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Professor Cris Print
BHB, MBChB, PhD
Professor
Department of Molecular Medicine and Pathology
University of Auckland
The last five years has been an immensely exciting time for those
doctors and medical students who love new technologies, or more
importantly, who love what new technologies can do for patients. An
expanding range of technological advances competes for our atten-
tion, such as: 3-D printing of cells to generate replacement tissues;
augmented reality for clinical communication; health robots using arti-
ficial intelligence; cancer immunotherapy; and gene sequencing. Some
of these new technologies are destined to be used by all practitioners
in a specialty within the next five years. Other equally valuable tech-
nologies will remain research tools, used to build the evidence base
for future medical practice, but are unlikely to be used directly by
most doctors. Genomics and related ‘omic technologies’ sit in both
camps, rapidly penetrating into the mainstream of primary and sec-
ondary care, while in parallel, transforming our knowledge of disease
through research.
This article will argue that omic technologies are an advance that few
doctors and medical students can ignore. It will describe the general
landscape of omic technologies in New Zealand and overseas, then
use two examples of omic technologies to illustrate the potential of
this field: personal genomic testing; and polygenic risk scores. It will
then discuss two challenges that are currently being addressed: the
development of a genomically-literate health-care workforce; and is-
sues of equity. Pertinent web sites and peer-reviewed references will
be given for further reading.
What are omic technologies? Omic technologies generate masses
of data to characterise pools of biological molecules in cells and tis-
sues. Currently, the most widely used omic technology in medicine is
genomics – the characterisation of DNA sequence. This is often di-
vided into whole genome sequencing, exome sequencing (which se-
quences only that part of the human genome encoding proteins) and
targeted panels (sequencing small subsets of the genome that are as-
sociated with disease). Other omic technologies are rapidly catching
up to genomics, including: transcriptomics (RNA); proteomics (pro-
teins); metabolomics (metabolites); lipidomics (lipids); and glycomics
(carbohydrates). In all omic fields, the pace of technical advance is
rapid and dramatic. This is best illustrated by genomics, where the
shift from Sanger sequencing (sequencing one gene at a time) to mas-
sively parallel sequencing (capable of sequencing the whole genomes
of many patients simultaneously) has been described as ‘the most
transformative technological advance in biomedical science since the
development of the optical microscope’. 1,2
So where have medical genomics and related technologies reached
in Aotearoa New Zealand (NZ) and what is their future trajectory?
Genomic tests using single genes or small sets of genes have been
used in NZ for decades. Building on this expertise, NZ clinicians and
research scientists have started to use next-generation sequencing
in research studies where data can be fed back into patient care.
Local examples include paediatric exome sequencing analysis to di-
agnose rare syndromes, and sequencing of cancers. 3,4 These studies
are just a small part of a plethora of NZ medical-genomics initiatives,
including Auckland’s Genomics Into Medicine program and the na-
tional Genomics Aotearoa infrastructure. 5,6 In late 2018, a large-scale
collaboration between a network of NZ general practitioners and
an Australian genomics company was announced to undertake phar-
macogenomics testing (analysis of genetic variants that affect medica-
tions) for NZ patients. 7
However, despite this exciting activity, as a small nation with limited
resources, our implementation of omic technologies in health care
has lagged behind that of larger countries with similar health sys-
tems. For instance, as of December 2018, the United Kingdom (UK)
Genomics England organisation had sequenced 100,000 genomes
through its 13 Genomic Medicine Centres, facilitated by carefully gov-
erned partnerships with researchers and industry. 8 In Australia, the
2018 government budget provided a AU$500,000,000 investment
for genomics to save or transform the lives of 200,000 Australians
over ten years. 9 This seeded Australian Genomics, an alliance that
brings together 80 clinical and research organisations. Investments
in genomics for health and well-being are being made in many other
Western nations, complementing large health data research studies
such as ‘All of US’ in the United States of America.
