Local adaptation for seed banks in wild tomato species

种子长时间休眠促进所谓的种子库是一种重要的生理适应，它使植物能够成功地在大量可变和不可预测的环境中定殖。这种进化赌注对冲包括减少短期繁殖成功率，以有利于长期风险降低，并且在细菌，原生动物（包括人类寄生虫）和昆虫物种中也广泛流行。虽然，种子银行似乎是一个无处不在的机制，促进适应不可预测的环境，进化，遗传和基因组机制驱动种子银行的演变在很大程度上是未知的。到目前为止，我们还不知道1）这种赌注对冲策略的发展速度有多快，或者2）遗传基础是什么。在这个项目中，我们使用了四管齐下的方法来解决这两个问题相结合的发展人口基因组学的理论方法与基因组测序和种子休眠的经验措施。我们使用的模型系统，Solanum chilense，野生番茄种在不同的栖息地在南部秘鲁和北方智利，揭示负责赌注对冲evolution.First，在该项目的理论部分，我们的目标是开发一个隐马尔可夫模型（HMM）的统计方法来分析全基因组序列从植物种群。隐马尔可夫模型方法将从基因组沿着的多态性和重组率的变化中具体地推断种子库的过去进化。chilense，通过对来自两个已经测序的亲本植物的20个F1后代的全基因组进行测序。第三，我们将通过比较六个种群的S. chilense选择在不同的栖息地（北梅西奇，南部沿海和南部高海拔）。将获得来自这些群体的30种植物的全基因组序列，并使用我们的HMM方法进行研究。我们想测试新栖息地的殖民化是否在S。chilense与种子库的变化相关，重要的是揭示了对非生物因子的局部适应和种子休眠的新基因。第四，为了完善种子库适应的证据，我们将对六个测序的S.不同温湿度条件下的智利种群该项目提出了一个独特的组合的统计群体遗传学建模，全基因组数据分析，发芽的经验性测试，以提供在野生植物物种种子休眠最近的本地适应的第一个示范。

Seed dormancy for long periods of time promoting so called seed banks is a crucial physiological adaptation which allowed plants to successfully colonize a large number of variable and unpredictable environments. This evolutionary bet hedging consists in decreasing short-term reproductive success in favour of longer-term risk reduction, and is also widely prevalent in bacteria, protozoans (including human parasites), and insects species. Although, seed banking appears to be a ubiquitous mechanism facilitating adaptation to unpredictable environments, the evolutionary, genetic and genomic mechanisms driving the evolution of seed banking are largely unknown. Up to date we do not know 1) how fast such bet hedging strategies evolve in time, or 2) what the genetic underpinnings are. In this project, we use a four-pronged approach to address both questions by combining the development of a population genomics theoretical method with genome sequencing and empirical measures of seed dormancy. We use as a model system, Solanum chilense, a wild tomato species found in various habitats in Southern Peru and Northern Chile, to reveal loci responsible for bet hedging evolution.First, in the theoretical part of the project, we aim to develop a Hidden Markov Model (HMM) statistical method to analyse full genome sequences from plant populations. The HMM method will infer specifically the past evolution of seed banking from variation in polymorphism and recombination rates along genomes.Second, we will quantify the genomic mutation rate for the wild species S. chilense, by sequencing the full genome of 20 F1 progenies from two already sequenced parental plants. This step is crucial for obtaining accurate estimates in our inference method.Third, we will uncover local adaptation for seed banking and infer the speed of adaptation by contrasting six populations of S. chilense chosen in different habitats (North mesic, Southern coastal and Southern high altitudes). Full genomes sequences from 30 plants from these populations will be obtained and studied using our HMM method. We want to test if colonization of new habitats in S. chilense was correlated with changes in seed banking, and importantly reveal novel genes underlying local adaptation to abiotic factors and seed dormancy.Fourth, to complete the evidence for seed bank adaptation, we will perform germination experiments of seeds from the six sequenced S. chilense populations under different temperature and humidity regimes. This project presents a unique combination of statistical population genetics modelling, full genome data analyses, and empirical test of germination to provide the first demonstration of recent local adaptation for seed dormancy in a wild plant species.