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Thomas Lumley

References Anderson, R.A. (2001). Security Engineering. Hoboken, NJ: John Wiley & Sons. Binder, D.A. (1983). On the Variances of Asymptotically Normal Estimators from Complex Surveys. International Statistical Review, 51, 279-292. Church, G., Heeney, C., Hawkins, N., De Vries, J., Boddington, P., Kaye, J., Bobrow, M., and Weir, B. (2009). Public Access to Genome-Wide Data: Five Views on Balancing Research with Privacy and Protection. PLoS Genetics, 5(10), p. e1000665. Estevao, V

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Mark Elliot, Elaine Mackey, Susan O’Shea, Caroline Tudor and Keith Spicer

.3233/978-1-61499-450-3-253 . Gymrek, M., A.L. McGuire, D. Golan, E. Halperin, and Y. Erlich. 2013. “Identifying Personal Genomes by Surname Inference.” Science 339: 321–324. http://dx.doi.org/10.1126/science.1229566 . Ma, Z.M., G. Pant, and O.R.L. Sheng. 2010. “Examining Organic and Sponsored Search Results: A Vendor Reliability Perspective.” Journal of Computer Information Systems 50: 30–38. Available at: http://bit.ly/1MSpcni (accessed 9 November 2015). Mackey, E. 2009. A Framework for Understanding Statistical Disclosure Control Processes: A Case Study Using the UK

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Paulo C. Rodrigues

Summary

Genotype-by-environment interaction (GEI) is frequently encountered in multi-environment trials, and represents differential responses of genotypes across environments. With the development of molecular markers and mapping techniques, researchers can go one step further and analyse the whole genome to detect specific locations of genes which influence a quantitative trait such as yield. Such a location is called a quantitative trait locus (QTL), and when these QTLs have different expression across environments we talk about QTL-by-environment interaction (QEI), which is the basis of GEI. Good understanding of these interactions enables researchers to select better genotypes across different environmental conditions, and consequently to improve crops in developed and developing countries. In this paper we present an overview of statistical methods and models commonly used to detect and to understand GEI and QEI, ranging from the simple joint regression model to complex eco-physiological genotype-to-phenotype simulation models.

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Jan Bocianowski, Kamila Nowosad, Alina Liersch, Wiesława Popławska and Agnieszka Łącka

content and chemoprotective potency of broccoli. Plant Breeding 123: 60-65. Friedt W., Snowdon R.J. (2009): Oilseed rape. In: Vollmann J., Rajcan I. (eds.) Handbook of plant breeding. Oil crops, vol. 4. Springer, New York: 91-126. Gauch H.G., Zobel R.W. (1990): Imputing missing yield trial data. Theoretical and Applied Genetics 79: 753-761. Howell P.M., Sharpe A.G., Lydiate D.J. (2003): Homoeologous loci control the accumulation of seed glucosinolates in oilseed rape (Brassica napus). Genome 46: 454

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Bogna Zawieja, Sylwia Lewandowska, Tomasz Mikulski and Wiesław Pilarczyk

importance of genotypic and interaction effects in plant breeding using the example of winter wheat]. Buletyn IHAR 240/241:13-32. Werner R., Lunwen Q., Voss-Fels K.P., Abbadi A., Leckband G., Frisch M., Snowdon R. (2018): Genome-wide regression models considering general and specific combining ability predict hybrid performance in oilseed rape with similar accuracy regardless of trait architecture. Theoretical and Applied Genetics 131: 299-317. Węgrzyn S. (2003): Ocena genotypowo-statystyczna wyników doświadczeń polowych z rodami hodowlanymi na przykładzie

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Zrinka Knezović, Ana Mandić, Nikica Perić, Jure Beljo and Maja Žulj Mihaljević

sequence repeat in loci in grape ( Vitis vinifera L.). Genome , Vol. 39, No. 4, pp. 628-633. 4. Bulić, S. (1949). Dalmatinska ampelografija . Poljoprivredni nakladni zavod, Zagreb. 5. Dettweiler, E., Jung, A., Zyprian, E., Töpfer, R. (2000). Grapevine cultivar Muller-Thurgau and its true to type descent. Vitis , Vol. 39, No. 2, pp. 63-65. 6. European Commission (2017). Agriculture and rural development, Agriculture and the environment . Available at http://www1.montpellier.inra.fr/grapegen06/ [10 June 2017]. 7. Leko, M., Žulj Mihaljević, M