Cloud-based genomics pipelines for ophthalmology: Reviewed from research to clinical practice

Aim: To familiarize clinicians with clinical genomics, and to describe the potential of cloud computing for enabling the future routine use of genomics in eye hospital settings. Design: Review article exploring the potential for cloud-based genomic pipelines in eye hospitals. Methods: Narrative review of the literature relevant to clinical genomics and cloud computing, using PubMed and Google Scholar. A broad overview of these fields is provided, followed by key examples of their integration. Results: Cloud computing could benefit clinical genomics due to scalability of resources, potentially lower costs, and ease of data sharing between multiple institutions. Challenges include complex pricing of services, costs from mistakes or experimentation, data security, and privacy concerns. Conclusions and future perspectives: Clinical genomics is likely to become more routinely used in clinical practice. Currently this is delivered in highly specialist centers. In the future, cloud computing could enable delivery of clinical genomics services in non-specialist hospital settings, in a fast, cost-effective way, whilst enhancing collaboration between clinical and research teams

Doctor of Philosophy

dissertationThe widespread use of genomic information to improve clinical care has long been a goal of clinicians, researchers, and policy-makers. With the completion of the Human Genome Project over a decade ago, the feasibility of attaining this goal on a widespread basis is becoming a greater reality. In fact, new genome sequencing technologies are bringing the cost of obtaining a patient's genomic information within reach of the general population. While this is an exciting prospect to health care, many barriers still remain to effectively use genomic information in a clinically meaningful way. These barriers, if not overcome, will limit the ability of genomic information to provide a significant impact on health care. Nevertheless, clinical decision support (CDS), which entails the provision of patient-specific knowledge to clinicians at appropriate times to enhance health care, offers a feasible solution. As such, this body of work represents an effort to develop a functional CDS solution capable of leveraging whole genome sequence information on a widespread basis. Many considerations were made in the design of the CDS solution in order to overcome the complexities of genomic information while aligning with common health information technology approaches and standards. This work represents an important advancement in the capabilities of integrating actionable genomic information within the clinical workflow using health informatics approaches

Integrative Molecular Pathological Epidemiology of Congenital and Infant Acute Leukemia

Congenital and infant acute leukemia remain one of the most puzzling clinical issues in pediatric hematology-oncology. There is a paucity of studies focused on these rare, aggressive, acute leukemias; specifically, there is little study on the differences in disease in the youngest of infants less than 1 year of age unlike the numerous studies of the disease in older children. The United States National Cancer Institute’s Surveillance, Epidemiology, and End Results (SEER) cancer population registry program has been integral for a plethora of clinical population and pathology research studies for numerous diseases in the last 40 years and has an excellent resource for investigation of the infant population. Laboratory medicine and pathology professionals must use pathology results not only to diagnose individuals after the disease has been discovered, but the information must be applied retrospectively to develop new testing strategies. By classifying the intense heterogeneity within these cancers, the distinct changes of the diseases within individuals can be established, ultimately reshaping diagnostic methodologies. Through the application of Integrative Molecular Pathological Epidemiology to a 325-infant case series from the SEER program from 2008 to 2014, this dissertation study was used to evolve the classification of these pediatric cancers with the application of scientific nosology. This dissertation study has documented characteristics of this population for application in further precision medicine investigations to influence laboratory medicine algorithms for diagnosis and management of patients guiding health policy that are aimed at improving outcomes in the youngest of children

