The concept of precision medicine has really taken hold in recent years. However, the majority of current studies have used European and US datasets to identify how different diseases can respond to different treatments.

A new “gene bank” for the indigenous people of New Zealand, the Māori, has been approved with a $5m injection from the government, and will be led by Genomics Aoteroa Rakeiora in an aim to identify unique genetic differences prevalent in the Maori population that could play a role in the treatment of a number of diseases.

It is well known that genetics can play a role in therapeutic response, and different regional populations are at higher risks for different types of diseases and can respond differently to medicines. Data banks can provide insights into how different populations may respond to a particular drug, and have already been successful in developing a new generation of cancer treatments that work better than previous treatments.

A previous ground-breaking discovery by the Ngāti Porou Hauroa Charitable Trust and researchers from Auckland and Otago universities identified a gene variant that only exists in Māori and Pacific people, and further studies could identify more gene variants that could potentially be used in the planning of precision medicine.

New Zealand’s first data bank will be led by geneticist Professor Stephen Robertson from Otago University alongside the Ngāti Porou Hauroa Trust. To begin with, 500 local people in the Tairawhiti district will undergo genome sequencing, and this data will be combined with personal health records to investigate a link between the response to different prescribed drugs and certain genetic factors.

Using genomic data from 100 cancer patients in Auckland, a second project led by Professor Cristin Print at the University of Auckland will be carried out and will use genomic data to deliver the best possible treatment to each patient.

New Zealand’s first “gene bank” is a step in the right direction for precision medicine, and in the future will widen the study to predict risk levels for different diseases within the population.

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