Publications

Understanding the relationship between genetic variation and gene expression is a central question in genetics. With the availability of data from high-throughput technologies such as ChIP-Chip, expression, and genotyping arrays, we can begin to not only identify associations but to understand how genetic variations perturb the underlying transcription regulatory networks to induce differential gene expression. In this study, we describe a simple model of transcription regulation where the expression of a gene is completely characterized by two properties: the concentrations and promoter affinities of active transcription factors. We devise a method that extends Network Component Analysis (NCA) to determine how genetic variations in the form of single nucleotide polymorphisms (SNPs) perturb these two properties. Applying our method to a segregating population of Saccharomyces cerevisiae, we found statistically significant examples of trans-acting SNPs located in regulatory hotspots that perturb transcription factor concentrations and affinities for target promoters to cause global differential expression and cis-acting genetic variations that perturb the promoter affinities of transcription factors on a single gene to cause local differential expression. Although many genetic variations linked to gene expressions have been identified, it is not clear how they perturb the underlying regulatory networks that govern gene expression. Our work begins to fill this void by showing that many genetic variations affect the concentrations of active transcription factors in a cell and their affinities for target promoters. Understanding the effects of these perturbations can help us to paint a more complete picture of the complex landscape of transcription regulation. The software package implementing the algorithms discussed in this work is available as a MATLAB package upon request.

This study estimated the sensitivity and specificity of self-reported breast cancer and their associations with patient factors and pathologic findings using data from the Breast Cancer Surveillance Consortium. We included 24,631 women with and 463,804 women without a prior diagnosis of breast cancer who completed a questionnaire (including breast cancer history) at participating US mammography facilities between 1996 and 2006. We determined "true" cancer status using cancer registries and pathology databases. Multivariable logistic regression models were used to examine associations with patient factors and pathologic findings. Sensitivity of self-reported breast cancer was higher for women with invasive cancer (96.9%) than for those with ductal carcinoma in situ (DCIS) (90.2%). Specificity was high overall (99.7%) but much lower for women with a history of lobular carcinoma in situ (LCIS) (65.0%). In multivariable models, women reporting older ages, a nonwhite race/ethnicity, or less education had lower sensitivities and specificities. Sensitivity was reduced when there was evidence of prior DCIS, especially when this diagnosis had been made more than 2 years before questionnaire completion. Women reporting a family history of breast cancer had higher sensitivity. Evidence of prior LCIS was associated with lower specificity. The accuracy of self-reported breast cancer depends on the respondent's characteristics and prior diagnoses. Accuracy is lower among nonwhite women and women reporting less education. There appears to be uncertainty surrounding breast findings such as DCIS and LCIS. These results have important implications for research relying on self-report and for patient communication and care.