Linkage mapping and whole-genome predictions in canola (Brassica napus) subjected to differing temperature treatments
Chadwick B. Koscielny
A , Stuart W. Gardner B , Frank Technow C and Robert W. Duncan D E
+ Author Affiliations
- Author Affiliations
A Plant Breeding Research & Development, Corteva agriscience, Carman, MB R0G 0J0, Canada.
B Biostatistics, Corteva agriscience, 8305 NW 62nd Avenue, Johnston, IA 50131-7060, USA.
C Breeding Technologies, Corteva agriscience, 596779 Country Road 59N, Woodstock, ON N4S 7W1, Canada.
D Department of Plant Science, University of Manitoba, 222 Agriculture Building, Winnipeg, MB R3T 2N2, Canada.
E Corresponding author. Email: rob.duncan@umanitoba.ca
Crop and Pasture Science 71(3) 229-238 https://doi.org/10.1071/CP19387
Submitted: 4 October 2019 Accepted: 25 December 2019 Published: 1 April 2020
Journal compilation © CSIRO 2020 Open Access CC BY-NC-ND
Abstract
Canola (Brassica napus L.) is grown on >8 Mha in Canada and is sensitive to high temperatures; therefore, research on breeding methodologies to improve heat-stress tolerance is warranted. This study utilised a doubled-haploid population created from two parents (PB36 and PB56) that differed in their ability to set seed following growth at high temperatures. The experiment was designed to identify potential quantitative trait loci (QTLs) responsible for conferring tolerance to increased temperatures, and to utilise this population as a test case for evaluating the prospects of whole-genome prediction. The population was phenotyped in a split-plot, randomised complete block experimental design at three locations with two planting-date treatments. The first planting date was during the normal planting period (control), and the second planting was timed to experience increased average temperatures (1.7°C, 2.0°C and 1.2°C) and increased number of days with maximum temperatures above the critical temperature of 29.5°C (4, 12 and 3 days). The stress treatment reduced yield on average by 16.7%. There were 66 QTLs discovered across the nine traits collected. Given the quantitative nature of the traits collected, the ability to use whole-genome prediction was investigated. The prediction accuracies ranged from 0.14 (yield) to 0.66 (1000-seed weight). Prediction had higher accuracy within the stress treatment than within the control treatment for seven of the nine traits, demonstrating that phenotyping within a stress environment can provide valuable data for whole-genome predictions.
Additional keywords: breeding methods, climate change, predictive breeding.
References
Aslam MN, Nelson MN, Kailis SG, Bayliss KL, Speijers J, Cowling WA (2009) Canola oil increases in polyunsaturated fatty acids and decreases in oleic acid in drought-stressed Mediterranean-type environments. Plant Breeding 128, 348–355.| Canola oil increases in polyunsaturated fatty acids and decreases in oleic acid in drought-stressed Mediterranean-type environments.Crossref | GoogleScholarGoogle Scholar |
Bernardo R (2016) Bandwagons I, too, have known. Theoretical and Applied Genetics 129, 2323–2332.
| Bandwagons I, too, have known.Crossref | GoogleScholarGoogle Scholar | 27681088PubMed |
Bita CE, Gerats T (2013) Plant tolerance to high temperature in a changing environment: scientific fundamentals and production of heat stress-tolerant crops. Frontiers in Plant Science 4, 273
| Plant tolerance to high temperature in a changing environment: scientific fundamentals and production of heat stress-tolerant crops.Crossref | GoogleScholarGoogle Scholar | 23914193PubMed |
Chen G, Geng J, Rahman M, Liu X, Tu J, Fu T, Li G, McVetty PBE, Tahir M (2010) Identification of QTL for oil content, seed yield, and flowering time in oilseed rape (Brassica napus). Euphytica 175, 161–174.
| Identification of QTL for oil content, seed yield, and flowering time in oilseed rape (Brassica napus).Crossref | GoogleScholarGoogle Scholar |
Collard BCY, Jahufer MZZ, Brouwer JB, Pang ECK (2005) An introduction to markers, quantitative trait loci (QTL) mapping and marker-assisted selection for crop improvement: The basic concepts. Euphytica 142, 169–196.
| An introduction to markers, quantitative trait loci (QTL) mapping and marker-assisted selection for crop improvement: The basic concepts.Crossref | GoogleScholarGoogle Scholar |
Collins NC, Tardieu F, Tuberosa R (2008) Quantitative trait loci and crop performance under abiotic stress: where do we stand? Plant Physiology 147, 469–486.
