Phillip Wilcox’s research while affiliated with University of Otago and other places

Aims/hypothesis Metformin is an important first-line treatment for type 2 diabetes and acts by increasing the body’s ability to dispose of glucose. Metformin’s efficacy can be affected by genetic variants in the transporters that regulate its uptake into cells. The SLC22A3 gene (also known as EMT; EMTH; OCT3) codes for organic cation transporter 3 (OCT3), which is a broad-specificity cation transporter that also transports metformin. Most SLC22A3 variants reduce the rate of metformin transport but the rs8187715 variant (p.Thr44Met) is reported to increase uptake of metformin in vitro. However, the impact of this on in vivo metformin transport and efficacy is unknown. Very few carriers of this variant have been reported globally, but, notably, all were of Pacific Island descent. Therefore, this study aims to understand the prevalence of this variant in Polynesian peoples (Māori and Pacific peoples) and to understand its impact on metformin transport and efficacy in vivo. Methods rs8187715 was genotyped in 310 individuals with Māori and Pacific ancestry recruited in Aotearoa New Zealand. To study this variant in a physiological context, an orthologous knockin mouse model with C57BL/6J background was used. Pharmacokinetic analysis compared uptake rate of metformin into tissues. Plasma growth/differentiation factor 15 (GDF-15) was also measured as a marker of metformin efficacy. Glucose and insulin tolerance was assessed after acute or sustained metformin treatment in knockin and wild-type control mice to examine the impact of the variant on metformin’s glycaemic control. Results The minor allele frequency of this variant in the Māori and Pacific participants was 15.4%. There was no association of the variant with common metabolic parameters including diabetes status, BMI, blood pressure, lipids, or blood glucose and HbA1c. However, in the orthologous knockin mouse model, the rate of metformin uptake into the blood and tissues was increased. Acute metformin dosing increased insulin sensitivity in variant knockin mice but this effect was lost after longer-term metformin treatment. Metformin’s effects on GDF-15 levels were also lost in variant knockin mice with longer-term metformin treatment. Conclusions/interpretation These data provide evidence that the SLC22A3 rs8187715 variant accelerates metformin uptake rate in vivo. While this acutely improves insulin sensitivity, there was no increased effect of metformin with longer-term dosing. Thus, our finding of a high prevalence of this variant specifically in Māori and Pacific peoples identifies it as a potential population-specific pharmacogenetic marker with potential to guide metformin therapy in these peoples. Graphical Abstract

Māori perspectives on gene technologies are evolving, and traditional cultural constructs continue to inform a wide diversity of views. Here we summarise a series of research activities aimed at identifying evolving Māori perspectives on gene editing and how these inform engagement at the co-innovation interface.

Achieving a diagnosis for Indigenous people living with a rare, often genetic, disease is crucial for equitable healthcare. The International Rare Disease Research Consortium convened a global Task Force to bridge the gap in diagnosing Indigenous rare diseases, and identify solutions to tackle the health inequity faced by Indigenous people.

Aims Monogenic diabetes accounts for 1-2% of diabetes cases yet is often misdiagnosed as type 2 diabetes. The aim of this study was to examine in Māori and Pacific adults clinically diagnosed with type 2 diabetes within 40 years of age, (a) the prevalence of monogenic diabetes in this population (b) the prevalence of beta-cell autoantibodies and (c) the pre-test probability of monogenic diabetes. Methods Targeted sequencing data of 38 known monogenic diabetes genes was analyzed in 199 Māori and Pacific peoples with BMI of 37.9 ± 8.6 kg/m² who had been diagnosed with type 2 diabetes between 3 and 40 years of age. A triple-screen combined autoantibody assay was used to test for GAD, IA-2, and ZnT8. MODY probability calculator score was generated in those with sufficient clinical information (55/199). Results No genetic variants curated as likely pathogenic or pathogenic were found. One individual (1/199) tested positive for GAD/IA-2/ZnT8 antibodies. The pre-test probability of monogenic diabetes was calculated in 55 individuals with 17/55 (31%) scoring above the 20% threshold considered for diagnostic testing referral. Discussion Our findings suggest that monogenic diabetes is rare in Māori and Pacific people with clinical age, and the MODY probability calculator likely overestimates the likelihood of a monogenic cause for diabetes in this population.

Identifying population-specific genetic variants associated with disease and disease-predisposing traits is important to provide insights into the genetic determinants of health and disease between populations, as well as furthering genomic justice. Various common pan-population polymorphisms at CETP associate with serum lipid profiles and cardiovascular disease. Here, sequencing of CETP identified a missense variant rs1597000001 (p.Pro177Leu) specific to Māori and Pacific people that associates with higher HDL-C and lower LDL-C levels. Each copy of the minor allele associated with higher HDL-C by 0.236 mmol/L and lower LDL-C by 0.133 mmol/L. The rs1597000001 effect on HDL-C is comparable with CETP Mendelian loss-of-function mutations that result in CETP deficiency, consistent with our data, which shows that rs1597000001 lowers CETP activity by 27.9%. This study highlights the potential of population-specific genetic analyses for improving equity in genomics and health outcomes for population groups underrepresented in genomic studies.

