Doctor of Philosophy

dissertationRapidly evolving technologies such as chip arrays and next-generation sequencing are uncovering human genetic variants at an unprecedented pace. Unfortunately, this ever growing collection of gene sequence variation has limited clinical utility without clear association to disease outcomes. As electronic medical records begin to incorporate genetic information, gene variant classification and accurate interpretation of gene test results plays a critical role in customizing patient therapy. To verify the functional impact of a given gene variant, laboratories rely on confirming evidence such as previous literature reports, patient history and disease segregation in a family. By definition variants of uncertain significance (VUS) lack this supporting evidence and in such cases, computational tools are often used to evaluate the predicted functional impact of a gene mutation. This study evaluates leveraging high quality genotype-phenotype disease variant data from 20 genes and 3986 variants, to develop gene-specific predictors utilizing a combination of changes in primary amino acid sequence, amino acid properties as descriptors of mutation severity and Naïve Bayes classification. A Primary Sequence Amino Acid Properties (PSAAP) prediction algorithm was then combined with well established predictors in a weighted Consensus sum in context of gene-specific reference intervals for known phenotypes. PSAAP and Consensus were also used to evaluate known variants of uncertain significance in the RET proto-oncogene as a model gene. The PSAAP algorithm was successfully extended to many genes and diseases. Gene-specific algorithms typically outperform generalized prediction tools. Characteristic mutation properties of a given gene and disease may be lost when diluted into genomewide data sets. A reliable computational phenotype classification framework with quantitative metrics and disease specific reference ranges allows objective evaluation of novel or uncertain gene variants and augments decision making when confirming clinical information is limited

MS

thesisAnalysis of organic acids in urine is a valuable tool in the diagnosis of the inborn errors of metabolism known as organic acidurias. This test is commonly ordered in newborns with symptoms such as lethargy, failure to thrive, hepatic failure, and suspected familial disorders. A drawback of published methods is the overwhelming amount of data to examine for each patient, prior to the final laboratory report. Physicians will wait as long as two weeks for these time critical results. The goal of this research was to develop and export system to automate the process of screening for metabolic disorders of urine organic acids. The Xaminer® pattern recognition software (ThermoFinnigan, San Jose, CA) was adapted to predict and identify patterns of urine organic acid disorders. The gas chromatography-mass spectrometry (GC-MS) full scan spectra of organic acids were used to build the pattern match library and train the software to recognized methylmalonic aciduria (MMA) and associated vitamin B12 deficiency, as well as, a subset of fatty acid oxidation defects (FAOD), including medium chain acyl-CoA dehydrogenase (MCAD) deficiency. Patient data files were de-identified and reprocessed using the expert system. The expert system results were compared to the original laboratory findings. From a total of 2573 samples, the original laboratory findings were 20 positives for MMA and 29 positives for FAOD. The Xaminer software identified 17 of the 20 MMA positives, plus 4 additional candidate samples that matched the search pattern criteria. The software found 26 of the 29 FAOD positives. Five additional samplers found to be candidates for FAOD. Software analysis time averaged less than 10 seconds per sample. This expert system can use pattern recognition of full scan GC-MS data to aid in patient screening for MMA and fatty acid oxidation disorders. The performance of Xaminer shows promise for refining or expanding the reference library to include other metabolic disorders as well

Utility of gene-specific algorithms for predicting pathogenicity of uncertain gene variants

ManuscriptThe rapid advance of gene sequencing technologies has produced an unprecedented rate of discovery for genome variation in humans. A growing numbered of authoritative clinical repositories archive gene variants and disease phenotype, yet there are currently many more gene variants that lack clear annotation or disease association. To date, there has been very limited coverage of gene-specific predictors in the literature. Here we present the evaluation of ?gene-specific? predictor models based on a Na?ve Bayesian classifier for 20 gene-disease data sets, containing 3,986 variants with clinically characterized patient conditions. Utility of gene-specific prediction is then compared ?all-gene? generalized prediction and also to existing popular predictors. Gene-specific computational prediction models derived from clinically curated gene variant disease data sets often outperform established generalized algorithms for novel and uncertain gene variants

Computational Feature of Selection and Classification of RET Phenotypic Severity

pre-printAlthough many reported mutations in the RET oncogene have been directly associated with hereditary thyroid carcinoma, other mutations are labelled as uncertain gene variants because they have not been clearly associated with a clinical phenotype. The process of determining the severity of a mutation is costly and time consuming. Informatics tools and methods may aid to bridge this genotype-phenotype gap. Towards this goal, machine-learning classification algorithms were evaluated for their ability to distinguish benign and pathogenic RET gene variants as characterized by differences in values of physicochemical properties of the residue present in the wild type and the one in the mutated sequence. Representative algorithms were chosen from different categories of machine learning classification techniques, including rules, bayes, and regression, nearest neighbour, support vector machines and trees. Machine-learning models were then compared to well-established techniques used for mutation severity prediction. Machine-learning classification can be used to accurately predict RET mutation status using primary sequence information only. Existing algorithms that are based on sequence homology (ortholog conservation) or protein structural data are not necessarily superior