| Quantitative trait loci and crop performance under abiotic stress: where do we stand?Crossref | GoogleScholarGoogle Scholar | 18524878PubMed |
Combs E, Bernardo R (2013) Accuracy of genome-wide selection for different traits with constant population size, heritability, and number of markers. The Plant Genome 6, 1–7.
| Accuracy of genome-wide selection for different traits with constant population size, heritability, and number of markers.Crossref | GoogleScholarGoogle Scholar |
Cooper M, Messina CD, Podlich D, Totir LR, Baumgarten A, Hausmann NJ, Wright D, Graham G (2014) Predicting the future of plant breeding: complementing empirical evaluation with genetic prediction. Crop & Pasture Science 65, 311–336.
| Predicting the future of plant breeding: complementing empirical evaluation with genetic prediction.Crossref | GoogleScholarGoogle Scholar |
Cullis BR, Smith AB, Coombes NE (2006) On the design of early generation variety trials with correlated data. Journal of Agricultural Biological & Environmental Statistics 11, 381–393.
| On the design of early generation variety trials with correlated data.Crossref | GoogleScholarGoogle Scholar |
Daetwyler HD, Pong-Wong R, Villanueva B, Woolliams JA (2010) The impact of genetic architecture on genome-wide evaluation methods. Genetics 185, 1021–1031.
| The impact of genetic architecture on genome-wide evaluation methods.Crossref | GoogleScholarGoogle Scholar | 20407128PubMed |
Daun J (2007) Quality of canola (Brassica napus L.) varieties in Western Canada: evaluation of variability due to genetic, year and environmental conditions using data from Canadian Grain Commission Harvest Surveys and from Environment Canada meteorological stations. In ‘Proceedings 12th International Rapeseed Congress’. 26–30 March 2007, Wuhan, China. (Eds T Fu, C Guan) pp. 10–14. (Science Press: Beijing)
Daun JK, Clear KM, Williams P (1994) Comparison of three whole seed near-infrared analyzers for measuring quality components of canola seed. Journal of the American Oil Chemists’ Society 71, 1063–1068.
| Comparison of three whole seed near-infrared analyzers for measuring quality components of canola seed.Crossref | GoogleScholarGoogle Scholar |
Delourme R, Falentin C, Huteau V, Clouet V, Horvais R, Gandon B, Specel S, Hanneton L, Dheu JE, Deschamps M, Margale E, Vincourt P, Renard M (2006) Genetic control of oil content in oilseed rape (Brassica napus L.). Theoretical and Applied Genetics 113, 1331–1345.
| Genetic control of oil content in oilseed rape (Brassica napus L.).Crossref | GoogleScholarGoogle Scholar | 16960716PubMed |
Ding G, Zhao Z, Liao Y, Hu Y, Shi L, Long Y, Xu F (2012) Quantitative trait loci for seed yield and yield-related traits, and their responses to reduced phosphorus supply in Brassica napus. Annals of Botany 109, 747–759.
| Quantitative trait loci for seed yield and yield-related traits, and their responses to reduced phosphorus supply in Brassica napus.Crossref | GoogleScholarGoogle Scholar | 22234558PubMed |
Driedonks N, Rieu I, Vriezen WH (2016) Breeding for plant heat tolerance at vegetative and reproductive stages. Plant Reproduction 29, 67–79.
| Breeding for plant heat tolerance at vegetative and reproductive stages.Crossref | GoogleScholarGoogle Scholar | 26874710PubMed |
Endelman JB (2011) Ridge regression and other kernels for genomic selection with R package rrBLUP. The Plant Genome 4, 250–255.
| Ridge regression and other kernels for genomic selection with R package rrBLUP.Crossref | GoogleScholarGoogle Scholar |
Fan C, Cai G, Qin J, Li Q, Yang M, Wu J, Fu T, Liu K, Zhou Y (2010) Mapping of quantitative trait loci and development of allele-specific markers for seed weight in Brassica napus. Theoretical and Applied Genetics 121, 1289–1301.
| Mapping of quantitative trait loci and development of allele-specific markers for seed weight in Brassica napus.Crossref | GoogleScholarGoogle Scholar | 20574694PubMed |
Franks SJ (2011) Plasticity and evolution in drought avoidance and escape in the annual plant Brassica rapa. New Phytologist 190, 249–257.
