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Developments in statistical analysis in quantitative genetics

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Abstract

A remarkable research impetus has taken place in statistical genetics since the last World Conference. This has been stimulated by breakthroughs in molecular genetics, automated data-recording devices and computer-intensive statistical methods. The latter were revolutionized by the bootstrap and by Markov chain Monte Carlo (McMC). In this overview a number of specific areas are chosen to illustrate the enormous flexibility that McMC has provided for fitting models and exploring features of data that were previously inaccessible. The selected areas are inferences of the trajectories over time of genetic means and variances, models for the analysis of categorical and count data, the statistical genetics of a model postulating that environmental variance is partly under genetic control, and a short discussion of models that incorporate massive genetic marker information. We provide an overview of the application of McMC to study model fit, and finally, a discussion is presented on the development of efficient McMC updating schemes for non-standard models.

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  1. Department of Genetics and Biotechnology, Faculty of Agricultural Sciences, University of Aarhus, P.O. Box 50, 8830, Tjele, Denmark

    Daniel Sorensen

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Correspondence to Daniel Sorensen.

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Sorensen, D. Developments in statistical analysis in quantitative genetics. Genetica 136, 319–332 (2009). https://doi.org/10.1007/s10709-008-9303-5

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