Understanding the genetic basis of potato development using a multi-trait QTL analysis
Hurtado-Lopez, P.X. ; Tessema, B.B. ; Schnabel, S.K. ; Maliepaard, C.A. ; Linden, C.G. van der; Eilers, P.H.C. ; Jansen, J. ; Eeuwijk, F.A. van; Visser, R.G.F. - \ 2015
Euphytica 204 (2015)1. - ISSN 0014-2336 - p. 229 - 241.
solanum-tuberosum - complex traits - night temperature - linkage maps - mixed-model - loci - architecture - population - maturity - growth
Understanding the genetic basis of plant development in potato requires a proper characterization of plant morphology over time. Parameters related to different aging stages can be used to describe the developmental processes. It is attractive to map these traits simultaneously in a QTL analysis; because the power to detect a QTL will often be improved and it will be easier to identify pleiotropic QTLs. We included complex, agronomic traits together with plant development parameters in a multi-trait QTL analysis. First, the results of our analysis led to coherent insight into the genetic architecture of complex traits in potato. Secondly, QTL for parameters related to plant development were identified. Thirdly, pleiotropic regions for various types of traits were identified. Emergence, number of main stems, number of tubers and yield were explained by 9, 5, 4 and 6 QTL, respectively. These traits were measured once during the growing season. The genetic control of flowering, senescence and plant height, which were measured at regular time intervals, was explained by 9, 10 and 12 QTL, respectively. Genetic relationships between aboveground and belowground traits in potato were observed in 14 pleiotropic QTL. Some of our results suggest the presence of QTL-by-Environment interactions. Therefore, additional studies comparing development under different photoperiods are required to investigate the plasticity of the crop.
A weighted AMMI Algorithm to Study Genotype-by-Environment Interaction and QTL-by-Environment Interaction
Rodrigues, P.C. ; Malosetti, M. ; Gauch, H.G. - \ 2014
Crop Science 54 (2014)4. - ISSN 0011-183X - p. 1555 - 1570.
principal component analysis - multiplicative interaction-model - joint regression-analysis - additive main - cross-validation - yield trials - barley cross - mixed-model - selection - gene
Genotype-by-environment (G × E) interaction (GEI) and quantitative trait locus (QTL)-by-environment interaction (QEI) are common phenomena in multiple-environment trials and represent a major challenge to breeders. The additive main effects and multiplicative interaction (AMMI) model is a widely used tool for the analysis of multiple-environment trials, where the data are represented by a two-way table of G × E means. For complete tables, least squares estimation for the AMMI model is equivalent to fitting an additive two-way ANOVA model for the main effects and applying a singular value decomposition to the interaction residuals, thereby implicitly assuming equal weights for all G × E means. However, multiple-environment data with strong GEI are often also characterized by strong heterogeneous error variation. To improve the performance of the AMMI model in the latter situation, we introduce a generalized estimation scheme, the weighted AMMI or W-AMMI algorithm. This algorithm is useful for studying GEI and QEI. For QEI, the W-AMMI algorithm can be used to create predicted values per environment that are subjected to QTL analysis. We compare the performance of this combined W-AMMI and QTL mapping strategy to direct QTL mapping on G × E means and to QTL mapping on AMMI-predicted values, again with QTL analyses for individual environments. Finally, we compare the W-AMMI QTL mapping strategy, with a multi-environment mixed model QTL mapping approach. Two data sets are used: (i) data from a simulated pepper (Capsicum annuum L.) back cross population using a crop growth model to relate genotypes to phenotypes in a nonlinear way, and (ii) the doubled-haploid Steptoe × Morex barley (Hordeum vulgare L.) population. The QTL analyses on the W-AMMI-predicted values outperformed the QTL analyses on the G × E means and on the AMMI-predicted values, and were very similar to the mixed model QTL mapping approach with regard to the number and location of the true positive QTLs detected, especially for QTLs associated with the interaction and for environments with higher error variance. W-AMMI analysis for GEI and QEI provides an easy-to-use and robust tool with wide applicability.