| Plasticity and evolution in drought avoidance and escape in the annual plant Brassica rapa.Crossref | GoogleScholarGoogle Scholar | 21210818PubMed |
Fu Y, Wei D, Dong H, He Y, Cui Y, Mei J, Wan H, Li J, Snowdon R, Friedt W, Li X, Qian W (2015) Comparative quantitative trait loci for silique length and seed weight in Brassica napus. Scientific Reports 5, 14407
| Comparative quantitative trait loci for silique length and seed weight in Brassica napus.Crossref | GoogleScholarGoogle Scholar | 26394547PubMed |
Gilmour AR, Cullis BR, Verbyla AP (1997) Accounting for natural and extraneous variation in the analysis of field experiments. Journal of Agricultural, Biological, & Environmental Statistics 2, 269–293.
| Accounting for natural and extraneous variation in the analysis of field experiments.Crossref | GoogleScholarGoogle Scholar |
Gilmour AR, Gogel BJ, Cullis BR, Thompson R (2009) ‘ASReml user guide. Release 3.0.’ (VSN International: Hemel Hempstead, UK)
Gunasekera CP, Martin LD, Siddique KHM, Walton GH (2006) Genotype by environment interactions of Indian mustard (Brassica juncea L.) and canola (B. napus L.) in Mediterranean-type environments: 1. Crop growth and seed yield. European Journal of Agronomy 25, 1–12.
| Genotype by environment interactions of Indian mustard (Brassica juncea L.) and canola (B. napus L.) in Mediterranean-type environments: 1. Crop growth and seed yield.Crossref | GoogleScholarGoogle Scholar |
Heffner EL, Lorenz AJ, Jannink J-L, Sorrells ME (2010) Plant breeding with genomic selection: gain per unit time and cost. Crop Science 50, 1681–1690.
| Plant breeding with genomic selection: gain per unit time and cost.Crossref | GoogleScholarGoogle Scholar |
Jan HU, Abbadi A, Lücke S, Nichols RA, Snowdon RJ (2016) Genomic prediction of testcross performance in canola (Brassica napus). PLoS One 11, e0147769
| Genomic prediction of testcross performance in canola (Brassica napus).Crossref | GoogleScholarGoogle Scholar | 26824924PubMed |
Jannink J-L, Lorenz AJ, Iwata H (2010) Genomic selection in plant breeding: from theory to practice. Briefings in Functional Genomics 9, 166–177.
| Genomic selection in plant breeding: from theory to practice.Crossref | GoogleScholarGoogle Scholar | 20156985PubMed |
Javed N, Geng J, Tahir M, McVetty PBE, Li G, Duncan RW (2016) Identification of QTL influencing seed oil content, fatty acid profile and days to flowering in Brassica napus L. Euphytica 207, 191–211.
| Identification of QTL influencing seed oil content, fatty acid profile and days to flowering in Brassica napus L.Crossref | GoogleScholarGoogle Scholar |
Jha UC, Bohra A, Singh NP (2014) Heat stress in crop plants: its nature, impacts and integrated breeding strategies to improve heat tolerance. Plant Breeding 133, 679–701.
| Heat stress in crop plants: its nature, impacts and integrated breeding strategies to improve heat tolerance.Crossref | GoogleScholarGoogle Scholar |
Jung C, Müller AE (2009) Flowering time control and applications in plant breeding. Trends in Plant Science 14, 563–573.
| Flowering time control and applications in plant breeding.Crossref | GoogleScholarGoogle Scholar | 19716745PubMed |
Koscielny CB, Gardner SW, Duncan RW (2018a) Impact of high temperature on heterosis and general combining ability in spring canola (Brassica napus L.). Field Crops Research 221, 61–70.
| Impact of high temperature on heterosis and general combining ability in spring canola (Brassica napus L.).Crossref | GoogleScholarGoogle Scholar |
Koscielny CB, Hazebroek J, Duncan RW (2018b) Phenotypic and metabolic variation among spring Brassica napus L. genotypes during heat stress. Crop & Pasture Science 69, 284–295.
| Phenotypic and metabolic variation among spring Brassica napus L. genotypes during heat stress.Crossref | GoogleScholarGoogle Scholar |
Kutcher HR, Warland JS, Brandt SA (2010) Temperature and precipitation effects on canola yields in Saskatchewan, Canada. Agricultural and Forest Meteorology 150, 161–165.
| Temperature and precipitation effects on canola yields in Saskatchewan, Canada.Crossref | GoogleScholarGoogle Scholar |
Legarra A, Robert-Granié C, Manfredi E, Elsen J-M (2008) Performance of genomic selection in mice. Genetics 180, 611–618.
| Performance of genomic selection in mice.Crossref | GoogleScholarGoogle Scholar | 18757934PubMed |
Li Z, Mei S, Mei Z, Liu X, Fu T, Zhou G, Tu J (2014) Mapping of QTL associated with waterlogging tolerance and drought resistance during the seedling stage in oilseed rape (Brassica napus). Euphytica 197, 341–353.
