Analysis of the gene coding for the BRCA2-Interacting protein PALB2 in hereditary prostate cancer

BACKGROUND The genetic basis of susceptibility to prostate cancer (PRCA) remains elusive. Mutations in BRCA2 have been associated with increased prostate cancer risk and account for around 2% of young onset (<56 years) prostate cancer cases. PALB2 is a recently identified breast cancer susceptibility gene whose protein is closely associated with BRCA2 and is essential for BRCA2 anchorage to nuclear structures. This functional relationship made PALB2 a candidate PRCA susceptibility gene. METHODS We sequenced PALB2 in probands from 95 PRCA families, 77 of which had two or more cases of early onset PRCA (age at diagnosis <55 years), and the remaining 18 had one case of early onset PRCA and five or more total cases of PRCA. RESULTS Two previously unreported variants, K18R and V925L were identified, neither of which is in a known PALB2 functional domain and both of which are unlikely to be pathogenic. No truncating mutations were identified. CONCLUSIONS These results indicate that deleterious PALB2 mutations are unlikely to play a significant role in hereditary prostate cancer. Prostate 68: 675–678, 2008. © 2008 Wiley-Liss, Inc.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/58069/1/20729_ftp.pd

Identification of a novel germline SPOP mutation in a family with hereditary prostate cancer

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/106871/1/pros22818.pd

Molecular Characterization of the Onset and Progression of Colitis in Inoculated Interleukin-10 Gene-Deficient Mice: A Role for PPARα

The interleukin-10 gene-deficient (Il10−/−) mouse is a model of human inflammatory bowel disease and Ppara has been identified as one of the key genes involved in regulation of colitis in the bacterially inoculated Il10−/− model. The aims were to (1) characterize colitis onset and progression using a histopathological, transcriptomic, and proteomic approach and (2) investigate links between PPARα and IL10 using gene network analysis. Bacterial inoculation resulted in severe colitis in Il10−/− mice from 10 to 12 weeks of age. Innate and adaptive immune responses showed differences in gene expression relating to colitis severity. Actin cytoskeleton dynamics, innate immunity, and apoptosis-linked gene and protein expression data suggested a delayed remodeling process in 12-week-old Il10−/− mice. Gene expression changes in 12-week-old Il10−/− mice were related to PPARα signaling likely to control colitis, but how PPARα activation might regulate intestinal IL10 production remains to be determined

Prevalence of the HOXB13 G84E prostate cancer risk allele in men treated with radical prostatectomy

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/106912/1/bju12522.pd

Post hoc Analysis for Detecting Individual Rare Variant Risk Associations Using Probit Regression Bayesian Variable Selection Methods in Case-Control Sequencing Studies

Rare variants (RVs) have been shown to be significant contributors to complex disease risk. By definition, these variants have very low minor allele frequencies and traditional single-marker methods for statistical analysis are underpowered for typical sequencing study sample sizes. Multimarker burden-type approaches attempt to identify aggregation of RVs across case-control status by analyzing relatively small partitions of the genome, such as genes. However, it is generally the case that the aggregative measure would be a mixture of causal and neutral variants, and these omnibus tests do not directly provide any indication of which RVs may be driving a given association. Recently, Bayesian variable selection approaches have been proposed to identify RV associations from a large set of RVs under consideration. Although these approaches have been shown to be powerful at detecting associations at the RV level, there are often computational limitations on the total quantity of RVs under consideration and compromises are necessary for large-scale application. Here, we propose a computationally efficient alternative formulation of this method using a probit regression approach specifically capable of simultaneously analyzing hundreds to thousands of RVs. We evaluate our approach to detect causal variation on simulated data and examine sensitivity and specificity in instances of high RV dimensionality as well as apply it to pathway-level RV analysis results from a prostate cancer (PC) risk case-control sequencing study. Finally, we discuss potential extensions and future directions of this work