The GLAD study’s commitment to Diversity, Inclusion and Ethical Science
We recognise that our research involves sensitive research topics, thus we have set out our commitments to our participants below:
*we will be creating FAQs for specific aspects of the below statements. Until then, please email us on firstname.lastname@example.org with any queries.
GLAD’s Commitment to participants
- The NIHR BioResource Centre Maudsley, and its studies in the NIHR Mental Health BioResource such as GLAD, recognises that research involving genetics is especially sensitive and as such, we have a responsibility to be transparent about how we collect, analyse and store your data, as well as who has access to this.
- NIHR Mental Health BioResource data is stored securely in accordance with the necessary legislation. We take the protection and privacy of your data very seriously, and you can find out how we do this here https://gladstudy.org.uk/confidentiality/.
- New collaborators’ requests to use the research data we collect will be closely examined by a multidisciplinary data access panel, and will be assessed for research justification and potential, and ethicality. Where appropriate, public representatives from the groups involved in research will sit on this panel.
- Any data that is shared with researchers or companies for ethically approved projects will be securely transferred with your personal details (such as name) removed prior to sharing it, so that it is anonymous to anyone outside NIHR Mental Health BioResource or NIHR BioResource Centre Maudsley teams. If for some reason we need to pass on your contact details, for example so a clinical trial team can contact, we will ensure you are contacted by us to specifically consent to this.
- Specifically, we commit that NIHR Mental Health BioResource or NIHR BioResource Centre Maudsley will never knowingly share data with projects that intend to link genetics to race. Researchers within the NIHR Mental Health BioResource or NIHR BioResource Centre Maudsley at King’s College London firmly state that the social construct of race is not based on science and that this idea has no place in our research.
- We are committed to building relationships with underrepresented communities, to achieve better diversity within our research studies and research staff.
Population Genetics: Race, Ethnicity and Ancestry
How will we use information about self-reported ancestry?
- Analyses that stratify by self-reported ethnicity will only be done to compare social risk factors and specific symptoms or disorders rates between groups. The way we ask people about their ethnicity follows NHS guidance and practise, and allows us to compare the cohorts we recruit with NHS baseline data. These analyses will be undertaken in collaboration with individuals from representative communities to ensure fair and balanced assessment of results.
How do we use population genetics in our studies?
Population genetics is the study of genetic variation within populations, and involves the examination and modelling of changes in the frequencies of genetic variants in populations over space (distance) and time (measured in thousands of years). Specifically, it examines genetic variations that differ in their frequency between (1) European ancestral populations, (2) diverse South Asican ancestral populations and (3) highly diverse African ancestral populations as well as other populations.
[You’ll find we use the term “ancestry” over “race” or “ethnicity” when discussing genetics research. Race and ethnicity are not valid indicators for any underlying biological differences between individuals (e.g., genetics). For these reasons, racial categories and ethnic groups should only be used when discussing social constructs such as racial or cultural experiences.]
Research has shown that the vast majority of genetic variations that differ between ancestries have no association with either diseases or traits. So, if a disease occurs more frequently in a certain group, it is not definitively because this group has a genetic predisposition that makes them more vulnerable to this disease, but rather it would be important and helpful to examine both genetic and social/environmental potential risk factors. To prevent false positive and false negative findings that mistake genetic differences for social differences, or the reverse, we use the methods of population genetics to adjust for population ancestry in our analyses.
Most frequently, we use population genetic methods to prevent us reaching false conclusions due to slightly different ancestry between the groups we wish to compare in a study, such as when we compare cases of depression to an unaffected control group. Another benefit is that, by allowing us to find true associations in different ancestry groups, they allow the discovery of new biological causes of the disorders we look at. We know if we look at just one population we will not be able to find all of the biological mechanisms relevant to a disorder, including mechanisms that may suggest new treatments or interventions.
The NIHR Mental Health BioResource and NIHR BioResource Centre Maudsley team commits to:
- designing research that is thoughtful and proactive regarding diverse recruitment.
- actively pursuing diversity within the team.
- acknowledging and supporting all forms of diversity, including within race, gender, sexuality, socio-economic status and the intersectionality of diversity.
- standing with the Black Lives Matter movement.
- supporting the LGBTQIA+ community and acknowledging the difference between sex and gender.
- influencing local research practice via participation in local Anti-Racism Groups.
- constantly learning more about issues of diversity, cultural sensitivity and regularly discussing these within team meetings.
- ensuring our staff group include those with lived experience of these issues, working with external highly experienced consultants on sensitive issues when necessary.
- sharing and learning from best practice on all matters of inclusivity within our department and across wider KCL faculties.
- supporting individuals from groups that are under-represented in academia, and to offer mentoring on a case by case basis (please email email@example.com if interested).
- We will publish a yearly report detailing our efforts and work within diversity and inclusion.
- We particularly focus on collaborative approaches, across populations and cultures, and inclusive, replicable science. We discuss open science strategies including preprints, the Open Science Framework, open access publication and publishing of analysis scripts regularly in the group, with more established team members providing support and examples to newer members of the lab.
- From 2021, we are pre-registering all new analysis plans on the Open Science Framework and support local open, reproducible science networks.
- Lab members are actively facilitating collaborative research practices to improve peer based review and sharing of code.
- We support sharing of code and data (where possible) on publication of our papers.
- We archive our papers at the point of submission whenever possible.
- In general we view mistakes made by those at all levels as opportunities for shared learning; this is not a blame culture.