An interesting example of medical genomics is Personal Genomic
Testing (PGT). PGT involves individuals ordering their own genomic
analysis online and is a rapidly growing industry. PGT is sometimes
perceived as a route to ‘precision health’ – optimising the wellness
of already healthy people. Although individuals using PGT are some-
times perceived as consumers of health care rather than patients,
PGT is rapidly evolving from a purely direct-to-consumer model, into
a model where health-care providers, directed by their patients, are
intimately involved. PGT can generate a range of information, includ-
ing: ancestry; predicted traits related to fitness and nutrition; phar-
macogenomics; and carrier status for inherited disease. 10 As a result,
medical practitioners play a difficult role in PGT, since only a subset
of this information has a clear medical indication, a scientific evidence
base, and rigorous regulation. 11 The scientific evidence base of some other information included in these tests is either still emerging or
downright absent. This complexity makes it difficult for individuals to
interpret their own PGT results using readily available, but sometimes
conflicting, web tools and blogs. The bandwidth of secondary-care
genetic counsellors and clinical geneticists to assist with PGT impe-
tration, and their knowledge about the ever-changing smorgasbord
of PGT available, is also limited. Therefore, primary-care doctors and
nurses will increasingly be called upon to order and interpret PGT.
This will require them to both learn new material, and use their ex-
isting skills and experience to communicate a nuanced interpretation
of the range of information provided by these tests in the context of
the person in front of them and their medical history. This is a current
reality, not just a future possibility. In a 2016–2017 survey of more
than 2800 Australians, ~10% had undertaken PGT; of these ~60%
would seek help from their general practitioner for interpretation of
medical aspects of the test results. Even more challenging for general
practice, ~ 25% would seek help from their general practitioner to
interpret non-medical test information such as ancestry and traits. 12
Another example of medical genomic technologies is Polygenic Risk
Scores (PRS). PRS involve a set of tens to hundreds of single nucleo-
tide variants in an individual’s genome that is being sequenced, which
are then summarised statistically. 13 PRS are emerging as important
predictive tools to guide screening programs, clinical interventions,
and life planning. 14 They are often more predictive of a disease than
any single genetic variant is alone. This is in line with large-scale ge-
nome-wide association studies, which frequently identify hundreds
of individually-weak genetic variants that interact synergistically to
strongly influence the incidence or outcome of a disease. PRS have
been used for everything from cardiovascular risk prediction to pre-
diction of breast cancer risk and sub-type. 15,16 However, with current
methods, the ‘uncertainty’ in PRS predictions at the level of an in-
dividual person can make them difficult to interpret. 14 In addition,
many PRS have been derived from limited populations, so biases and
inaccuracies can be introduced when they are then applied to popu-
lations with different genetic characteristics than those in which they
were generated. 17 Since most of these limitations appear resolvable,
especially if PRS are intelligently combined with existing clinical data,
PRS are a technology likely to reach further into both primary and
secondary care over the next five years.
The largest challenge we face today is generating a genomically-liter-
ate health care workforce and genomically-literate patients. The 2016
UK Chief Medical Officer’s report stated ‘modern genomic science
has evolved into a new concept of the “clinical team” which now in-
cludes: diagnostic staff in laboratories and imaging; computer scientists;
statisticians; (bio)informaticians’. 18 A major challenge seems to be cli-
nicians acquiring the data science skills needed to integrate genomic
information with health records, pathology tests, and their traditional
clinical acumen. However, this integration is essential, since medical
genomics is only effective when driven by, and interpreted alongside,
patient-specific clinical information. 1 For nurses, general practitioners,
pathologists, physicians, and surgeons to undertake this complex in-
tegration, significant capability development is often needed as part
of their continuing medical education. For instance, in February 2019,
Professor Eric Topol’s UK National Health Service review noted that,
‘within 20 years, 90% of all jobs in the NHS will require some element
of digital skills’, and that ‘all staff will need digital and genomics litera-
cy’. 19
The rate with which medical genomics is developing has forced us to
address issues in equity of access, genomic data governance, data se-
curity, and medical ethics, which have not previously been resolved. 20
For instance, current genomic technologies may serve some ethnic-
ities much better than others, due to disparities in the inclusion of
different ethnicities in the genomic databases used to interpret gene
sequence data. 21 This has encouraged a group of NZ genomic sci-
entists and clinicians to initiate a NZ ‘variome’ project, which will
be co-governed by Māori and Pacific People in order to define the
distribution of genomic features across NZers. 22 An additional chal-
lenge recently in the news is the ethical issues about genomically-di-
rected technologies for genetic repair in utero using CRISPR-Cas9
and related methods. 22 This has recently resulted in a World Health
Organisation panel proposing an international global registry for all
CRISPR-Cas9 experiments in humans. 23
This article has summarised the potential of medical genomics and
their challenges. Right back in 2016, Dame Sally Davies, the UK’s Chief
Medical Officer, said in her annual report ‘Genomics is not tomorrow.
