Brooke L. Fridley, Ph.D.

Associate Professor, Department of Biostatistics
Director, Biostatistics and Informatics Shared Resource, The University of Kansas Cancer Center
Site Director, K-INBRE Bioinformatics Core

B.S., Mathematics, Truman State University, Kirksville, MO
M.S., Statistics, Iowa State University, Ames, IA
Ph.D., Statistics, Iowa State University, Ames, IA

Research Focus

Statistical Genetics / Genomics, Molecular Epidemiology, Bioinformatics, Cancer Genomics and Ovarian Cancer, Bayesian Methods.

Personal Mission Statement

Discovering the role of genomics in human diseases will pave the way toward improvements in disease diagnosis, treatment and prevention. My research is focused on the development of sophisticated statistical and bioinformatics tools for the analysis of high-dimensional "omic" data. These tools will aid researchers in the analysis and interpretation of genomic, epigenomic and transcriptomic data to determine functionally relevant loci associated with complex traits and diseases. My research is focused primarily on development methods for data integration, including Bayesian methods, molecular clustering and gene set analysis approaches. Recently, I also started investigating and developing analysis methods for next-generation sequence data that will aid researchers in the interpretation of this high-dimensional data, including rare variants. In addition to my statistical research, I am actively involved in numerous genomic projects, primary in the area of cancer and cancer pharmacogenomics. Many of these studies deal with both candidate genes and genome-wide approaches, along with multiple types of genomic data, such as genotypic, methylation and mRNA expression.


Click here to view Dr. Fridley's Analysis Code & Software for Genomic Studies


Top 15 Publications

1.    Shen H, Fridley BL, Song H, Lawrenson K, Cunningham JM, Ramus SJ, Cicek MS, Tyrer J, Stram D, Larson MC, Köbel M, PRACTICAL Consortium, Ziogas A, Zheng W, Yang HP, Wu AH, Wozniak EL, Woo YL, Winterhoff B, Wik E, Whittemore AS, Wentzensen N, Weber RP, Vitonis AF, Vincent D, Vierkant RA, Vergote I,...,  Australian Ovarian Cancer Study Group, Australian Cancer Study, Schildkraut JM, Sellers TA, Huntsman D, Berchuck A, Chenevix-Trench G, Gayther SA, Pharoah PDP, Laird PW, Goode EL#, and Pearce CL#, "Epigenetic analysis leads to identification of HNF1B as a subtype-specific susceptibility gene for ovarian cancer," Nature Communications, In press.

2.    Pharoah, P.D.P., Tsai, Y.Y., Ramus, S.J., Phelan, C.M., Goode, E.L., Lawrenson, K., Price, P.D.P., Fridley, B.L., Tyrer, J.P., Shen, H., et al. (2012). GWAS meta-analysis and replication identifies three novel common susceptibility loci for ovarian cancer. Nat Genet, In press.

3.    Larson NB, Jenkins GD, Larson MC, Vierkant RA, Sellers TA, Phelan CM, Schildkraut JM, Sutphen R, Pharoah PPD, Gayther SA, Wentzensen N, Ovarian Cancer Association Consortium, Goode EL, and Fridley BL, “Kernel canonical correlation analysis for assessing gene-gene interactions and application to ovarian cancer”. European Journal of Human Genetics. In Press

4.    Fridley, B.L., Lund, S., Jenkins, G.D., and Wang, L. (2012). A Bayesian integrative genomic model for pathway analysis of complex traits. Genet Epidemiol 36, 352-359.

 5.    Fridley, B.L., Jenkins, G.D., Tsai, Y.Y., Song, H., Bolton, K.L., Fenstermacher, D., Tyrer, J., Ramus, S.J., Cunningham, J.M., Vierkant, R.A., et al. (2012). Gene set analysis of survival following ovarian cancer implicates macrolide binding and intracellular signaling genes. Cancer Epidemiol Biomarkers Prev 21, 529-536

 6.    Breheny, P., Chalise, P., Batzler, A., Wang, L., and Fridley, B.L. (2012). Genetic association studies of copy-number variation: should assignment of copy number states precede testing? PLoS One 7, e34262. 

7. Brisbin, A., Jenkins, G.D., Ellsworth, K.A., Wang, L., and Fridley, B.L. (2012). Localization of association signal from risk and protective variants in sequencing studies. Front Genet 3, 173.

 8. Chalise, P., Batzler, A., Abo, R., Wang, L., and Fridley, B.L. (2012). Simultaneous analysis of multiple data types in pharmacogenomic studies using weighted sparse canonical correlation analysis. Omics: a journal of integrative biology 16, 363-373.

9.    Abo, R., Jenkins, G.D., Wang, L., and Fridley, B.L. (2012). Identifying the Genetic Variation of Gene Expression Using Gene Sets: Application of Novel Gene Set eQTL Approach to PharmGKB and KEGG. PLoS One 7, e43301.

10. Biernacka, J.M., Jenkins, G.D., Wang, L., Moyer, A.M., and Fridley, B.L. (2012). Use of the gamma method for self-contained gene-set analysis of SNP data. Eur J Hum Genet 20, 565-571.

11.  Fridley, B.L., Batzler, A., Li, L., Li, F., Matimba, A., Jenkins, G.D., Ji, Y., Wang, L., and Weinshilboum, R.M. (2011). Gene set analysis of purine and pyrimidine antimetabolites cancer therapies. Pharmacogenet Genomics.

12. Fridley, B.L., and Biernacka, J.M. (2011). Gene set analysis of SNP data: benefits, challenges, and future directions. Eur J Hum Genet 19, 837-843.

13.  Fridley, B.L., Iversen, E., Tsai, Y.Y., Jenkins, G.D., Goode, E.L., and Sellers, T.A. (2011). A Latent Model for Prioritization of SNPs for Functional Studies. PLoS One 6, e20764.

14.  Fridley, B.L., and Jenkins, G.D. (2010). Localizing putative markers in genetic association studies by incorporating linkage disequilibrium into bayesian hierarchical models. Hum Hered 70, 63-73.

15. Fridley, B.L., Jenkins, G.D., and Biernacka, J.M. (2010). Self-contained gene-set analysis of expression data: an evaluation of existing and novel methods. PLoS One 5, e12693.

Last modified: Mar 13, 2013


Brooke L. Fridley, Ph.D.
Associate Professor, Department of Biostatistics
Director, Biostatistics and Informatics Shared Resource, The University of Kansas Cancer Center
Site Director, K-INBRE Bioinformatics Core

P: (913) 945-5039
F: (913) 588-0252