Abstract
Many quantitative trait loci (QTL) detection methods ignore QTL-by-environment interaction (QEI) and are limited in accommodation of error and environment-specific variance. This paper outlines a mixed model approach using a recombinant inbred spring wheat population grown in six drought stress trials. Genotype estimates for yield, anthesis date and height were calculated using the best design and spatial effects model for each trial. Parsimonious factor analytic models best captured the variance–covariance structure, including genetic correlations, among environments. The 1RS.1BL rye chromosome translocation (from one parent) which decreased progeny yield by 13.8 g m−2 was explicitly included in the QTL model. Simple interval mapping (SIM) was used in a genome-wide scan for significant QTL, where QTL effects were fitted as fixed environment-specific effects. All significant environment-specific QTL were subsequently included in a multi-QTL model and evaluated for main and QEI effects with non-significant QEI effects being dropped. QTL effects (either consistent or environment-specific) included eight yield, four anthesis, and six height QTL. One yield QTL co-located (or was linked) to an anthesis QTL, while another co-located with a height QTL. In the final multi-QTL model, only one QTL for yield (6 g m−2) was consistent across environments (no QEI), while the remaining QTL had significant QEI effects (average size per environment of 5.1 g m−2). Compared to single trial analyses, the described framework allowed explicit modelling and detection of QEI effects and incorporation of additional classification information about genotypes.
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References
Akaike H (1974) A new look at statistical model identification. IEEE Trans Automat Contr AU 19:716–722
Boer M, Wright D, Feng L, Podlich D, Luo L, Cooper M, Van Eeuwijk F (2007) A mixed model QTL analysis for multiple environment trial data using environmental covariables for QTLxE with an example in maize. Genetics 177:1801–1813
Brennan PS, Byth DE (1979) Genotype × environmental interactions for wheat yields and selection for widely adapted wheat genotypes. Aust J Agric Res 30:221–232
Broman KW, Wu H, Sen S, Churchill GA (2003) R/qtl: QTL mapping in experimental crosses. Bioinformatics 19:889–890
Cheverud JM (2001) A simple correction for multiple comparisons in interval mapping genome scans. Heredity 87:52–58
Comstock RE (1977) Quantitative genetics and the design of breeding programs. In: Pollak E, Kempthorne O, Bailey TB (eds) International conference on quantitative genetics. Iowa State University Press, Ames, IA, pp 705–718
Cooper M, Woodruff DR (1993) Predicting grain-yield in Australian environments using data from CIMMYT international wheat performance trials. 3. Testing predicted correlated eesponse to selection. Field Crops Res 35:191–204
Cooper M, Byth DE, Woodruff DR (1994) An investigation of the grain-yield adaptation of advanced CIMMYT wheat lines to water-stress environments in Queensland. 1. Crop physiological analysis. Aust J Agric Res 45:965–984
Cooper M, Stucker RE, DeLacy IH, Harch BD (1997) Wheat breeding nurseries, target environments, and indirect selection for grain yield. Crop Sci 37:1168–1176
Crossa J, Cornelius PL (1997) Sites regression and shifted multiplicative model clustering of cultivar trial sites under heterogeneity of error variances. Crop Sci 37:406–415
Crossa J, Yang R-C, Cornelius PL (2004) Studying crossover genotype environment interaction using linear-bilinear models and mixed models. J Agric Biol Environ Stat 9:362–380
Cullis B, Gogel B, Verbyla A, Thompson R (1998) Spatial analysis of multi-environment early generation variety trials. Biometrics 54:1–18
Cullis BR, Smith AB, Coombes NE (2006) On the design of early generation variety trials with correlated data. J Agric Biol Environ Stat 11:381–393
Darvasi A, Weinreb A, Minke V, Weller JI, Soller M (1993) Detecting marker-QTL linkage and estimating QTL gene effect and map location using a saturated genetic map. Genetics 134:943–951
van Eeuwijk FA, Malosetti M, Yin X, Struik PC, Stam P (2005) Statistical models for genotype by environment data: from conventional ANOVA models to eco-physiological QTL models. Aust J Agric Res 56:883–894
Gilmour AR (2007) Mixed model regression mapping for QTL detection in experimental crosses. Comput Stat Data Anal 51:3749–3764
Gilmour AR, Cullis BR, Verbyla AP (1997) Accounting for natural and extraneous variation in the analysis of field experiments. J Agric Biol Environ Stat 2:269–293
Hanson WD (1963) Heritability. In: Hanson WD, Robinson HF (eds) Statistical genetics and plant breeding. NAS-NRC, Washington, DC