It’s here today’. 18 However, it is clear that omics technologies have
reached the clinic in some places earlier than in others. A historical
quotation from the writer William Gibson aptly describes the current
state of omics in NZ health care: ‘The future is already here – it’s just
not very evenly distributed’. 24 In NZ, despite lagging behind some of
our large international partners, we can look forward to an exciting
future in medical genomics. Yet, in among this excitement, we need
to be vigilant that the genomics we do in NZ has a firm evidence base,
that it includes appropriate levels of co-governance with Māori, and
that we add data science to our list of required skills.
References
1. Harris G, O’Toole S, George P, Browett P, Print C. Massive parallel
sequencing of solid tumours – challenges and opportunities for
pathologists. Histopathology. 2017;70(1):123–33. DOI:10.1111/his.13067
2. Lek M, MacArthur D. The challenge of next generation sequencing
in the context of neuromuscular diseases. J Neuromuscul Dis.
2014;1(2):135–49.
3. McKeown C, Connors S, Stapleton R, Morgan T, Hayes I, Neas K,
et al. A pilot study of exome sequencing in a diverse New Zealand
cohort with undiagnosed disorders and cancer. J. R. Soc. N. Z.
2018;48(4):262–79. DOI:10.1080/03036758.2018.1464033
4. Lawrence B, Blenkiron C, Parker K, Tsai P, Fitzgerald S, Shields P, et
al. Recurrent loss of heterozygosity correlates with clinical outcome in
pancreatic neuroendocrine cancer. NPJ Genom Med. 2018;3:18. DOI:
10.1038/s41525-018-0058-3
5. Genomics into medicine [Internet]. The University of Auckland.
Available from: https://www.genomicsinmedicine.auckland.ac.nz
6. Genomics Aotearoa [Internet]. Genomics Aotearoa. Available
from: https://www.genomics-aotearoa.org.nz
7. Van Delden A. GPs at heart of NZ’s first large-scale genomics
programme [Internet]. New Zealand Doctor;2018. Available from:
https://www.nzdoctor.co.nz/article/news/gps-heart-nzs-first-large-
scale-genomics-programme
8. Genomics England [Internet]. England:Genomics England. About
Genomics England. Available from: https://www.genomicsengland.
co.uk/about-genomics-england/.
9. The Department of Health [Internet]. Australia:Australian
Government. National Health and Medical Industry Growth
Plan – Australian Genomics Health Futures Mission [updated 8 May
2018]. Available from: http://www.health.gov.au/internet/budget/
publishing.nsf/Content/budget2018-factsheet65.htm
10. Ramos E, Weissman SM. The dawn of consumer-directed
testing. Am J Med Genet C Semin Med Genet. 2018;178(1):89–97.
DOI:10.1002/ajmg.c.31603
11. Federal Drug Administration [Internet]. FDA allows marketing of
first direct-to-consumer tests that provide genetic risk information
for certain conditions; 2017. https://www.fda.gov/news-events/press-
announcements/fda-allows-marketing-first-direct-consumer-tests-
provide-genetic-risk-information-certain-conditions
12. Metcalfe SA, Hickerton C, Savard J, Stackpoole e, Tytherleigh
R, Tutty E, et al. Australians’ perspectives on support around use of
personal genomic testing: findings from the Genioz study. Eur J Med
Genet. 2018. DOI:10.1016/j.ejmg.2018.11.002
13. Spiliopoulou A, Nagy R, Bermingham ML, Huffman JE, Hayward
C, Vitart V, et al. Genomic prediction of complex human traits:
relatedness, trait architecture and predictive meta-models. Hum Mol
Genet. 2015;24(14):4167–82. DOI:10.1093/hmg/ddv145
14. Torkamani A, Wineinger NE, Topol EJ. The personal and clinical
utility of polygenic risk scores. Nat Rev Genet. 2018;19(9):581–90.
