Phillip Wilcox

Extraction of high purity genomic DNA from pine for use in a high-throughput Genotyping Platform

Standard protocols for extracting genomic DNA from Pinus radiata D. Don needles, such as CTAB-based methods, can yield large quantities of DNA. However, final DNA purity can be an issue due to carry over of contaminants that can impede accurate high throughput genotyping. This study evaluated eight DNA extraction and purification protocols to determine which method provided the greatest improvement in call rates and accuracy when using the Sequenom iPLEX® Gold MassARRAY® genotyping technology. Of the methods tested, genomic DNA extracted using the Machery-Nagel NucleoSpin®-96 Plant II kit performed the best overall, and was more efficiently and accurately genotyped than genomic DNA extracted using the standard CTAB method. This study also demonstrated that the quality and assay performance of CTAB-extracted genomic DNA is greatly improved by further purification with the Qiagen® QIAquick 96 PCR Purification kit. Using these improvements, the Sequenom iPLEX® Gold MassARRAY® genotyping technology is now a viable option for genotyping plant genomes such as Pinus radiata.

Engaging Māori in biobanking and genomic research: a model for biobanks to guide culturally informed governance, operational, and community engagement activities

Purpose:He Tangata Kei Tua, a relationship model for biobanks, was developed to facilitate best practice in addressing Māori ethical concerns by guiding culturally informed policy and practice for biobanks in relation to governance, operational, and community engagement activities.Methods:The model is based on key issues of relevance to Māori that were identified as part of the Health Research Council of New Zealand–funded research project, Te Mata Ira (2012–2015).Results:This project identified Māori perspectives on biobanking and genetic research, and along with tikanga Māori it developed cultural guidelines for ethical biobanking and genetic research involving biospecimens. The model draws on a foundation of mātauranga (Indigenous knowledge) and tikanga Māori (Māori protocols and practices) and will be useful for biobanks, researchers, ethics committee members, and those who engage in consultation or advice about biobanking in local, regional, national, or international settings.Conclusion:This article describes the model and considers the policy and practice implications for biobanks seeking to address Māori ethical concerns. Although the model has focused on Māori aspirations in the New Zealand context, it provides a framework for considering cultural values in relation to other community or indigenous contexts.Genet Med 19 3, 345–351.

Building strong relationships between conservation genetics and primary industry leads to mutually beneficial genomic advances

Several reviews in the past decade have heralded the benefits of embracing high‐throughput sequencing technologies to inform conservation policy and the management of threatened species, but few have offered practical advice on how to expedite the transition from conservation genetics to conservation genomics. Here, we argue that an effective and efficient way to navigate this transition is to capitalize on emerging synergies between conservation genetics and primary industry (e.g., agriculture, fisheries, forestry and horticulture). Here, we demonstrate how building strong relationships between conservation geneticists and primary industry scientists is leading to mutually‐beneficial outcomes for both disciplines. Based on our collective experience as collaborative New Zealand‐based scientists, we also provide insight for forging these cross‐sector relationships.

Lack of direct evidence for natural selection at the candidate thrifty gene locus, PPARGC1A

BackgroundThe gene PPARGC1A, in particular the Gly482Ser variant (rs8192678), had been proposed to be subject to natural selection, particularly in recent progenitors of extant Polynesian populations. Reasons include high levels of population differentiation and increased frequencies of the derived type 2 diabetes (T2D) risk 482Ser allele, and association with body mass index (BMI) in a small Tongan population. However, no direct statistical tests for selection have been applied.MethodsUsing a range of Polynesian populations (Tongan, Māori, Samoan) we re-examined evidence for association between Gly482Ser with T2D and BMI as well as gout. Using also Asian, European, and African 1000 Genome Project samples a range of statistical tests for selection (FST, integrated haplotype score (iHS), cross population extended haplotype homozygosity (XP-EHH), Tajima’s D and Fay and Wu’s H) were conducted on the PPARGC1A locus.ResultsNo statistically significant evidence for association between Gly482Ser and any of BMI, T2D or gout was found. Population differentiation (FST) was smallest between Asian and Pacific populations (New Zealand Māori ≤ 0.35, Samoan ≤ 0.20). When compared to European (New Zealand Māori ≤ 0.40, Samoan ≤ 0.25) or African populations (New Zealand Māori ≤ 0.80, Samoan ≤ 0.66) this differentiation was larger. We did not find any strong evidence for departure from neutral evolution at this locus when applying any of the other statistical tests for selection. However, using the same analytical methods, we found evidence for selection in specific populations at previously identified loci, indicating that lack of selection was the most likely explanation for the lack of evidence of selection in PPARGC1A.ConclusionWe conclude that there is no compelling evidence for selection at this locus, and that this gene should not be considered a candidate thrifty gene locus in Pacific populations. High levels of population differentiation at this locus and the reported absence of the derived 482Ser allele in some Melanesian populations, can alternatively be explained by multiple out-of-Africa migrations by ancestral progenitors, and subsequent genetic drift during colonisation of Polynesia. Intermediate 482Ser allele frequencies in extant Western Polynesian populations could therefore be due to recent admixture with Melanesian progenitors.

