Our results in New Zealand Polynesian adults replicate, with very similar effect sizes, the association of the A allele of rs373863828 with higher BMI but lower odds of type 2 diabetes among Samoan adults living in Samoa and American Samoa.

Genomic mapping has been used to identify a region of the host genome that determines resistance to fusiform rust disease in loblolly pine where no discrete, simply inherited resistance factors had been previously found by conventional genetic analysis over four decades. A resistance locus, behaving as a single dominant gene, was mapped by association with genetic markers, even though the disease phenotype deviated from the expected Mendelian ratio. The complexity of forest pathosystems and the limitations of genetic analysis, based solely on phenotype, had led to an assumption that effective long-term disease resistance in trees should be polygenic. However, our data show that effective long-term resistance can be obtained from a single qualitative resistance gene, despite the presence of virulence in the pathogen population. Therefore, disease resistance in this endemic coevolved forest pathosystem is not exclusively polygenic. Genomic mapping now provides a powerful tool for characterizing the genetic basis of host pathogen interactions in forest trees and other undomesticated, organisms, where conventional genetic analysis often is limited or not feasible.

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.

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The detection of “signatures of selection” is now possible on a genome-wide scale in many plant and animal species, and can be performed in a population-specific manner due to the wealth of per-population genome-wide genotype data that is available. With genomic regions that exhibit evidence of having been under selection shown to also be enriched for genes associated with biologically important traits, detection of evidence of selective pressure is emerging as an additional approach for identifying novel gene-trait associations. While high-density genotype data is now relatively easy to obtain, for many researchers it is not immediately obvious how to go about identifying signatures of selection in these data sets. Here we describe a basic workflow, constructed from open source tools, for detecting and examining evidence of selection in genomic data. Code to install and implement the pipeline components, and instructions to run a basic analysis using the workflow described here, can be downloaded from our public GitHub repository: http://www.github.com/smilefreak/selectionTools/