Master of Science

thesisLack of information is a serious concern for clinicians. Information resources can address this problem, leading to improvements in decision making and patient outcomes. Genomics is an information-rich domain where searching for information can be complex. For example, most physicians agree that pharmacogenomics can be used to improve the quality of care, and there is evidence that many patients harbor actionable pharmacogenomic variation. However, surveys have shown that physicians feel their knowledge of pharmacogenomics to be inadequate. This represents an information need. A natural approach to meet this need is to provide context-aware access to the precise information needed. The Health Level 7 Context-Aware Knowledge Retrieval Standard, a.k.a the Infobutton, offers a modality to deliver context-aware knowledge into electronic health record (EHR) systems. OpenInfobutton is a reference implementation of this standard that offers an open-source instantiation. In this thesis, we aimed to provide insight into pharmacogenomics information needs and an automated mechanism for addressing these needs. Such work can aid the design of tools that support clinical decisions in genomics

Challenges and strategies of the Genetics Home Reference

pre-printObjective: This paper focuses on the first two years of operation of Genetics Home Reference (GHR), a Web-based resource for the general public that helps to explain the health implications of findings from the Human Genome Project. Methods and Findings: Key challenges of Web-based consumer health communication encountered in the growth and maintenance of GHR are discussed: prioritizing topics for GHR, streamlining the development process while keeping genetic information accurate, and designing a system that helps consumers navigate complex genetic relationships. Various strategies are used to address these challenges. Tying content development to topics of national priority and addressing topics requested by users makes the site increasingly important for both consumers and health professionals. Informatics methods are essential for quality control, particularly for genetic information that changes frequently. Indexing and hierarchical browsing features help to facilitate navigation. Conclusions: GHR is a credible, dynamic Website that uses lay language to explain the effects of genetic variation on human health. Informatics strategies are key to effective management of a large and expanding body of genetics information. Feedback from formal and informal sources indicates increasing usage and favorable acceptance of GHR

DECIPHER: Improving genetic diagnosis through dynamic integration of genomic and clinical data.

DECIPHER (Database of Genomic Variation and Phenotype in Humans Using Ensembl Resources) shares candidate diagnostic variants and phenotypic data from patients with genetic disorders to facilitate research and improve the diagnosis, management, and therapy of rare diseases. The platform sits at the boundary between genomic research and the clinical community. DECIPHER aims to ensure that the most up-to-date data are made rapidly available within its interpretation interfaces to improve clinical care. Newly integrated cardiac case-control data that provide evidence of gene-disease associations and inform variant interpretation exemplify this mission. New research resources are presented in a format optimized for use by a broad range of professionals supporting the delivery of genomic medicine. The interfaces within DECIPHER integrate and contextualize variant and phenotypic data, helping to determine a robust clinico-molecular diagnosis for rare-disease patients, which combines both variant classification and clinical fit. DECIPHER supports discovery research, connecting individuals within the rare-disease community to pursue hypothesis-driven research. Expected final online publication date for the Annual Review of Genomics and Human Genetics, Volume 24 is August 2023. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates

Systematising and scaling literature curation for genetically determined developmental disorders