Huang XQ, Coster H, Ganal MW, Roder MS (2003) Advanced backcross QTL analysis for the identification of quantitative trait loci alleles from wild relatives of wheat (Triticum aestivum L.). Theor Appl Genet 106:1379–1389
Isidore E, van Os H, Andrzejewski S, Bakker J, Barrena I, Bryan GJ, Caromel B, van Eck H, Ghareeb B, de Jong W, van Koert P, Lefebvre V, Milbourne D, Ritter E, van der Voort JR, Rousselle-Bourgeois F, van Vliet J, Waugh R (2003) Toward a marker-dense meiotic map of the potato genome: lessons from linkage group I. Genetics 165:2107–2116
Jiang C, Zeng Z-B (1997) Mapping quantitative trait loci with dominant and missing markers in various crosses from two inbred lines. Genetica 101:47–58
Kato K, Miura H, Sawada S (2000) Mapping QTLs controlling grain yield and its components on chromosome 5A of wheat. Theor Appl Genet 101:1114–1121
Keurentjes JJB, Fu J, Ric de Vos CH, Lommen A, Hall RD, Bino RJ, van der Plas LHW, Jansen RC, Vreugdenhil D, Koornneef M (2006) The genetics of plant metabolisms. Nat Genet 38:842–849
Koebner RMD (1995) Generation of PCR-based markers for the detection of rye chromatin in a wheat background. Theor Appl Genet 90:740–745
Kuchel H, Hollamby G, Langridge P, Williams K, Jefferies S (2006) Identification of genetic loci associated with ear-emergence in bread wheat. Theor Appl Genet 113:1103–1112
Kumar R, Venuprasad R, Atlin GN (2007) Genetic analysis of rainfed lowland rice drought tolerance under naturally-occurring stress in eastern India: heritability and QTL effects. Field Crops Res 103:42–52
Lander ES, Green P (1987) Construction of multilocus genetic-linkage maps in humans. Proc Natl Acad Sci USA 84:2363–2367
Li J, Ji L (2005) Adjusting multiple testing in multilocus analyses using the eigenvalues of a correlation matrix. Heredity 95:221–227
Li S, Jia J, Wei X, Zhang X, Li L, Chen H, Fan Y, Sun H, Zhao X, Lei T, Xu Y, Jiang F, Wang H, Li L (2007) A intervarietal genetic map and QTL analysis for yield traits in wheat. Mol Br 20:167–178
Loss SP, Siddique KHM (1994) Morphological and physiological traits associated with wheat yield increases in Mediterranean environments. Advances in Agronomy, Vol 52 52:229–276
Mago R, Spielmeyer W, Lawrence GJ, Lagudah ES, Ellis JG, Pryor A (2002) Identification and mapping of molecular markers linked to rust resistance genes located on chromosome 1RS of rye using wheat-rye translocation lines. Theor Appl Genet 104:1317–1324
Malosetti M, Voltas J, Romagosa I, Ullrich SE, van Eeuwijk FA (2004) Mixed models including environmental covariables for studying QTL by environment interaction. Euphytica 137:139–145
Malosetti M, Ribaut J, Vargas M, Crossa J, van Eeuwijk F (2008) A multi-trait multi-environment QTL mixed model with an application to drought and nitrogen stress trials in maize (Zea mays L.). Euphytica 161:241–257
Mathews KL, Chapman SC, Butler DG, Cooper M, DeLacy IH, Sheppard J, Kelly A, Sahama T (2002) Inter-annual changes in genotypic and genotype by environment variance components for different stages of the Northern Wheat Improvement Program. In: McComb J (ed) Plant breeding for the 11th millennium. The Australasian Plant Breeding Association Inc., Perth
Mathews KL, Chapman SC, Trethowan R, Pfeiffer W, van Ginkel M, Crossa J, Payne T, DeLacy I, Fox PN, Cooper M (2007) Global adaptation patterns of Australian and CIMMYT spring bread wheat. Theor Appl Genet 115:819–835
McIntyre CL, Chapman SC, Mathews KL, Van Herwaarden A, Reynolds M, Shorter R (2006) Identification of genomic regions of traits relevant to wheat production in drought environments. In: Mercer CF (ed) 13th Australasian plant breeding conference, Christchurch, New Zealand
McLaren CG, Bruskiewich RM, Portugal AM, Cosico AB (2005) The International Rice Information System. A platform for meta-analysis of rice crop data. Plant Physiol 139:637–642
Merker A (1982) “VEERY”—a CIMMYT spring wheat with the 1B/1R chromosome translocation. Cereal Res Commun 10:105–106
Mohler V, Hsam SLK, Zeller FJ, Wenzel G (2001) An STS marker distinguishing the rye-derived powdery mildew resistance alleles at the Pm8/Pm17 locus of common wheat. Plant Breed 120:448–450
Oakey H, Verbyla A, Pitchford W, Cullis B, Kuchel H (2006) Joint modeling of additive and non-additive genetic line effects in single field trials. Theor Appl Genet 113:809–819
Olivares-Villegas JJ, Reynolds MP, McDonald GK (2007) Drought-adaptive attributes in the Seri/Babax hexaploid wheat population. Funct Plant Biol 34:189–203
Payne RW, Harding SA, Murray DA, Soutar DM, Baird DB, Welham SJ, Kane AF, Gilmour AR, Thompson R, Webster R, Tunnicliffe Wilson G (2006) GenStat release 9 reference manual, part 2 directives. VSN International, Hemel Hempstead
