome. Front Plant Sci 2014, 5:461. An attempt to empirically estimate the true number of QTLs affecting quantitative traits in plants. The article emphasizes the importance of considering how much of the species diversity was included in QTL studies. Results showed that the genetic complexity might be much higher than expected. 48. Kliebenstein DJ: Systems biology uncovers the foundation of natural genetic diversity. Plant Physiol 2010, 152:480-486. 49. Orr HA: The genetic theory of adaptation: a brief history. Nat Rev Genet 2005, 6:119-127. 50. Wallace JG, Larsson SJ, Buckler ES: Entering the second century of maize quantitative genetics. Heredity 2014, 112:30-38. An informed review which discusses many of the key topics in quantitative genetics relevant for maize breeding, including an assessment of the main current challenges, namely searching for the rare beneficial QTL alleles, controlling recombination rates, understanding the role of epigenetics, and others. 51. Xing Y, Zhang Q: Genetic and molecular bases of rice yield. Annu Rev Plant Biol 2010, 61:421-442. 52. Maccaferri M, Sanguineti MC, Demontis A, El-Ahmed A, Garcia del Moral L, Maalouf F, Nachit M, Nserallah N, Ouabbou H, Rhouma S et al.: Association mapping in durum wheat grown across a broad range of water regimes. J Exp Bot 2011, 62:409-438. 53. Zanke CD, Ling J, Plieske J, Kollers S, Ebmeyer E, Korzun V, Argillier O, Stiewe G, Hinze M, Neumann K et al.: Whole genome association mapping of plant height in winter wheat (Triticum aestivum L.). PLoS One 2014, 9:113287. Straightforward GWAS in wheat identified approx. 100 plant height QTLs, including Green Revolution genes but also apparently equally effective additional unexploited loci. High congruency with former QTL metaanalysis and candidate genes was noted. 54. McMullen MD, Kresovich S, Villeda HS, Bradbury P, Li H, Sun Q, Flint-Garcia S, Thornsberry J, Acharya C, Bottoms C et al.: Genetic properties of the maize nested association mapping population. Science 2009, 325:737-740. Current Opinion in Biotechnology 2015, 32:179–185
55. Maccaferri M, Sanguineti MC, Corneti S, Ortega JL, Salem MB, Bort J, DeAmbrogio E, del Moral LF, Demontis A, El-Ahmed A et al.: Quantitative trait loci for grain yield and adaptation of durum wheat (Triticum durum Desf.) across a wide range of water availability. Genetics 2008, 178:489-511. 56. Wurschum T, Maurer HP, Schulz B, Mohring J, Reif JC: Genomewide association mapping reveals epistasis and genetic interaction networks in sugar beet. Theor Appl Genet 2011, 123:109-118. 57. Quarrie SA, Steed A, Calestani C, Semikhodskii A, Lebreton C, Chinoy C, Steele N, Pljevljakusic D, Waterman E, Weyen J et al.: A high-density genetic map of hexaploid wheat (Triticum aestivum L.) from the cross Chinese Spring SQ1 and its use to compare QTLs for grain yield across a range of environments. Theor Appl Genet 2005, 110:865-880. 58. Huang A, Xu S, Cai X: Whole-genome quantitative trait locus mapping reveals major role of epistasis on yield of rice. PLoS One 2014, 9:e87330. Using immortalized F2-like populations from RILs of a highly productive hybrid cultivar, the authors thoroughly mapped additive and epistatic (digenic) QTLs. The latter ones were found to be the majority. 59. Mackay TF: Epistasis and quantitative traits: using model organisms to study gene–gene interactions. Nat Rev Genet 2014, 15:22-33. 60. Cane` MA, Maccaferri M, Nazemi G, Salvi S, Francia R, Colalongo C, Tuberosa R:: Association mapping for root architectural traits in durum wheat seedlings as related to agronomic performance. Mol Breed 2014, 34:1629-1645. 61. Peleman JD, van der Voort JR: Breeding by design. Trends Plant Sci 2003, 8:330-334. 62. Yamamoto T, Nagasaki H, Yonemaru J, Ebana K, Nakajima M, Shibaya T, Yano M: Fine definition of the pedigree haplotypes of closely related rice cultivars by means of genome-wide discovery of single-nucleotide polymorphisms. BMC Genomics 2010, 11:267. 