DOI:10.1038/s41576-018-0018-x
15. Benes LB, Brandt DJ, Brandt EJ, Davidson MH. How genomics is
personalizing the management of dyslipidemia and cardiovascular
disease prevention. Curr Cardiol Rep. 2018;20(12):138. DOI:10.1007/
s11886-018-1079-3
16. Mavaddat N, Michailidou K, Dennis J, Lush M, Fachal L, Lee A,
Tyrer JP, et al. Polygenic risk scores for prediction of breast cancer
and breast cancer subtypes. Am J Hum Genet. 2019;104(1):21-34.
DOI:10.1016/j.ajhg.2018.11.002
17. De La Vega FM, Bustamante CD. Polygenic risk scores: a biased
prediction? Genome Med. 2018;10(1):100. DOI:10.1186/s13073-018-
0610-x
18. Davies SC. Annual Report of the Chief Medical Officer 2016,
Generation Genome. [Internet]. London: Department of Health;
2017. Available from: https://www.ndph.ox.ac.uk/news/cmo_annual_
report_generation_genome.pdf
19. Topol E. Preparing the healthcare workforce to deliver the digital
future: an independent report on behalf of the Secretary of State
for Health and Social Care. [Internet] England:Health Education
England;2019. Available from: https://topol.hee.nhs.uk/wp-content/
uploads/HEE-Topol-Review-2019.pdf
20. Cornwall J, Slatter T, Guilford P, Print CG, Henaghan M, Wee
R. Culture, law, ethics, and social implications: is society ready for
advanced genomic medicine? Australas Med J. 2014;7(4):200-2.
DOI:10.4066/AMJ.2014.2069
21. Popejoy AB, Fullerton SM. Genomics is failing on diversity. Nature.
2016;538(7624):161-4. DOI:10.1038/538161a
22. Robertson SP, Hindmarsh JH, Berry S, Cameron VA, Cox MP,
Dewes O, et al. Genomic medicine must reduce, not compound,
health inequities: the case for hauora-enhancing genomic resources
for New Zealand. N Z Med J. 2018;131(1480):81-9.
23. Cohen J. WHO panel proposes new global registry for all CRISPR
human experiments. Science. Mar 2019. DOI:10.1126/science.aax3948
24. Gibson W. The science in science fiction. Talk of the Nation,
National Public Radio;1999.
Professor Cris Print
BHB, MBChB, PhD
Professor
Department of Molecular Medicine and Pathology
University of Auckland
The last five years has been an immensely exciting time for those
doctors and medical students who love new technologies, or more
importantly, who love what new technologies can do for patients. An
expanding range of technological advances competes for our atten-
tion, such as: 3-D printing of cells to generate replacement tissues;
augmented reality for clinical communication; health robots using arti-
ficial intelligence; cancer immunotherapy; and gene sequencing. Some
of these new technologies are destined to be used by all practitioners
in a specialty within the next five years. Other equally valuable tech-
nologies will remain research tools, used to build the evidence base
for future medical practice, but are unlikely to be used directly by
most doctors. Genomics and related ‘omic technologies’ sit in both
camps, rapidly penetrating into the mainstream of primary and sec-
ondary care, while in parallel, transforming our knowledge of disease
through research.
This article will argue that omic technologies are an advance that few
doctors and medical students can ignore. It will describe the general
landscape of omic technologies in New Zealand and overseas, then
use two examples of omic technologies to illustrate the potential of
this field: personal genomic testing; and polygenic risk scores. It will
then discuss two challenges that are currently being addressed: the
development of a genomically-literate health-care workforce; and is-
sues of equity. Pertinent web sites and peer-reviewed references will
be given for further reading.
