Selection and Metabolic Disease in the Pacific

An example of a "signature of selection" in a population is a region of the genome that exhibits a reduction in genetic variability with a particular linkage disequilibrium pattern. This reduction in variation can arise when the phenotype of a neutral beneficial allele experiences a favourable change in environmental conditions. This results in an increased frequency of both the allele, and linked sites, within a population. Polynesian populations share a common genetic ancestry with East Asia, but little characterisation of genetic selection has been undertaken in Polynesian populations. Serum urate has been associated with metabolic disorders such as obesity, type 2 diabetes, renal disease and metabolic syndrome. It is hypothesised that serum urate may have undergone positive selection in Polynesians due to some of the beneficial properties, such as its role as an anti-oxidant, or as an adjuvant for the innate immune system. New Zealand Polynesians have inherently elevated serum urate levels and increased rates of gout. This thesis presents the results of a genome-wide study of selection in Polynesian (and other) populations, focusing on testing the hypothesis that genomic loci containing genes involved in urate processing have undergone selection. There was no evidence of wide-spread selection at genes associated with urate and gout, or related metabolic disorders, but there was evidence at some individual loci. Pathway analysis showed that of the significant pathways, there was a dominance of metabolic pathways that were enriched for genes with signatures of selection. Calcium related transport and signalling was a theme amongst loci that displayed signs of possible selection. Regions of the genome that were possibly selected in modern-day Polynesian populations also had similarities to those of modern-day East Asian populations. This thesis has provided identification and characterisation of regions in the genome with possible evidence of genetic selection in Polynesian populations, that previously was not available. It has also provided insight into the role of genetic selection with respect to urate and metabolic disease

A bioinformatics workflow for detecting signatures of selection in genomic data

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

Amplification of Inflammation by Lubricin Deficiency Implicated in Incident, Erosive Gout Independent of Hyperuricemia

Objective In gout, hyperuricemia promotes urate crystal deposition that stimulates the NLRP3 inflammasome and IL-1β-mediated arthritis. Incident gout without background hyperuricemia is rarely reported. To identify hyperuricemia-independent mechanisms driving gout incidence and progression, we characterized erosive urate crystalline inflammatory arthritis meeting ACR/EULAR gout classification criteria in a normouricemic young adult female. Methods Whole genome sequencing, quantitative proteomics, whole blood RNA-seq, and IL-1β-induced murine knee synovitis characterized proband candidate genes, biomarkers, and pathogenic mechanisms. Results Lubricin was attenuated in proband serum, associated with elevated acute phase reactants and inflammatory whole blood transcripts and transcriptional pathways. The proband had predicted damaging gene variants of NLRP3 and of Inter-Alpha-Trypsin Inhibitor Heavy Chain 3, an inhibitor of lubricin-degrading Cathepsin G. Proband serum protein interactome network changes supported enhanced lubricin degradation, with Cathepsin G activity increased relative to its inhibitors SERPINB6 and Thrombospondin1. TLR2 activation suppressed cultured human synovial fibroblast lubricin mRNA and release (p\u3c0.01). Lubricin blunted urate crystal precipitation, and IL-1β induction of xanthine oxidase and urate in cultured macrophages (p\u3c0.001). In lubricin-deficient mice, IL-1β knee injection increased xanthine oxidase positive synovial resident M1 macrophages (p\u3c0.05). Conclusion We linked normouricemic erosive gout to attenuated lubricin, with impaired control of Cathepsin G activity, compounded by deleterious NLRP3 variants. Lubricin suppressed monosodium urate crystallization, and blunted IL-1β-induced increases in macrophage xanthine oxidase and urate. Collective activities of articular lubricin that could limit incident and erosive gouty arthritis independently of hyperuricemia are subject to disruption by inflammation, activated Cathepsin G, and synovial fibroblast TLR2 signaling

A COL17A1 Splice-Altering Mutation Is Prevalent in Inherited Recurrent Corneal Erosions

