Jonathan Pritchard
Research Summary / Selected Publications
I work 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.
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 current interest concerns the problem of population structure leading to false positives in association studies. I have recently worked on 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] . I 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 and Peter Donnelly, I 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.
I work 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.
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 current interest concerns the problem of population structure leading to false positives in association studies. I have recently worked on 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] . I 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...
J.K. Pritchard and M. Przeworski, 2001. Linkage disequilibrium in humans: models and data. Am. J. Hum. Genet. 69:1-14.
J.K. Pritchard, 2001. Are rare variants responsible for susceptibility to complex diseases? Am. J. Hum. Genet. 69:124-137.
J.K. Pritchard, M. Stephens and P. J. Donnelly, 2000. Inference of population structure using multilocus genotype data. Genetics 155: 945-959.
J.K. Pritchard, M. Stephens, N.A. Rosenberg and P. Donnelly, 2000. Association mapping in structured populations. Am J. Hum Genet. 67:170-181.
R. Thompson, J.K. Pritchard, P. Shen, P.J. Oefner and M.W. Feldman, 2000. Recent common ancestry of human Y chromsomes: Evidence from DNA sequence data. PNAS 97:7360-7365.
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.
J.K. Pritchard and M. Przeworski, 2001. Linkage disequilibrium in humans: models and data. Am. J. Hum. Genet. 69:1-14.
J.K. Pritchard, 2001. Are rare variants responsible for susceptibility to complex diseases? Am. J. Hum. Genet. 69:124-137.
J.K. Pritchard, M. Stephens and P. J. Donnelly, 2000. Inference of population structure using multilocus genotype data. Genetics 155: 945-959.
J.K. Pritchard, M. Stephens, N.A. Rosenberg and P. Donnelly, 2000. Association mapping in structured populations. Am J. Hum Genet. 67:170-181.
R. Thompson, J.K. Pritchard, P. Shen, P.J. Oefner and M.W. Feldman, 2000. Recent common ancestry of human Y chromsomes: Evidence from DNA sequence data. PNAS 97:7360-7365.