Peake A (2003) Inheritance of grain yield and effect of the 1BL/1RS translocation in three bi-parental wheat (Triticum aestivum L.) populations in production environments of north eastern Australia. School of Land and Food Sciences, The University of Queensland, Brisbane
Piepho HP (1997) Analyzing genotype-environment data by mixed models with multiplicative terms. Biometrics 53:761–766
Piepho H-P (2000) A mixed-model approach to mapping quantitative trait loci in barley on the basis of multiple environment data. Genetics 156:2043–2050
Piepho HP (2005) Statistical tests for QTL and QTL-by-environment effects in segregating populations derived from line crosses. Theor Appl Genet 110:561–566
Piepho HP, Möhring J (2007) Computing heritability and selection response from unbalanced plant breeding trials. Genetics 177:1881–1888
R Development Core Team (2008) R: a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. ISBN 3-900051-07-0, URL http://www.R-project.org
Raftery A (1986) Choosing models for cross-classification. Am Sociol Rev 51:145–146
Rajaram S, Mann CE, Ortiz-Ferrara G, Mujeeb-Kazi A (1983) Adaptation, stability and high yield potential of certain 1B/1R CIMMYT wheats. In: Sakamoto S (ed) The 6th international wheat genetics symposium. CIMMYT: Mexico City, Kyoto, Japan, pp 613–621
Rebetzke G, Herwaarden A, Jenkins C, Ruuska S, Tabe L, Fettell N, Lewis D, Weiss M, Richards R (2007) Genetic control of water-soluble carbohydrate reserves in bread wheat. Wheat Production in Stressed Environments, pp 349–356
Searle SR, Casella G, McCulloch CE (1992) Variance components. Wiley, New York
Smith A, Cullis B, Gilmour A (2001a) The analysis of crop variety evaluation data in Australia. Aust NZ J Stat 43:129–145
Smith A, Cullis B, Thompson R (2001b) Analyzing variety by environment data using multiplicative mixed models and adjustments for spatial field trend. Biometrics 57:1138–1147
van Ooijen JW, Voorrips RE (2001) JoinMap 3.0: software for the calculation of genetic linkage maps. Plant Research International B·V, Wageningen
Utz HF, Melchinger AE (1996) PLABQTL: a program for composite interval mapping of QTL. J. Agric. Genomics 2. (http://www.cabi-publishing.org/jag/papers96/paper196/indexp196.htm)
Verbeke G, Molenberghs G (2000) Linear mixed models for longitudinal data. Springer-Verlag Inc., Berlin
Verbyla AP, Eckermann PJ, Thompson R, Cullis BR (2003) The analysis of quantitative trait loci in multi-environment trials using a multiplicative mixed model. Aust J Agric Res 54:1395–1408
Verbyla AP, Cullis BR, Thompson R (2007) The analysis of QTLs by simultaneous use of the full linkage map. Theor Appl Genet 116:95–111
Villareal R, Bañuelos O, Mujeeb-Kazi A, Rajaram S (1998) Agronomic performance of chromosomes 1B and T1BL.1RS near-isolines in the spring bread wheat Seri M82. Euphytica 103:195–202
Wang S, Basten CJ, Gaffney P, Zeng Z-B (2005) Window QTL Cartographer, V2.0. North Carolina State University, Raleigh, NC (http://statgen.ncsu.edu/qtlcart/WQTLCart.htm)
Welham SJ, Gogel BJ, Smith AB, Thomson R, Cullis BR (2006) Evaluation of models for late-stage variety evaluation trials. Australasian GenStat/StatGen Conference. 5–8 December 2006, Victor Harbor, Australia pp 44 (http://www.biometricssa.adelaide.edu.au/genstat2006/Talk%20PDFs/Welham_Sue.pdf)
Wenzl P, Carling J, Kudrna D, Jaccoud D, Huttner E, Kleinhofs A, Kilian A (2004) Diversity Arrays Technology (DArT) for whole-genome profiling of barley. Proc Natl Acad Sci 101:9915–9920
Yan WK, Hunt LA, Sheng QL, Szlavnics Z (2000) Cultivar evaluation and mega-environment investigation based on the GGE biplot. Crop Sci 40:597–605
Yang J, Hu C, Ye X, Zhu J. (2005) QTL Network 2.0. Zhejang University, Hangzhou, China (http://ibi.zju.edu.cn/software/qtlnetwork/)
Zeng ZB (1993) Theoretical basis for separation of multiple linked gene effects in mapping quantitative trait loci. Proc Natl Acad Sci 90:10972–10976
Zeng ZB (1994) Precision mapping of quantitative trait loci. Genetics 136:1457–1468
Acknowledgments
We thank P. Jackson, H. Jansen, G. Rebetzke, P. Stam and J. Stringer for their valuable comments. We acknowledge the Generation Challenge Program (GCP Project No. 4) and Grains Research and Development Corporation of Australia (Project CSP00053) for funding this research. Comments from two anonymous reviewers were greatly appreciated.
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Communicated by C.-C. Schön.
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Mathews, K.L., Malosetti, M., Chapman, S. et al. Multi-environment QTL mixed models for drought stress adaptation in wheat. Theor Appl Genet 117, 1077–1091 (2008). https://doi.org/10.1007/s00122-008-0846-8
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DOI: https://doi.org/10.1007/s00122-008-0846-8