Recently, genomes from a large number of organisms are available for us to research. By looking at these genomes, we can observe genetic variations within and between species that were generated by processes such as mutations to the genome, natural selection and demographic changes. Because these genetic variations reflect the footprint of evolutionary mechanisms functioning over a long period of time, we can use the patterns of genetic variations to understand the mechanisms that actually drive evolution. Our group are taking two approaches: one is the modeling of evolutionary processes, and the other is genomic data analysis.
Although evolutionary processes are extremely complicated, we can make high resolution models and predict characteristics of genetic variations thanks to the recent availability of powerful computing technologies. By using computational and statistical methods, we are trying to understand the patterns of genetic footprints left by each of the evolutionary processes and their interactions. By using results of these investigations, we hope to explain the mechanisms that actually shaped variations by performing genomic data analysis. We are currently interested in the following questions: What are the characteristics of adaptive evolution? Can we differentiate the effect of adaptation from the other processes? How accurately can we identify past events?
Where do we come from? What are we? Where are we going? These are fundamental questions for human beings. To answer these questions, genomic data are analyzed from the multiple viewpoints such as population genetics, evolutionary biology, medical science, cultural anthropology, and so on. For example, we are trying to elucidate the genetic basis of cumulative cultural evolution by tracing the evolution of the genes involved in mental illnesses.