Molecular diagnosis of developmental disorders

ABSTRACT Idiopathic developmental disorders (DDs) affect ~1% of the population worldwide. This being a considerable amount, efforts are being made to elucidate the disease mechanisms. One or several genetic factors cause 30-40% of DDs, and only 10% are caused by environmental factors. The remaining 50% of DD patients go undiagnosed, mostly due to a lack of diagnostic techniques. The cause in most undiagnosed cases is though to be a genetic factor or a combination of genetic and environmental factors. Despite the surge of new technologies entering the market, their implementation into diagnostic laboratories is hampered by costs, lack of information about the expected diagnostic yield, and the wide range of selection. This study evaluates new microarray methods in diagnosing idiopathic DDs, providing information about their added diagnostic value. Study I analysed 150 patients by array comparative genomic hybridization (array CGH, 44K and 244K), with a subsequent 18% diagnostic yield. These results are supported by other studies, indicating an enourmous added diagnostic value of array CGH, compared with conventional cytogenetic analysis. Nevertheless, 80% of the patients remained undiagnosed in Study I. In an effort to diagnose more patients, in Study IV the resolution was increased from 8.9 Kb of the 244K CGH array to 0.7 Kb, by using a single-nucleotide polymorphism (SNP) array. However, no additional pathogenic changes were detected in the 35 patients assessed, and thus, for diagnostic purposes, an array platform with ca 9 Kb resolution appears adequate. The recent vast increase in reports of detected aberrations and associated phenotypes has enabled characterization of several new syndromes first based on a common aberration and thereafter by delineation of common clinical characteristics. In Study II, a familial deletion at 9q22.2q22.32 with variable penetrance was described. Despite several reports of aberrations in the adjacent area at 9q associated with Gorlin syndrome, the patients in this family had a unique phenotype and did not present with the syndrome. In Study III, a familial duplication of chromosome 6p22.2 was described. The duplication caused increased expression of an important enzyme of the γ-aminobutyric acid (GABA) degradation pathway, causing oxidative stress of the brain, and thus, very likely, the mild mental retardation of these patients. These two case studies attempted to pinpoint candidate genes and to resolve the pathogenic mechanism causing the clinical characteristics of the patients. Presenting rare genetic and clinical findings to the international science and medical community enables interpretation of similar findings in other patients. The added value of molecular karyotyping in patients with idiopathic DD is evident. As a first line of testing, arrays with a median resolution of at least 9 Kb should be considered and further characterization of detected aberrations undertaken when possible. Diagnostic whole-exome sequencing may be the best option for patients who remain undiagnosed after high-resolution array analysis.Doktorsavhandlingens målsättning har varit att utvärdera nya analyseringsmetoder och den fördel de medför vid försök att fastställa genetiska orsaker som orsak till uppkomsten av utvecklingsstörningar. Ungefär 1% av världens befolkning har någon form av utvecklingsstörning, vars orsak inte är känd, d.v.s. den är idiopatisk. Det uppskattas att genetiska faktorer förorsakar ca 30-40%, medan miljöfaktorer förorsakar ca 10% av alla utvecklingsstörningar, medan de resterande 50% av patienterna saknar en diagnos. Uppskattningsvis förorsakas utvecklingsstörningar till största delen av genetiska faktorer, eller av en kombination av genetiska och miljöfaktorer. Uteblivna diagnoser beror vanligtvis på bristfälliga undersökningsmetoder. I de första delarbetet (Studie I) undersöktes 150 patienter med idopatisk utvecklingsstörning genom att använda en mikromatrismetod, array CGH (eng. microarray comparative genomic hybridisation). Metoden går ut på att patientens DNA jämförs med DNAt från en frisk person och skillnaderna i kopietal registreras. På så kan man upptäcka eventuella genetiska avvikelser. Med den här metoden kunde 18% av patienterna diagnostiseras i Studie I. Resultatet får stöd i andra motsvarande studier. Trots det positiva resultatet i Studie I saknade ca 80% av patienterna fortfarande en diagnos. I den fjärde Studien (IV) användes en annan mikromatrismetod, en SNP-array (eng. single-nucleotide polymorphism), med högre resolution (0,7Kb gentemot 8,9Kb), för att analysera 35 patienter med idiopatisk utvecklingsstörning och normalt array CGH resultat. Inga nya diagnoser kunde däremot fastställas. Resultatet fråm Studie IV antyder att det för diagnostik förhållandevis bästa resultatet fås med en platform med måttlig resolution (ca 9Kb). Tack vare ny analyseringsmetoder framställs mycket ny information om de genetiska avvikelser och kliniska egenskaper som karakteriserar olika utveckilingsstörningar och därigenom har nya syndrom fastställts. I Studie II karakteriserades den variation av kliniska egenskaper som förekom inom en familj p.g.a. en deletion i kromosom 9q33.2q22.32. I Studie III beskrevs den mekanism som ledde till att en duplikation i kromosom 6p22.2 förorsakade mild utvecklinsstörning i en familj. Dessa två delarbeten strävade till att definiera kandidatgener och den mekanism som hade lett till familjernas utvecklingsstörning. Dylik information är viktig att föra fram för att befrämja diagnostik av andra patienter med liknande symptom. Som slutsats av den här doktorsavhandlingen konstateras att användning av mikromatriser med en resolution på minst 9Kb är rekommenderat som första diagnostiseringsmetod för patienter med idiopatisk utvecklingsstörning