Te Mata Ira—Faces of the Gene: Developing a cultural foundation for biobanking and genomic research involving Māori

Te Mata Ira was a three-year research project (2012–2015) that explored Māori views on genomic research and biobanking for the development of culturally appropriate guidelines. A key component of this process has been to identify Māori concepts that provide cultural reference points for engaging with biobanking and genomic research. These cultural cues provide the basis for describing the cultural logic that underpins engagement in this context in a culturally acceptable manner. This paper outlines the role of two wānanga (workshops) conducted as part of the larger project that were used to make sense of the Māori concepts that emerged from other data-collection activities. The wānanga involved six experts who worked with the research team to make sense of the Māori concepts. The wānanga process created the logic behind the cultural foundation for biobanking and genomic research, providing a basis for understanding Māori concepts, Māori ethical principles and their application to biobanking and genomic research.

Re: “Widespread prevalence of a CREBRF variant among Māori and Pacific children is associated with weight and height in early childhood”

Dear Editor: Your journal recently published the article “Widespread prevalence of a CREBRF variant amongst Māori and Pacific children is associated with weight and height in early childhood” [1]. The minor A-allele of the CREBRF rs373863828 variant (p.Arg457Gln) was analyzed in a pooled sample of New Zealand (NZ) children of maternally self-reported Māori, Pacific, European, or Asian ethnicity. Berry et al. [1] reported A-allele frequencies of 1.5% (European) and 1.1% (Asian). Other studies report that the A-allele has a prevalence of 26% in Samoa and American Samoa [2] and 15% in Tonga [3], but is extremely rare in other genotyped populations. In the Genome Aggregation Database [4], it was observed in 5/76,104 European (0.0033%) and 2/24,773 Asian individuals (0.0040%) (Table 1), and in TOPMed (www.nhlbiwgs.org) in 3/62,787 individuals (0.0024%). (In the original TOPMed release, a Samoan data set was included, thus an A-allele prevalence of 0.007 is recorded for TOPMed on Ensembl (as of 10 October 2017). However, in the latest Bravo Freeze 5 release (used here) that Samoan data set was not included.). Therefore, the frequencies reported by Berry et al. [1] are considerably greater than those observed in the Genome Aggregation Database and are extremely unlikely to have occurred by chance (European PFisher= 4 × 10; Asian 9 × 10). They are also inconsistent with our own findings. We genotyped rs373863828 in two NZ European sample sets and observed A-allele frequencies of 0.15% in the Screening for Pregnancy Endpoints (SCOPE) study and 0.19% in the Gout Genetics study (Table 1). These frequencies were considerably higher than observed in the Genome Aggregation Database (PFisher= 1 × 10 and 4 × 10, respectively), consistent with the introduction of the A-allele into the NZ European population by admixture with the Māori and Pacific populations. However, the frequencies in our sample sets were tenfold lower than those reported by Berry et al. [1] and differed significantly (PFisher

Accounting for Errors in Low Coverage High-Throughput Sequencing Data When Constructing Genetic Maps Using Biparental Outcrossed Populations

