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Jonathan Pritchard grew up in England before moving to Pennsylvania during high school. He received his BSc in Biology and Mathematics from Penn State University in 1994, and his PhD in Biology at Stanford in 1998. After that he moved to a postdoc in the Department of Statistics at Oxford University and then to his first faculty job at the University of Chicago in 2001. Pritchard returned to Stanford University in 2013, where he is now a Professor in the Departments of Biology and Genetics.
My group has expertise in the development of new statistical methods for genetic analysis and in their application to genomic data from humans and other organisms. We focus on questions relating to genetic variation and evolution: How does genetic variation impact phenotypic traits and evolution, both at the organismal and cellular level? What can we learn from genome sequences of modern and ancient humans about the relationships among human populations, and the the nature of adaptation in these populations?We often work on problems where there are no off-the-shelf statistical methods. Thus, an important part of our work is in developing appropriate statistical and computational approaches that can yield new insights into biological data. In the past, we have made important contributions to a variety of problems in human population genetics, including methods for complex trait mapping, inference of population structure and history, and studies of natural selection. We have a strong track record of producing user-friendly resources that are widely used in the community, and in applied data analysis to tackle important biological questions. Notably, our Structure algorithm and software package for inferring population structure from genetic data have received >30,000 total citations spread across several papers.Since 2008 an important emphasis of my group has focused on understanding gene regulation, and in particular how genetic variation may impact regulation. Ultimately, we would like to be able to predict which noncoding variants in the genome are likely to have regulatory effects in any given cell type, and how these link to phenotypic variation and disease. My lab has been deeply involved in developing new computational methods to interpret various types of modern genomic assays and in linking these to genetic variation. Secondly, we have had a major focus on understanding the genetic architecture of complex traits, and the implications for understanding evolution. We have argued that much--if not most--evolution in humans likely proceeds through a process that we call "polygenic adaptation" in which populations evolve through small allele frequency shifts at many loci. We have also written extensively about conceptual models for understanding the genetic architecture of trait variation (Boyle et al, 2017). We have argued that the data are consistent with a model in which essentially every regulatory variant in disease-relevant cell types can affect risk, and proposed that most of these effects act through trans-regulatory networks. Testing this model is an ongoing focus of our work.