Evolutionary Design of Search and Triage Interfaces for Large Document Sets

This dissertation is concerned with the design of visual interfaces for searching and triaging large document sets. Data proliferation has generated new and challenging information-based tasks across various domains. Yet, as the document sets of these tasks grow, it has become increasingly difficult for users to remain active participants in the information-seeking process, such as when searching and triaging large document sets. During information search, users seek to understand their document set, align domain knowledge, formulate effective queries, and use those queries to develop document set mappings which help generate encounters with valued documents. During information triage, users encounter the documents mapped by information search to judge relevance to information-seeking objectives. Yet, information search and triage can be challenging for users. Studies have found that when using traditional design strategies in tool interfaces for search and triage, users routinely struggle to understand the domain being searched, apply their expertise, communicate their objectives during query building, and assess the relevance of search results during information triage. Users must understand and apply domain- specific vocabulary when communicating information-seeking objectives. Yet, task vocabularies typically do not align with those of users, especially in tasks of complex domains. Ontologies can be valuable mediating resources for bridging between the vocabularies of users and tasks. They are created by domain experts to provide a standardized mapping of knowledge that can be leveraged both by computational- as well as human-facing systems. We believe that the activation of ontologies within user-facing interfaces has a potential to help users when searching and triaging large document sets, however more research is required

Visual Analytics for Performing Complex Tasks with Electronic Health Records

Electronic health record systems (EHRs) facilitate the storage, retrieval, and sharing of patient health data; however, the availability of data does not directly translate to support for tasks that healthcare providers encounter every day. In recent years, healthcare providers employ a large volume of clinical data stored in EHRs to perform various complex data-intensive tasks. The overwhelming volume of clinical data stored in EHRs and a lack of support for the execution of EHR-driven tasks are, but a few problems healthcare providers face while working with EHR-based systems. Thus, there is a demand for computational systems that can facilitate the performance of complex tasks that involve the use and working with the vast amount of data stored in EHRs. Visual analytics (VA) offers great promise in handling such information overload challenges by integrating advanced analytics techniques with interactive visualizations. The user-controlled environment that VA systems provide allows healthcare providers to guide the analytics techniques on analyzing and managing EHR data through interactive visualizations. The goal of this research is to demonstrate how VA systems can be designed systematically to support the performance of complex EHR-driven tasks. In light of this, we present an activity and task analysis framework to analyze EHR-driven tasks in the context of interactive visualization systems. We also conduct a systematic literature review of EHR-based VA systems and identify the primary dimensions of the VA design space to evaluate these systems and identify the gaps. Two novel EHR-based VA systems (SUNRISE and VERONICA) are then designed to bridge the gaps. SUNRISE incorporates frequent itemset mining, extreme gradient boosting, and interactive visualizations to allow users to interactively explore the relationships between laboratory test results and a disease outcome. The other proposed system, VERONICA, uses a representative set of supervised machine learning techniques to find the group of features with the strongest predictive power and make the analytic results accessible through an interactive visual interface. We demonstrate the usefulness of these systems through a usage scenario with acute kidney injury using large provincial healthcare databases from Ontario, Canada, stored at ICES

