Genetic Testing and Personalised Medicine: Ethical, Social and Regulatory Challenges

Question 1

Definition of geneticisation

Answer 1

Tendency to define differences between individuals as a result of genetics

Question 2

Definition of biosociality

Answer 2

Social bonds created between people with similarities in genetic characteristics

Question 3

Definition of personalised medicine

Answer 3

Matching drug treatments to groups with certain genetic markers

Question 4

Definition of stratified medicine

Answer 4

Optimise treatment by identifying subgroups with similar disease profiles/drug responses

Question 5

Definition of precision medicine

Answer 5

Tailor treatment to patients during prevention, diagnosis, treatment and monitoring
Uses data beyond the clinic

Question 6

Definition of individual medicine

Answer 6

Consider why this patient is different and adapt treatment to match characteristics of the patient

Question 7

Definition of filter bubble

Answer 7

Only info that is relevant to the use can be seen

Question 8

Definition of social exclusion effect

Answer 8

Only those with the resources can contribute to the data set

Question 9

What are the 3 viewpoints of ethics

Answer 9

Medical ethics
-philosophers and physicians

Bioethics
-identify and address issues raised by genomic research

Social sciences
-explore political, social, individual conditions and practices that shape what new technology and science is adopted by society

Question 10

What were the 2 possible effects of genetic testing on identity

Answer 10

Geneticisation of society => enlightened geneticisation

• expected the increased tendency to define differences between individuals as a result of genetics
• however in complex traits that have several factors, genetics is assumed to play the most dominant role

Biosociality
-genetic characteristic similarities => social groups built around these differences

Question 11

What are the 2 main social effects of genetic testing on identity and behaviour

Answer 11

People often integrate genetic info into existing identities and narratives

Growing proliferation of genetic predisposition testing for complex diseases does not affect behaviour

We have no good data on effects of identities and long term strategies

Question 12

What are the 2 negative personal effects of genetic testing

Answer 12

People who are at risk for an illness => harm caused by excess worry and unecessary treatment

People are disempowered by their result

Question 13

What are the 2 positive personal effects of genetic testing

Answer 13

More participation, personalised, preventative, predictive

People are empowered by results, somatic individualisation

Question 14

What are the 4 main effects of genetic testing on justice

Answer 14

Risk
Discrimination
Individualisation of duties and responsibilities
End of genetic exceptionalisation

Question 15

Describe the risks that can arise as a result of genetic testing

Answer 15

People in lower SE groups have higher health risks and worse health outcomes

Question 16

Describe the discrimination that can arise as a result of genetic testing

Answer 16

Fear of genetic discrimination by employers, insurance

Unlikely as info less useful than family history

Current data not representative of all ethinicities, non white people may not benefit from knowing their result

Question 17

Describe the individualisation of duties and responsibilities that can arise as a result of genetic testing

Answer 17

People are forced to become more responsible for their actions and consequences for not changing their behaviour as they have a genetic predisposition

Question 18

Describe the end of genetic exceptionalisation that can arise as a result of genetic testing

Answer 18

Other data such as family history are just as sensitive and predictive as genetic info

Question 19

Describe how patient participation in healthcare has changed over time

Answer 19

Increased emphasis of patient participation and shared decision making

Increased use of ‘beyond the clinic’ health data generation
-people add their own symptoms and treatments to an online database

Question 20

What are the effects of increased participation in health

In what parts of healthcare are they participating in

Answer 20

Increased self diagnoses especially if people cannot access HC services easily and buy drugs online

Increased participation in

• data generation
• analysis
• interpretation
• translation

Question 21

What is personalised medicine

What is the main criticism of personalised medicine

Answer 21

Initially focus on matching drug treatments to groups with certain genetic markers

Not tailored to the patient
Having a specific genetic marker does not necasserily mean that you will benefit

Question 22

What is stratified medicine

Answer 22

Designed to optimise treatment by identifying subgroups with similar disease profiles/drug responses

Question 23

What is precision medicine

Answer 23

Tailoring treatment to patients during prevention, diagnosis, treatment, monitoring

Uses data beyond the clinic

Question 24

What is individualised medicine

Answer 24

Takes into consideration why this patient is different and adapt treatment to match characteristics of the patient

Question 25

Where does the data for precision medicine come from

Answer 25

Data from medical science and research
Quanitification of info in clinical domain
Outside scientific and clinical domain (social media, apps, online databases)

Question 26

What are the 5 main ethical and social challenges of genetic testing

Answer 26

Autonomy and rights to know/not know

Rights and interests of family members

Data protection

Avoiding discrimination

Effects on identity

Question 27

What are some social, ethical and regulatory challenges of genetic testing

Answer 27

More emphasis on collaboration and distribution of power of data

Data analysis has become a market

Relies on patient contributions

Must not forget the importance of other types of evidence

How to include info that is important to the patient

Who controls personal health data and algorithms

Filter bubble and social exclusion effect