Genomic prediction using single nucleotide polymorphisms (SNPs) has been implemented in plant breeding. However, applications of this method have been constrained for allogamous plant species, by factors including a higher level of diversity at the sequence level, and rapid linkage disequilibrium decay. To overcome these challenges, we proposed to utilise ShortHaps (i.e., Short Haplotypes) in genomic prediction. We define ShortHaps as multiple SNPs located within each GBS read. Using ShortHaps will, in theory, improve the accuracy of genomic prediction as they are more likely to be in stronger linkage disequilibrium (LD) with causative variants. In this talk, we will describe a bioinformatics workflow to identify ShortHaps from ryegrass GBS data, and methods to utilise ShortHaps in genomic prediction.

Objective: The minor allele (A) of the rs373863828 variant (p.Arg457Gln) in CREBRF is restricted to indigenous peoples of the Pacific islands (including New Zealand Māori and peoples of Polynesia), with a frequency of up to 25% in these populations. This allele associates with a large increase in body mass index (BMI) but with significantly lower risk of type-2 diabetes (T2D). It remains unclear whether the increased BMI is driven by increased adiposity or by increased lean mass. Methods: We undertook body composition analysis using DXA in 189 young men of Māori and Pacific descent living in Aotearoa New Zealand. Further investigation was carried out in two orthologous Arg458Gln knockin mouse models on FVB/NJ and C57BL/6j backgrounds. Results: The rs373863828 A allele was associated with lower fat mass when adjusted for BMI (p < 0.05) and was associated with significantly lower circulating levels of the muscle inhibitory hormone myostatin (p < 0.05). Supporting the human data, significant reductions in adipose tissue mass were observed in the knockin mice. This was more significant in older mice in both backgrounds and appeared to be the result of reduced age-associated increases in fat mass. The older male knockin mice on C57BL/6j background also had increased grip strength (p < 0.01) and lower levels of myostatin (p < 0.05). Conclusion: Overall, these results prove that the rs373863828 A-allele is associated with a reduction of myostatin levels which likely contribute to an age-dependent lowering of fat mass, at least in males.

Sequencing of CETP in Māori and Pacific peoples identified a common (MAF ~2.4%-5.4%) population-specific missense variant (rs1597000001, CETP:c.530C>T p.Pro177Leu) that associates with higher HDL-C levels ("β" ̂HDLmeta = 0.236 mmol/L [95% CI 0.211; 0.260]) and lower LDL-C ("β" ̂LDLmeta = -0.133 mmol/L [95% CI -0.209; -0.058]). In a subsample of the study cohort (n = 11), heterozygous carriers of the population-specific variant had lower plasma CETP activity (P = 0.028). Our study identifies a population-specific missense variant in CETP which lowers CETP activity with an effect on HDL-C that is comparable to Mendelian CETP loss-of-function mutations.

Existing methods of SNP calling aim to optimise the efficiency by aggressively filtering and trimming GBS data. Conversely, we aim to capture more variation by following a mild workflow, which we have named snpGBS. Our results showed that including the extra variation yields a substantially larger number of SNPs from a ryegrass GBS dataset, which raised the question of whether this method is more appropriate for identifying SNPs from ryegrass GBS data.

Haplotypes are defined as a type of genomic variant at the multiple-nucleotide level. Previous efforts focused extensively on phased haplotypes or “long-haplotypes”, where remarkable progress has been made in genome-wide association and prediction studies, particularly in human genetics research. Single nucleotide polymorphisms (SNPs), in contrast, are still the marker of choice in plant and animal breeding. Reduced-representation sequencing methods, such as Genotyping-by-Sequencing, offer a cost-effective way to obtain genomic information for many non-model species of agricultural importance. Such genomic information has been successfully utilised in genome-wide association and prediction at the SNP level. Multiple SNPs located within each GBS fragment also naturally form and ascertain short haplotypes (or “shortHaps”). We hypothesise that shortHaps are more informative than their contributing (independent) SNPs, and therefore a more powerful marker system for genomic prediction. To investigate the usefulness of shortHaps, we developed a software with the team at AgResearch to simulate GBS data, and collaborated with a group from ILVO, Belgium to develop a bioinformatics workflow to capture shortHaps. We also extended the SNP-based approach to construct shortHap-based genomic relationship matrices (GRMs). Using the simulated data, these shortHap-based GRMs can then be used for genomic prediction and compared to the results obtained using SNP-based approach.