New advances in insecticide resistance genomics: using Machine Learning to predict resistance phenotype from large-scale genomic data.

疟疾是一种热带寄生虫病，每年造成数十万人死亡，主要是撒哈拉以南非洲（SSA）的儿童。这种疾病是由蚊子传播的，蚊子在从感染者那里吸血后获得寄生虫。因此，消除疟疾依赖于有效减少蚊子数量，以打破传播循环。这主要是通过使用杀虫剂来实现的，或者是在蚊子叮咬的房屋墙壁上喷洒杀虫剂，或者是用杀虫剂处理过的蚊帐保护人类。因此，有文件记载的携带疟疾的蚊子对杀虫剂的抗药性的演变令人极为关切，控制方案的持续有效性需要了解蚊子种群易受的杀虫剂。目前，这是通过实验性地将蚊子暴露于杀虫剂以直接测量它们的抗性来实现的，但这个过程缓慢而费力，并且不是一个很好的影响指标。理想情况下，应该有可能筛选蚊子种群中涉及抗性的关键基因，但我们对杀虫剂抗性遗传学的理解仍然限于少数基因。科学界目前正处于一个令人兴奋的基因组学时代的到来，现代基因组测序能力正在迅速增加基因组数据可以产生的规模。一直缺乏的是可以利用大规模数据并整合其包含的所有信息来预测抗性表型的分析技术。机器学习是一种允许计算机使用现有数据来“学习”如何分析新数据并使用它进行预测的方法。例如，给定一个足够大的蚊子数据集，其遗传学和抗性特征是已知的，机器学习工具可以找到遗传学和抗性之间的关联，然后可以使用遗传学来测量抗性。机器学习工具尚未应用于杀虫剂抗性领域，因为它们需要大量的数据来“学习”，而必要的基因组数据一直缺乏。我们和我们的合作者目前正在收集迄今为止最大的物种集合，结合全基因组测序数据和杀虫剂抗性测量，产生前所未有的大量抗性相关基因组数据。我们将利用这些数据，使用机器学习来提高我们使用基因组数据估计蚊子杀虫剂抗性的能力。最重要的是，该项目将有助于提高我们对蚊子种群进行杀虫剂耐药性筛查的能力，并因此为疟疾控制政策提供信息。我们将与撒哈拉以南非洲密切参与蚊子控制计划的合作伙伴合作，确定我们的方法可以最有效地应用于哪些领域，以帮助改善疟疾的控制。

Malaria is a parasitic tropical disease which kills hundreds of thousands of people every year, predominantly children in Sub-Saharan Africa (SSA). The disease is transmitted by mosquitoes who acquire the parasites after taking a blood meal from an infected person. Elimination of malaria therefore relies on effectively reducing mosquito numbers to break the cycle of transmission. This is primarily achieved through the application of insecticides, either by spraying the walls of houses in which mosquitoes bite or by protecting humans with insecticide-treated bed nets. The documented evolution of resistance to insecticides in mosquitoes that carry malaria is therefore of great concern, and the continued effectiveness of control programmes requires knowledge of the insecticides to which a mosquito population is susceptible. Currently, this is achieved by experimentally exposing mosquitoes to insecticides to directly measure their resistance, but this process is slow and laborious, and not a good indicator of impact. Ideally, it should be possible to screen a mosquito population for key genes involved resistance, but our understanding of the genetics of insecticide resistance is still limited to a handful of genes. The scientific community is currently at the advent of an exciting era in genomics where modern genome sequencing capacity is rapidly increasing the scale at which genomic data can be produced. What have been lacking are analytical techniques that can utilise the huge scale of data and integrate all of the information it contains to predict resistance phenotypes. Machine learning is an approach that allows computers to use existing data to "learn" how to analyse new data and use it to make predictions. For example, given a sufficiently large dataset of mosquitoes whose genetics and resistance characteristics are known, machine learning tools can find associations between genetics and resistance, which can then be used to measure resistance using only genetics. Machine learning tools have yet to be applied to the field of insecticide resistance because they require large amounts of data from which to "learn", and the necessary genomic data have been lacking.We and our collaborators are currently amassing the largest collection of any species to date combining both genome-wide sequencing data and measures of insecticide resistance, producing unprecedented amounts of resistance-associated genomic data. We will leverage these data, using machine learning to improve our ability to estimate the insecticide resistance profile of a mosquito using genomic data. Most importantly, this project will help improve our ability to screen mosquito populations for insecticide resistance and will inform malaria control policy as a result. In collaboration with our partners in SSA who are closely involved with the mosquito control programmes, we will identify areas where our method can be most effectively applied to help improve the control of malaria.