| Mapping of QTL associated with waterlogging tolerance and drought resistance during the seedling stage in oilseed rape (Brassica napus).Crossref | GoogleScholarGoogle Scholar |
Lorieux M (2012) MapDisto: Fast and efficient computation of genetic linkage maps. Molecular Breeding 30, 1231–1235.
| MapDisto: Fast and efficient computation of genetic linkage maps.Crossref | GoogleScholarGoogle Scholar |
Meuwissen THE, Hayes BJ, Goddard ME (2001) Prediction of total genetic value using genome-wide dense marker maps. Genetics 157, 1819–1829.
Morrison MJ, Stewart DW (2002) Heat stress during flowering in summer Brassica. Crop Science 42, 797–803.
| Heat stress during flowering in summer Brassica.Crossref | GoogleScholarGoogle Scholar |
Nelson MN, Rajasekaran R, Smith A, Chen S, Beeck CP, Siddique KHM, Cowling WA (2014) Quantitative trait loci for thermal time to flowering and photoperiod responsiveness discovered in summer annual-type Brassica napus L. PLoS ONE 9, e102611
| Quantitative trait loci for thermal time to flowering and photoperiod responsiveness discovered in summer annual-type Brassica napus L.Crossref | GoogleScholarGoogle Scholar | 25061822PubMed |
Nuttall WF, Moulin AP, Townly-Smith LJ (1992) Yield response of canola to nitrogen, phosphorus, precipitation, and temperature. Agronomy Journal 84, 765–768.
| Yield response of canola to nitrogen, phosphorus, precipitation, and temperature.Crossref | GoogleScholarGoogle Scholar |
Pinto RS, Reynolds M, Mathews K, McIntyre CL, Olivares-Villegas J-J, Chapman S (2010) Heat and drought adaptive QTL in a wheat population designed to minimize confounding agronomic effects. Theoretical and Applied Genetics 121, 1001–1021.
| Heat and drought adaptive QTL in a wheat population designed to minimize confounding agronomic effects.Crossref | GoogleScholarGoogle Scholar | 20523964PubMed |
Riedelsheimer C, Melchinger AE (2013) Optimizing the allocation of resources for genomic selection in one breeding cycle. Theoretical and Applied Genetics 126, 2835–2848.
| Optimizing the allocation of resources for genomic selection in one breeding cycle.Crossref | GoogleScholarGoogle Scholar | 23982591PubMed |
Riedelsheimer C, Czedik-Eysenberg A, Grieder C, Lisec J, Technow F, Sulpice R, Altmann T, Stitt M, Willmitzer L, Melchinger AE (2012) Genomic and metabolic prediction of complex heterotic traits in hybrid maize. Nature Genetics 44, 217–220.
| Genomic and metabolic prediction of complex heterotic traits in hybrid maize.Crossref | GoogleScholarGoogle Scholar | 22246502PubMed |
Shafii B, Mahler KA, Price WJ, Auld DL (1992) Genotype × environment interaction effects on winter rapeseed yield and oil content. Crop Science 32, 922–927.
| Genotype × environment interaction effects on winter rapeseed yield and oil content.Crossref | GoogleScholarGoogle Scholar |
Shi J, Li R, Qiu D, Jiang C, Long Y, Morgan C, Bancroft I, Zhao J, Meng J (2009) Unraveling the complex trait of crop yield with quantitative trait loci mapping in Brassica napus. Genetics 182, 851
| Unraveling the complex trait of crop yield with quantitative trait loci mapping in Brassica napus.Crossref | GoogleScholarGoogle Scholar | 19414564PubMed |
Smith AB, Cullis BR, Thompson R (2005) The analysis of crop cultivar breeding and evaluation trials: an overview of current mixed model approaches. The Journal of Agricultural Science 143, 449–462.
| The analysis of crop cultivar breeding and evaluation trials: an overview of current mixed model approaches.Crossref | GoogleScholarGoogle Scholar |
Statistics Canada (2016) CANSIM tables: Field and special crops. Statistics Canada, Ottawa. Available at: http://www.statcan.gc.ca/tables-tableaux/sum-som/l01/cst01/prim11a-eng.htm (accessed 5 December 2017).