Cross-platform comparative analyses of genetic variation in amino acid content in potato tubers
Carreno-Quintero, N. ; Undas, A.K. ; Bachem, C.W.B. ; Mumm, R. ; Visser, R.G.F. ; Bouwmeester, H.J. ; Keurentjes, J.J.B. - \ 2014
Metabolomics 10 (2014)6. - ISSN 1573-3882 - p. 1239 - 1257.
mass-spectrometry - metabolic networks - mixed-model - late blight - qtl - arabidopsis - methionine - biosynthesis - diversity - chromatography
Many of the biochemical pathways for plant amino acid metabolism are known and, at least in model species, most of the genes encoding the biosynthetic enzymes have been identified. How the accumulation of amino acids is regulated is much less well understood and for this genetic analysis can be instrumental. In potato, the nutritional value of the tubers is often determined by the content of essential amino acids such as lysine, tyrosine, methionine and cysteine. Better insight into the genetic determinants underlying the variation in amino acid accumulation in potato could support efforts to improve tuber nutritional quality by breeding. In this study, we used a diploid potato mapping population to explore the genetic basis of amino acid content. Hereto, we compared the use of one non-targeted and two targeted analytical approaches for amino acid analysis, allowing the evaluation of the robustness of amino acid quantification and the number and strength of detected quantitative trait locis (QTLs) across the different analytical platforms. Assessment of the three methodologies revealed a comparable detection of amino acids using non-targeted and targeted approaches. QTL detection across the different analytical platforms was similar, although slight differences in strength and explained variance were observed. The QTL regions were subsequently studied to provide candidate genes for the genetic regulation of amino acid accumulation in potato. Our results are discussed in the context of the detection of amino acid variation and its implications for the identification of QTLs.
Genotype x environment interaction QTL mapping in plants: lessons from Arabidopsis
El-Soda, M. ; Malosetti, M. ; Zwaan, B.J. ; Koornneef, M. ; Aarts, M.G.M. - \ 2014
Trends in Plant Science 19 (2014)6. - ISSN 1360-1385 - p. 390 - 398.
quantitative trait loci - genome-wide association - adaptive phenotypic plasticity - flowering time - natural variation - mixed-model - missing heritability - drought tolerance - complex traits - life-history
Plant growth and development are influenced by the genetic composition of the plant (G), the environment (E), and the interaction between them (G × E). To produce suitable genotypes for multiple environments, G × E should be accounted for and assessed in plant-breeding programs. Here, we review the genetic basis of G × E and its consequence for quantitative trait loci (QTL) mapping in biparental and genome-wide association (GWA) mapping populations. We also consider the implications of G × E for understanding plant fitness trade-offs and evolutionary ecology
Novel genomic approaches unravel genetic architecture of complex traits in apple.
Kumar, S. ; Garrick, D.J. ; Bink, M.C.A.M. ; Whitworth, C. ; Chagné, D. - \ 2013
BMC Genomics 14 (2013). - ISSN 1471-2164
x domestica borkh. - wide association - mixed-model - linkage disequilibrium - fruit - selection - predictions - accuracy - pedigree - mdmyb10
BACKGROUND: Understanding the genetic architecture of quantitative traits is important for developing genome-based crop improvement methods. Genome-wide association study (GWAS) is a powerful technique for mining novel functional variants. Using a family-based design involving 1,200 apple (Malus × domestica Borkh.) seedlings genotyped for an 8K SNP array, we report the first systematic evaluation of the relative contributions of different genomic regions to various traits related to eating quality and susceptibility to some physiological disorders. Single-SNP analyses models that accounted for population structure, or not, were compared with models fitting all markers simultaneously. The patterns of linkage disequilibrium (LD) were also investigated. RESULTS: A high degree of LD even at longer distances between markers was observed, and the patterns of LD decay were similar across successive generations. Genomic regions were identified, some of which coincided with known candidate genes, with significant effects on various traits. Phenotypic variation explained by the loci identified through a whole-genome scan ranged from 3% to 25% across different traits, while fitting all markers simultaneously generally provided heritability estimates close to those from pedigree-based analysis. Results from 'Q+K' and 'K' models were very similar, suggesting that the SNP-based kinship matrix captures most of the underlying population structure. Correlations between allele substitution effects obtained from single-marker and all-marker analyses were about 0.90 for all traits. Use of SNP-derived realized relationships in linear mixed models provided a better goodness-of-fit than pedigree-based expected relationships. Genomic regions with probable pleiotropic effects were supported by the corresponding higher linkage group (LG) level estimated genetic correlations. CONCLUSIONS: The accuracy of artificial selection in plants species can be increased by using more precise marker-derived estimates of realized coefficients of relationships. All-marker analyses that indirectly account for population- and pedigree structure will be a credible alternative to single-SNP analyses in GWAS. This study revealed large differences in the genetic architecture of apple fruit traits, and the marker-trait associations identified here will help develop genome-based breeding methods for apple cultivar development.
