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Development of human-computer interactive approaches for rare disease genomics

University of British Columbia

Clinical genome sequencing is becoming a tool for standard clinical practice. Many studies have presented sequencing as effective for both diagnosing and informing the management of genetic diseases. However, the task of finding the causal variant(s) of a rare genetic disease within an individual is often difficult due to the large number of identified variants and lack of direct evidence of causality. Current computational solutions harness existing genetic knowledge in order to infer the pathogenicity of the variant(s), as well as filter those unlikely to be pathogenic. Such methods can bring focus to a compact set (less than hundreds) of variants. However, they are not sufficient to interpret causality of variants for patient phenotypes; interpretation involves expert examination and synthesis of complex evidence, clinical knowledge, and experience. To accelerate interpretation and avoid diagnostic delay, computational methods are emerging for automated prioritization that capture, translate, and exploit clinical knowledge. While automation provides efficiency, it does not replace the expert-driven interpretation process. Moreover, knowledge and experience of human experts can be challenging to fully encode computationally. This thesis, therefore, explores an alternative space between expert-driven and computer-driven solutions, where human expertise is deeply embedded within computer-assisted analytic and diagnostic processes via facilitated human-computer interactions. First, clinical experts and their work environment were observed via collaborations in an interdisciplinary exome analysis project as well as in a clinical resource development project. From these observations, we identified two elements of human-computer interaction: characteristic cognitive processes underlying the diagnostic process and information visualization. Exploiting these findings, we designed and evaluated an interactive variant interpretation strategy that augments cognitive processes of clinical experts. We found that this strategy could expedite variant interpretation. We then qualitatively assessed current information visualization practices during clinical exome and genome analyses. Based on the findings of this assessment, we formulated design requirements that can enhance visual interpretation of complex genetic evidence. In summary, this research highlights the synergistic utility of human-computer interaction in clinical exome and genome analyses for rare genetic diagnoses. Furthermore, it exemplifies the importance of empowering the skills of human experts in digital medicine.

Text

2019-12-31

Attribution-NonCommercial-NoDerivatives 4.0 International

http://hdl.handle.net/2429/67981

Genome Science and Technology

University of British Columbia

UBCV

http://creativecommons.org/licenses/by-nc-nd/4.0/