Project Title: Prediction of cell-type specific effects of genetic variants in Alzheimer's disease
Supervisor: Dr Nathan Skene
Location: Level 7, Sir Michael Uren Hub, White City Campus, 86 Wood Lane, W12 0BZ

About Me

I have a background in data analytics and machine learning, undertaking my undergraduate degree in the statistics department of Trinity College Dublin. Thereafter, I gained three years of experience in industry, working as a data analytics consultant on numerous statistical projects across a wide range of industries from the public sector to banking.

More recently, I worked as part of Dr. Overton’s group in Queen’s University Belfast. My research focused on methods of integrating multi-omic patient data, through network biology approaches, to predict the instigated molecular players in differing biological contexts such as the Epithelial to Mesenchymal Transition (EMT) process. 

I joined Dr. Nathan Skene’s group as a bioinformatician focusing on the analysis of multi-omic single-cell data in neurodegenerative diseases before starting my PhD. The primary focus of my PhD research is in the prediction of cell type-specific regulatory effects of genetic variants in neurodegenerative diseases.

Outside of academia, I’m obsessed with all things food and hiking.


  • 2020-2021: MSc Bioinformatics and Computational Genomics, Queen's University Belfast
  • 2010-2013: BA Management Science and Information Systems Studies (MSISS), Trinity College Dublin

Research Interests 

All major neurodegenerative diseases are characterized by substantial heritability and while recent large-scale genetic efforts have identified variants associated with disease, these often lie in non-coding, regulatory regions and cannot be linked to any functional outcomes. Recent work has repeatedly highlighted that the genetic risk for Alzheimer’s disease acts primarily via microglia - the resident macrophages of the central nervous system.

However, most of these variants do not directly affect protein function, instead they are suspected of influencing gene expression by altering genomic regulatory elements. In fact, approximately 98% of SNPs in a recent genome-wide meta-analysis of Alzheimer’s disease were found in non-coding regions. One of the main challenges is that gene regulatory mechanisms are highly cell type specific. Genetic sequence variants which are associated with the function or activity of regulatory elements, quantitative trait loci (QTLs), will exert their effects in a cell type specific manner. Mapping out molecular and regulatory QTLs comprehensively in disease-relevant cell types would enable the interpretation of functional outcomes of genetic variants on gene expression and regulation. My project focuses on solving this problem using machine learning, prediciting the cell-type effects genetic variation and will be applied to Alzheimer’s disease.

Selected publications

Murphy, A.E., Skene, N.G. A balanced measure shows superior performance of pseudobulk methods in single-cell RNA-sequencing analysis. Nat Commun 13, 7851 (2022).

AE Murphy, BM Schilder, NG Skene (2021). MungeSumstats: a Bioconductor package for the standardization and quality control of many GWAS summary statistics. Bioinformatics, 37

All publications available on Google Scholar


Presentations and Conferences

  • Talk Kipoi Summit - predicting the cell type-specific effects of genetic variants on the epigenome - September 2023

  • Invited talk ADDI Summer Learning Series - single-cell genomics for AD - June 2023

  • A balanced measure shows superior performance of pseudobulk methods over mixed models and pseudoreplication approaches in single-cell RNA- sequencing analysis at Probabilistic Modelling in Genomics (Probgen) - March 2022

  • Differential expression in single-cell RNA-Seq analysis, re-analysis of published data at UK DRI Single Cell and Spatial Transcriptomics Workshop - May 2021
  • Turing Project, predicting epigenetic marks from DNA sequence and chromatin accessibility data at UK Dementia Research Institute Connectome - October 2021

Contact Details

LinkedIn: alan-murphy-23aa019a/