Genetic and QTL analyses of yield and a set of physiological traits in pepper
Alimi, N.A. ; Bink, M.C.A.M. ; Dieleman, J.A. ; Nicolaï, M. ; Wubs, M. ; Heuvelink, E. ; Magan, J. ; Voorrips, R.E. ; Jansen, J. ; Rodrigues, P.C. ; Heijden, G.W.A.M. van der; Vercauteren, A. ; Vuylsteke, M. ; Song, Y. ; Glasbey, C. ; Barocsi, A. ; Lefebvre, V. ; Palloix, A. ; Eeuwijk, F.A. van - \ 2013
Euphytica 190 (2013)2. - ISSN 0014-2336 - p. 181 - 201.
plant-breeding trials - cucumber mosaic-virus - capsicum-annuum - phytophthora-capsici - environment interactions - capsaicinoid content - mixed-model - fruit size - resistance - loci
An interesting strategy for improvement of a complex trait dissects the complex trait in a number of physiological component traits, with the latter having hopefully a simple genetic basis. The complex trait is then improved via improvement of its component traits. As first part of such a strategy to improve yield in pepper, we present genetic and QTL analyses for four pepper experiments. Sixteen traits were analysed for a population of 149 recombinant inbred lines, obtained from a cross between the largefruited pepper cultivar ‘Yolo Wonder’ (YW) and the small fruited pepper ‘Criollo de Morelos 334’(CM334). The marker data consisted of 493 markers assembled into 17 linkage groups covering 1,775 cM. The trait distributions were unimodal, although sometimes skewed. Many traits displayed heterosis and transgression. Heritabilities were high (mean 0.86, with a range between 0.43 and 0.96). A multiple QTL mapping approach per trait and environment yielded 24 QTLs. The average numbers of QTLs per trait was two, ranging between zero and six. The total explained trait variance by QTLs varied between 9 and 61 %. QTL effects differed quantitatively between environments, but not qualitatively. For stem-related traits, the trait-increasing QTL alleles came from parent CM334, while for leaf and fruit related traits the increasing QTL alleles came from parent YW. The QTLs on linkage groups 1b, 2, 3a, 4, 6 and 12 showed pleiotropic effects with patterns that were consistent with the genetic correlations. These results contribute to a better understanding of the genetics of yieldrelated physiological traits in pepper and represent a first step in the improvement of the target trait yield.
High throughput screening with chlorophyll fluorescence imaging and its use in crop improvement
Harbinson, J. ; Prinzenberg, A.E. ; Kruijer, W.T. ; Aarts, M.G.M. - \ 2012
Current Opinion in Biotechnology 23 (2012)2. - ISSN 0958-1669 - p. 221 - 226.
quantitative trait loci - genome-wide association - arabidopsis-thaliana - population-structure - genetic-variation - mixed-model - photosynthesis - design - potato - leaves
Marker assisted plant breeding is a powerful technique for targeted crop improvement in horticulture and agriculture. It depends upon the correlation of desirable phenotypic characteristics with specific genetic markers. This can be determined by statistical models that relate the variation in the value of genetic markers to variation in phenotypic traits. It therefore depends upon the convergence of three technologies; the creation of genetically characterised (and thus marked) populations, high throughput screening procedures, and statistical procedures. While a large number of high throughput screening technologies are available, real-time screening techniques are usually based on some kind of imaging technologies, such as chlorophyll fluorescence imaging, that offers physiological data that are eminently suitable as a quantitative trait for genetic mapping.
Whole-genome association study for milk protein composition in dairy cattle
Schopen, G.C.B. ; Visker, M.H.P.W. ; Koks, P.D. ; Mullaart, E. ; Arendonk, J.A.M. van; Bovenhuis, H. - \ 2011
Journal of Dairy Science 94 (2011)6. - ISSN 0022-0302 - p. 3148 - 3158.
quantitative trait loci - genetic-polymorphism - beta-lactoglobulin - missense mutation - alpha-lactalbumin - multiple levels - mixed-model - bovine-milk - casein - yield
Our objective was to perform a genome-wide association study for content in bovine milk of aS1-casein (aS1-CN), aS2-casein (aS2-CN), ß-casein (ß-CN), ¿-casein (¿-CN), a-lactalbumin (a-LA), ß-lactoglobulin (ß-LG), casein index, protein percentage, and protein yield using a 50K single nucleotide polymorphism (SNP) chip. In total, 1,713 Dutch Holstein-Friesian cows were genotyped for 50,228 SNP and a 2-step association study was performed. The first step involved a general linear model and the second step used a mixed model accounting for all family relationships. Associations with milk protein content and composition were detected on 20 bovine autosomes. The main genomic regions associated with milk protein composition or protein percentage were found on chromosomes 5, 6, 11, and 14. The number of chromosomal regions showing significant (false discovery rate
Detection and use of QTL for complex traits in multiple environments.