Investigation of the molecular basis of inherited developmental conditions in high risk population isolates

The Amish communities of Ohio (USA) are a distinct group of endogamous, rural-living Anabaptist Christians. An ancestral bottleneck, caused by migratory events in the 17th century and subsequent rapid population expansion, has led to the enrichment of a number of inherited conditions within these communities. This provides significantly enhanced power to identify genes responsible for rare monogenic disorders, as well traits with more complex inheritance patterns. The studies detailed in this thesis aims to provide diagnoses to individuals and their families for the underlying genetic causes responsible for the difficulties they experience and contributes to a long-running, non-profit community clinical-genetic research programme called the Windows of Hope (WoH). Forming part of a wider Amish Hearing Loss Program the studies described in chapter three document the discovery of the genetic causes of hearing loss for eight Amish families. Through a combination of targeted gene sequencing, genome-wide SNP mapping and exome sequencing this study identified a variant in the Gap junction beta-2 (GJB2) gene, not previously reported in the Amish, as the cause of non-syndromic hearing loss in six families. Additionally, one family initially thought to be affected by a neurodevelopment disorder which included syndromic hearing loss, was found to possess two distinct genetic disorders; a 16p11.2 microdeletion, responsible for the developmental delay, and a homozygous GJB2 variant, responsible for the hearing loss. Finally, this chapter proposes two novel hearing loss genes and details the functional work undertaken to assess the pathogenicity of one of these genes (SLC15A5). This work provided important diagnoses for many families and acquired significant information regarding the spectrum and frequency of hearing loss-associated gene variants across distinct Amish communities. Chapter four details work undertaken to define the clinical phenotype and molecular basis of a novel complex autosomal recessive neurological disorder. Work undertaken by one of our collaborators, Dr Zineb Ammous, was instrumental in precisely defining the clinical phenotype of this disorder. A combination of genome-wide SNP mapping and exome sequence identified a sequence variant in Smad Nuclear Interacting Protein 1 (SNIP1), which encodes an evolutionary-conserved transcriptional regulator, as the likely underlying genetic cause. Due to its role as a transcription regulator whole transcriptome sequencing was undertaken to determine the impact of this gene mutation. This work provided important information regarding the specific biological role of SNIP1 and identified gene expression pathways of direct relevance to the clinical phenotype, highlighting therapeutic approaches likely to benefit affected individuals. Additionally, this study determined that SNIP1-associated syndrome is one of the most common conditions across many Amish communities. In recent years the WoH Project has accumulated extensive single nucleotide polymorphisms (SNP) and exome sequencing datasets from patients and individuals from the Amish community. Chapter five outlines a pilot, proof-of-principle study undertaken to explore this data with the aim characterising the architecture of the Amish genome. The interrogation of 26 exomes identified the presence of 12 pathogenic variants known to cause autosomal recessive (AR) diseases that have not yet been reported in the Amish but are likely to be present. Additionally, a PLEXseq sequencing approach was implemented to determine the prevalence of 165 pathogenic variants in 171 unaffected Amish individuals. The findings indicated diverse carrier frequencies within the different Amish communities and contributed to the consolidation of two genes responsible for ultra-rare inherited AR diseases (CEP55, MNS1). By developing approaches to improve knowledge of the specific causes of inherited diseases in the community, this work has laid the foundation for the development of a new genetic-based approach to diagnostic testing in the community. This thesis, and the wider programme of work of Windows of Hope, occupies a privileged positioned at the interface between scientific research and clinical care. The findings described here have made a significant contribution to our understanding of the pathomolecular cause of a number of rare inherited disorders by increasing our knowledge of the nature and spectrum of inherited disease within the Amish laying the foundations to aid the future discovery of new disease genes and improving clinical outcomes by enabling focussed clinical diagnostic and management strategies to be implemented

Visual Analytics of Electronic Health Records with a focus on Acute Kidney Injury

