Graduation Date


Document Type

Master's Thesis

Degree Name

Master of Science

Department or Program

Biological Sciences

Department or Program Chair

Mary Sevigny, PhD

First Reader

Jonathan H. LeBowitz, PhD

Second Reader

Maggie Louie, PhD


The ability to sequence patient DNA has led to an explosion in the reports of mutations for a number of diseases. Frequently, published reports include in silico predictions of the probability that the mutations are disease-associated. The question asked here is how well these in silico methods predict the effects of new mutations, i.e., mutations not included in the dataset(s) used for training and testing the in silico method. To address this question, we examined mutations associated, or potentially associated, with Morquio A (MPS IVA), a rare, autosomal recessive lysosomal storage disorder (LSD) caused by a deficiency of lysosomal enzyme N-acetylgalactosamine-6-sulfatase (GALNS). In the severe form of the disease, life expectancy is less than 30 years. More than 200 unique missense mutations have been identified in the GALNS gene, with effects ranging from no change in function (wild-type) to significant reduction in functional effect (severe forms of the disease). Using GALNS as the model gene, we evaluated the ability of select publicly available in silico methods to predict the functional effects of these mutations. Specifically, the predictions of GALNS mutations on enzyme activity were evaluated and compared to both published and unpublished Morquio A mutations. Functional effects for unpublished Morquio A mutations were determined by measuring the enzyme activity of cells transiently transfected with mutant gene cassettes. Although some of the in silico packages perform better than others, they may not be used either individually or in combination to predict with any level of certainty whether or not any particular mutation is deleterious. Our results strongly suggest that testing enzyme activity is still required to determine the functional impact of a specific mutation.