Eeuwijk, F.A. van; Bink, M.C.A.M. ; Chenu, K. ; Chapman, S.C. - \ 2010
Current Opinion in Plant Biology 13 (2010)2. - ISSN 1369-5266 - p. 193 - 205.
model selection approach - chain monte-carlo - mixed-model - water-deficit - leaf growth - experimental crosses - quantitative traits - plant-populations - breeding program - flanking markers
QTL mapping methods for complex traits are challenged by new developments in marker technology, phenotyping platforms, and breeding methods. In meeting these challenges, QTL mapping approaches will need to also acknowledge the central roles of QTL by environment interactions (QEI) and QTL by trait interactions in the expression of complex traits like yield. This paper presents an overview of mixed model QTL methodology that is suitable for many types of populations and that allows predictive modeling of QEI, both for environmental and developmental gradients. Attention is also given to multi-trait QTL models which are essential to interpret the genetic basis of trait correlations. Biophysical (crop growth) model simulations are proposed as a complement to statistical QTL mapping for the interpretation of the nature of QEI and to investigate better methods for the dissection of complex traits into component traits and their genetic controls
Modeling QTL for complex traits: detection and context for plant breeding
Cooper, M. ; Eeuwijk, F.A. van; Hammer, G.L. ; Podlich, D.W. ; Messina, C. - \ 2009
Current Opinion in Plant Biology 12 (2009)2. - ISSN 1369-5266 - p. 231 - 240.
root architectural traits - gene regulatory networks - mixed-model - water-deficit - leaf growth - arabidopsis-thaliana - quantitative traits - inbred lines - maize - environment
The genetic architecture of a trait is defined by the set of genes contributing to genetic variation within a reference population of genotypes together with information on their location in the genome and the effects of their alleles on traits, including intra-locus and inter-locus interactions, environmental dependencies, and pleiotropy. Accumulated evidence from trait mapping studies emphasizes that plant breeders work within a trait genetic complexity continuum. Some traits show a relatively simple genetic architecture while others, such as grain yield, have a complex architecture. An important advance is that we now have empirical genetic models of trait genetic architecture obtained from mapping studies (multi-OTL models including various genetic effects that may vary in relation to environmental factors) to ground theoretical investigations on the merits of alternative breeding strategies. Such theoretical studies indicate that as the genetic complexity of traits increases the opportunities for realizing benefits from molecular enhanced breeding strategies increase. To realize these potential benefits and enable the plant breeder to increase rate of genetic gain for complex traits it is anticipated that the empirical genetic models of trait genetic architecture used for predicting trait variation will need to incorporate the effects of genetic interactions and be interpreted within a genotype-environment-management framework for the target agricultural production system
Expert opinion as 'validiation' of risk assessment applied to calf welfare
Bracke, M.B.M. ; Edwards, S.A. ; Engel, B. ; Buist, W.G. ; Algers, B. - \ 2008
Acta Veterinaria Scandinavica 50 (2008). - ISSN 0044-605X
decision-support-system - enrichment materials - animal-welfare - mixed-model - pigs
Background - Recently, a Risk Assessment methodology was applied to animal welfare issues in a report of the European Food Safety Authority (EFSA) on intensively housed calves. Methods - Because this is a new and potentially influential approach to derive conclusions on animal welfare issues, a so-called semantic-modelling type 'validation' study was conducted by asking expert scientists, who had been involved or quoted in the report, to give welfare scores for housing systems and for welfare hazards. Results - Kendall's coefficient of concordance among experts (n = 24) was highly significant (P <0.001), but low (0.29 and 0.18 for housing systems and hazards respectively). Overall correlations with EFSA scores were significant only for experts with a veterinary or mixed (veterinary and applied ethological) background. Significant differences in welfare scores were found between housing systems, between hazards, and between experts with different backgrounds. For example, veterinarians gave higher overall welfare scores for housing systems than ethologists did, probably reflecting a difference in their perception of animal welfare. Systems with the lowest scores were veal calves kept individually in so-called "baby boxes" (veal crates) or in small groups, and feedlots. A suckler herd on pasture was rated as the best for calf welfare. The main hazards were related to underfeeding, inadequate colostrum intake, poor stockperson education, insufficient space, inadequate roughage, iron deficiency, inadequate ventilation, poor floor conditions and no bedding. Points for improvement of the Risk Assessment applied to animal welfare include linking information, reporting uncertainty and transparency about underlying values. Conclusion - The study provides novel information on expert opinion in relation to calf welfare and shows that Risk Assessment applied to animal welfare can benefit from a semantic modelling approach
Analysis of the real EADGENE data set: Comparison of methods and guidelines for data normalisation and selection of differentially expressed genes
Jafrezic, F. ; Koning, D.J. de; Boettcher, P. ; Bonnet, A. ; Buitenhuis, B. ; Closset, R. ; Dejean, S. ; Delmas, C. ; Detilleux, J.C. ; Dovc, P. ; Duval, M. ; Foulley, J.L. ; Hedegaard, J. ; Hoprnshoj, H. ; Hulsegge, B. ; Janss, L. ; Jensen, K. ; Jiang, L. ; Lavric, M. ; Cao Le, K.A. ; Lund, M.S. ; Malinverni, R. ; Marot, G. ; Nie, H. ; Petzl, W. ; Pool, M.H. ; Robert-Granie, C. ; Cristobal, M. ; Schothorst, E.M. van; Schuberth, H.J. ; Sorensen, P. ; Stella, A. ; Tosser-klopp, G. ; Waddington, D. ; Watson, M. ; Yang, M. ; Zerbe, H. ; Seyfert, H.M. - \ 2007
Genetics, Selection, Evolution 39 (2007). - ISSN 0999-193X - p. 633 - 650.
cdna microarray data - mixed-model
A large variety of methods has been proposed in the literature for microarray data analysis. The aim of this paper was to present techniques used by the EADGENE (European Animal Disease Genomics Network of Excellence) WP1.4 participants for data quality control, normalisation and statistical methods for the detection of differentially expressed genes in order to provide some more general data analysis guidelines. All the workshop participants were given a real data set obtained in an EADGENE funded microarray study looking at the gene expression changes following artificial infection with two different mastitis causing bacteria: Escherichia coli and Staphylococcus aureus. It was reassuring to see that most of the teams found the same main biological results. In fact, most of the differentially expressed genes were found for infection by E. coli between uninfected and 24 h challenged udder quarters. Very little transcriptional variation was observed for the bacteria S. aureus. Lists of differentially expressed genes found by the different research teams were, however, quite dependent on the method used, especially concerning the data quality control step. These analyses also emphasised a biological problem of cross-talk between infected and uninfected quarters which will have to be dealt with for further microarray studies
Statistical models for genotype by environment data: from conventional ANOVA models to eco-physiological QTL models
Eeuwijk, F.A. van; Malosetti, M. ; Yin, X. ; Struik, P.C. ; Stam, P. - \ 2005
Australian Journal of Agricultural Research 56 (2005)9. - ISSN 0004-9409 - p. 883 - 894.
quantitative trait loci - affecting grain-sorghum - recombinant inbred lines - least-squares - factorial regression - mixed-model - barley - trials - growth - yield
To study the performance of genotypes under different growing conditions, plant breeders evaluate their germplasm in multi-environment trials. These trials produce genotype × environment data. We present statistical models for the analysis of such data that differ in the extent to which additional genetic, physiological, and environmental information is incorporated into the model formulation. The simplest model in our exposition is the additive 2-way analysis of variance model, without genotype × environment interaction, and with parameters whose interpretation depends strongly on the set of included genotypes and environments. The most complicated model is a synthesis of a multiple quantitative trait locus (QTL) model and an eco-physiological model to describe a collection of genotypic response curves. Between those extremes, we discuss linear-bilinear models, whose parameters can only indirectly be related to genetic and physiological information, and factorial regression models that allow direct incorporation of explicit genetic, physiological, and environmental covariables on the levels of the genotypic and environmental factors. Factorial regression models are also very suitable for the modelling of QTL main effects and QTL × environment interaction. Our conclusion is that statistical and physiological models can be fruitfully combined for the study of genotype × environment interaction