CAREER: Dynamic models of isolation and admixture for community-scale population genomic inference

人们能够快速、轻松地越来越多地获取大量DNA数据的速度远远超过了计算性能随着时间的推移而增长的速度(摩尔定律)。为了弥合即将到来的数据洪流和我们分析它的能力之间的差距，这项研究将开发计算工具和模型，以适应在不同物种的多个个体水平上分析基因组数据所固有的复杂性。这将有助于回答长期存在的、以前难以解决的问题，即物种群落是如何形成的，是什么创造了生物多样性，物种群体如何应对气候变化。建模和工具开发将侧重于过去两百万年主导地球的气候变化的周期性历史，重点是由此产生的物种栖息地收缩、扩张和灭绝的循环如何塑造整个群落的基因组模式。迫在眉睫的基因组数据洪流也将随着个人基因组学试剂盒的日益普及而改变社会。为了预测公众将如何通过个人基因组学越来越多地成为事实上的科学参与者，这项研究将通过实施基于基因组参与者的人类群体基因组学课程和三个公共活动，展示应用于人类的群体基因组推理的力量、复杂性和警告。参与者将有选择地从他们自己那里获得大量与医学无关的基因组数据，这一经验部分将使公众能够就与医学、历史、进化、伦理、统计学和信息素养有关的一系列复杂问题向公众进行交流。

The rate at which one can increasingly obtain large amounts of DNA data quickly and easily has far outpaced the rate at which computational performance increases over time (Moore's law). To bridge the gap between this impending flood of data and our ability to analyze it, this research will develop computational tools and models that can accommodate the complexity inherent in analyzing genomic data at the level of multiple individuals across different species. This will allow answering long-standing and previously intractable questions about how communities of species form, what creates biodiversity, and how groups of species respond to climate change. The modeling and tool development will focus on the cyclical history of climate change that dominated the Earth for the last two million years with an emphasis on how the resulting cycles of species habitat retraction, expansion and extinction shape the genomic patterns of whole communities. The impending flood of genomic data will also transform society with the increasing availability of personal genomics kits. To anticipate how the public will increasingly become de facto scientific participants through personal genomics, the research will demonstrate the power, complexities and cautions of population genomic inference as applied to humans by implementing a genomic participant-based human population genomics course and three public events. Participants will optionally obtain vast amounts of non-medically relevant genomic data from themselves and this experiential component will enable communicating to the public about a wide array of complex issues relevant to medicine, history, evolution, ethics, statistics and information literacy.