What are omic technologies? Omic technologies generate masses
of data to characterise pools of biological molecules in cells and tis-
sues. Currently, the most widely used omic technology in medicine is
genomics – the characterisation of DNA sequence. This is often di-
vided into whole genome sequencing, exome sequencing (which se-
quences only that part of the human genome encoding proteins) and
targeted panels (sequencing small subsets of the genome that are as-
sociated with disease). Other omic technologies are rapidly catching
up to genomics, including: transcriptomics (RNA); proteomics (pro-
teins); metabolomics (metabolites); lipidomics (lipids); and glycomics
(carbohydrates). In all omic fields, the pace of technical advance is
rapid and dramatic. This is best illustrated by genomics, where the
shift from Sanger sequencing (sequencing one gene at a time) to mas-
sively parallel sequencing (capable of sequencing the whole genomes
of many patients simultaneously) has been described as ‘the most
transformative technological advance in biomedical science since the
development of the optical microscope’. 1,2
So where have medical genomics and related technologies reached
in Aotearoa New Zealand (NZ) and what is their future trajectory?
Genomic tests using single genes or small sets of genes have been
used in NZ for decades. Building on this expertise, NZ clinicians and
research scientists have started to use next-generation sequencing
in research studies where data can be fed back into patient care.
Local examples include paediatric exome sequencing analysis to di-
agnose rare syndromes, and sequencing of cancers. 3,4 These studies
are just a small part of a plethora of NZ medical-genomics initiatives,
including Auckland’s Genomics Into Medicine program and the na-
tional Genomics Aotearoa infrastructure. 5,6 In late 2018, a large-scale
collaboration between a network of NZ general practitioners and
an Australian genomics company was announced to undertake phar-
macogenomics testing (analysis of genetic variants that affect medica-
tions) for NZ patients. 7
However, despite this exciting activity, as a small nation with limited
resources, our implementation of omic technologies in health care
has lagged behind that of larger countries with similar health sys-
tems. For instance, as of December 2018, the United Kingdom (UK)
Genomics England organisation had sequenced 100,000 genomes
through its 13 Genomic Medicine Centres, facilitated by carefully gov-
erned partnerships with researchers and industry. 8 In Australia, the
2018 government budget provided a AU$500,000,000 investment
for genomics to save or transform the lives of 200,000 Australians
over ten years. 9 This seeded Australian Genomics, an alliance that
brings together 80 clinical and research organisations. Investments
in genomics for health and well-being are being made in many other
Western nations, complementing large health data research studies
such as ‘All of US’ in the United States of America.
An interesting example of medical genomics is Personal Genomic
Testing (PGT). PGT involves individuals ordering their own genomic
analysis online and is a rapidly growing industry. PGT is sometimes
perceived as a route to ‘precision health’ – optimising the wellness
of already healthy people. Although individuals using PGT are some-
times perceived as consumers of health care rather than patients,
PGT is rapidly evolving from a purely direct-to-consumer model, into
a model where health-care providers, directed by their patients, are
intimately involved. PGT can generate a range of information, includ-
ing: ancestry; predicted traits related to fitness and nutrition; phar-
macogenomics; and carrier status for inherited disease. 10 As a result,
medical practitioners play a difficult role in PGT, since only a subset
of this information has a clear medical indication, a scientific evidence
base, and rigorous regulation. 11 The scientific evidence base of some other information included in these tests is either still emerging or
downright absent. This complexity makes it difficult for individuals to
interpret their own PGT results using readily available, but sometimes
conflicting, web tools and blogs. The bandwidth of secondary-care
genetic counsellors and clinical geneticists to assist with PGT impe-
tration, and their knowledge about the ever-changing smorgasbord
of PGT available, is also limited. Therefore, primary-care doctors and
nurses will increasingly be called upon to order and interpret PGT.