PurposeCorneal dystrophies are a genetically heterogeneous group of disorders. We previously described a family with an autosomal dominant epithelial recurrent erosion dystrophy (ERED). We aimed to identify the underlying genetic cause of ERED in this family and 3 additional ERED families. We sought to characterize the potential function of the candidate genes using the human and zebrafish cornea.DesignCase series study of 4 white families with a similar ERED. An experimental study was performed on human and zebrafish tissue to examine the putative biological function of candidate genes.ParticipantsFour ERED families, including 28 affected and 17 unaffected individuals.MethodsHumanLinkage-12 arrays (Illumina, San Diego, CA) were used to genotype 17 family members. Next-generation exome sequencing was performed on an uncle–niece pair. Segregation of potential causative mutations was confirmed using Sanger sequencing. Protein expression was determined using immunohistochemistry in human and zebrafish cornea. Gene expression in zebrafish was assessed using whole-mount in situ hybridization. Morpholino-induced transient gene knockdown was performed in zebrafish embryos.Main Outcome MeasuresLinkage microarray, exome analysis, DNA sequence analysis, immunohistochemistry, in situ hybridization, and morpholino-induced genetic knockdown results.ResultsLinkage microarray analysis identified a candidate region on chromosome chr10:12,576,562–112,763,135, and exploration of exome sequencing data identified 8 putative pathogenic variants in this linkage region. Two variants segregated in 06NZ–TRB1 with ERED: COL17A1 c.3156C→T and DNAJC9 c.334G→A. The COL17A1 c.3156C→T variant segregated in all 4 ERED families. We showed biologically relevant expression of these proteins in human cornea. Both proteins are expressed in the cornea of zebrafish embryos and adults. Zebrafish lacking Col17a1a and Dnajc9 during development show no gross corneal phenotype.ConclusionsThe COL17A1 c.3156C→T variant is the likely causative mutation in our recurrent corneal erosion families, and its presence in 4 independent families suggests that it is prevalent in ERED. This same COL17A1 c.3156C→T variant recently was identified in a separate pedigree with ERED. Our study expands the phenotypic spectrum of COL17A1 disease from autosomal recessive epidermolysis bullosa to autosomal dominant ERED and identifies COL17A1 as a key protein in maintaining integrity of the corneal epithelium

Selection and Metabolic Disease in the Pacific

An example of a "signature of selection" in a population is a region of the genome that exhibits a reduction in genetic variability with a particular linkage disequilibrium pattern. This reduction in variation can arise when the phenotype of a neutral beneficial allele experiences a favourable change in environmental conditions. This results in an increased frequency of both the allele, and linked sites, within a population. Polynesian populations share a common genetic ancestry with East Asia, but little characterisation of genetic selection has been undertaken in Polynesian populations. Serum urate has been associated with metabolic disorders such as obesity, type 2 diabetes, renal disease and metabolic syndrome. It is hypothesised that serum urate may have undergone positive selection in Polynesians due to some of the beneficial properties, such as its role as an anti-oxidant, or as an adjuvant for the innate immune system. New Zealand Polynesians have inherently elevated serum urate levels and increased rates of gout. This thesis presents the results of a genome-wide study of selection in Polynesian (and other) populations, focusing on testing the hypothesis that genomic loci containing genes involved in urate processing have undergone selection. There was no evidence of wide-spread selection at genes associated with urate and gout, or related metabolic disorders, but there was evidence at some individual loci. Pathway analysis showed that of the significant pathways, there was a dominance of metabolic pathways that were enriched for genes with signatures of selection. Calcium related transport and signalling was a theme amongst loci that displayed signs of possible selection. Regions of the genome that were possibly selected in modern-day Polynesian populations also had similarities to those of modern-day East Asian populations. This thesis has provided identification and characterisation of regions in the genome with possible evidence of genetic selection in Polynesian populations, that previously was not available. It has also provided insight into the role of genetic selection with respect to urate and metabolic disease

Performance of gout definitions for genetic epidemiological studies: analysis of UK Biobank