Technow F, Totir RL (2015) Using Bayesian multilevel whole genome regression models for partial pooling of training sets in genomic prediction. Genes, Genomes, Genetics 5, 1603–1612.
| Using Bayesian multilevel whole genome regression models for partial pooling of training sets in genomic prediction.Crossref | GoogleScholarGoogle Scholar |
Thudi M, Upadhyaya HD, Rathore A, Gaur PM, Krishnamurthy L, Roorkiwal M, Nayak SN, Chaturvedi SK, Basu PS, Gangarao NVPR, Fikre A, Kimurto P, Sharma PC, Sheshashayee MS, Tobita S, Kashiwagi J, Ito O, Killian A, Varshney RK (2014) Genetic dissection of drought and heat tolerance in chickpea through genome-wide and candidate gene-based association mapping approaches. PLoS One 9, e96758
| Genetic dissection of drought and heat tolerance in chickpea through genome-wide and candidate gene-based association mapping approaches.Crossref | GoogleScholarGoogle Scholar | 24801366PubMed |
Varshney RK, Singh VK, Hickey JM, Xun X, Marshall DF, Wang J, Edwards D, Ribaut J-M (2016) Analytical and decision support tools for genomics-assisted breeding. Trends in Plant Science 21, 354–363.
| Analytical and decision support tools for genomics-assisted breeding.Crossref | GoogleScholarGoogle Scholar | 26651919PubMed |
Vivek BS, Krishna GK, Vengadessan V, Babu R, Zaidi PH, Kha LQ, Mandal SS, Grudloyma P, Takalkar S, Krothapalli K, Singh IS, Ocampo ETM, Xingming F, Burgueño J, Azrai M, Singh RP, Crossa J (2017) Use of genomic estimated breeding values results in rapid genetic gains for drought tolerance in maize. The Plant Genome 10, 1–8.
| Use of genomic estimated breeding values results in rapid genetic gains for drought tolerance in maize.Crossref | GoogleScholarGoogle Scholar |
Wang S, Basten CJ, Zeng ZB (2012) ‘Windows QTL Cartographer 2.5.’ (Department of Statistics, North Carolina State: Raleigh, NC, USA)
Witcombe JR, Hollington PA, Howarth CJ, Reader S, Steele KA (2008) Breeding for abiotic stresses for sustainable agriculture. Philosophical Transactions of the Royal Society of London. Series B, Biological Sciences 363, 703–716.
| Breeding for abiotic stresses for sustainable agriculture.Crossref | GoogleScholarGoogle Scholar | 17761467PubMed |
Würschum T, Abel S, Zhao Y (2014) Potential of genomic selection in rapeseed (Brassica napus L.) breeding. Plant Breeding 133, 45–51.
| Potential of genomic selection in rapeseed (Brassica napus L.) breeding.Crossref | GoogleScholarGoogle Scholar |
Xu Y (2010) ‘Molecular plant breeding.’ (CABI: Wallingford, UK)
Yan XY, Li JN, Fu FY, Jin MY, Chen L, Liu LZ (2009) Co-location of seed oil content, seed hull content and seed coat color QTL in three different environments in Brassica napus L. Euphytica 170, 355–364.
| Co-location of seed oil content, seed hull content and seed coat color QTL in three different environments in Brassica napus L.Crossref | GoogleScholarGoogle Scholar |
Zhang X, Perez-Rodriguez P, Semagn K, Beyene Y, Babu R, Lopez-Cruz MA, San Vincent F, Olsen M, Buckler E, Jannick JL, Prasanna BM, Crossa J (2015) Genomic prediction in biparental tropical maize populations in water-stressed and well-watered environments using low-density and GBS SNPs. Heredity 114, 291–299.
| Genomic prediction in biparental tropical maize populations in water-stressed and well-watered environments using low-density and GBS SNPs.Crossref | GoogleScholarGoogle Scholar | 25407079PubMed |
Zhang A, Wang H, Beyene Y, Samagn K, Liu Y, Cao S, Ciu Z, Ruan Y, Burgueno J, San Vincent F, Olsen M, Prasanna BM, Crossa J, Yu H, Zhang X (2017) Effect of trait heritability, training population size and marker density on genomic prediction accuracy estimation in 22 bi-parental tropical maize populations. Frontiers in Plant Science 8, 1916
| Effect of trait heritability, training population size and marker density on genomic prediction accuracy estimation in 22 bi-parental tropical maize populations.Crossref | GoogleScholarGoogle Scholar | 29167677PubMed |
Zou J, Zhao Y, Liu P, Shi L, Wang X, Wang M, Meng J, Reif JC (2016) Seed quality traits can be predicted with high accuracy in Brassica napus using genomic data. PLoS One 11, e0166624
| Seed quality traits can be predicted with high accuracy in Brassica napus using genomic data.Crossref | GoogleScholarGoogle Scholar | 28030633PubMed |
