Effect and interaction of mutation and recombination in the dynamics of genetic variation in modern chickens

解决如何在定向选择下保持封闭群体的遗传变异是数量遗传学中的基本问题之一，但仍然是悬而未决的问题。解决这一问题对支撑全球主食生产系统的遗传改良方案的可持续性也具有重要影响。相当大的研究努力集中在研究选择如何减少遗传变异，以及如何在基因组中识别其足迹。很少有人努力研究遗传变异是如何从头产生并在几代人之间重新洗牌的。原因是，到目前为止，由于技术和经济的限制，在分析所需的大规模范围内追踪突变是不可行的，突变是造成新的基因变异的进化力量。遗传学家可用的唯一选择是依赖于对突变率的低精度估计，因为它们大多是在实验人群中进行的小规模研究。然后，这些参数将被用于旨在描述遗传变异动态的模型中，由于缺乏关于观察到的突变率的数据，这些模型基于许多理论假设。所用参数的不确定性，再加上我们对遗传变异力量相互作用的有限了解，降低了我们目前模型捕捉潜在过程的能力，因此解释了为什么“仍然没有关于负责维持变异的进化力量的明确解决方案”，正如“数量遗传学”(Lynch和Walsh，2018)的黄金标准教科书之一强调的那样。这一建议的主要假设是，在有限的封闭群体中，例如精英育种群体，遗传变异的创造和洗牌速度可以抵消选择、遗传漂移和近亲繁殖的影响。我们的目标是采用数据驱动的方法，以前所未有的精度量化突变和重组过程，这可以解释在处于密集定向选择下的封闭种群中，遗传变异似乎是如何保持的。为了实现这一目标，我们将使用一个独特的基因组库，该库由数十万只鸟类(无论是高深度测序还是用SNP阵列进行基因分型)组成，跨越25代，具有完整的系谱和表型信息，以研究遗传变异是如何随着时间的推移而保持的。这个数据集为我们提供了精确估计重要参数所需的能力，例如每一代发生多少突变事件，它们往往发生在整个基因组的哪里，以及由于突变而产生的新的遗传变异如何平衡它的去除。随着我们的数据跨越许多世代，我们将有能力跟踪谱系中的突变和染色体片段，以消除可能夸大估计的假阳性观察。我们的研究还将专注于量化重组，作为洗牌的力量遗传变异，我们将探索它与突变的相互作用。最后，我们将应用我们更精确的估计来更新选择界限的理论预期，因此我们将致力于调和理论预测和观察。除了解决如何维持遗传变异这一关键问题的潜力之外，我们的发现可能直接适用于动植物育种组织，以提高其遗传改良计划的弹性和可持续性。

Resolving how genetic variation is maintained in closed populations under directional selection is one of the fundamental, yet still open questions in quantitative genetics. Addressing this question has also vital implications for the sustainability of genetic improvement programmes that underpin global staple food production systems. Considerable research effort has focused on studying how selection reduces genetic variation and how its footprint can be identified in the genome. Little effort has been placed to study how genetic variation is de-novo created and shuffled across generations. The reason is that until now, it was not feasible to track mutation, the evolutionary force creating new genetic variation, on the large-scale that analysis required, due to technological and economic constraints. The only option available to geneticists was to rely on low precision estimates for the mutation rate, since they mostly arose from small scale studies in experimental populations. These parameters then would be used in models aiming to describe the dynamics of genetic variation, which were based on many theoretical assumptions due to lack of data on the observed rate of mutation. The uncertainty of the parameters used, combined with our limited understanding of the interplay of forces on genetic variation, reduce the power of our current models to capture the underlying processes and therefore, explain why "there is still no clear resolution on the evolutionary forces responsible for the maintenance of variation", as one of the gold standard textbooks in Quantitative Genetics (Lynch and Walsh, 2018) emphatically states. The main hypothesis of this proposal is that the rate of creation and shuffling of genetic variation can counterbalance the effects of selection, genetic drift and inbreeding in finite closed populations, such as the elite breeding populations. Our goal is to take a data-driven approach to quantify with unprecedented accuracy the mutation and recombination processes that could explain how genetic variation appears to be maintained in closed populations that are under intense directional selection. To achieve this aim, we will use a unique genomic repository consisting of hundreds of thousands of birds (either sequenced in high-depth or genotyped with SNP arrays) extending over 25 generations with full pedigree and phenotypic information to study how genetic variation is maintained over time. This dataset gives us the required power to precisely estimate important parameters, such as how many mutational events occur per generation, where they tend to occur across the genome, and how the new genetic variation due to mutation balances its removal. As our data extend over many generations, we will have the power to track both mutations and chromosomal segments through the pedigree in order to remove false positive observations that can inflate estimates. Our study will also focus on quantifying recombination, as the force shuffling genetic variation, and we will explore its interplay with mutation. Finally, we will apply our more precise estimates to update theoretical expectations on where selection limits may lay and thus we will aim to reconcile theoretical predictions and observations. Beyond the potential to address the key question of how genetic variation is maintained, our findings could be directly applicable across animal and plant breeding organisations to improve the resilience and sustainability of their genetic improvement programmes.