Daniel Ortiz-Barrientos

Content Summary 1 I. Recent advances in evolutionary prediction 2 II. Integrative Bayesian framework for evolutionary forecasting 7 III. Validation and transferability 9 IV. Applications and implementation 10 V. Prospects 11 Acknowledgements 11 References 11 I. Recent advances in evolutionary prediction Evolution unfolds on timescales we can now observe directly.

Abstract Thousands of years of breeding and agronomic change have pushed genetic change into increasingly narrow corridors. The approach has worked effectively, but gains are slowing and much genetic variance expected from heritability estimates is not readily found. We propose that breeding constraints, the practical demands that make crops useful, force genetic exploration onto curved, lower-dimensional surfaces within the larger landscape of possibility.

1 Introduction Our understanding of biology relies heavily on inferences from studies of model organisms, chosen because of ease of culture and manipulation, short generation span and large reproductive capacity (Leonelli and Ankeny 2013). The underlying rationale for this approach is that discoveries made in model species can be broadly generalised to other organisms, especially those that are closely related to the models or share similar genetic, developmental or sexual systems.