Abstract Background Many different combinations of available data have been used to identify gout cases in large genetic studies. The aim of this study was to determine the performance of case definitions of gout using the limited items available in multipurpose cohorts for population-based genetic studies. Methods This research was conducted using the UK Biobank Resource. Data, including genome-wide genotypes, were available for 105,421 European participants aged 40–69 years without kidney disease. Gout definitions and combinations of these definitions were identified from previous epidemiological studies. These definitions were tested for association with 30 urate-associated single-nucleotide polymorphisms (SNPs) by logistic regression, adjusted for age, sex, waist circumference, and ratio of waist circumference to height. Heritability estimates under an additive model were generated using GCTA version 1.26.0 and PLINK version 1.90b3.32 by partitioning the genome. Results There were 2066 (1.96%) cases defined by self-report of gout, 1652 (1.57%) defined by urate-lowering therapy (ULT) use, 382 (0.36%) defined by hospital diagnosis, 1861 (1.76%) defined by hospital diagnosis or gout-specific medications and 2295 (2.18%) defined by self-report of gout or ULT use. Association with gout at experiment-wide significance (P < 0.0017) was observed for 13 SNPs with gout using the self-report of gout or ULT use definition, 12 SNPs using the self-report of gout definition, 11 SNPs using the hospital diagnosis or gout-specific medication definition, 10 SNPs using ULT use definition and 3 SNPs using hospital diagnosis definition. Heritability estimates ranged from 0.282 to 0.308 for all definitions except hospital diagnosis (0.236). Conclusions Of the limited items available in multipurpose cohorts, the case definition of self-report of gout or ULT use has high sensitivity and precision for detecting association in genetic epidemiological studies of gout

Flooding an HPC: Parallelism optimisation in the STRAND project

The STRAND Marsden Fund Project is an interdisciplinary project exploring climate-change flooding related risks to residential property values across space and time, and the related implications for financial stability. One workflow of the STRAND project aims at evaluating the flooding risks for Dunedin properties under various climate change scenarios, architecture choices and the interaction with data uncertainty. This workflow involves generating and aggregating thousands of simulations across many properties. The embarrassingly parallel nature of the problem made it a good fit for a highperformance computing (HPC) platform. In this talk, we will explore how RTIS (University of Otago) and NeSI partnered to optimise this code. After emphasizing the numerical challenges, we will highlight why and how we combined a workflow management system, Snakemake, and a parallel toolbox, Dask, to distribute tasks efficiently and limit bottlenecks, while keeping the solution portable across two different HPC platforms. ABOUT THE AUTHORS Dr Maxime Rio is a data science engineer and data scientist at NeSI and NIWA. He enjoys helping researchers to analyse their data, from visualisation to machine learning and probabilistic modelling. Dr Murray Cadzow is a Scientific Programmer within Research Teaching IT Support (RTIS) at the University of Otago. Prior to this he spent 11 years researching the genetic basis of gout and related diseases. Murray has been heavily involved in computational literacy and bioinformatic training at the University of Otago - organising Research Bazaar Dunedin and the Otago Bioinformatics Spring School. He is both a Carpentries instructor and instructor trainer. His teaching has focused on delivering digital literacy training to researchers, and the development and support of the local Carpentries community at Otago. Dr Quyen Nguyen is the STRAND Marsden Fund Project Postdoctoral Fellow (2021–2024) hosted at the School of Surveying, University of Otago. She is the modeller for the STRAND Marsden Fund Project entitled "Should I stay or should I go? Climate-change risks to property values across space and time, and the related implications for financial stability". Dr Nguyen is also working with GNS Science as a Climate Change Economist. Her research interests are in climate finance and data science.For more information about eResearch NZ / eRangahau Aotearoa, visit:https://eresearchnz.co.nz/</p

Identification of Cis-Regulatory Modules that Function in the Male Germline of Flowering Plants.

The male germline of flowering plants develops within the vegetative cell of the male gametophyte and displays a distinct transcriptional profile. Key to understanding the development of this unique cell lineage is determining how gene expression is regulated within germline cells. This knowledge impacts upon our understanding of cell specification, differentiation, and plant fertility. Here, we describe methods to identify cis-regulatory modules (CRMs) that act as key regulatory regions in the promoters of germline-expressed genes. We detail the complimentary techniques of phylogenetic footprinting and the use of fluorescent reporters in pollen for the identification and verification of CRMs

