Genomics-based breeding in forest trees

林木遗传改良计划是长期的，需要资源，因此与一年生作物相比，其遗传增益轨迹较浅。缩短育种和/或测试阶段将确保快速的代周转率，从而在单位时间和成本上获得可观的收益。标记辅助选择被认为是一种通过揭示基因型-表型关系来加速遗传改良程序的手段。不幸的是，这种方法只对影响较大的主要基因有效，而许多影响较小的基因是大多数复杂多基因性状的基础。群体遗传学方法，如DNA指纹和谱系重建（例如，育种不育种（BwB））已被证明在减少与育种和测试相关的成本和时间方面是有效的。DNA测序技术的最新进展，加上基因组选择（GS）这一革命性选择方法的发展，创造了一种育种模式的转变，即选择不再基于表型值，而是基于全基因组测序基因型预测的表型值。这种方法允许更快的选择和育种，因为表型可以预测，而不考虑年龄和发育阶段。在这里，我建议将BwB与GS联合起来，利用这两种方法的协同效应，这两种方法有望加速世代更替，从而创造更陡峭的遗传增益轨迹。由于GS基于已开发的模型预测表型值，因此这些模型的准确性和随时间和空间的可转移性至关重要。该提案旨在评估GS模型的准确性，并阐明它们在不同地点和年龄阶层的“普遍”使用。如果这些模型显示出跨地点和年龄的普遍性，那么将获得额外的节省。该研究是跨学科的，涵盖生物信息学、基因组学、高级统计学和数量遗传学。

Forest tree genetic improvement programs are long-term and resource demanding thus their genetic gain trajectory is shallow as compared to their annual crop counterparts. Shortening the breeding and/or testing phases would secure speedy generation turnover yielding substantial gains per unit time and cost. Marker-assisted selection has been proposed as a means to speeding genetic improvement programs through unravelling genotype-phenotype relationships. Unfortunately, this approach is only effective for major genes with large effects, whereas many genes of small effect underlie most complex polygenic traits. Population genetics methods such as DNA fingerprinting and pedigree reconstruction (e.g., Breeding without Breeding (BwB)) have been proven effective in reducing the cost and time associated with breeding and testing. Recent advances in DNA sequencing technology coupled with the development of a revolutionary approach to selection known as genomic selection (GS) have created a breeding paradigm shift where selection is no longer based on phenotypic values but rather is based on predicted phenotypic values from genome-wide sequencing genotypes. This approach allowed faster selection and breeding as phenotypes can be predicted irrespective of age and developmental phase. Here, I propose joining BwB with GS to capitalize on the synergetic effect of both approaches which is expected to speed generation turnover thus creating a steeper genetic gain trajectory. Since GS predicts phenotypic values based on developed models, these models' accuracy and transferability over time and space are of vital importance. This proposal seeks to evaluate GS models accuracies and to shed light on their "universal" use across sites and age classes. If these models show universality across sites and ages then additional savings will be attained. The proposed research is interdisciplinary covering bioinformatics, genomics, advanced statistics and quantitative genetics.