63. Yonemaru J, Mizobuchi R, Kato H, Yamamoto T, Yamamoto E, Matsubara K, Hirabayashi H, Takeuchi Y, Tsunematsu H, Ishii T et al.: Genomic regions involved in yield potential detected by genome-wide association analysis in Japanese high-yielding rice cultivars. BMC Genomics 2014, 15:346. This paper reports on the use of genomewide SNP-defined haplotypes to describe genetic variation for heading date, blast resistance and other yield related quantitative traits in rice. 64. Chen H, He H, Zhou F, Yu H, Deng XW: Development of genomics-based genotyping platforms and their applications in rice breeding. Curr Opin Plant Biol 2013, 16:247-254. 65. Varshney RK, Gaur PM, Chamarthi SK, Krishnamurthy L, Tripathi S, Kashiwagi J, Samineni S, Singh VK, Thudi M, Jaganathan D: Fast-Track introgression of ‘‘QTL hotspot’’ for root traits and other drought tolerance traits in JG 11, an elite and leading variety of chickpea. Plant Genome 2013:6. 66. Borrell AK, Mullet JE, George-Jaeggli B, van Oosterom EJ, Hammer GL, Klein PE, Jordan DR: Drought adaptation of stay-green sorghum is associated with canopy development, leaf anatomy, root growth, and water uptake. J Exp Bot 2014, 65:6251-6263. 67. Concibido VC, Diers BW, Arelli PR: A decade of QTL mapping for cyst nematode resistance in soybean. Crop Sci 2004, 44:1121-1131. 68. Anderson JA, Chao S, Liu S: Molecular breeding using a major QTL for Fusarium head blight resistance in wheat. Crop Sci 2007, 47:S-112-S-119. 69. James RA, Blake C, Byrt CS, Munns R: Major genes for Na+ exclusion, Nax1 and Nax2 (wheat HKT1;4 and HKT1;5), decrease Na+ accumulation in bread wheat leaves under saline and waterlogged conditions. J Exp Bot 2011, 62:2939-2947. 70. Salvi S, Sponza G, Morgante M, Tomes D, Niu X, Fengler KA, Meeley R, Ananiev EV, Svitashev S, Bruggemann E et al.: Conserved noncoding genomic sequences associated with a www.sciencedirect.com
The crop QTLome Salvi and Tuberosa 185
flowering-time quantitative trait locus in maize. Proc Natl Acad Sci USA 2007, 104:11376-11381. 71. Septiningsih EM, Pamplona AM, Sanchez DL, Neeraja CN, Vergara GV, Heuer S, Ismail AM, Mackill DJ: Development of submergence-tolerant rice cultivars: the Sub1 locus and beyond. Ann Bot 2009, 103:151-160. 72. Famoso AN, Zhao K, Clark RT, Tung CW, Wright MH, Bustamante C, Kochian LV, McCouch SR: Genetic architecture of aluminum tolerance in rice (Oryza sativa) determined through genome-wide association analysis and QTL mapping. PLoS Genet 2011, 7:e1002221. 73. Gamuyao R, Chin JH, Pariasca-Tanaka J, Pesaresi P, Catausan S, Dalid C, Slamet-Loedin I, Tecson-Mendoza EM, Wissuwa M, Heuer S: The protein kinase Pstol1 from traditional rice confers tolerance of phosphorus deficiency. Nature 2012, 488:535-539. 74. Steele KA, Price AH, Witcombe JR, Shrestha R, Singh BN, Gibbons JM, Virk DS: QTLs associated with root traits increase yield in upland rice when transferred through marker-assisted selection. Theor Appl Genet 2013, 126:101-108.
www.sciencedirect.com
75. Pallotta M, Schnurbusch T, Hayes J, Hay A, Baumann U, Paull J, Langridge P, Sutton T:: Molecular basis of adaptation to high soil boron in wheat landraces and elite cultivars. Nature 2014, 514:88-91. 76. Tardieu F, Tuberosa R:: Dissection and modelling of abiotic stress tolerance in plants. Curr Opin Plant Biol 2010, 13:206-212. 77. Cooper M, van Eeuwijk FA, Hammer GL, Podlich DW, Messina CQTL: Modeling for complex traits: detection and context for plant breeding. Curr Opin Plant Biol 2009, 12:231240. 78. Harrison MT, Tardieu F, Dong Z, Messina CD, Hammer GL: Characterizing drought stress and trait influence on maize yield under current and future conditions. Glob Chang Biol 2014, 20:867-878. 79. Tanksley SD, Medina-Filho H, Rick CM: Use of naturallyoccurring enzyme variation to detect and map genes controlling quantitative traits in an interspecific backcross of tomato. Heredity 1982, 49:11-25.
Current Opinion in Biotechnology 2015, 32:179–185
