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Our lab works on statistical methods for interpreting genetic data. Much of this work makes use of techniques from population genetics and computational statistics in order to model and analyze data on genetic variation in humans and other species. We are interested in a range of applications, including studying complex disease genetics and various problems in evolutionary and population biology. A major focus of the research is on techniques for linkage disequilibrium (LD) mapping of complex disease genes. LD mapping has been proposed as a powerful approach to finding genes of modest effect that contribute to disease susceptibility. However, there are still important open questions about how best to design the studies, how to analyze the data and, ultimately, how effective this type of approach is likely to be. One area of recent focus concerns the problem of population structure leading to false positives in association studies. We have developed techniques that use genetic data in order to detect, and correct for these problems and can potentially make these studies more powerful[1][2][3] . We have also recently worked on the extent of LD in humans [4] and developed population genetic models of complex disease mutations, with the goal that these models can be useful for designing more-powerful tests of association [5]. A second area of interest is in using multi-locus genotype data to learn about population structure. Working jointly with Matthew Stephens, Daniel Falush and Peter Donnelly, we have developed a Bayesian model-based clustering method which uses multi-locus data from a sample of individuals to infer population structure and assign individuals (probabilistically) to populations [6]. This method is implemented in a program called structure. This approach has proved useful in a range of problems, particularly in evolutionary and conservation genetics, as well as in various applications in human genetics. We are now working on various extensions of this type of approach. Our lab also maintains a focus in evolutionary biology, including the use of genetic variation to learn about the evolutionary history of humans and other species and to study selection (e.g., [7][8]). Selected publications are listed below; click on "Publications" for an updated and mostly complete list of published and in-press papers.
Linkage disequilibrium in humans: models and data. J.K. Pritchard and M. Przeworski, 2001. Am. J. Hum. Genet. 69:1-14 [Abstract] [PDF] Are rare variants responsible for susceptibility to complex diseases? J.K. Pritchard, 2001. Am. J. Hum. Genet. 69:124-137 [Abstract] [PDF] Inference of population structure using multilocus genotype data. J.K. Pritchard, M. Stephens and P. J. Donnelly, 2000. Genetics 155: 945-959. [Abstract], [PDF], [Software]. Association mapping in structured populations. J.K. Pritchard, M. Stephens, N.A. Rosenberg and P. Donnelly, 2000. Am J. Hum Genet. 67:170-181. [Abstract], [PDF], [Software] Recent common ancestry of human Y chromsomes: Evidence from DNA sequence data. R. Thompson, J.K. Pritchard, P. Shen, P.J. Oefner and M.W. Feldman, 2000. PNAS 97:7360-7365. [Abstract]. [PDF], Use of unlinked genetic markers to detect population stratification in association studies. JK Pritchard and NA Rosenberg 1999. Am. J. of Hum. Gen. 65: 220-228. [Abstract], [PDF] Population growth of human Y chromosomes: a study of Y chromosome microsatellites. JK Pritchard, MT Seielstad, A Perez-Lezaun and MW Feldman 1999. Mol. Biol. Evol., 16:1791-1798. [Abstract], [PDF], [Data] |
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