The widespread availability of genomic sequencing has transformed the diagnosis of genetically-determined developmental disorders (GDD). However, this type of test often generates a number of genetic variants, which have to be reviewed and related back to the clinical features (phenotype) of the individual being tested. This frequently entails a time-consuming review of the peer-reviewed literature to look for case reports describing variants in the gene(s) of interest. This is particularly true for newly described and/or very rare disorders not covered in phenotype databases. Therefore, there is a need for scalable, automated literature curation to increase the efficiency of this process. This should lead to improvements in the speed in which diagnosis is made, and an increase in the number of individuals who are diagnosed through genomic testing. Phenotypic data in case reports/case series is not usually recorded in a standardised, computationally-tractable format. Plain text descriptions of similar clinical features may be recorded in several different ways. For example, a technical term such as ‘hypertelorism’, may be recorded as its synonym ‘widely spaced eyes’. In addition, case reports are found across a wide range of journals, with different structures and file formats for each publication. The Human Phenotype Ontology (HPO) was developed to store phenotypic data in a computationally-accessible format. Several initiatives have been developed to link diseases to phenotype data, in the form of HPO terms. However, these rely on manual expert curation and therefore are not inherently scalable, and cannot be updated automatically. Methods of extracting phenotype data from text at scale developed to date have relied on abstracts or open access papers. At the time of writing, Europe PubMed Central (EPMC, https://europepmc.org/) contained approximately 39.5 million articles, of which only 3.8 million were open access. Therefore, there is likely a significant volume of phenotypic data which has not been used previously at scale, due to difficulties accessing non-open access manuscripts. In this thesis, I present a method for literature curation which can utilise all relevant published full text through a newly developed package which can download almost all manuscripts licenced by a university or other institution. This is scalable to the full spectrum of GDD. Using manuscripts identified through manual literature review, I use a full text download pipeline and NLP (natural language processing) based methods to generate disease models. These are comprised of HPO terms weighted according to their frequency in the literature. I demonstrate iterative refinement of these models, and use a custom annotated corpus of 50 papers to show the text mining process has high precision and recall. I demonstrate that these models clinically reflect true disease expressivity, as defined by manual comparison with expert literature reviews, for three well-characterised GDD. I compare these disease models to those in the most commonly used genetic disease phenotype databases. I show that the automated disease models have increased depth of phenotyping, i.e. there are more terms than those which are manually-generated. I show that, in comparison to ‘real life’ prospectively gathered phenotypic data, automated disease models outperform existing phenotype databases in predicting diagnosis, as defined by increased area under the curve (by 0.05 and 0.08 using different similarity measures) on ROC curve plots. I present a method for automated PubMed search at scale, to use as input for disease model generation. I annotated a corpus of 6500 abstracts. Using this corpus I show a high precision (up to 0.80) and recall (up to 1.00) for machine learning classifiers used to identify manuscripts relevant to GDD. These use hand-picked domain-specific features, for example utilising specific MeSH terms. This method can be used to scale automated literature curation to the full spectrum of GDD. I also present an analysis of the phenotypic terms used in one year of GDD-relevant papers in a prominent journal. This shows that use of supplemental data and parsing clinical report sections from manuscripts is likely to result in more patient-specific phenotype extraction in future. In summary, I present a method for automated curation of full text from the peer-reviewed literature in the context of GDD. I demonstrate that this method is robust, reflects clinical disease expressivity, outperforms existing manual literature curation, and is scalable. Applying this process to clinical testing in future should improve the efficiency and accuracy of diagnosis

Bioinformatics solution for clinical utilization of next generation DNA sequencing

University of Minnesota Ph.D. dissertation. September 2014. Major: Biomedical Informatics and Computational Biology. Advisor: Dr.Claudia Neuhauser. 1 computer file (PDF); x, 132 pages, appendix A.DNA sequencing as an application of Next Generation Sequencing (NGS) is beginning to reshape how physicians diagnose and make treatment decisions for their patients. These NGS technologies provide a great depth of information by bringing along unprecedented throughput of data, huge scalability and speed. The terabytes of data generated has precipitated a need for efficient bioinformatics analysis and interpretation processes. My dissertation provides an end-to-end solution to analyze DNA sequencing data, interpret and deliver results efficiently and effectively. I developed a modular, robust workflow Targeted RE-sequencing Annotation Tool (TREAT) to provide a backbone for NGS DNA analysis, in collaboration with Mayo Clinic's bioinformatics core [1]. TREAT is one of the first bioinformatics solutions to incorporate alignment, variant calling, annotation and visualization of DNA sequencing data. To better evaluate the increasing foray of NGS into the clinical domain, I designed a module for comprehensive depth of coverage evaluation for genes and variants of interest. This module extending upon the TREAT pipeline helps quantify the applicability of NGS for clinical gene panels [2]. With dwindling costs and increasing availability of whole genome sequencing, turnaround time remains a major factor for clinical adaptation of NGS. I developed a novel iterative bioinformatics approach to expedite whole genome analysis by focusing on clinically relevant genomic regions, reporting results in less than 10% of the original processing time [3]. Further research employing additional clinical annotation has given us insight into a comprehensive genotype phenotype correlation evaluation of clinically reportable variants. Here I report on the characteristics of clinically relevant variants typically expected per individual from whole exome DNA sequencing data. These data highlight challenges that need to be addressed including both phenotype issues of disease penetrance and uncertainty about what is clinically reportable, and sequencing issues like incomplete sequencing coverage, thresholds for data filtering and lack of high quality databases to determine functional annotation

Multiple acyl-CoA dehydrogenase deficiency and population newborn screening:Connecting the dots

