Population Simulation to Optimise Study Designs and Estimate Polygenic Disease Risk/Resilience in Aotearoa Māori Populations

For commonly occurring polygenically inherited conditions such as gout, type 2 diabetes, and cardiovascular conditions, disease risk/resilience estimates have most often been derived from GWAS (genome-wide association studies). Such studies require large sample sizes (n > 10⁴ participants) genotyped with 10⁴-10⁷ DNA markers.

These datasets often do not include indigenous peoples, who can have important genetic differences from more commonly represented populations of predominantly European descent. Moreover, existing datasets from Māori (and Pasifika) domiciled in New Zealand are few, and those that could be utilised consist of fewer than two thousand individuals - and thus are underpowered for clinically accurate disease risk/resilience prediction. In addition, establishing sufficiently large GWAS is unlikely in Aotearoa/NZ because of substantive costs associated with generating genotypic data and reluctance of many Māori to participate in such studies. 

In order to offset further health inequities arising from a lack of Māori-specific DR prediction models, new studies are required. Such studies require both (a) optimal designs that incorporate known genetic relationships on non-genotyped as well as genotyped individuals, and (b) analytical methods that more accurately predict phenotype than GWAS-based methods such as polygenic risk scores.

We have used a population simulator (SLiM) to model genetic structures of Māori communities (i.e., whānau/hapū/iwi), incorporating estimates of effective population sizes prior to European admixture, as well as post-colonisation admixture with Europeans. Through NeSI, we are using these simulations to explore what features of study design and analytical methods lead to optimal disease risk/resilience prediction. I will illustrate and present current results on this.

ABOUT THE AUTHORS

Dr Alastair Lamont is a Postdoctoral Fellow with the Department of Mathematics and Statistics at the University of Otago. He is interested in applying statistical methodologies to improve prediction of human genetic traits, particularly in indigenous peoples.

Professor Mik Black is a statistician, bioinformatician and data scientist whose research focuses on the development of methods for the analysis of genomic data, with a strong emphasis on cancer and other human diseases. A common theme is the use of techniques that allow high-dimensional and often very disparate data sets to be combined in ways that provide new insights into disease development and progression. In addition to his own work, Mik has been heavily involved in establishing national research infrastructure in high performance computing through the NZ eScience Infrastructure, and in genomics and bioinformatics through Genomics Aotearoa, where he is Co-Director and chair of the Bioinformatics Leadership Team.

Associate Professor Phillip Wilcox’s Māori tribal affiliations are Ngāti Rakaipaaka, Ngāti Kahungunu ki te Wairoa, Rongomaiwahine and Te Aitanga a Mahaki. He is based in the University of Otago’s Department of Mathematics and Statistics, and has experience in applied genomics and statistical genetics, as well as engagement with indigenous communities regarding gene technologies. He is also an Affiliate of the University of Otago’s Bioethics Centre, and is the current convenor of MapNet, a NZ-wide collective of gene mapping scientists, and led the Virtual Institute for Statistical Genetics from 2008 to 2013. He is also a Deputy Director of the Maurice Wilkins Centre. For over 20 years he has worked in the interface of genetic sciences and Te Ao Māori, and co-leads four genomics-based projects focussing on Māori health. A/Prof Wilcox  has also worked on genetics of plant species (particularly forest trees) and Māori health. He also co-initiated the Summer Internship of iNdigenous peoples in Genomics Aotearoa (SING-Aotearoa), and was a member of the Health Research Council of New Zealand’s Ethics Committee which oversees New Zealand’s institutional and regional ethics committees. 

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