Next generation sequencing-based genotyping platforms allow for the construction of high density genetic linkage maps. However, data generated using these platforms often contain errors resulting from miscalled bases and missing parental alleles that are due... Next-generation sequencing is an efficient method that allows for substantially more markers than previous technologies, providing opportunities for building high-density genetic linkage maps, which facilitate the development of nonmodel species’ genomic assemblies and the investigation of their genes. However, constructing genetic maps using data generated via high-throughput sequencing technology (e.g., genotyping-by-sequencing) is complicated by the presence of sequencing errors and genotyping errors resulting from missing parental alleles due to low sequencing depth. If unaccounted for, these errors lead to inflated genetic maps. In addition, map construction in many species is performed using full-sibling family populations derived from the outcrossing of two individuals, where unknown parental phase and varying segregation types further complicate construction. We present a new methodology for modeling low coverage sequencing data in the construction of genetic linkage maps using full-sibling populations of diploid species, implemented in a package called GUSMap. Our model is based on the Lander–Green hidden Markov model but extended to account for errors present in sequencing data. We were able to obtain accurate estimates of the recombination fractions and overall map distance using GUSMap, while most existing mapping packages produced inflated genetic maps in the presence of errors. Our results demonstrate the feasibility of using low coverage sequencing data to produce genetic maps without requiring extensive filtering of potentially erroneous genotypes, provided that the associated errors are correctly accounted for in the model.

Cardio-metabolic disease genetic risk factors among Māori and Pacific Island people in Aotearoa New Zealand: current state of knowledge and future directions

Abstract Context: Cardio-metabolic conditions in Aotearoa New Zealand (NZ) Māori and non-indigenous Polynesian (Pacific) populations have been increasing in prevalence and severity, especially over the last two decades. Objectives: To assess knowledge on genetic and non-genetic risk factors for cardio-metabolic disease in the Māori and Pacific populations residing in Aotearoa NZ by a semi-systematic review of the PubMed database. To outline possible future directions in genetic epidemiological research with Māori and Pacific communities. Results: There have been few studies to confirm that risk factors in other populations also associate with cardio-metabolic conditions in Māori and Pacific populations. Such data are important when interventions are considered. Genetic studies have been sporadic, with no genome-wide association studies done. Conclusions: Biomedical research with Māori and Pacific communities is important to reduce the prevalence and impact of the cardio-metabolic diseases, as precision medicine is implemented in other Aotearoa NZ populations using overseas findings. Genuine engagement with Māori and Pacific communities is needed to ensure positive outcomes for genetic studies, from data collection through to analysis and dissemination. Important is building trust, understanding by researchers of fundamental cultural concepts and implementing protocols that minimise risks and maximise benefits. Approaches that utilise information such as genealogical information and whole genome sequencing technologies will provide new insights into cardio-metabolic conditions—and new interventions for affected individuals and families.

A DNA-based diagnostic for differentiating among New Zealand endemic Podocarpus

Species of the genus Podocarpus are primarily found in the Southern Hemisphere. In New Zealand, there are four endemic species—Podocarpus acutifolius, Podocarpus nivalis, Podocarpus totara and Podocarpus cunninghamii. The last mentioned two species, tōtara and Hall’s tōtara, are the most economically and culturally important and have been used extensively for carving, timber and medicinal purposes. However, these species are often difficult to distinguish morphologically as seedlings and adults. Useable Po. totara and Po. cunninghamii timber resources are now scarce, and replanting of tōtara is very costly; therefore, cheap diagnostics for ensuring species identity would be useful for replanting. Using expressed sequence tag (EST)-aligned genomic DNA sequences from putative Po. totara × Po. cunninghamii hybrids, we designed 120 primers for high-resolution melting (HRM) assays. These were evaluated in a multi-stage screening process to identify markers that discriminate among New Zealand endemic Podocarpus species. Ten markers reproducibly differentiated at least one species from the other three, and six differentiated two or more species. One marker differentiated all four species. Moreover, two markers were able to identify ‘artifical’ F1 hybrids of Po. totara and Po. cunninghamii that had been created from mixing equal amounts of DNA from one genotype of each species. Markers also differentiated a non-New Zealand endemic, Podocarpus lawrencei. Phylogenetic analyses indicated that Po. acutifolius accessions were genetically most similar to those of Po. totara, while Po. nivalis was the most genetically distinct species. Our results show that HRM markers can be easily developed from small amounts of next-generation sequence data and used to identify species and determine their phylogenetic relationships.