Multiple acyl-CoA dehydrogenase deficiency (MADD; also known as glutaric aciduria type II) is an ultra-rare inborn error of metabolism (IEM). The disorder is not included in the Dutch newborn blood spot (NBS) screening program due to a lack of evidence for sufficient health gain upon early detection. Complicating factors concern the limited knowledge on the natural history, disease severity prediction and monitoring of the spectrum of MADD patients, and the absence of systematic evidence of an effective treatment for severely affected patients. MADD is also an exemplary IEM that can escape identification due to nonspecific symptoms and unexpected childhood death. In this thesis, we combined reviews of the literature, with experimental research and studies on clinical outcome in patients. In part I, we present the IEMs that are associated with unexpected death in early childhood, and how their detection through acylcarnitine profile analysis can be improved. We recommend that every child participates in a population NBS program, even after death. In part II, we describe that functional studies in patient fibroblasts can predict the MADD phenotype, we propose a clinical monitoring system, and we describe the efficacy and safety of D,L-3-hydroxybutyrate treatment in severe MADD. The results of this thesis can guide clinicians in their care of MADD patients and their families, and can also support decision-makers in their aims to improve NBS programs and facilitate access to novel treatments for (ultra-)rare diseases

A Systems Genetics Approach to Drosophila melanogaster Models of Rare and Common Neurodevelopmental Disorders

Fetal Alcohol Spectrum Disorders are a group of disorders resulting from prenatal alcohol exposure, presenting with neurodevelopmental and facial abnormalities of varying severity. SSRIDDs and CdLS are rare disorders of chromatin modification, resulting in patients with a wide range of craniofacial, digit and/or neurodevelopmental abnormalities. All of these disorders have a wide range of clinical phenotypes and disease severity, yet the role of potential genetic modifiers and gene-gene or gene-environment interactions in disease pathogenesis is largely unknown and cannot be studied in humans. Insufficient numbers of patients with a single rare disorder prevent investigation of genetic factors beyond the focal disease-associated variant, while experimental study of the more common FASD using human subjects is prohibited due to ethical constraints. Drosophila melanogaster is an excellent model system for neurodevelopmental disorders, as Drosophila neurobiology is largely conserved in humans and experiments performed in Drosophila are low-cost, easily controlled, and exempt from regulation. Here, we take advantage of the Drosophila model system and identify genetic factors contributing to these neurodevelopmental disorders. Specifically, we used the Drosophila Genetic Reference Panel (DGRP) of inbred lines with full genome sequences and single cell RNA sequencing to identify genetic networks in adult Drosophila after developmental ethanol exposure and demonstrate that changes in sleep, activity, and time to sedation as a result of the developmental ethanol exposure are dependent on genetic background. We also developed a novel assay measuring time to ethanol-induced sedation of individual flies to better assess this phenotype in our research and characterized a previously unstudied long noncoding RNA critical for Drosophila fitness and stress-response. We then established Drosophila models for multiple SSRIDD and CdLS subtypes and determined the extent to which behavioral and transcriptomic phenotypes vary within and across these rare disorders. Finally, we used SSRIDD Drosophila models to present evidence for the role of genetic modifiers in ARID1B-associated SSRIDD and identify candidate genetic modifiers for multiple SSRIDD subtypes. Taken together, these results show that the Drosophila model system is a powerful tool for investigating the genetic underpinnings of both rare and common neurodevelopmental disorders that cannot be currently identified using human populations

Genetic Testing for Rare Diseases

Rare diseases, or orphan diseases, are those that individually affect a small number of patients, but taken together affect over 300 million people worldwide. They are characterized by their etiological, diagnostic and evolutionary complexity, important morbi-mortality, with high levels of disability that entail and hinder the development of a normal vital subject, not only in those who suffer from them, but also their families; therefore, a comprehensive social health approach is necessary to address this problem.About 80% of rare diseases have a genetic origin, mainly monogenic; thus, genetic testing is mandatory for the confirmation of clinical diagnostics and to ensure correct genetic counseling. Next-generation sequencing (NGS) has enabled a revolution in genetic diseases, specially in rare diseases. However, their complexity makes diagnoses difficult even with the advent of NGS.In this Special Issue, we present several examples of the complexity of genetic diagnosis for most of these diseases and the consequences that genetic testing implies for genetic counseling. There are examples of the genetic heterogeneity of hearing loss, some metabolic and lisosomal disorders, ataxia, Prader–Willi syndrome, and three comprehensive reviews on syndromic retinal dystrophies, the complexity of the molecular diagnosis of neuromuscular disorders, and the value of genetic counseling before and after a genetic test