Reanalyses of the historical series of UK variety trials to quantify the contributions of genetic and environmental factors to trends and variability in yield over time

作者: I. Mackay , A. Horwell , J. Garner , J. White , J. McKee

DOI: 10.1007/S00122-010-1438-Y

关键词: AgronomyPlant disease resistanceAgriculturePlant geneticsPlant breedingSugar beetSeasonalityBiologyForageGermplasm

摘要: Historical datasets have much to offer. We analyse data from winter wheat, spring and barley, oil seed rape, sugar beet forage maize the UK National List Recommended trials over period 1948–2007. find that since 1982, for cereal crops at least 88% of improvement in yield is attributable genetic improvement, with little evidence changes agronomy improved yields. In contrast, same time period, plant breeding contributed almost equally increased yields beet. For cereals prior contributions were 42, 60 86% wheat respectively. These results demonstrate overwhelming importance increasing crop productivity UK. Winter are analysed more detail exemplify use historical series study detect disease resistance breakdown, sensitivity varieties climatic factors, also test methods genomic selection. show breakdown can cause biased estimates variety year effects, but comparison between fungicide treated untreated years may be a means screen durable resistance. greatest sensitivities germplasm seasonal differences rainfall temperature summer temperature. Finally, selection, correlations observed predicted ranged 0.17 0.83. The high correlation resulted markers predicting kinship amongst lines rather than tagging multiple QTL. believe full value these will come exploiting links other experiments experimental populations. However, not exploit such valuable wasteful.

参考文章(36)
Robert Tibshirani, John D. Storey, Statistical Significance for Genome-Wide Experiments ,(2003)
Hugh G. Gauch, Manjit S. Kang, Genotype-by-Environment Interaction ,(1996)
Charles R Henderson, Applications of linear models in animal breeding Published in <b>1984</b> in Guelph by University of Guelph. ,(1984)
J. D. Storey, R. Tibshirani, Statistical significance for genomewide studies Proceedings of the National Academy of Sciences of the United States of America. ,vol. 100, pp. 9440- 9445 ,(2003) , 10.1073/PNAS.1530509100
Richard Frankham, Introduction to quantitative genetics (4th edn) Trends in Genetics. ,vol. 12, pp. 280- ,(1996) , 10.1016/0168-9525(96)81458-2
Shengqiang Zhong, Jack C. M. Dekkers, Rohan L. Fernando, Jean-Luc Jannink, Factors Affecting Accuracy From Genomic Selection in Populations Derived From Multiple Inbred Lines: A Barley Case Study Genetics. ,vol. 182, pp. 355- 364 ,(2009) , 10.1534/GENETICS.108.098277
Sang Hong Lee, Julius H. J. van der Werf, Ben J. Hayes, Michael E. Goddard, Peter M. Visscher, Predicting Unobserved Phenotypes for Complex Traits from Whole-Genome SNP Data PLoS Genetics. ,vol. 4, pp. e1000231- ,(2008) , 10.1371/JOURNAL.PGEN.1000231
H. D. PATTERSON, E. R. WILLIAMS, A new class of resolvable incomplete block designs Biometrika. ,vol. 63, pp. 83- 92 ,(1976) , 10.1093/BIOMET/63.1.83