Genomics-based breeding in forest trees

林木遗传改良计划具有长期性和资源需求性，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世代更替，产生单位时间和成本的巨大收益。分子标记辅助选择已经被认为是一种通过解开基因型-表型关系来加速遗传改良计划的手段。不幸的是，这种方法只对大效应的主基因有效，而许多小效应的基因是大多数复杂的多基因性状的基础。种群遗传学方法，如DNA指纹和谱系重建(例如，不繁育(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，再加上一种革命性的选择方法--基因组选择(Gs)的发展，创造了一种育种范式的转变，即选择不再基于表型值，而是基于全基因组测序基因型的预测表型值。这种方法允许更快的选择和繁殖，因为无论年龄和发育阶段都可以预测表型。在这里，我建议将BWB和GS结合起来，以利用这两种方法的协同效应，预计这将加快世代周转，从而创造出更陡峭的遗传收益轨迹。由于GS基于已开发的模型来预测表型值，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。这项提案旨在评估GS模型的准确性，并阐明它们在不同地点和年龄段的“普遍”使用情况。如果这些模式显示出跨地点和跨年龄段的普遍性，那么将获得额外的节省。拟议的研究是跨学科的，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.