Upskilling researchers in Machine Learning

Emerging tools and techniques in the space of Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL) are set to change many aspects of our daily lives and research is no exception. But how do we ensure that researchers are equipped to understand and utilise these tools and techniques across the vast spectrum of research domains? In this birds-of-a-feather session we aim to survey ongoing efforts around Aotearoa/New Zealand to introduce and train researchers in ML and DL, and aim to communally discuss some of the following topics: developing content and training materials; challenges in teaching and facilitating these techniques; reaching, teaching, and engaging with a broad audience; encouraging the growing community of practice; and providing support for learners and practitioners through compute and other services. ABOUT THE AUTHORSMike Laverick is a solutions specialist for the Centre for eResearch at the University of Auckland. Formerly an atomic astrophysicist at KU Leuven, Mike now uses his experience in research and programming to help tackle the ever-growing digital needs of researchers. As part of the Rongowai mission, a collaboration between NASA and the New Zealand Space Agency, Mike has helped develop operational data workflows and visualisation tools. Mike is also a Python aficionado, helping to train and upskill researchers as a Carpentries workshop instructor. Maxime Rio is Data Research Software Engineer for NeSI and NIWA. Maxime helps researchers build data science pipelines on NeSI platforms. Ben Collings is an engagement specialist at the Centre for eResearch at the University of Auckland. With a background in Physical Geography, he stumbled into the world of geographical information systems and remote sensing discovering how useful machine learning could be for resolving landcover classification problems with satellite imagery. He is completing his PhD developing new tools for coastal change detection with satellite imagery. He enjoys learning and teaching to provide researchers with tools to excel in their work and has been involved in several machine learning workshops with CeR and NeSI. Dr Murray Cadzow is a Scientific Programmer within Research Teaching IT Support at the University of Otago. Prior to this he spent 11 years researching the genetic basis of gout and related diseases. Murray has been heavily involved in computational literacy and bioinformatic training at the University of Otago - organising Research Bazaar Dunedin and the Otago Bioinformatics Spring School. He is both a Carpentries instructor and instructor trainer. His teaching has focused on delivering digital literacy training to researchers, and the development and support of the local Carpentries community at Otago.For more information about eResearch NZ / eRangahau Aotearoa, visit:https://eresearchnz.co.nz/</p

Bridging the digital divide: Supporting Aotearoa's journey to digital literacy excellence

Raising digital skills literacy in New Zealand is essential for the advancement and progress of the nation. As technology continues to transform every aspect of our lives, it is essential to ensure that researchers are equipped with the necessary skills to navigate the changing digital landscape effectively. Universities, research centres and crown research institutes across the motu strive to support a research workforce proficient in data science, high-performance computing, and advanced research methodologies, contributing significantly to the country's digital competitiveness and knowledge economy. This Birds-of-a feather session aims to spotlight the diverse initiatives for digital upskilling led by organizations nationwide. It will encourage a focused dialogue on identifying the research requirements of the community as we prepare for next year's training programs. The session is geared towards addressing the training necessities of the research community, striving to meet their specific needs effectively. ABOUT THE AUTHORSDr Murray Cadzow is a Scientific Program mer within Research Teaching IT Support at the University of Otago. Prior to this he spent 11 years researching the genetic basis of gout and related diseases. Murray has been heavily involved in computational literacy and bioinformatic training at the University of Otago - organising Research Bazaar Dunedin and the Otago Bioinformatics Spring School. He is both a Carpentries instructor and instructor trainer. His teaching has focused on delivering digital literacy training to researchers, and the development and support of the local Carpentries community at Otago. Dr Nisha Ghatak is the Research Communities Advisor and Training Lead at NeSI. She is also the Carpentries Regional Coordinator for Aotearoa New Zealand. and an Executive Council member for the Carpentries. Dr Tyler McInnes is the Bioinformatics Training Coordinator for Genomics Aotearoa. He has research experience in the field of genetics, studying cancer epigenetics in colorectal cancer and limb and spinal development in Xenopus laevis. In his previous role as a Teaching Fellow, Tyler developed and implemented a series of workshops to support student learning, as well as new lecture content and practical labs with a focus on bioinformatics. He is now a certified Carpentries instructor.Dr Tom Saunders has a background in entomology and biosecurity but is now an Engagement Specialist in the Centre for eResearch at the University of Auckland. Tom organises, hosts, and delivers digital research skills workshops, and helps researchers to manage their data throughout the research lifecycle. He collaborates on the organisation and delivery of ResBaz Aotearoa and cross-institution Carpentries events as a certified instructor.For more information about eResearch NZ / eRangahau Aotearoa, visit:https://eresearchnz.co.nz/</p