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Barwell et al. J Transl Genet Genom 2018;2:13. I  https://doi.org/10.20517/jtgg.2018.17                                                Page 7 of 10

               To aid interpretation of sequence variants in the future, we anticipate that the test results will be linked to a
               national database for phenotypes and genomic variants, but at the time of writing this is subject to confir-
               mation. If busy secondary care clinicians are required to submit extensive phenotypic data when ordering a
               genetic test, we anticipate that they will need support to fit this into their existing patterns of work.

                                                                                                        [14]
               The test directory has been generated through combining the current UK Genetic Testing Network (UKGTN)
               dossier of approved tests with PanelApp. Little detail on how this list was generated or evidence based with
               respect to variant call detection rate or economic modelling has been published, which makes critical analy-
               sis difficult.


               Anecdotal patient stories from the 100,000 Genomes Project have been published about the impact of some
               test findings in individual cases. We look forward to seeing results collated from the different diseases and
               gene panels used to assess its health benefits in a more systematic way.

               In lieu of arranging diagnostic testing in sporadic and non-syndromic disease and cancer, clinical geneti-
               cists will have vital additional roles in driving local innovation, education, supporting patient identification
               and variant interpretation. Patients with a complex family history, potentially syndromic disease, an identi-
               fied variant, family dynamic/psychosocial challenges or a previous history of mental health problems will
               continue to need to be referred for genetic counselling. The focus will be to concentrate more on identifying
               clinically actionable variants in affected patients and then cascading results to at risk relatives. This contrasts
               with starting with the “worried well” approaching clinical genetics departments concerned about their fam-
               ily history, which is often currently the case.

               Legacy
               As the 100,000 Genomes Project finishes, it may be a challenge to maintain the wider engagement and dia-
               logue around consent and data-sharing. The development of personalised medicine will target screening and
               specific treatments on those most likely to benefit rather than everyone in a population. Data storage and
               access are a controversial new currency in the digital age. Data about the health of an individual and his or
               her close relatives is highly sensitive information. There is a risk of increasing societal concerns about the
               direction that genomic medicine could take. We recommend that governments consider this an important
               aspect of transformational change otherwise technological advances may be unable to be successfully and
               appropriately implemented.

               Challenges in clinical transformation
               There is an often unspoken cynicism regarding whether new molecular diagnostics can improve healthcare
               in standard clinical settings when considering the benefits against other well-deserving healthcare priorities.
               This includes diagnostics for patients with acute medical problems or to guide treatment in patients with
               common chronic disease. This is partially due to two problems: firstly, a failure to recognize progress where
               present and secondly, a resistance to change in roles [Figure 3]. Failure to recognize the impact of molecular
               developments on patient outcomes when such innovations move from research into routine care is common.
               Education with evidence based economic modelling may be required here to demonstrate the value of such
               innovations. All diagnostics cost money while the benefits may come downstream in the patient pathway,
               and beyond. When economic modelling is used to plan healthcare the analysis must include the costs of
               companion diagnostics and any impact on any at risk relatives.

               Secondly, change can be challenging and threatening to established clinicians, in spite of the simple fact that
               medicine changes as knowledge and technology grows. Change can be underpinned by evidence that the
               new intervention will solve an important problem and that it is worth changing priorities and effort along-
               side adapting or discarding tried and tested current practices.
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