The increasing use of electronic platforms in healthcare has resulted in the generation of unprecedented amounts of data in recent years. The amount of data available to clinical researchers, physicians, and healthcare administrators continues to grow, which creates an untapped resource with the ability to improve the healthcare system drastically. Despite the enthusiasm for adopting electronic health records (EHRs), some recent studies have shown that EHR-based systems hardly improve the ability of healthcare providers to make better decisions. One reason for this inefficacy is that these systems do not allow for human-data interaction in a manner that fits and supports the needs of healthcare providers. Another reason is the information overload, which makes healthcare providers often misunderstand, misinterpret, ignore, or overlook vital data. The emergence of a type of computational system known as visual analytics (VA), has the potential to reduce the complexity of EHR data by combining advanced analytics techniques with interactive visualizations to analyze, synthesize, and facilitate high-level activities while allowing users to get more involved in a discourse with the data. The purpose of this research is to demonstrate the use of sophisticated visual analytics systems to solve various EHR-related research problems. This dissertation includes a framework by which we identify gaps in existing EHR-based systems and conceptualize the data-driven activities and tasks of our proposed systems. Two novel VA systems (VISA_M3R3 and VALENCIA) and two studies are designed to bridge the gaps. VISA_M3R3 incorporates multiple regression, frequent itemset mining, and interactive visualization to assist users in the identification of nephrotoxic medications. Another proposed system, VALENCIA, brings a wide range of dimension reduction and cluster analysis techniques to analyze high-dimensional EHRs, integrate them seamlessly, and make them accessible through interactive visualizations. The studies are conducted to develop prediction models to classify patients who are at risk of developing acute kidney injury (AKI) and identify AKI-associated medication and medication combinations using EHRs. Through healthcare administrative datasets stored at the ICES-KDT (Kidney Dialysis and Transplantation program), London, Ontario, we have demonstrated how our proposed systems and prediction models can be used to solve real-world problems

An Analysis of Next Generation Sequencing in Mendelian Disorders: Diagnostic Potential and Clinical Utility

Mendelian disorders are rare, heritable conditions that cause medical, financial, psychological, and social burdens. Diagnosis is key for improving patient care but is challenging due to frequent genetic and phenotypic heterogeneity. The genome-wide next generation sequencing (NGS) technologies of whole exome sequencing (WES) and whole genome sequencing (WGS) have revolutionised Mendelian disorder diagnosis in little more than a decade. Yet, the rapid adoption and complexity of genomic technologies have resulted in a gap between implementation and understanding how to utilise their full potential. The main objective of this research was to establish the benefits of NGS to Mendelian disorder diagnosis for clinical translation. To achieve this, the diagnostic potential and utility of WES and WGS were assessed in a cohort of individuals with suspected, but undiagnosed Mendelian disorders. Both WES and WGS were able to dramatically improve diagnostic rates over traditional testing, with diagnostic yields of first-line WES of 52% and WGS, 61%. WES reanalysis with improved pipelines and scientific knowledge demonstrated gains in diagnostic yield at 12 months, and again 2 years later. A WES diagnosis of PLOD3-related disease, an ultra-rare Mendelian disorder, was extended with phenotype delineation, tissue expression, and protein modelling. The resulting disease description and proposed classification is expected to positively impact future patient diagnosis. To assess the economic implications of genomic testing pathways, comparisons of WES to traditional testing and to WGS were made. The early application of WES in intellectual disability saved AU$782 for each additional diagnosis compared to the traditional model. While WGS was shown to have a greater diagnostic yield than WES, when used as a first-line test each incremental additional WGS diagnosis cost AUD$29,000. Thus, from an economic perspective, first-line WES is preferred for routine testing, with WGS reserved for situations where a diagnosis would have a high chance of clinical intervention. Notwithstanding this, WGS additional diagnostic gains should not be underestimated given the increased potential for future diagnosis and the gap in understanding the potential downstream costs benefits of a diagnosis, which may dwarf the initial test cost. In conclusion, NGS technologies provide significant gains over traditional methods and should be adopted early in Mendelian disorder diagnosis to positively impact patient care