Fred Wright

Dr. Wright’s group studies a number of areas in statistical genetics and biomedical computation including the development of methods for mapping genetic diseases and analyzing gene expression profiles. The role of statistics and computational methods is increasingly important in biomedical research as developments in the Human Genome Project provide vast quantities of data to interpret. The Wright group is particularly interested in three main areas:

(1) Developing statistical methods for genetic mapping.
The objective of genetic linkage mapping is to use the segregation of genetic markers in a pedigree to obtain an approximate chromosomal location for a gene of interest. Association mapping is a related technique that uses a direct comparison of unrelated diseased vs. non-diseased individuals to discover founder effects of a disease mutation from the distant past. Both forms of genetic mapping require continually improved statistical techniques to keep pace with the volume and quality of data provided by newer mapping technologies.

(2) Developing statistical methods for analyzing microarray-based gene expression data.
The Wright group is developing new methods for estimating gene expression from Affymetrix oligonucleotide arrays, and for identifying differentially expressed genes in a comparison of two or more groups of samples when the sample sizes are very small. Related interests in this area include new methods for sample-size and power calculations, as well as improved methods for normalizing and scaling microarray data.

(3) Applying bioinformatic and computational tools to genome annotation.
Several years ago, Dr. Wright began to pursue bioinformatics problems enabled by the availability of the draft human genome sequence, and was involved in some initial annotations of the draft human sequence. His group continues to work in this area, developing improved methods for hierarchical genome assembly and the computational assembly of expressed sequence tags (ESTs).

Selected Publications:
Nadler JJ, Zou F, Huang H, Moy SS, Lauder J, Crawley JN, Threadgill DW, Wright FA, Magnuson TR. (2006) Large-scale gene expression differences across brain regions and inbred strains correlate with a behavioral phenotype. Genetics. 174(3):1229-36.

Drumm ML, Konstan MW, Schluchter MD, Handler A, Pace R, Zou F, Zariwala M, Fargo D, Xu A, Dunn JM, Darrah RJ, Dorfman R, Sandford AJ, Corey M, Zielenski J, Durie P, Goddard K, Yankaskas JR, Wright FA, Knowles MR; Gene Modifier Study Group. (2005) Genetic modifiers of lung disease in cystic fibrosis. N Engl J Med 353:1443-53.

Hu J, Zou F, Wright FA. (2005) Practical FDR-based sample size calculations in microarray experiments. Bioinformatics 21:3264-72.

Barry WT, Nobel AB, and Wright FA. (2005) Significance analysis of functional categories in gene expression studies: a structured permutation approach. Bioinformatics 21(9):1943-9.

Miller BJ, Wang D, Krahe R and Wright FA. (2003) Pooled analysis of loss of heterozygosity in breast cancer: a genome scan provides comparative evidence for multiple tumor suppressors and identifies novel candidate regions. Am J Hum Genet 73:748-67.




				Dr. Wright’s group studies a number of areas in statistical genetics and biomedical computation including the development of methods for mapping genetic diseases and analyzing gene expression profiles. The role of statistics and computational methods is increasingly important in biomedical research as developments in the Human Genome Project provide vast quantities of data to interpret. The Wright group is particularly interested in three main areas: (1) Developing statistical methods for genetic mapping. The objective of genetic linkage mapping is to use the segregation of genetic markers in a pedigree to obtain an approximate chromosomal location for a gene of interest. Association mapping is a related technique that uses a direct comparison of unrelated diseased vs. non-diseased individuals to discover founder effects of a disease mutation from the distant past. Both forms of genetic mapping require continually improved statistical techniques to keep pace with the volume and quality of data provided by newer mapping technologies. (2) Developing statistical methods for analyzing microarray-based gene expression data. The Wright group is developing new methods for estimating gene expression from Affymetrix oligonucleotide arrays, and for identifying differentially expressed genes in a comparison of two or more groups of samples when the sample sizes are very small. Related interests in this area include new methods for sample-size and power calculations, as well as improved methods for normalizing and scaling microarray data. (3) Applying bioinformatic and computational tools to genome annotation. Several years ago, Dr. Wright began to pursue bioinformatics problems enabled by the availability of the draft human genome sequence, and was involved in some initial annotations of the draft human sequence. His group continues to work in this area, developing improved methods for hierarchical genome assembly and the computational assembly of expressed sequence tags (ESTs). Selected Publications: Nadler JJ, Zou F, Huang H, Moy SS, Lauder J, Crawley JN, Threadgill DW, Wright FA, Magnuson TR. (2006) Large-scale gene expression differences across brain regions and inbred strains correlate with a behavioral phenotype. Genetics. 174(3):1229-36. Drumm ML, Konstan MW, Schluchter MD, Handler A, Pace R, Zou F, Zariwala M, Fargo D, Xu A, Dunn JM, Darrah RJ, Dorfman R, Sandford AJ, Corey M, Zielenski J, Durie P, Goddard K, Yankaskas JR, Wright FA, Knowles MR; Gene Modifier Study Group. (2005) Genetic modifiers of lung disease in cystic fibrosis. N Engl J Med 353:1443-53. Hu J, Zou F, Wright FA. (2005) Practical FDR-based sample size calculations in microarray experiments. Bioinformatics 21:3264-72. Barry WT, Nobel AB, and Wright FA. (2005) Significance analysis of functional categories in gene expression studies: a structured permutation approach. Bioinformatics 21(9):1943-9. Miller BJ, Wang D, Krahe R and Wright FA. (2003) Pooled analysis of loss of heterozygosity in breast cancer: a genome scan provides comparative evidence for multiple tumor suppressors and identifies novel candidate regions. Am J Hum Genet 73:748-67.


	contact information: [phone] (919) 843-3655 [email] [website]