Predicting phenotypic severity of uncertain gene variants in the RET Proto-Oncogene

pre-printAlthough reported gene variants in the RET oncogene have been directly associated with multiple endocrine neoplasia type 2 and hereditary medullary thyroid carcinoma, other mutations are classified as variants of uncertain significance (VUS) until the associated clinical phenotype is made clear. Currently, some 46 non-synonymous VUS entries exist in curated archives. In the absence of a gold standard method for predicting phenotype outcomes, this follow up study applies feature selected amino acid physical and chemical properties feeding a Bayes classifier to predict disease association of uncertain gene variants into categories of benign and pathogenic. Algorithm performance and VUS predictions were compared to established phylogenetic based mutation prediction algorithms. Curated outcomes and unpublished RET gene variants with known disease association were used to benchmark predictor performance. Reliable classification of RET uncertain gene variants will augment current clinical information of RET mutations and assist in improving prediction algorithms as knowledge increases

Identification of histoplasma -specific peptides in human urine

pre-printHistoplasmosis is a severe dimorphic fungus infection, which is often difficult to diagnose due to similarity in symptoms to other diseases and lack of specific diagnostic tests. Urine samples from histoplasma-antigen-positive patients and appropriate controls were prepared using various sample preparation strategies including immunoenrichment, ultrafiltration, high-abundant protein depletion, deglycosylation, reverse-phase fractions, and digest using various enzymes. Samples were then analyzed by nanospray tandem mass spectrometry. Accurate mass TOF scans underwent molecular feature extraction and statistical analysis for unique disease makers, and acquired MS/MS data were searched against known human and histoplasma proteins. In human urine, some 52 peptides from 37 Histoplasma proteins were identified with high confidence. This is the first report of identification of a large number of Histoplasma-specific peptides from immunoassay-positive patient samples using tandem mass spectrometry and bioinformatics techniques. These findings may lead to novel diagnostic markers for histoplasmosis in human urine

The SPRED1 Variants Repository for Legius Syndrome

Legius syndrome (LS) is an autosomal dominant disorder caused by germline loss-of-function mutations in the sprouty-related, EVH1 domain containing 1 (SPRED1) gene. The phenotype of LS is multiple café au lait macules (CALM) with other commonly reported manifestations, including intertriginous freckling, lipomas, macrocephaly, and learning disabilities including ADHD and developmental delays. Since the earliest signs of LS and neurofibromatosis type 1 (NF1) syndrome are pigmentary findings, the two are indistinguishable and individuals with LS may meet the National Institutes of Health diagnostic criteria for NF1 syndrome. However, individuals are not known to have an increased risk for developing tumors (compared with NF1 patients). It is therefore important to fully characterize the phenotype differences between NF1 and LS because the prognoses of these two disorders differ greatly. We have developed a mutation database that characterizes the known variants in the SPRED1 gene in an effort to facilitate this process for testing and interpreting results. This database is free to the public and will be updated quarterly

Identification of Histoplasma-Specific Peptides in Human Urine

Histoplasmosis is a severe dimorphic fungus infection, which is often difficult to diagnose due to similarity in symptoms to other diseases and lack of specific diagnostic tests. Urine samples from histoplasma-antigen-positive patients and appropriate controls were prepared using various sample preparation strategies including immunoenrichment, ultrafiltration, high-abundant protein depletion, deglycosylation, reverse-phase fractions, and digest using various enzymes. Samples were then analyzed by nanospray tandem mass spectrometry. Accurate mass TOF scans underwent molecular feature extraction and statistical analysis for unique disease makers, and acquired MS/MS data were searched against known human and histoplasma proteins. In human urine, some 52 peptides from 37 Histoplasma proteins were identified with high confidence. This is the first report of identification of a large number of Histoplasma-specific peptides from immunoassay-positive patient samples using tandem mass spectrometry and bioinformatics techniques. These findings may lead to novel diagnostic markers for histoplasmosis in human urine

Consensus: a framework for evaluation of uncertain gene variants in laboratory test reporting

Accurate interpretation of gene testing is a key component in customizing patient therapy. Where confirming evidence for a gene variant is lacking, computational prediction may be employed. A standardized framework, however, does not yet exist for quantitative evaluation of disease association for uncertain or novel gene variants in an objective manner. Here, complementary predictors for missense gene variants were incorporated into a weighted Consensus framework that includes calculated reference intervals from known disease outcomes. Data visualization for clinical reporting is also discussed