This will require them to both learn new material, and use their ex-
isting skills and experience to communicate a nuanced interpretation
of the range of information provided by these tests in the context of
the person in front of them and their medical history. This is a current
reality, not just a future possibility. In a 2016–2017 survey of more
than 2800 Australians, ~10% had undertaken PGT; of these ~60%
would seek help from their general practitioner for interpretation of
medical aspects of the test results. Even more challenging for general
practice, ~ 25% would seek help from their general practitioner to
interpret non-medical test information such as ancestry and traits. 12
Another example of medical genomic technologies is Polygenic Risk
Scores (PRS). PRS involve a set of tens to hundreds of single nucleo-
tide variants in an individual’s genome that is being sequenced, which
are then summarised statistically. 13 PRS are emerging as important
predictive tools to guide screening programs, clinical interventions,
and life planning. 14 They are often more predictive of a disease than
any single genetic variant is alone. This is in line with large-scale ge-
nome-wide association studies, which frequently identify hundreds
of individually-weak genetic variants that interact synergistically to
strongly influence the incidence or outcome of a disease. PRS have
been used for everything from cardiovascular risk prediction to pre-
diction of breast cancer risk and sub-type. 15,16 However, with current
methods, the ‘uncertainty’ in PRS predictions at the level of an in-
dividual person can make them difficult to interpret. 14 In addition,
many PRS have been derived from limited populations, so biases and
inaccuracies can be introduced when they are then applied to popu-
lations with different genetic characteristics than those in which they
were generated. 17 Since most of these limitations appear resolvable,
especially if PRS are intelligently combined with existing clinical data,
PRS are a technology likely to reach further into both primary and
secondary care over the next five years.
The largest challenge we face today is generating a genomically-liter-
ate health care workforce and genomically-literate patients. The 2016
UK Chief Medical Officer’s report stated ‘modern genomic science
has evolved into a new concept of the “clinical team” which now in-
cludes: diagnostic staff in laboratories and imaging; computer scientists;
statisticians; (bio)informaticians’. 18 A major challenge seems to be cli-
nicians acquiring the data science skills needed to integrate genomic
information with health records, pathology tests, and their traditional
clinical acumen. However, this integration is essential, since medical
genomics is only effective when driven by, and interpreted alongside,
patient-specific clinical information. 1 For nurses, general practitioners,
pathologists, physicians, and surgeons to undertake this complex in-
tegration, significant capability development is often needed as part
of their continuing medical education. For instance, in February 2019,
Professor Eric Topol’s UK National Health Service review noted that,
‘within 20 years, 90% of all jobs in the NHS will require some element
of digital skills’, and that ‘all staff will need digital and genomics litera-
cy’. 19
The rate with which medical genomics is developing has forced us to
address issues in equity of access, genomic data governance, data se-
curity, and medical ethics, which have not previously been resolved. 20
For instance, current genomic technologies may serve some ethnic-
ities much better than others, due to disparities in the inclusion of
different ethnicities in the genomic databases used to interpret gene
sequence data. 21 This has encouraged a group of NZ genomic sci-
entists and clinicians to initiate a NZ ‘variome’ project, which will
be co-governed by Māori and Pacific People in order to define the
distribution of genomic features across NZers. 22 An additional chal-
lenge recently in the news is the ethical issues about genomically-di-
rected technologies for genetic repair in utero using CRISPR-Cas9
and related methods. 22 This has recently resulted in a World Health
Organisation panel proposing an international global registry for all
CRISPR-Cas9 experiments in humans. 23
This article has summarised the potential of medical genomics and
their challenges. Right back in 2016, Dame Sally Davies, the UK’s Chief
Medical Officer, said in her annual report ‘Genomics is not tomorrow.
It’s here today’. 18 However, it is clear that omics technologies have
reached the clinic in some places earlier than in others. A historical
quotation from the writer William Gibson aptly describes the current
state of omics in NZ health care: ‘The future is already here – it’s just
not very evenly distributed’. 24 In NZ, despite lagging behind some of
our large international partners, we can look forward to an exciting
future in medical genomics. Yet, in among this excitement, we need
to be vigilant that the genomics we do in NZ has a firm evidence base,
that it includes appropriate levels of co-governance with Māori, and
that we add data science to our list of required skills.
References
1. Harris G, O’Toole S, George P, Browett P, Print C. Massive parallel
sequencing of solid tumours – challenges and opportunities for
pathologists. Histopathology. 2017;70(1):123–33. DOI:10.1111/his.13067
2. Lek M, MacArthur D. The challenge of next generation sequencing
in the context of neuromuscular diseases. J Neuromuscul Dis.
