POPULATION GENOMICS OF ADAPTATION

适应的群体基因组学

• 批准号: 9753261
• 负责人: ANDREW D KERN
• 金额: $ 29.5万
• 依托单位: UNIVERSITY OF OREGON
• 依托单位国家: 美国
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-09-01 至 2021-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9753261
• 关键词: Affect; Africa South of the Sahara; Anopheles Genus; Anopheles gambiae; Awareness; Back; Beds; Behavioral; Catalogs; Cessation of life; Chromosomes; Classification; Complex; Coupled; Culicidae; Data; Data Set; Dependence; Detection; Development; Distant; Equipment and supply inventories; Evolution; Frequencies; Funding; Genome; Genomic approach; Genomics; Geography; Goals; Health; Human; Individual; Insecticide Resistance; Insecticides; Link; Location; Machine Learning; Malaria; Methodology; Methods; Mosquito-borne infectious disease; Mutation; Pattern; Phase; Plasmodium; Plasmodium falciparum; Population; Prevalence; Production; Recording of previous events; Research; Residual state; Resistance; Risk; Sampling; Techniques; Time; Variant; Work; deep neural network; fight against; genomic data; global health; human disease; learning strategy; malaria infection; malaria mosquito; malaria transmission; markov model; novel; recurrent neural network; resistance allele; response; supervised learning; tool; vector; vector control; vector mosquito

Project Summary Malaria that results from Plasmodium falciparum is among the most globally devastating human diseases. The principle vector of malaria, mosquitoes of the Anopheles gambiae species complex, are thus central targets for controlling the human health burden of Plasmodium. For nearly two decades, there have been large-scale, coordinated efforts to diminish mosquito populations, generally through spraying and insecticide treated bed nets. Indeed such control efforts have now led to a nearly 50% decrease in the rates of malaria infection in many parts of sub-Saharan Africa. At present, however, control efforts of A. gambiae are being threatened by evolutionary responses within mosquitos: A. gambiae populations have shown increases in insecticide resistance as well as behavioral adaptations that allow mosquitos to avoid spraying all together. Thus adaptation of mosquitos to the control efforts themselves is currently a risk to maintain the gains made in the fight against malaria. In this proposal we lay out an integrated population genomic approach for systematically identifying regions of the A. gambiae genome that are evolving adaptively in response to ongoing control efforts. Our approach centers upon state-of-the-art supervised machine learning techniques that we have recently introduced for finding the signatures of selective sweeps in genomes (Schrider and Kern, 2016), coupled with the large-scale population genomic datasets currently in production by the Ag1000G consortium.

项目摘要 由恶性疟原虫引起的疟疾是全球最严重的疟疾之一， 毁灭性的人类疾病疟疾的主要传播媒介， 因此，冈比亚按蚊种复合体是控制冈比亚按蚊的中心目标。 疟原虫对人类健康的负担。近二十年来， 大规模、协调一致的努力，以减少蚊子种群， 通过喷洒和杀虫剂处理过的蚊帐。事实上，这种控制努力 现已使许多国家的疟疾感染率下降了近50%， 撒哈拉以南非洲的部分地区。目前，A.冈比亚按蚊 受到蚊子进化反应的威胁：A.冈比亚按蚊 种群对杀虫剂的抗药性和行为 使蚊子能够避免一起喷洒。因此，适应 蚊子对控制工作本身的影响目前是维持 在防治疟疾方面取得的成果。 在这项提案中，我们提出了一个综合的人口基因组方法， 系统地确定了A.冈比亚的基因组正在进化 自适应地响应于正在进行的控制努力。我们的方法集中在 最先进的监督机器学习技术， 介绍了在基因组中寻找选择性扫描的签名（Schrider 和克恩，2016年），再加上大规模的人口基因组数据集 目前由Ag1000G财团生产。