Associations between genetic variants and the effect of letrozole and exemestane on bone mass and bone turnover.

Adjuvant therapy for hormone receptor (HR) positive postmenopausal breast cancer patients includes aromatase inhibitors (AI). While both the non-steroidal AI letrozole and the steroidal AI exemestane decrease serum estrogen concentrations, there is evidence that exemestane may be less detrimental to bone. We hypothesized that single nucleotide polymorphisms (SNP) predict effects of AIs on bone turnover.

Genetic associations with toxicity-related discontinuation of aromatase inhibitor therapy for breast cancer.

Up to 25 % of patients discontinue adjuvant aromatase inhibitor (AI) therapy due to intolerable symptoms. Predictors of which patients will be unable to tolerate these medications have not been defined. We hypothesized that inherited variants in candidate genes are associated with treatment discontinuation because of AI-associated toxicity.

The impact of glucocorticoid polymorphisms on markers of neonatal respiratory disease after antenatal betamethasone administration.

Objective: We previously demonstrated that maternal and fetal genotypes are associated independently with neonatal respiratory distress syndrome. The objective of the current study was to determine the impact of maternal and fetal single-nucleotide polymorphisms (SNPs) in key betamethasone pathways on respiratory outcomes that serve as markers for severity of disease.

Study Design: DNA was obtained from women who were given betamethasone and from their infants.

The impact of drug metabolizing enzyme polymorphisms on outcomes after antenatal corticosteroid use.

Objective: To determine the impact of maternal and fetal single nucleotide polymorphisms in key betamethasone pathways on neonatal outcomes.

Study Design: DNA was obtained from women given betamethasone and their infants. Samples were genotyped for 73 exploratory drug metabolism and glucocorticoid pathway single nucleotide polymorphisms.

Identification of multiple rare variants associated with a disease.

Identifying rare variants that are responsible for complex disease has been promoted by advances in sequencing technologies. However, statistical methods that can handle the vast amount of data generated and that can interpret the complicated relationship between disease and these variants have lagged. We apply a zero-inflated Poisson regression model to take into account the excess of zeros caused by the extremely low frequency of the 24,487 exonic variants in the Genetic Analysis Workshop 17 data.

Identifying viral integration sites using SeqMap 2.0.

Unlabelled: Retroviral integration has been implicated in several biomedical applications, including identification of cancer-associated genes and malignant transformation in gene therapy clinical trials. We introduce an efficient and scalable method for fast identification of viral vector integration sites from long read high-throughput sequencing. Individual sequence reads are masked to remove non-genomic sequence, aligned to the host genome and assembled into contiguous fragments used to pinpoint the position of integration.

MutDB: update on development of tools for the biochemical analysis of genetic variation.

Understanding how genetic variation affects the molecular function of gene products is an emergent area of bioinformatic research. Here, we present updates to MutDB (http://www.mutdb.

MutDB services: interactive structural analysis of mutation data.

Non-synonymous single nucleotide polymorphisms (SNPs) and mutations have been associated with human phenotypes and disease. As more and more SNPs are mapped to phenotypes, understanding how these variations affect the function and expression of genes and gene products becomes an important endeavor. We have developed a set of tools to aid in the understanding of how amino acid substitutions affect protein structures.