2014;1(2):135–49.
3. McKeown C, Connors S, Stapleton R, Morgan T, Hayes I, Neas K,
et al. A pilot study of exome sequencing in a diverse New Zealand
cohort with undiagnosed disorders and cancer. J. R. Soc. N. Z.
2018;48(4):262–79. DOI:10.1080/03036758.2018.1464033
4. Lawrence B, Blenkiron C, Parker K, Tsai P, Fitzgerald S, Shields P, et
al. Recurrent loss of heterozygosity correlates with clinical outcome in
pancreatic neuroendocrine cancer. NPJ Genom Med. 2018;3:18. DOI:
10.1038/s41525-018-0058-3
5. Genomics into medicine [Internet]. The University of Auckland.
Available from: https://www.genomicsinmedicine.auckland.ac.nz
6. Genomics Aotearoa [Internet]. Genomics Aotearoa. Available
from: https://www.genomics-aotearoa.org.nz
7. Van Delden A. GPs at heart of NZ’s first large-scale genomics
programme [Internet]. New Zealand Doctor;2018. Available from:
https://www.nzdoctor.co.nz/article/news/gps-heart-nzs-first-large-
scale-genomics-programme
8. Genomics England [Internet]. England:Genomics England. About
Genomics England. Available from: https://www.genomicsengland.
co.uk/about-genomics-england/.
9. The Department of Health [Internet]. Australia:Australian
Government. National Health and Medical Industry Growth
Plan – Australian Genomics Health Futures Mission [updated 8 May
2018]. Available from: http://www.health.gov.au/internet/budget/
publishing.nsf/Content/budget2018-factsheet65.htm
10. Ramos E, Weissman SM. The dawn of consumer-directed
testing. Am J Med Genet C Semin Med Genet. 2018;178(1):89–97.
DOI:10.1002/ajmg.c.31603
11. Federal Drug Administration [Internet]. FDA allows marketing of
first direct-to-consumer tests that provide genetic risk information
for certain conditions; 2017. https://www.fda.gov/news-events/press-
announcements/fda-allows-marketing-first-direct-consumer-tests-
provide-genetic-risk-information-certain-conditions
12. Metcalfe SA, Hickerton C, Savard J, Stackpoole e, Tytherleigh
R, Tutty E, et al. Australians’ perspectives on support around use of
personal genomic testing: findings from the Genioz study. Eur J Med
Genet. 2018. DOI:10.1016/j.ejmg.2018.11.002
13. Spiliopoulou A, Nagy R, Bermingham ML, Huffman JE, Hayward
C, Vitart V, et al. Genomic prediction of complex human traits:
relatedness, trait architecture and predictive meta-models. Hum Mol
Genet. 2015;24(14):4167–82. DOI:10.1093/hmg/ddv145
14. Torkamani A, Wineinger NE, Topol EJ. The personal and clinical
utility of polygenic risk scores. Nat Rev Genet. 2018;19(9):581–90.
DOI:10.1038/s41576-018-0018-x
15. Benes LB, Brandt DJ, Brandt EJ, Davidson MH. How genomics is
personalizing the management of dyslipidemia and cardiovascular
disease prevention. Curr Cardiol Rep. 2018;20(12):138. DOI:10.1007/
s11886-018-1079-3
16. Mavaddat N, Michailidou K, Dennis J, Lush M, Fachal L, Lee A,
Tyrer JP, et al. Polygenic risk scores for prediction of breast cancer
and breast cancer subtypes. Am J Hum Genet. 2019;104(1):21-34.
DOI:10.1016/j.ajhg.2018.11.002
17. De La Vega FM, Bustamante CD. Polygenic risk scores: a biased
prediction? Genome Med. 2018;10(1):100. DOI:10.1186/s13073-018-
0610-x
18. Davies SC. Annual Report of the Chief Medical Officer 2016,
Generation Genome. [Internet]. London: Department of Health;
2017. Available from: https://www.ndph.ox.ac.uk/news/cmo_annual_
report_generation_genome.pdf
19. Topol E. Preparing the healthcare workforce to deliver the digital
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