Identifying and cataloguing individual and population-level DNA sequence variations is a critical step towards understanding the genetic basis of disease and clinically significant human variation. Recent advances in molecular microarray technology have made it feasible to rapidly screen DNA samples for possible genetic mutations. This dissertation focuses on evaluating the efficacy of resequencing arrays as a tool for variant detection and proposes mechanistic bases and computational algorithms that can be employed for an improvement in performance. We present results from hearing loss arrays developed in two different research facilities and highlight some of the approaches we adopted to enhance the applicability of the arrays in a clinical setting.
We leveraged sequence and intensity pattern features responsible for diminished coverage and accuracy and developed a novel algorithm, sPROFILER, which resolved >80% of no-calls from Affymetrix™ GSEQ and allowed 99.6% (range: 99.2-99.8%) of sequence to be called, while maintaining overall accuracy at >99.8% based upon dideoxy sequencing comparison. We implemented a bioinformatics pipeline that incorporated sPROFILER to support clinical genetic testing of hearing loss patients at the Cincinnati Children’s Hospital Medical Center.
The utility of any molecular diagnostic tool in determining the genetic basis of a disease is fully realized only when an effective variant detection method is complemented by a rigorous framework for evaluating the potential clinical significance of these variants. We evaluated the contribution of various properties related to amino acid substitution in determining whether a residue change is damaging or not. We developed a machine learning-based framework to assess the functional impact of missense variants using childhood Sensorineural Hearing Loss and Hypertrophic/Dilated Cardiomyopathy as specific instances of application of the methodology. We compared our method with some of the representative tools for missense variant classification and present results that demonstrate the improvement in classification accuracy. Additionally, we developed customized classifiers trained on proteins sharing Gene Ontology terms with the protein being tested and observed a smaller training set could be used to provide better or same prediction accuracy as compared to utilizing a large generic training set for all proteins.