QUEX who?…

The University of Queensland and the University of Exeter have partnered to establish the QUEX Institute, a new initiative designed to bolster our joint global research impact.Through the QUEX joint PhD program, you can receive a scholarship to study at two leading universities - The University of Queensland and the University of Exeter - on projects linked with the virtual QUEX Institute of Global Sustainability and Wellbeing. I was very fortunate to make part of the first QUEX PhD cohort and so far I’m absolutely loving this experience. You can find out more about QUEX here

This is a website for hosting my most relevant PhD analyses. Please feel free to comment or send me any suggestions/criticism to m.nabais@uq.edu.au.

List of most relevant analyses

  • Smoking Prediction: Briefly, We aimed to develop a DNA methylation-based predictor of smoking status, using a least absolute shrinkage and selection operator (LASSO) multinomial regression model, to perform variable selection and regularization to enhance predictive accuracy of smoking status, assessed by out-of-sample prediction. Models were fit by 10-fold cross-validation, using CpG methylation values as independent variables and smoking status as response variable; data were taken from the Lothian Birth Cohort 1936 + 1921 (n = 988). > 0 coefficients were extracted and multiplied by raw methylation values of a Motor Neuron Disease cohort (n = 905) giving a methylation index for each individual. Predictive accuracy was then assessed by logistic regression. I presented this as a poster at Genemappers 2018

  • Significant out-of-sample classification from methylation profile scoring for amyotrophic lateral sclerosis: We conducted DNA methylation association analyses using Illumina 450K data from whole blood for an Australian amyotrophic lateral sclerosis (ALS) case–control cohort (782 cases and 613 controls). Analyses used mixed linear models as implemented in the OSCA software. We found a significantly higher proportion of neutrophils in cases compared to controls which replicated in an independent cohort from the Netherlands (1159 cases and 637 controls). The OSCA MOMENT linear mixed model has been shown in simulations to best account for confounders. When combined in a methylation profile score, the 25 most-associated probes identified by MOMENT significantly classified case–control status in the Netherlands sample (area under the curve, AUC = 0.65, CI95% = [0.62–0.68], p = 8.3x10-22 ). The maximum AUC achieved was 0.69 (CI95% = [0.66–0.71], p = 4.3x10-34) when cell-type proportion was included in the predictor. I presented this work as a poster at the European Society for Human Genetics Conference, in 2019 and published this work in npj Genomic Medicine, in 2020.

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